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[ "_The documentation is not available anymore as the PR was closed or merged._", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.006173 / 0.011353 (-0.005180) | 0.003773 / 0.011008 (-0.007235) | 0.099499 / 0.038508 (0.060991) | 0.037918 / 0.023109 (0.014809) | 0.321329 / 0.275898 (0.045431) | 0.379739 / 0.323480 (0.056259) | 0.004664 / 0.007986 (-0.003322) | 0.002943 / 0.004328 (-0.001385) | 0.077759 / 0.004250 (0.073509) | 0.055271 / 0.037052 (0.018219) | 0.329428 / 0.258489 (0.070939) | 0.378731 / 0.293841 (0.084890) | 0.027737 / 0.128546 (-0.100810) | 0.008566 / 0.075646 (-0.067081) | 0.313220 / 0.419271 (-0.106052) | 0.047101 / 0.043533 (0.003568) | 0.316211 / 0.255139 (0.061072) | 0.341826 / 0.283200 (0.058626) | 0.020838 / 0.141683 (-0.120845) | 1.550064 / 1.452155 (0.097909) | 1.706518 / 1.492716 (0.213801) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.203093 / 0.018006 (0.185087) | 0.425345 / 0.000490 (0.424856) | 0.004800 / 0.000200 (0.004600) | 0.000077 / 0.000054 (0.000022) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.024590 / 0.037411 (-0.012821) | 0.098115 / 0.014526 (0.083589) | 0.108274 / 0.176557 (-0.068282) | 0.170804 / 0.737135 (-0.566332) | 0.110560 / 0.296338 (-0.185778) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.425251 / 0.215209 (0.210042) | 4.239075 / 2.077655 (2.161421) | 1.955601 / 1.504120 (0.451481) | 1.774796 / 1.541195 (0.233602) | 1.826641 / 1.468490 (0.358150) | 0.558777 / 4.584777 (-4.026000) | 3.361697 / 3.745712 (-0.384015) | 1.764468 / 5.269862 (-3.505394) | 1.032280 / 4.565676 (-3.533396) | 0.067872 / 0.424275 (-0.356403) | 0.010998 / 0.007607 (0.003391) | 0.525682 / 0.226044 (0.299637) | 5.254356 / 2.268929 (2.985427) | 2.384332 / 55.444624 (-53.060292) | 2.045578 / 6.876477 (-4.830898) | 2.170914 / 2.142072 (0.028841) | 0.674782 / 4.805227 (-4.130445) | 0.135351 / 6.500664 (-6.365314) | 0.066591 / 0.075469 (-0.008878) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.209181 / 1.841788 (-0.632606) | 14.044518 / 8.074308 (5.970210) | 13.184705 / 10.191392 (2.993313) | 0.130836 / 0.680424 (-0.549588) | 0.016582 / 0.534201 (-0.517619) | 0.360005 / 0.579283 (-0.219279) | 0.379519 / 0.434364 (-0.054845) | 0.422174 / 0.540337 (-0.118164) | 0.515546 / 1.386936 (-0.871390) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.006293 / 0.011353 (-0.005060) | 0.003784 / 0.011008 (-0.007224) | 0.079248 / 0.038508 (0.040739) | 0.038452 / 0.023109 (0.015343) | 0.444727 / 0.275898 (0.168829) | 0.500535 / 0.323480 (0.177055) | 0.003455 / 0.007986 (-0.004531) | 0.002873 / 0.004328 (-0.001455) | 0.077439 / 0.004250 (0.073189) | 0.047855 / 0.037052 (0.010803) | 0.448049 / 0.258489 (0.189560) | 0.509517 / 0.293841 (0.215676) | 0.028359 / 0.128546 (-0.100188) | 0.008503 / 0.075646 (-0.067143) | 0.084961 / 0.419271 (-0.334310) | 0.042880 / 0.043533 (-0.000653) | 0.436628 / 0.255139 (0.181489) | 0.456574 / 0.283200 (0.173375) | 0.019539 / 0.141683 (-0.122144) | 1.561273 / 1.452155 (0.109118) | 1.572018 / 1.492716 (0.079301) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.230250 / 0.018006 (0.212244) | 0.415189 / 0.000490 (0.414700) | 0.003213 / 0.000200 (0.003013) | 0.000080 / 0.000054 (0.000026) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.025541 / 0.037411 (-0.011871) | 0.102326 / 0.014526 (0.087800) | 0.110258 / 0.176557 (-0.066298) | 0.162488 / 0.737135 (-0.574647) | 0.112782 / 0.296338 (-0.183556) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.457936 / 0.215209 (0.242727) | 4.581503 / 2.077655 (2.503848) | 2.237659 / 1.504120 (0.733540) | 2.029960 / 1.541195 (0.488765) | 2.082911 / 1.468490 (0.614421) | 0.556485 / 4.584777 (-4.028292) | 3.384418 / 3.745712 (-0.361295) | 1.748809 / 5.269862 (-3.521053) | 1.034759 / 4.565676 (-3.530917) | 0.067500 / 0.424275 (-0.356776) | 0.011425 / 0.007607 (0.003818) | 0.561340 / 0.226044 (0.335295) | 5.623629 / 2.268929 (3.354701) | 2.733587 / 55.444624 (-52.711038) | 2.401578 / 6.876477 (-4.474899) | 2.524569 / 2.142072 (0.382496) | 0.673170 / 4.805227 (-4.132057) | 0.136681 / 6.500664 (-6.363983) | 0.068060 / 0.075469 (-0.007409) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.318651 / 1.841788 (-0.523137) | 14.362123 / 8.074308 (6.287815) | 14.385964 / 10.191392 (4.194572) | 0.149914 / 0.680424 (-0.530510) | 0.016877 / 0.534201 (-0.517324) | 0.358406 / 0.579283 (-0.220877) | 0.394349 / 0.434364 (-0.040015) | 0.422471 / 0.540337 (-0.117866) | 0.513807 / 1.386936 (-0.873129) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#1b9ce11d1b94e6178df663ff5fcad029849d10fb \"CML watermark\")\n", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.006272 / 0.011353 (-0.005080) | 0.003903 / 0.011008 (-0.007105) | 0.100180 / 0.038508 (0.061672) | 0.037799 / 0.023109 (0.014690) | 0.385627 / 0.275898 (0.109729) | 0.446518 / 0.323480 (0.123038) | 0.004811 / 0.007986 (-0.003175) | 0.003032 / 0.004328 (-0.001296) | 0.077063 / 0.004250 (0.072812) | 0.055564 / 0.037052 (0.018512) | 0.397346 / 0.258489 (0.138857) | 0.443242 / 0.293841 (0.149401) | 0.027904 / 0.128546 (-0.100642) | 0.008386 / 0.075646 (-0.067260) | 0.315013 / 0.419271 (-0.104259) | 0.047943 / 0.043533 (0.004410) | 0.378443 / 0.255139 (0.123304) | 0.411472 / 0.283200 (0.128272) | 0.020465 / 0.141683 (-0.121218) | 1.526594 / 1.452155 (0.074439) | 1.547018 / 1.492716 (0.054301) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.219377 / 0.018006 (0.201370) | 0.430254 / 0.000490 (0.429764) | 0.003218 / 0.000200 (0.003018) | 0.000072 / 0.000054 (0.000017) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.023667 / 0.037411 (-0.013744) | 0.099143 / 0.014526 (0.084617) | 0.106044 / 0.176557 (-0.070513) | 0.166186 / 0.737135 (-0.570949) | 0.108736 / 0.296338 (-0.187603) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.437971 / 0.215209 (0.222762) | 4.363675 / 2.077655 (2.286021) | 2.011993 / 1.504120 (0.507873) | 1.845189 / 1.541195 (0.303994) | 1.831848 / 1.468490 (0.363358) | 0.562402 / 4.584777 (-4.022375) | 3.365259 / 3.745712 (-0.380453) | 1.781491 / 5.269862 (-3.488371) | 1.023454 / 4.565676 (-3.542223) | 0.067857 / 0.424275 (-0.356418) | 0.011076 / 0.007607 (0.003469) | 0.532267 / 0.226044 (0.306223) | 5.340344 / 2.268929 (3.071415) | 2.388649 / 55.444624 (-53.055976) | 2.055373 / 6.876477 (-4.821104) | 2.205047 / 2.142072 (0.062975) | 0.672909 / 4.805227 (-4.132318) | 0.135244 / 6.500664 (-6.365420) | 0.066184 / 0.075469 (-0.009285) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.206838 / 1.841788 (-0.634950) | 13.967075 / 8.074308 (5.892767) | 13.143971 / 10.191392 (2.952579) | 0.143991 / 0.680424 (-0.536433) | 0.016673 / 0.534201 (-0.517527) | 0.376180 / 0.579283 (-0.203103) | 0.386550 / 0.434364 (-0.047814) | 0.440590 / 0.540337 (-0.099747) | 0.529974 / 1.386936 (-0.856962) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.006299 / 0.011353 (-0.005054) | 0.003784 / 0.011008 (-0.007224) | 0.077875 / 0.038508 (0.039367) | 0.038689 / 0.023109 (0.015580) | 0.421684 / 0.275898 (0.145786) | 0.472649 / 0.323480 (0.149169) | 0.003570 / 0.007986 (-0.004415) | 0.004448 / 0.004328 (0.000120) | 0.077867 / 0.004250 (0.073616) | 0.049514 / 0.037052 (0.012462) | 0.375983 / 0.258489 (0.117494) | 0.470632 / 0.293841 (0.176791) | 0.028238 / 0.128546 (-0.100308) | 0.008462 / 0.075646 (-0.067185) | 0.082452 / 0.419271 (-0.336819) | 0.043617 / 0.043533 (0.000084) | 0.400874 / 0.255139 (0.145735) | 0.426191 / 0.283200 (0.142992) | 0.020602 / 0.141683 (-0.121081) | 1.567658 / 1.452155 (0.115504) | 1.572610 / 1.492716 (0.079893) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.246144 / 0.018006 (0.228138) | 0.419402 / 0.000490 (0.418913) | 0.001691 / 0.000200 (0.001491) | 0.000071 / 0.000054 (0.000017) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.026105 / 0.037411 (-0.011306) | 0.104734 / 0.014526 (0.090208) | 0.110257 / 0.176557 (-0.066300) | 0.161429 / 0.737135 (-0.575706) | 0.114367 / 0.296338 (-0.181972) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.453352 / 0.215209 (0.238143) | 4.537924 / 2.077655 (2.460269) | 2.196193 / 1.504120 (0.692073) | 2.002087 / 1.541195 (0.460892) | 2.041722 / 1.468490 (0.573231) | 0.561643 / 4.584777 (-4.023134) | 3.449108 / 3.745712 (-0.296605) | 2.862800 / 5.269862 (-2.407062) | 1.387895 / 4.565676 (-3.177782) | 0.068076 / 0.424275 (-0.356199) | 0.011568 / 0.007607 (0.003961) | 0.559279 / 0.226044 (0.333235) | 5.598738 / 2.268929 (3.329809) | 2.676649 / 55.444624 (-52.767975) | 2.334588 / 6.876477 (-4.541889) | 2.376215 / 2.142072 (0.234142) | 0.673109 / 4.805227 (-4.132118) | 0.137587 / 6.500664 (-6.363077) | 0.069131 / 0.075469 (-0.006338) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.307332 / 1.841788 (-0.534456) | 14.536036 / 8.074308 (6.461728) | 14.173734 / 10.191392 (3.982342) | 0.145143 / 0.680424 (-0.535281) | 0.016662 / 0.534201 (-0.517539) | 0.366901 / 0.579283 (-0.212383) | 0.394498 / 0.434364 (-0.039866) | 0.430546 / 0.540337 (-0.109792) | 0.518950 / 1.386936 (-0.867986) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#682d21e94ab1e64c11b583de39dc4c93f0101c5a \"CML watermark\")\n", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.008122 / 0.011353 (-0.003231) | 0.005585 / 0.011008 (-0.005424) | 0.121219 / 0.038508 (0.082711) | 0.047616 / 0.023109 (0.024507) | 0.440576 / 0.275898 (0.164678) | 0.491053 / 0.323480 (0.167573) | 0.004774 / 0.007986 (-0.003211) | 0.006758 / 0.004328 (0.002430) | 0.103852 / 0.004250 (0.099602) | 0.071560 / 0.037052 (0.034508) | 0.463107 / 0.258489 (0.204618) | 0.516904 / 0.293841 (0.223063) | 0.048052 / 0.128546 (-0.080494) | 0.013679 / 0.075646 (-0.061968) | 0.428383 / 0.419271 (0.009112) | 0.069468 / 0.043533 (0.025936) | 0.432593 / 0.255139 (0.177454) | 0.471810 / 0.283200 (0.188611) | 0.037541 / 0.141683 (-0.104142) | 1.823490 / 1.452155 (0.371335) | 1.922558 / 1.492716 (0.429842) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.252315 / 0.018006 (0.234309) | 0.541757 / 0.000490 (0.541267) | 0.000373 / 0.000200 (0.000173) | 0.000083 / 0.000054 (0.000028) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.030361 / 0.037411 (-0.007050) | 0.125928 / 0.014526 (0.111402) | 0.145102 / 0.176557 (-0.031455) | 0.209798 / 0.737135 (-0.527337) | 0.147349 / 0.296338 (-0.148990) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.627554 / 0.215209 (0.412345) | 5.917422 / 2.077655 (3.839767) | 2.491083 / 1.504120 (0.986963) | 2.147078 / 1.541195 (0.605883) | 2.167511 / 1.468490 (0.699021) | 0.903061 / 4.584777 (-3.681716) | 5.518537 / 3.745712 (1.772825) | 2.654348 / 5.269862 (-2.615514) | 1.645121 / 4.565676 (-2.920556) | 0.103782 / 0.424275 (-0.320493) | 0.013048 / 0.007607 (0.005441) | 0.756732 / 0.226044 (0.530687) | 7.622873 / 2.268929 (5.353945) | 3.122689 / 55.444624 (-52.321936) | 2.537735 / 6.876477 (-4.338742) | 2.640090 / 2.142072 (0.498018) | 1.128635 / 4.805227 (-3.676593) | 0.228089 / 6.500664 (-6.272575) | 0.086207 / 0.075469 (0.010738) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.561591 / 1.841788 (-0.280197) | 18.110299 / 8.074308 (10.035991) | 20.718017 / 10.191392 (10.526625) | 0.225741 / 0.680424 (-0.454682) | 0.031738 / 0.534201 (-0.502463) | 0.530789 / 0.579283 (-0.048495) | 0.607364 / 0.434364 (0.173000) | 0.581593 / 0.540337 (0.041256) | 0.726033 / 1.386936 (-0.660903) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.009323 / 0.011353 (-0.002030) | 0.005360 / 0.011008 (-0.005649) | 0.103608 / 0.038508 (0.065100) | 0.050158 / 0.023109 (0.027049) | 0.499906 / 0.275898 (0.224008) | 0.561005 / 0.323480 (0.237525) | 0.005093 / 0.007986 (-0.002892) | 0.008285 / 0.004328 (0.003956) | 0.103446 / 0.004250 (0.099196) | 0.061478 / 0.037052 (0.024426) | 0.494016 / 0.258489 (0.235527) | 0.537550 / 0.293841 (0.243709) | 0.048829 / 0.128546 (-0.079717) | 0.017032 / 0.075646 (-0.058614) | 0.107748 / 0.419271 (-0.311524) | 0.065607 / 0.043533 (0.022074) | 0.488709 / 0.255139 (0.233570) | 0.512023 / 0.283200 (0.228823) | 0.032067 / 0.141683 (-0.109616) | 1.907585 / 1.452155 (0.455431) | 1.960994 / 1.492716 (0.468278) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.278378 / 0.018006 (0.260371) | 0.551474 / 0.000490 (0.550985) | 0.006886 / 0.000200 (0.006686) | 0.000106 / 0.000054 (0.000051) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.030674 / 0.037411 (-0.006737) | 0.135179 / 0.014526 (0.120654) | 0.133703 / 0.176557 (-0.042853) | 0.198923 / 0.737135 (-0.538212) | 0.155108 / 0.296338 (-0.141231) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.690566 / 0.215209 (0.475357) | 6.789594 / 2.077655 (4.711940) | 2.940668 / 1.504120 (1.436549) | 2.562431 / 1.541195 (1.021236) | 2.554232 / 1.468490 (1.085742) | 0.888470 / 4.584777 (-3.696307) | 5.672318 / 3.745712 (1.926606) | 2.741626 / 5.269862 (-2.528236) | 1.818336 / 4.565676 (-2.747340) | 0.110434 / 0.424275 (-0.313841) | 0.014114 / 0.007607 (0.006507) | 0.830632 / 0.226044 (0.604588) | 8.270787 / 2.268929 (6.001859) | 3.723486 / 55.444624 (-51.721139) | 2.993671 / 6.876477 (-3.882806) | 2.918273 / 2.142072 (0.776201) | 1.105337 / 4.805227 (-3.699891) | 0.222976 / 6.500664 (-6.277688) | 0.085290 / 0.075469 (0.009820) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.816027 / 1.841788 (-0.025760) | 18.496850 / 8.074308 (10.422541) | 20.457032 / 10.191392 (10.265640) | 0.243533 / 0.680424 (-0.436891) | 0.027044 / 0.534201 (-0.507157) | 0.500752 / 0.579283 (-0.078531) | 0.620963 / 0.434364 (0.186599) | 0.607995 / 0.540337 (0.067658) | 0.722915 / 1.386936 (-0.664021) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#682d21e94ab1e64c11b583de39dc4c93f0101c5a \"CML watermark\")\n" ]

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[ "Hi ! Do you think this can be fixed at the Pool level ? Ideally it should be the Pool responsibility to handle this, not the `map` code. We could even subclass Pool if needed (at least the one from `multiprocess`)", "@lhoestq it makes sense to me. Just pushed a refactoring creating a `class ProcessPool(multiprocess.pool.Pool)` to keep track of the PID changes.", "_The documentation is not available anymore as the PR was closed or merged._", "I managed to raise an error without subclassing Pool with two additions to `iflatmap_unordered`:\r\n\r\n1. at the beggining\r\n```python\r\noriginal_pool = list(pool._pool)\r\n```\r\n\r\n2. in the loop\r\n```python\r\nif any(async_result._pool != original_pool for async_result in async_results) and queue.empty():\r\n raise RuntimeError(\r\n \"One of the subprocesses has abruptly died during map operation.\"\r\n \"To debug the error, disable multiprocessing.\"\r\n )\r\n```\r\n\r\nIt's still a fix that only works for `iflatmap_unordered` (so not for map, imap etc) but is maybe simpler that subclassing. It also works for both multiprocessing.Pool and multiprocess.Pool", "@lhoestq sorry for the delay. Busy weeks here. \r\n\r\nI just pushed the change you requested. It looks closer to the original proposal, actually.\r\n\r\nIt seems that `map` actually uses `iflatmap_unordered` ([here](https://github.com/huggingface/datasets/blob/819bb4346434912eb405ce3f3e9f21dc25a2fe85/src/datasets/arrow_dataset.py#L1509)). I think this solution works fine for the `map` method (which is the one being tested by the new `tests/test_arrow_dataset.py::BaseDatasetTest::test_map_crash_subprocess`, right?).", "Yes fixing iflatmap_unordered does fix Dataset.map, but it won't fix any Pool.map that we may use elsewhere so we'll have to keep this in mind.", "It looks all good to me, feel free to fix code formatting by running `make style` and we can merge :)", "> Yes fixing iflatmap_unordered does fix Dataset.map, but it won't fix any Pool.map that we may use elsewhere so we'll have to keep this in mind.\r\n\r\nRight, I agree. The best way moving forward is probably not using the buggy `multiprocess.Pool` anymore, and replace it with `concurrent.futures.ProcessPoolExecutor` as much as possible.\r\n\r\nAnyway, I've run `make style` now. Thanks for the support!", "It looks like checking the async_result._pool doesn't always work - sorry about that. We might just go back to your original solution then. Would also be cool to open an issue in `multiprocess` to ask if they have a solution or if they plan to fix this.", "@lhoestq no problem! Reverted to the previous version.\r\n\r\nTBH, given the discussions [in this python issue](https://github.com/python/cpython/issues/66587), I don't think the error in `multiprocess` will be merged upstream any time soon...", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.006060 / 0.011353 (-0.005293) | 0.003695 / 0.011008 (-0.007313) | 0.080484 / 0.038508 (0.041976) | 0.061894 / 0.023109 (0.038785) | 0.312510 / 0.275898 (0.036612) | 0.352398 / 0.323480 (0.028918) | 0.004638 / 0.007986 (-0.003348) | 0.002918 / 0.004328 (-0.001410) | 0.062932 / 0.004250 (0.058681) | 0.050859 / 0.037052 (0.013807) | 0.316812 / 0.258489 (0.058323) | 0.357684 / 0.293841 (0.063843) | 0.027622 / 0.128546 (-0.100924) | 0.008012 / 0.075646 (-0.067634) | 0.260970 / 0.419271 (-0.158302) | 0.045807 / 0.043533 (0.002275) | 0.321235 / 0.255139 (0.066096) | 0.343162 / 0.283200 (0.059962) | 0.021136 / 0.141683 (-0.120547) | 1.465886 / 1.452155 (0.013731) | 1.500216 / 1.492716 (0.007500) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.187286 / 0.018006 (0.169279) | 0.428724 / 0.000490 (0.428235) | 0.003029 / 0.000200 (0.002829) | 0.000063 / 0.000054 (0.000008) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.022703 / 0.037411 (-0.014708) | 0.072740 / 0.014526 (0.058215) | 0.083436 / 0.176557 (-0.093120) | 0.144559 / 0.737135 (-0.592577) | 0.083958 / 0.296338 (-0.212380) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.435729 / 0.215209 (0.220520) | 4.351146 / 2.077655 (2.273491) | 2.316627 / 1.504120 (0.812508) | 2.144587 / 1.541195 (0.603393) | 2.209182 / 1.468490 (0.740692) | 0.501131 / 4.584777 (-4.083646) | 3.077085 / 3.745712 (-0.668627) | 4.353706 / 5.269862 (-0.916156) | 2.621523 / 4.565676 (-1.944154) | 0.058976 / 0.424275 (-0.365299) | 0.006467 / 0.007607 (-0.001141) | 0.506690 / 0.226044 (0.280646) | 5.085787 / 2.268929 (2.816858) | 2.731336 / 55.444624 (-52.713289) | 2.419451 / 6.876477 (-4.457025) | 2.583649 / 2.142072 (0.441577) | 0.589869 / 4.805227 (-4.215359) | 0.131040 / 6.500664 (-6.369624) | 0.061332 / 0.075469 (-0.014137) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.220542 / 1.841788 (-0.621245) | 18.169643 / 8.074308 (10.095335) | 13.251704 / 10.191392 (3.060312) | 0.142952 / 0.680424 (-0.537472) | 0.016639 / 0.534201 (-0.517562) | 0.334851 / 0.579283 (-0.244432) | 0.361865 / 0.434364 (-0.072499) | 0.380933 / 0.540337 (-0.159404) | 0.527374 / 1.386936 (-0.859562) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.006319 / 0.011353 (-0.005034) | 0.003778 / 0.011008 (-0.007231) | 0.062388 / 0.038508 (0.023880) | 0.062228 / 0.023109 (0.039119) | 0.373727 / 0.275898 (0.097829) | 0.399442 / 0.323480 (0.075962) | 0.005434 / 0.007986 (-0.002551) | 0.003020 / 0.004328 (-0.001308) | 0.062774 / 0.004250 (0.058524) | 0.052784 / 0.037052 (0.015732) | 0.376428 / 0.258489 (0.117939) | 0.405039 / 0.293841 (0.111198) | 0.027884 / 0.128546 (-0.100662) | 0.008086 / 0.075646 (-0.067561) | 0.067078 / 0.419271 (-0.352194) | 0.042927 / 0.043533 (-0.000606) | 0.372142 / 0.255139 (0.117003) | 0.389604 / 0.283200 (0.106405) | 0.021582 / 0.141683 (-0.120101) | 1.473332 / 1.452155 (0.021177) | 1.536018 / 1.492716 (0.043302) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.184729 / 0.018006 (0.166723) | 0.421065 / 0.000490 (0.420575) | 0.002681 / 0.000200 (0.002481) | 0.000070 / 0.000054 (0.000015) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.026067 / 0.037411 (-0.011344) | 0.077138 / 0.014526 (0.062612) | 0.085178 / 0.176557 (-0.091379) | 0.139681 / 0.737135 (-0.597454) | 0.087528 / 0.296338 (-0.208810) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.444899 / 0.215209 (0.229690) | 4.459168 / 2.077655 (2.381513) | 2.408792 / 1.504120 (0.904672) | 2.237243 / 1.541195 (0.696048) | 2.296298 / 1.468490 (0.827808) | 0.498508 / 4.584777 (-4.086269) | 3.067064 / 3.745712 (-0.678648) | 4.470577 / 5.269862 (-0.799284) | 2.701972 / 4.565676 (-1.863705) | 0.057711 / 0.424275 (-0.366564) | 0.006443 / 0.007607 (-0.001164) | 0.524046 / 0.226044 (0.298002) | 5.229928 / 2.268929 (2.961000) | 2.862101 / 55.444624 (-52.582523) | 2.545972 / 6.876477 (-4.330504) | 2.606459 / 2.142072 (0.464387) | 0.593285 / 4.805227 (-4.211942) | 0.124913 / 6.500664 (-6.375751) | 0.061942 / 0.075469 (-0.013527) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.322162 / 1.841788 (-0.519625) | 18.745796 / 8.074308 (10.671488) | 13.955443 / 10.191392 (3.764051) | 0.145610 / 0.680424 (-0.534814) | 0.016817 / 0.534201 (-0.517384) | 0.331180 / 0.579283 (-0.248103) | 0.343019 / 0.434364 (-0.091345) | 0.379459 / 0.540337 (-0.160878) | 0.526403 / 1.386936 (-0.860533) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#aca4cdcc79f16ec5157a2a3a665fdef0e3aa176d \"CML watermark\")\n" ]

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[ "Duplicate of #", "Hi ! can you try to set the upper case environment variables `HTTP_PROXY` and `HTTPS_PROXY` ?\r\n\r\nWe use `aiohttp` for streaming and it uses case sensitive environment variables", "Hi, thanks for the quick reply.\r\n\r\nI set the uppercase env variables with\r\n\r\n`\r\nos.environ['HTTP_PROXY'] = \"http://example.com:xxxx\" \r\nos.environ['HTTPS_PROXY'] = \"http://example.com:xxxx\" \r\n`\r\n\r\nHowever, I still get the same error.\r\n\r\nOne thing that could be helpfull: When downloading a dataset without streaming i get the following message:\r\n_HF google storage unreachable. Downloading and preparing it from source_.\r\nThe download does however work as expected.\r\n", "Are you able to use `aiohttp` to get the file at `https://huggingface.co/datasets/facebook/voxpopuli/resolve/main/data/n_files.json` using your proxy ?", "It only works when passing trust_env=True when creating the ClientSession, as well as setting ssl=False.\r\n\r\nWorking Example:\r\n\r\n```\r\nimport os\r\n\r\nos.environ['HTTP_PROXY'] = \"xyz\"\r\nos.environ['HTTPS_PROXY'] = \"xyz\"\r\n\r\nimport asyncio\r\nimport aiohttp\r\n\r\nasync def download_pep(url):\r\n async with aiohttp.ClientSession(trust_env=True) as session:\r\n print(\"1\")\r\n async with session.get(url, ssl=False) as resp:\r\n print(\"2\")\r\n content = await resp.text()\r\n print(content)\r\n return content\r\n\r\nasyncio.run(download_pep(\"https://huggingface.co/datasets/facebook/voxpopuli/resolve/main/data/n_files.json\"))\r\n```\r\n\r\n\r\n\r\nSSL Verification has been a problem with other packages as well. Usually I circumvent the problem by setting\r\n```\r\nimport ssl\r\nssl._create_default_https_context = ssl._create_unverified_context\r\n```\r\n(probably not the best idea for security), although here aiohttp does not seem to use this default context.", "We do pass `trust_env` as well. Could you share the full stack trace you get when streaming using `datasets` ? That could help locate where we might have forgotten to pass `trust_env`", "Is there a way to disable ssl verification when streaming a dataset. I suspect this might be the isssue with my proxy.\r\n\r\n\r\nHere you go:\r\n\r\n```\r\nFileNotFoundError Traceback (most recent call last)\r\nCell In[8], line 3\r\n 1 from datasets import load_dataset\r\n----> 3 ds = load_dataset(\"facebook/voxpopuli\", name=\"de\", streaming=True)\r\n 5 sample = next(iter(ds))\r\n\r\nFile [~/.conda/envs/audio_hf/lib/python3.10/site-packages/datasets/load.py:1790](https://vscode-remote+ssh-002dremote-002bml-002er-002dsoftware-002eat.vscode-resource.vscode-cdn.net/home/wrsbri/projects/audio_course/~/.conda/envs/audio_hf/lib/python3.10/site-packages/datasets/load.py:1790), in load_dataset(path, name, data_dir, data_files, split, cache_dir, features, download_config, download_mode, verification_mode, ignore_verifications, keep_in_memory, save_infos, revision, use_auth_token, task, streaming, num_proc, storage_options, **config_kwargs)\r\n 1788 # Return iterable dataset in case of streaming\r\n 1789 if streaming:\r\n-> 1790 return builder_instance.as_streaming_dataset(split=split)\r\n 1792 # Some datasets are already processed on the HF google storage\r\n 1793 # Don't try downloading from Google storage for the packaged datasets as text, json, csv or pandas\r\n 1794 try_from_hf_gcs = path not in _PACKAGED_DATASETS_MODULES\r\n\r\nFile [~/.conda/envs/audio_hf/lib/python3.10/site-packages/datasets/builder.py:1281](https://vscode-remote+ssh-002dremote-002bml-002er-002dsoftware-002eat.vscode-resource.vscode-cdn.net/home/wrsbri/projects/audio_course/~/.conda/envs/audio_hf/lib/python3.10/site-packages/datasets/builder.py:1281), in DatasetBuilder.as_streaming_dataset(self, split, base_path)\r\n 1274 dl_manager = StreamingDownloadManager(\r\n 1275 base_path=base_path or self.base_path,\r\n 1276 download_config=DownloadConfig(use_auth_token=self.use_auth_token, storage_options=self.storage_options),\r\n 1277 dataset_name=self.name,\r\n 1278 data_dir=self.config.data_dir,\r\n 1279 )\r\n 1280 self._check_manual_download(dl_manager)\r\n-> 1281 splits_generators = {sg.name: sg for sg in self._split_generators(dl_manager)}\r\n 1282 # By default, return all splits\r\n 1283 if split is None:\r\n\r\nFile [~/.cache/huggingface/modules/datasets_modules/datasets/facebook--voxpopuli/b5ff837284f0778eefe0f642734e142d8c3f574eba8c9c8a4b13602297f73604/voxpopuli.py:120](https://vscode-remote+ssh-002dremote-002bml-002er-002dsoftware-002eat.vscode-resource.vscode-cdn.net/home/wrsbri/projects/audio_course/~/.cache/huggingface/modules/datasets_modules/datasets/facebook--voxpopuli/b5ff837284f0778eefe0f642734e142d8c3f574eba8c9c8a4b13602297f73604/voxpopuli.py:120), in Voxpopuli._split_generators(self, dl_manager)\r\n 118 def _split_generators(self, dl_manager):\r\n 119 n_shards_path = dl_manager.download_and_extract(_N_SHARDS_FILE)\r\n--> 120 with open(n_shards_path) as f:\r\n 121 n_shards = json.load(f)\r\n 123 if self.config.name == \"en_accented\":\r\n\r\nFile [~/.conda/envs/audio_hf/lib/python3.10/site-packages/datasets/streaming.py:71](https://vscode-remote+ssh-002dremote-002bml-002er-002dsoftware-002eat.vscode-resource.vscode-cdn.net/home/wrsbri/projects/audio_course/~/.conda/envs/audio_hf/lib/python3.10/site-packages/datasets/streaming.py:71), in extend_module_for_streaming..wrap_auth..wrapper(*args, **kwargs)\r\n 69 @wraps(function)\r\n 70 def wrapper(*args, **kwargs):\r\n---> 71 return function(*args, use_auth_token=use_auth_token, **kwargs)\r\n\r\nFile [~/.conda/envs/audio_hf/lib/python3.10/site-packages/datasets/download/streaming_download_manager.py:517](https://vscode-remote+ssh-002dremote-002bml-002er-002dsoftware-002eat.vscode-resource.vscode-cdn.net/home/wrsbri/projects/audio_course/~/.conda/envs/audio_hf/lib/python3.10/site-packages/datasets/download/streaming_download_manager.py:517), in xopen(file, mode, use_auth_token, *args, **kwargs)\r\n 515 except FileNotFoundError:\r\n 516 if file.startswith(config.HF_ENDPOINT):\r\n--> 517 raise FileNotFoundError(\r\n 518 file + \"\\nIf the repo is private or gated, make sure to log in with `huggingface-cli login`.\"\r\n 519 ) from None\r\n 520 else:\r\n 521 raise\r\n\r\nFileNotFoundError: https://huggingface.co/datasets/facebook/voxpopuli/resolve/main/data/n_files.json\r\nIf the repo is private or gated, make sure to log in with `huggingface-cli login`.\r\n```", "> Is there a way to disable ssl verification when streaming a dataset.\r\n\r\nI don't think so.\r\n\r\nWe use `fsspec` HTTPFileSystem implementation that is based on `aiohttp`. If you register a subclass of HTTPFileSystem that has SSL disabled by default it could work, but I wouldn't recommended it because it can raise security issues.", "Okay thanks for your help! I guess I have to figure out how to improve the proxy environment / see if I can make it work with ssl connections." ]

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[ "_The documentation is not available anymore as the PR was closed or merged._", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.006518 / 0.011353 (-0.004835) | 0.004121 / 0.011008 (-0.006887) | 0.103350 / 0.038508 (0.064842) | 0.045030 / 0.023109 (0.021920) | 0.351670 / 0.275898 (0.075772) | 0.408110 / 0.323480 (0.084630) | 0.003883 / 0.007986 (-0.004102) | 0.003352 / 0.004328 (-0.000977) | 0.078786 / 0.004250 (0.074535) | 0.063977 / 0.037052 (0.026925) | 0.369759 / 0.258489 (0.111270) | 0.415103 / 0.293841 (0.121262) | 0.033069 / 0.128546 (-0.095477) | 0.008863 / 0.075646 (-0.066783) | 0.353660 / 0.419271 (-0.065611) | 0.055714 / 0.043533 (0.012181) | 0.350458 / 0.255139 (0.095319) | 0.369505 / 0.283200 (0.086305) | 0.022822 / 0.141683 (-0.118861) | 1.537588 / 1.452155 (0.085433) | 1.590569 / 1.492716 (0.097853) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.206826 / 0.018006 (0.188819) | 0.471625 / 0.000490 (0.471135) | 0.005188 / 0.000200 (0.004988) | 0.000316 / 0.000054 (0.000261) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.028148 / 0.037411 (-0.009263) | 0.111941 / 0.014526 (0.097415) | 0.122106 / 0.176557 (-0.054451) | 0.181127 / 0.737135 (-0.556009) | 0.127534 / 0.296338 (-0.168805) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.409520 / 0.215209 (0.194311) | 4.098455 / 2.077655 (2.020800) | 1.852447 / 1.504120 (0.348327) | 1.657036 / 1.541195 (0.115842) | 1.709624 / 1.468490 (0.241134) | 0.542806 / 4.584777 (-4.041970) | 3.809352 / 3.745712 (0.063640) | 1.855412 / 5.269862 (-3.414449) | 1.109180 / 4.565676 (-3.456497) | 0.066801 / 0.424275 (-0.357474) | 0.011832 / 0.007607 (0.004225) | 0.518338 / 0.226044 (0.292293) | 5.190108 / 2.268929 (2.921179) | 2.320602 / 55.444624 (-53.124023) | 1.991416 / 6.876477 (-4.885060) | 2.106989 / 2.142072 (-0.035084) | 0.668914 / 4.805227 (-4.136313) | 0.145325 / 6.500664 (-6.355340) | 0.065145 / 0.075469 (-0.010324) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.254706 / 1.841788 (-0.587082) | 14.707264 / 8.074308 (6.632956) | 14.615423 / 10.191392 (4.424031) | 0.170764 / 0.680424 (-0.509659) | 0.017905 / 0.534201 (-0.516296) | 0.435606 / 0.579283 (-0.143677) | 0.434648 / 0.434364 (0.000284) | 0.520813 / 0.540337 (-0.019524) | 0.633902 / 1.386936 (-0.753034) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.007212 / 0.011353 (-0.004141) | 0.004301 / 0.011008 (-0.006707) | 0.080767 / 0.038508 (0.042258) | 0.051949 / 0.023109 (0.028840) | 0.398473 / 0.275898 (0.122575) | 0.465038 / 0.323480 (0.141558) | 0.005580 / 0.007986 (-0.002406) | 0.003556 / 0.004328 (-0.000773) | 0.080682 / 0.004250 (0.076431) | 0.059517 / 0.037052 (0.022464) | 0.421171 / 0.258489 (0.162682) | 0.459752 / 0.293841 (0.165911) | 0.032960 / 0.128546 (-0.095586) | 0.009107 / 0.075646 (-0.066539) | 0.086382 / 0.419271 (-0.332889) | 0.056053 / 0.043533 (0.012520) | 0.393357 / 0.255139 (0.138218) | 0.412972 / 0.283200 (0.129772) | 0.031115 / 0.141683 (-0.110568) | 1.576961 / 1.452155 (0.124806) | 1.627249 / 1.492716 (0.134533) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.227618 / 0.018006 (0.209612) | 0.444640 / 0.000490 (0.444150) | 0.004376 / 0.000200 (0.004176) | 0.000092 / 0.000054 (0.000038) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.030826 / 0.037411 (-0.006586) | 0.117587 / 0.014526 (0.103062) | 0.127467 / 0.176557 (-0.049089) | 0.184440 / 0.737135 (-0.552695) | 0.133664 / 0.296338 (-0.162675) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.443183 / 0.215209 (0.227974) | 4.408312 / 2.077655 (2.330658) | 2.132487 / 1.504120 (0.628367) | 1.923632 / 1.541195 (0.382438) | 1.967882 / 1.468490 (0.499392) | 0.552954 / 4.584777 (-4.031823) | 3.777701 / 3.745712 (0.031989) | 1.857686 / 5.269862 (-3.412176) | 1.104847 / 4.565676 (-3.460829) | 0.068350 / 0.424275 (-0.355925) | 0.012437 / 0.007607 (0.004830) | 0.559258 / 0.226044 (0.333214) | 5.593258 / 2.268929 (3.324330) | 2.648059 / 55.444624 (-52.796565) | 2.277428 / 6.876477 (-4.599049) | 2.351685 / 2.142072 (0.209612) | 0.678750 / 4.805227 (-4.126477) | 0.145550 / 6.500664 (-6.355114) | 0.066556 / 0.075469 (-0.008913) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.327128 / 1.841788 (-0.514659) | 15.649079 / 8.074308 (7.574771) | 14.478659 / 10.191392 (4.287267) | 0.147633 / 0.680424 (-0.532791) | 0.018502 / 0.534201 (-0.515699) | 0.438556 / 0.579283 (-0.140727) | 0.433381 / 0.434364 (-0.000983) | 0.514367 / 0.540337 (-0.025970) | 0.618347 / 1.386936 (-0.768589) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#16aa1c886c5b499641a4bb3d8ce4a4f7de8244b7 \"CML watermark\")\n", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.006078 / 0.011353 (-0.005275) | 0.003914 / 0.011008 (-0.007095) | 0.102039 / 0.038508 (0.063531) | 0.037660 / 0.023109 (0.014551) | 0.348963 / 0.275898 (0.073065) | 0.407284 / 0.323480 (0.083804) | 0.004661 / 0.007986 (-0.003324) | 0.003253 / 0.004328 (-0.001076) | 0.078276 / 0.004250 (0.074025) | 0.054144 / 0.037052 (0.017091) | 0.376715 / 0.258489 (0.118225) | 0.418499 / 0.293841 (0.124658) | 0.027627 / 0.128546 (-0.100919) | 0.008494 / 0.075646 (-0.067152) | 0.316894 / 0.419271 (-0.102377) | 0.046560 / 0.043533 (0.003027) | 0.339835 / 0.255139 (0.084696) | 0.374628 / 0.283200 (0.091428) | 0.020729 / 0.141683 (-0.120954) | 1.502769 / 1.452155 (0.050615) | 1.548756 / 1.492716 (0.056040) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.229192 / 0.018006 (0.211186) | 0.426245 / 0.000490 (0.425756) | 0.005190 / 0.000200 (0.004990) | 0.000081 / 0.000054 (0.000026) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.024271 / 0.037411 (-0.013140) | 0.098869 / 0.014526 (0.084343) | 0.105079 / 0.176557 (-0.071477) | 0.164707 / 0.737135 (-0.572428) | 0.110337 / 0.296338 (-0.186002) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.426593 / 0.215209 (0.211383) | 4.293977 / 2.077655 (2.216323) | 1.928502 / 1.504120 (0.424382) | 1.728623 / 1.541195 (0.187428) | 1.792084 / 1.468490 (0.323594) | 0.568737 / 4.584777 (-4.016040) | 3.438534 / 3.745712 (-0.307178) | 1.797798 / 5.269862 (-3.472063) | 1.054078 / 4.565676 (-3.511598) | 0.068711 / 0.424275 (-0.355564) | 0.011250 / 0.007607 (0.003643) | 0.529299 / 0.226044 (0.303255) | 5.283965 / 2.268929 (3.015037) | 2.358274 / 55.444624 (-53.086350) | 2.012818 / 6.876477 (-4.863659) | 2.109923 / 2.142072 (-0.032149) | 0.679556 / 4.805227 (-4.125671) | 0.138346 / 6.500664 (-6.362318) | 0.066349 / 0.075469 (-0.009120) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.193994 / 1.841788 (-0.647794) | 14.073158 / 8.074308 (5.998850) | 13.488525 / 10.191392 (3.297133) | 0.144536 / 0.680424 (-0.535888) | 0.016748 / 0.534201 (-0.517453) | 0.362703 / 0.579283 (-0.216580) | 0.389511 / 0.434364 (-0.044853) | 0.427296 / 0.540337 (-0.113041) | 0.513227 / 1.386936 (-0.873709) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.006215 / 0.011353 (-0.005138) | 0.003834 / 0.011008 (-0.007174) | 0.078001 / 0.038508 (0.039493) | 0.036537 / 0.023109 (0.013428) | 0.369724 / 0.275898 (0.093826) | 0.426761 / 0.323480 (0.103281) | 0.003602 / 0.007986 (-0.004383) | 0.003001 / 0.004328 (-0.001327) | 0.075989 / 0.004250 (0.071739) | 0.048618 / 0.037052 (0.011566) | 0.374296 / 0.258489 (0.115807) | 0.430330 / 0.293841 (0.136489) | 0.028299 / 0.128546 (-0.100247) | 0.008537 / 0.075646 (-0.067109) | 0.083275 / 0.419271 (-0.335997) | 0.043136 / 0.043533 (-0.000397) | 0.359072 / 0.255139 (0.103933) | 0.387391 / 0.283200 (0.104192) | 0.021202 / 0.141683 (-0.120481) | 1.520832 / 1.452155 (0.068677) | 1.567030 / 1.492716 (0.074313) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.230944 / 0.018006 (0.212938) | 0.422159 / 0.000490 (0.421669) | 0.003447 / 0.000200 (0.003247) | 0.000125 / 0.000054 (0.000071) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.025442 / 0.037411 (-0.011969) | 0.103944 / 0.014526 (0.089418) | 0.110577 / 0.176557 (-0.065979) | 0.161393 / 0.737135 (-0.575743) | 0.113482 / 0.296338 (-0.182857) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.485765 / 0.215209 (0.270556) | 4.845737 / 2.077655 (2.768083) | 2.556732 / 1.504120 (1.052612) | 2.348638 / 1.541195 (0.807443) | 2.379289 / 1.468490 (0.910799) | 0.561261 / 4.584777 (-4.023516) | 3.482468 / 3.745712 (-0.263244) | 3.061319 / 5.269862 (-2.208543) | 1.483938 / 4.565676 (-3.081738) | 0.067584 / 0.424275 (-0.356691) | 0.011333 / 0.007607 (0.003726) | 0.594342 / 0.226044 (0.368297) | 5.935477 / 2.268929 (3.666548) | 3.025029 / 55.444624 (-52.419595) | 2.687032 / 6.876477 (-4.189445) | 2.752470 / 2.142072 (0.610398) | 0.674470 / 4.805227 (-4.130757) | 0.136777 / 6.500664 (-6.363887) | 0.068335 / 0.075469 (-0.007134) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.336456 / 1.841788 (-0.505332) | 14.376007 / 8.074308 (6.301699) | 14.171375 / 10.191392 (3.979983) | 0.159620 / 0.680424 (-0.520804) | 0.016685 / 0.534201 (-0.517516) | 0.364344 / 0.579283 (-0.214939) | 0.395358 / 0.434364 (-0.039006) | 0.424876 / 0.540337 (-0.115461) | 0.513267 / 1.386936 (-0.873669) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#6ed837325cb539a5deb99129e5ad181d0269e050 \"CML watermark\")\n" ]

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[ "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.006983 / 0.011353 (-0.004369) | 0.004473 / 0.011008 (-0.006535) | 0.105158 / 0.038508 (0.066650) | 0.048973 / 0.023109 (0.025864) | 0.358771 / 0.275898 (0.082873) | 0.432389 / 0.323480 (0.108909) | 0.005689 / 0.007986 (-0.002297) | 0.003584 / 0.004328 (-0.000744) | 0.080852 / 0.004250 (0.076601) | 0.066133 / 0.037052 (0.029081) | 0.370981 / 0.258489 (0.112492) | 0.406942 / 0.293841 (0.113101) | 0.032123 / 0.128546 (-0.096424) | 0.009313 / 0.075646 (-0.066333) | 0.355220 / 0.419271 (-0.064051) | 0.055768 / 0.043533 (0.012235) | 0.370545 / 0.255139 (0.115406) | 0.375619 / 0.283200 (0.092419) | 0.024258 / 0.141683 (-0.117425) | 1.559073 / 1.452155 (0.106918) | 1.616520 / 1.492716 (0.123804) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.277893 / 0.018006 (0.259887) | 0.535447 / 0.000490 (0.534957) | 0.004877 / 0.000200 (0.004677) | 0.000092 / 0.000054 (0.000037) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.029444 / 0.037411 (-0.007968) | 0.114366 / 0.014526 (0.099841) | 0.130957 / 0.176557 (-0.045599) | 0.189604 / 0.737135 (-0.547531) | 0.131682 / 0.296338 (-0.164656) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.412315 / 0.215209 (0.197106) | 4.093879 / 2.077655 (2.016225) | 1.856169 / 1.504120 (0.352050) | 1.655358 / 1.541195 (0.114164) | 1.758190 / 1.468490 (0.289699) | 0.545829 / 4.584777 (-4.038948) | 3.871436 / 3.745712 (0.125724) | 1.938244 / 5.269862 (-3.331618) | 1.122727 / 4.565676 (-3.442950) | 0.067107 / 0.424275 (-0.357168) | 0.012012 / 0.007607 (0.004405) | 0.518868 / 0.226044 (0.292824) | 5.235081 / 2.268929 (2.966153) | 2.335115 / 55.444624 (-53.109509) | 2.013074 / 6.876477 (-4.863402) | 2.219808 / 2.142072 (0.077735) | 0.674602 / 4.805227 (-4.130626) | 0.147051 / 6.500664 (-6.353613) | 0.068444 / 0.075469 (-0.007025) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.245600 / 1.841788 (-0.596188) | 15.537727 / 8.074308 (7.463419) | 15.074300 / 10.191392 (4.882908) | 0.194217 / 0.680424 (-0.486207) | 0.018536 / 0.534201 (-0.515665) | 0.437085 / 0.579283 (-0.142198) | 0.441123 / 0.434364 (0.006759) | 0.530681 / 0.540337 (-0.009657) | 0.649154 / 1.386936 (-0.737782) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.007243 / 0.011353 (-0.004110) | 0.004688 / 0.011008 (-0.006320) | 0.079809 / 0.038508 (0.041301) | 0.046915 / 0.023109 (0.023805) | 0.415144 / 0.275898 (0.139246) | 0.474867 / 0.323480 (0.151388) | 0.004550 / 0.007986 (-0.003435) | 0.004585 / 0.004328 (0.000257) | 0.080837 / 0.004250 (0.076587) | 0.061667 / 0.037052 (0.024614) | 0.411321 / 0.258489 (0.152832) | 0.464195 / 0.293841 (0.170354) | 0.032510 / 0.128546 (-0.096037) | 0.009306 / 0.075646 (-0.066340) | 0.086637 / 0.419271 (-0.332635) | 0.053335 / 0.043533 (0.009802) | 0.402302 / 0.255139 (0.147163) | 0.424864 / 0.283200 (0.141664) | 0.026573 / 0.141683 (-0.115110) | 1.566793 / 1.452155 (0.114639) | 1.628118 / 1.492716 (0.135401) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.317802 / 0.018006 (0.299796) | 0.544593 / 0.000490 (0.544103) | 0.005690 / 0.000200 (0.005490) | 0.000107 / 0.000054 (0.000053) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.033015 / 0.037411 (-0.004397) | 0.121940 / 0.014526 (0.107414) | 0.132920 / 0.176557 (-0.043637) | 0.191481 / 0.737135 (-0.545655) | 0.139139 / 0.296338 (-0.157199) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.460382 / 0.215209 (0.245173) | 4.610046 / 2.077655 (2.532392) | 2.296573 / 1.504120 (0.792453) | 2.099735 / 1.541195 (0.558540) | 2.213913 / 1.468490 (0.745423) | 0.544871 / 4.584777 (-4.039906) | 3.814174 / 3.745712 (0.068462) | 3.246397 / 5.269862 (-2.023464) | 1.480236 / 4.565676 (-3.085440) | 0.068464 / 0.424275 (-0.355811) | 0.012651 / 0.007607 (0.005043) | 0.564989 / 0.226044 (0.338944) | 5.639188 / 2.268929 (3.370259) | 2.827601 / 55.444624 (-52.617023) | 2.473743 / 6.876477 (-4.402734) | 2.567413 / 2.142072 (0.425340) | 0.674351 / 4.805227 (-4.130876) | 0.146248 / 6.500664 (-6.354416) | 0.067553 / 0.075469 (-0.007916) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.346703 / 1.841788 (-0.495085) | 16.494787 / 8.074308 (8.420479) | 15.179487 / 10.191392 (4.988095) | 0.181864 / 0.680424 (-0.498560) | 0.018857 / 0.534201 (-0.515344) | 0.437787 / 0.579283 (-0.141496) | 0.431770 / 0.434364 (-0.002594) | 0.507116 / 0.540337 (-0.033221) | 0.608899 / 1.386936 (-0.778037) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#0fd5b7412f907675e76b183a6e39ef6d176fdcc0 \"CML watermark\")\n", "_The documentation is not available anymore as the PR was closed or merged._", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.005963 / 0.011353 (-0.005390) | 0.003743 / 0.011008 (-0.007265) | 0.098519 / 0.038508 (0.060011) | 0.037392 / 0.023109 (0.014283) | 0.322706 / 0.275898 (0.046808) | 0.380032 / 0.323480 (0.056552) | 0.004694 / 0.007986 (-0.003292) | 0.002897 / 0.004328 (-0.001432) | 0.078664 / 0.004250 (0.074414) | 0.052646 / 0.037052 (0.015594) | 0.335523 / 0.258489 (0.077034) | 0.375464 / 0.293841 (0.081623) | 0.027537 / 0.128546 (-0.101010) | 0.008452 / 0.075646 (-0.067194) | 0.313844 / 0.419271 (-0.105427) | 0.047368 / 0.043533 (0.003835) | 0.313833 / 0.255139 (0.058694) | 0.342284 / 0.283200 (0.059085) | 0.021136 / 0.141683 (-0.120547) | 1.544764 / 1.452155 (0.092610) | 1.563850 / 1.492716 (0.071134) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.188609 / 0.018006 (0.170603) | 0.421686 / 0.000490 (0.421196) | 0.003336 / 0.000200 (0.003136) | 0.000077 / 0.000054 (0.000023) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.023678 / 0.037411 (-0.013733) | 0.099191 / 0.014526 (0.084665) | 0.105819 / 0.176557 (-0.070738) | 0.169654 / 0.737135 (-0.567481) | 0.110240 / 0.296338 (-0.186099) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.425497 / 0.215209 (0.210288) | 4.237165 / 2.077655 (2.159510) | 1.902953 / 1.504120 (0.398833) | 1.699012 / 1.541195 (0.157818) | 1.751107 / 1.468490 (0.282617) | 0.563326 / 4.584777 (-4.021451) | 3.394189 / 3.745712 (-0.351523) | 2.706129 / 5.269862 (-2.563732) | 1.361522 / 4.565676 (-3.204155) | 0.067776 / 0.424275 (-0.356499) | 0.010959 / 0.007607 (0.003352) | 0.530905 / 0.226044 (0.304860) | 5.322467 / 2.268929 (3.053538) | 2.384356 / 55.444624 (-53.060269) | 2.044196 / 6.876477 (-4.832281) | 2.119837 / 2.142072 (-0.022235) | 0.682236 / 4.805227 (-4.122991) | 0.136921 / 6.500664 (-6.363743) | 0.066784 / 0.075469 (-0.008685) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.210642 / 1.841788 (-0.631146) | 13.804572 / 8.074308 (5.730264) | 13.309229 / 10.191392 (3.117837) | 0.154356 / 0.680424 (-0.526068) | 0.016833 / 0.534201 (-0.517368) | 0.366503 / 0.579283 (-0.212780) | 0.385201 / 0.434364 (-0.049163) | 0.426713 / 0.540337 (-0.113624) | 0.516795 / 1.386936 (-0.870141) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.006144 / 0.011353 (-0.005209) | 0.003723 / 0.011008 (-0.007285) | 0.077427 / 0.038508 (0.038919) | 0.037636 / 0.023109 (0.014527) | 0.375048 / 0.275898 (0.099150) | 0.442254 / 0.323480 (0.118774) | 0.003506 / 0.007986 (-0.004480) | 0.003751 / 0.004328 (-0.000577) | 0.076771 / 0.004250 (0.072521) | 0.047915 / 0.037052 (0.010862) | 0.378918 / 0.258489 (0.120429) | 0.435300 / 0.293841 (0.141459) | 0.028317 / 0.128546 (-0.100230) | 0.008413 / 0.075646 (-0.067233) | 0.082774 / 0.419271 (-0.336497) | 0.043211 / 0.043533 (-0.000321) | 0.362022 / 0.255139 (0.106883) | 0.404928 / 0.283200 (0.121728) | 0.020692 / 0.141683 (-0.120991) | 1.527303 / 1.452155 (0.075148) | 1.596091 / 1.492716 (0.103375) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.225537 / 0.018006 (0.207530) | 0.399901 / 0.000490 (0.399412) | 0.000424 / 0.000200 (0.000224) | 0.000058 / 0.000054 (0.000004) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.026483 / 0.037411 (-0.010928) | 0.104373 / 0.014526 (0.089847) | 0.111271 / 0.176557 (-0.065286) | 0.163872 / 0.737135 (-0.573264) | 0.113991 / 0.296338 (-0.182347) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.456484 / 0.215209 (0.241275) | 4.572652 / 2.077655 (2.494998) | 2.374908 / 1.504120 (0.870788) | 2.207855 / 1.541195 (0.666661) | 2.260009 / 1.468490 (0.791519) | 0.562678 / 4.584777 (-4.022099) | 3.441778 / 3.745712 (-0.303934) | 1.729006 / 5.269862 (-3.540855) | 1.024937 / 4.565676 (-3.540739) | 0.068707 / 0.424275 (-0.355568) | 0.011334 / 0.007607 (0.003727) | 0.564293 / 0.226044 (0.338248) | 5.638367 / 2.268929 (3.369438) | 2.665654 / 55.444624 (-52.778970) | 2.320033 / 6.876477 (-4.556444) | 2.328706 / 2.142072 (0.186634) | 0.677433 / 4.805227 (-4.127794) | 0.137190 / 6.500664 (-6.363474) | 0.068585 / 0.075469 (-0.006885) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.312476 / 1.841788 (-0.529312) | 14.206685 / 8.074308 (6.132377) | 14.217928 / 10.191392 (4.026536) | 0.143416 / 0.680424 (-0.537007) | 0.016647 / 0.534201 (-0.517554) | 0.361228 / 0.579283 (-0.218055) | 0.396185 / 0.434364 (-0.038178) | 0.423275 / 0.540337 (-0.117063) | 0.512966 / 1.386936 (-0.873970) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#b424648fd68bd0b5279eb916cec4836d1220e268 \"CML watermark\")\n", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.008913 / 0.011353 (-0.002440) | 0.005142 / 0.011008 (-0.005866) | 0.133958 / 0.038508 (0.095449) | 0.049180 / 0.023109 (0.026071) | 0.389169 / 0.275898 (0.113270) | 0.481513 / 0.323480 (0.158033) | 0.006555 / 0.007986 (-0.001430) | 0.003806 / 0.004328 (-0.000522) | 0.102056 / 0.004250 (0.097806) | 0.083259 / 0.037052 (0.046207) | 0.392536 / 0.258489 (0.134047) | 0.447503 / 0.293841 (0.153662) | 0.047472 / 0.128546 (-0.081074) | 0.014748 / 0.075646 (-0.060899) | 0.475619 / 0.419271 (0.056348) | 0.107306 / 0.043533 (0.063773) | 0.421942 / 0.255139 (0.166803) | 0.419736 / 0.283200 (0.136536) | 0.044195 / 0.141683 (-0.097488) | 1.793840 / 1.452155 (0.341686) | 1.960204 / 1.492716 (0.467488) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.252046 / 0.018006 (0.234040) | 0.627725 / 0.000490 (0.627236) | 0.007435 / 0.000200 (0.007235) | 0.000526 / 0.000054 (0.000472) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.034656 / 0.037411 (-0.002755) | 0.114534 / 0.014526 (0.100008) | 0.135804 / 0.176557 (-0.040753) | 0.209309 / 0.737135 (-0.527826) | 0.140369 / 0.296338 (-0.155969) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.636736 / 0.215209 (0.421527) | 6.039985 / 2.077655 (3.962330) | 2.640141 / 1.504120 (1.136021) | 2.284492 / 1.541195 (0.743297) | 2.324956 / 1.468490 (0.856466) | 0.934499 / 4.584777 (-3.650278) | 5.673415 / 3.745712 (1.927703) | 5.184584 / 5.269862 (-0.085278) | 2.661911 / 4.565676 (-1.903766) | 0.150420 / 0.424275 (-0.273855) | 0.015655 / 0.007607 (0.008048) | 0.748290 / 0.226044 (0.522246) | 7.579755 / 2.268929 (5.310827) | 3.346732 / 55.444624 (-52.097892) | 2.708212 / 6.876477 (-4.168264) | 2.682423 / 2.142072 (0.540351) | 1.170389 / 4.805227 (-3.634838) | 0.215775 / 6.500664 (-6.284889) | 0.076360 / 0.075469 (0.000891) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.516794 / 1.841788 (-0.324993) | 18.709117 / 8.074308 (10.634809) | 22.492542 / 10.191392 (12.301150) | 0.237978 / 0.680424 (-0.442446) | 0.027828 / 0.534201 (-0.506373) | 0.499968 / 0.579283 (-0.079315) | 0.645899 / 0.434364 (0.211535) | 0.548599 / 0.540337 (0.008262) | 0.675428 / 1.386936 (-0.711508) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.008469 / 0.011353 (-0.002884) | 0.005420 / 0.011008 (-0.005589) | 0.093340 / 0.038508 (0.054832) | 0.045896 / 0.023109 (0.022786) | 0.533267 / 0.275898 (0.257369) | 0.596034 / 0.323480 (0.272555) | 0.004816 / 0.007986 (-0.003170) | 0.004379 / 0.004328 (0.000051) | 0.096356 / 0.004250 (0.092106) | 0.058339 / 0.037052 (0.021287) | 0.574464 / 0.258489 (0.315975) | 0.649301 / 0.293841 (0.355461) | 0.047599 / 0.128546 (-0.080947) | 0.013759 / 0.075646 (-0.061887) | 0.104672 / 0.419271 (-0.314599) | 0.061658 / 0.043533 (0.018125) | 0.560956 / 0.255139 (0.305817) | 0.585328 / 0.283200 (0.302128) | 0.034137 / 0.141683 (-0.107546) | 1.844528 / 1.452155 (0.392373) | 1.971398 / 1.492716 (0.478682) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.278666 / 0.018006 (0.260660) | 0.577342 / 0.000490 (0.576853) | 0.005496 / 0.000200 (0.005296) | 0.000131 / 0.000054 (0.000076) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.029863 / 0.037411 (-0.007549) | 0.161703 / 0.014526 (0.147177) | 0.132279 / 0.176557 (-0.044277) | 0.227345 / 0.737135 (-0.509791) | 0.138047 / 0.296338 (-0.158291) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.651535 / 0.215209 (0.436326) | 7.077949 / 2.077655 (5.000295) | 2.926990 / 1.504120 (1.422871) | 2.598872 / 1.541195 (1.057678) | 2.614192 / 1.468490 (1.145702) | 0.913845 / 4.584777 (-3.670932) | 5.704301 / 3.745712 (1.958589) | 2.796914 / 5.269862 (-2.472948) | 1.836096 / 4.565676 (-2.729580) | 0.106294 / 0.424275 (-0.317981) | 0.012705 / 0.007607 (0.005098) | 0.836336 / 0.226044 (0.610291) | 8.234079 / 2.268929 (5.965150) | 3.836410 / 55.444624 (-51.608215) | 3.116752 / 6.876477 (-3.759724) | 3.154258 / 2.142072 (1.012186) | 1.195794 / 4.805227 (-3.609434) | 0.240491 / 6.500664 (-6.260173) | 0.087913 / 0.075469 (0.012444) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.724723 / 1.841788 (-0.117064) | 19.492194 / 8.074308 (11.417885) | 21.443341 / 10.191392 (11.251949) | 0.245819 / 0.680424 (-0.434605) | 0.027024 / 0.534201 (-0.507177) | 0.481071 / 0.579283 (-0.098212) | 0.596359 / 0.434364 (0.161995) | 0.646462 / 0.540337 (0.106124) | 0.706380 / 1.386936 (-0.680556) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#67ca664e6d5ef137127b238aae1d0aff54e22db2 \"CML watermark\")\n", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.006634 / 0.011353 (-0.004719) | 0.004003 / 0.011008 (-0.007005) | 0.097874 / 0.038508 (0.059365) | 0.043528 / 0.023109 (0.020419) | 0.302293 / 0.275898 (0.026395) | 0.357041 / 0.323480 (0.033561) | 0.003761 / 0.007986 (-0.004225) | 0.004312 / 0.004328 (-0.000016) | 0.076253 / 0.004250 (0.072003) | 0.062807 / 0.037052 (0.025755) | 0.316737 / 0.258489 (0.058248) | 0.356722 / 0.293841 (0.062881) | 0.030816 / 0.128546 (-0.097730) | 0.008691 / 0.075646 (-0.066955) | 0.328366 / 0.419271 (-0.090906) | 0.062299 / 0.043533 (0.018766) | 0.293877 / 0.255139 (0.038738) | 0.319832 / 0.283200 (0.036632) | 0.024996 / 0.141683 (-0.116687) | 1.473912 / 1.452155 (0.021758) | 1.565439 / 1.492716 (0.072723) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.208428 / 0.018006 (0.190422) | 0.435618 / 0.000490 (0.435128) | 0.000695 / 0.000200 (0.000495) | 0.000056 / 0.000054 (0.000001) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.026253 / 0.037411 (-0.011158) | 0.106908 / 0.014526 (0.092382) | 0.117075 / 0.176557 (-0.059482) | 0.177969 / 0.737135 (-0.559166) | 0.123400 / 0.296338 (-0.172938) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.424970 / 0.215209 (0.209761) | 4.203233 / 2.077655 (2.125578) | 2.009679 / 1.504120 (0.505559) | 1.825691 / 1.541195 (0.284496) | 1.870639 / 1.468490 (0.402149) | 0.530758 / 4.584777 (-4.054019) | 3.718791 / 3.745712 (-0.026921) | 1.800206 / 5.269862 (-3.469656) | 1.071651 / 4.565676 (-3.494025) | 0.065126 / 0.424275 (-0.359149) | 0.011312 / 0.007607 (0.003704) | 0.532503 / 0.226044 (0.306458) | 5.353950 / 2.268929 (3.085021) | 2.463548 / 55.444624 (-52.981076) | 2.139832 / 6.876477 (-4.736645) | 2.238722 / 2.142072 (0.096650) | 0.655736 / 4.805227 (-4.149492) | 0.141689 / 6.500664 (-6.358975) | 0.063282 / 0.075469 (-0.012187) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.183523 / 1.841788 (-0.658265) | 14.146428 / 8.074308 (6.072120) | 14.312883 / 10.191392 (4.121491) | 0.169286 / 0.680424 (-0.511138) | 0.017343 / 0.534201 (-0.516858) | 0.397934 / 0.579283 (-0.181349) | 0.417791 / 0.434364 (-0.016573) | 0.463639 / 0.540337 (-0.076698) | 0.562787 / 1.386936 (-0.824149) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.006594 / 0.011353 (-0.004759) | 0.004086 / 0.011008 (-0.006922) | 0.075122 / 0.038508 (0.036614) | 0.041849 / 0.023109 (0.018740) | 0.362645 / 0.275898 (0.086747) | 0.464350 / 0.323480 (0.140870) | 0.003760 / 0.007986 (-0.004226) | 0.003327 / 0.004328 (-0.001001) | 0.076154 / 0.004250 (0.071904) | 0.053232 / 0.037052 (0.016180) | 0.407863 / 0.258489 (0.149374) | 0.460787 / 0.293841 (0.166946) | 0.031917 / 0.128546 (-0.096630) | 0.008770 / 0.075646 (-0.066876) | 0.082612 / 0.419271 (-0.336660) | 0.051311 / 0.043533 (0.007779) | 0.354508 / 0.255139 (0.099369) | 0.419533 / 0.283200 (0.136334) | 0.023980 / 0.141683 (-0.117703) | 1.491255 / 1.452155 (0.039100) | 1.536101 / 1.492716 (0.043384) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.178261 / 0.018006 (0.160255) | 0.444680 / 0.000490 (0.444190) | 0.013761 / 0.000200 (0.013561) | 0.000117 / 0.000054 (0.000063) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.027875 / 0.037411 (-0.009536) | 0.111269 / 0.014526 (0.096744) | 0.121096 / 0.176557 (-0.055461) | 0.174387 / 0.737135 (-0.562749) | 0.124714 / 0.296338 (-0.171624) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.445422 / 0.215209 (0.230213) | 4.435877 / 2.077655 (2.358222) | 2.221895 / 1.504120 (0.717775) | 2.030571 / 1.541195 (0.489376) | 2.074863 / 1.468490 (0.606373) | 0.543331 / 4.584777 (-4.041446) | 3.753615 / 3.745712 (0.007903) | 3.317074 / 5.269862 (-1.952787) | 1.630390 / 4.565676 (-2.935286) | 0.066726 / 0.424275 (-0.357549) | 0.011556 / 0.007607 (0.003949) | 0.546985 / 0.226044 (0.320941) | 5.460634 / 2.268929 (3.191705) | 2.705945 / 55.444624 (-52.738679) | 2.373425 / 6.876477 (-4.503052) | 2.401472 / 2.142072 (0.259399) | 0.663225 / 4.805227 (-4.142002) | 0.143694 / 6.500664 (-6.356970) | 0.065283 / 0.075469 (-0.010186) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.264804 / 1.841788 (-0.576983) | 14.803228 / 8.074308 (6.728919) | 14.178514 / 10.191392 (3.987122) | 0.162651 / 0.680424 (-0.517772) | 0.017586 / 0.534201 (-0.516615) | 0.398740 / 0.579283 (-0.180543) | 0.414478 / 0.434364 (-0.019886) | 0.465442 / 0.540337 (-0.074895) | 0.563450 / 1.386936 (-0.823486) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#76f75a9a3b2aaad05ea0ea5ab77e01fd2ca66760 \"CML watermark\")\n" ]

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[ "Loading a local dataset also works the same way when `data_files` are not specified, so I agree we should make this info easier to discover \r\n\r\ncc @stevhliu ", "Is this issue open? If so, I will self assign. ", "@benjaminbrown038 Yes, it is. Maybe @stevhliu can give some pointers on improving this doc page's discoverability.", "I think we can add a version of the [Main use-case](https://huggingface.co/docs/datasets/repository_structure#main-usecase) section to the [Share a dataset to the Hub](https://huggingface.co/docs/datasets/upload_dataset) tutorial. \r\n\r\nCurrently, it doesn't tell you *how* to structure the repository; it only tells you how to create it. So adding the \"main use-case\" will help bridge the gap and make it easier to find. We should also add a link to the [Structure your repository](https://huggingface.co/docs/datasets/repository_structure) guide for users who want to learn about the other options.", "#self-assign" ]

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[ "Here's a minimal way to reproduce the bug, for the sake of convenience.\r\n````\r\nfrom datasets import Dataset, DatasetInfo, load_dataset\r\n\r\n\r\nepisodes_dict = {\"test\":[1,2,3],\"test2\": [1,2,4]}\r\n\r\nhugging_face_dataset = Dataset.from_dict(\r\n episodes_dict, info=DatasetInfo(description=\"test_str\")\r\n)\r\nprint(hugging_face_dataset.info)\r\n\r\nhugging_face_dataset.push_to_hub(\"balisujohn/minari_test\", private=True)\r\n\r\nredownloaded_dataset= load_dataset(\"balisujohn/minari_test\")[\"train\"]\r\n\r\n\r\nprint(redownloaded_dataset.info)\r\n````\r\n", "Thanks for reporting !\r\n\r\nFor now I would recommend uploading a separate JSON file for your metadata.\r\n\r\nAlternatively you can upload a second configuration of the dataset containing your metadata but this feature is not released yet (though you can already use it from [here](https://github.com/huggingface/datasets/pull/5331), it will be released soon)" ]

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[ "_The documentation is not available anymore as the PR was closed or merged._", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.006770 / 0.011353 (-0.004583) | 0.004143 / 0.011008 (-0.006865) | 0.098928 / 0.038508 (0.060420) | 0.044893 / 0.023109 (0.021783) | 0.302630 / 0.275898 (0.026732) | 0.368173 / 0.323480 (0.044693) | 0.005631 / 0.007986 (-0.002354) | 0.003397 / 0.004328 (-0.000931) | 0.075748 / 0.004250 (0.071497) | 0.062582 / 0.037052 (0.025530) | 0.329586 / 0.258489 (0.071097) | 0.362625 / 0.293841 (0.068784) | 0.033250 / 0.128546 (-0.095296) | 0.008880 / 0.075646 (-0.066766) | 0.329683 / 0.419271 (-0.089588) | 0.054426 / 0.043533 (0.010893) | 0.297940 / 0.255139 (0.042801) | 0.319796 / 0.283200 (0.036597) | 0.023296 / 0.141683 (-0.118387) | 1.462142 / 1.452155 (0.009987) | 1.495796 / 1.492716 (0.003079) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.201771 / 0.018006 (0.183765) | 0.454514 / 0.000490 (0.454024) | 0.003333 / 0.000200 (0.003133) | 0.000081 / 0.000054 (0.000027) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.028084 / 0.037411 (-0.009327) | 0.109452 / 0.014526 (0.094926) | 0.119200 / 0.176557 (-0.057357) | 0.180302 / 0.737135 (-0.556834) | 0.125653 / 0.296338 (-0.170686) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.409819 / 0.215209 (0.194610) | 4.055117 / 2.077655 (1.977462) | 1.855279 / 1.504120 (0.351159) | 1.655281 / 1.541195 (0.114086) | 1.687938 / 1.468490 (0.219448) | 0.528352 / 4.584777 (-4.056425) | 3.750250 / 3.745712 (0.004538) | 3.386741 / 5.269862 (-1.883121) | 1.572036 / 4.565676 (-2.993640) | 0.065125 / 0.424275 (-0.359150) | 0.011259 / 0.007607 (0.003652) | 0.513449 / 0.226044 (0.287405) | 5.139421 / 2.268929 (2.870492) | 2.316973 / 55.444624 (-53.127651) | 1.984109 / 6.876477 (-4.892368) | 2.127915 / 2.142072 (-0.014158) | 0.653238 / 4.805227 (-4.151989) | 0.142686 / 6.500664 (-6.357978) | 0.063666 / 0.075469 (-0.011803) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.185174 / 1.841788 (-0.656614) | 14.790282 / 8.074308 (6.715974) | 13.089222 / 10.191392 (2.897830) | 0.146055 / 0.680424 (-0.534369) | 0.017835 / 0.534201 (-0.516366) | 0.399598 / 0.579283 (-0.179685) | 0.425296 / 0.434364 (-0.009068) | 0.478552 / 0.540337 (-0.061786) | 0.579702 / 1.386936 (-0.807234) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.006750 / 0.011353 (-0.004603) | 0.004156 / 0.011008 (-0.006853) | 0.074948 / 0.038508 (0.036440) | 0.043368 / 0.023109 (0.020259) | 0.355389 / 0.275898 (0.079491) | 0.429167 / 0.323480 (0.105687) | 0.003911 / 0.007986 (-0.004075) | 0.004340 / 0.004328 (0.000012) | 0.075940 / 0.004250 (0.071689) | 0.054293 / 0.037052 (0.017241) | 0.400317 / 0.258489 (0.141827) | 0.432001 / 0.293841 (0.138160) | 0.032340 / 0.128546 (-0.096206) | 0.008876 / 0.075646 (-0.066770) | 0.082284 / 0.419271 (-0.336987) | 0.050819 / 0.043533 (0.007286) | 0.351994 / 0.255139 (0.096855) | 0.375917 / 0.283200 (0.092717) | 0.022466 / 0.141683 (-0.119217) | 1.538824 / 1.452155 (0.086669) | 1.563995 / 1.492716 (0.071279) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.227330 / 0.018006 (0.209323) | 0.446380 / 0.000490 (0.445890) | 0.000408 / 0.000200 (0.000208) | 0.000058 / 0.000054 (0.000003) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.028534 / 0.037411 (-0.008878) | 0.113467 / 0.014526 (0.098941) | 0.123590 / 0.176557 (-0.052966) | 0.174309 / 0.737135 (-0.562827) | 0.130631 / 0.296338 (-0.165707) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.441020 / 0.215209 (0.225811) | 4.386564 / 2.077655 (2.308909) | 2.100704 / 1.504120 (0.596584) | 1.901484 / 1.541195 (0.360289) | 1.963494 / 1.468490 (0.495004) | 0.536838 / 4.584777 (-4.047939) | 3.739071 / 3.745712 (-0.006642) | 3.278981 / 5.269862 (-1.990881) | 1.515476 / 4.565676 (-3.050201) | 0.066388 / 0.424275 (-0.357887) | 0.011857 / 0.007607 (0.004250) | 0.545507 / 0.226044 (0.319463) | 5.441479 / 2.268929 (3.172550) | 2.602144 / 55.444624 (-52.842480) | 2.235583 / 6.876477 (-4.640894) | 2.293458 / 2.142072 (0.151385) | 0.658535 / 4.805227 (-4.146692) | 0.141327 / 6.500664 (-6.359337) | 0.063726 / 0.075469 (-0.011743) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.247819 / 1.841788 (-0.593968) | 15.234524 / 8.074308 (7.160216) | 14.592700 / 10.191392 (4.401308) | 0.141952 / 0.680424 (-0.538472) | 0.017747 / 0.534201 (-0.516454) | 0.396819 / 0.579283 (-0.182465) | 0.415902 / 0.434364 (-0.018462) | 0.464619 / 0.540337 (-0.075718) | 0.560866 / 1.386936 (-0.826070) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#4b7f6c59deb868e21f295917548fa2df10dd0158 \"CML watermark\")\n", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.008278 / 0.011353 (-0.003075) | 0.005044 / 0.011008 (-0.005964) | 0.123382 / 0.038508 (0.084874) | 0.054039 / 0.023109 (0.030929) | 0.382338 / 0.275898 (0.106440) | 0.453287 / 0.323480 (0.129807) | 0.006342 / 0.007986 (-0.001644) | 0.003930 / 0.004328 (-0.000398) | 0.094039 / 0.004250 (0.089789) | 0.076525 / 0.037052 (0.039472) | 0.394066 / 0.258489 (0.135577) | 0.445600 / 0.293841 (0.151759) | 0.039348 / 0.128546 (-0.089199) | 0.010485 / 0.075646 (-0.065161) | 0.433730 / 0.419271 (0.014459) | 0.082671 / 0.043533 (0.039138) | 0.375250 / 0.255139 (0.120111) | 0.416269 / 0.283200 (0.133070) | 0.038397 / 0.141683 (-0.103286) | 1.864834 / 1.452155 (0.412680) | 2.010453 / 1.492716 (0.517737) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.240008 / 0.018006 (0.222002) | 0.470975 / 0.000490 (0.470485) | 0.004001 / 0.000200 (0.003801) | 0.000097 / 0.000054 (0.000042) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.031107 / 0.037411 (-0.006304) | 0.129371 / 0.014526 (0.114846) | 0.141559 / 0.176557 (-0.034997) | 0.205571 / 0.737135 (-0.531564) | 0.144611 / 0.296338 (-0.151728) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.506972 / 0.215209 (0.291763) | 5.055951 / 2.077655 (2.978296) | 2.397438 / 1.504120 (0.893318) | 2.170435 / 1.541195 (0.629240) | 2.240296 / 1.468490 (0.771806) | 0.641559 / 4.584777 (-3.943218) | 4.644772 / 3.745712 (0.899060) | 4.064200 / 5.269862 (-1.205662) | 1.946991 / 4.565676 (-2.618685) | 0.086413 / 0.424275 (-0.337862) | 0.015082 / 0.007607 (0.007475) | 0.670413 / 0.226044 (0.444369) | 6.331346 / 2.268929 (4.062418) | 2.965813 / 55.444624 (-52.478812) | 2.547952 / 6.876477 (-4.328524) | 2.718390 / 2.142072 (0.576318) | 0.796657 / 4.805227 (-4.008571) | 0.173229 / 6.500664 (-6.327435) | 0.079606 / 0.075469 (0.004137) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.568761 / 1.841788 (-0.273026) | 18.485432 / 8.074308 (10.411124) | 15.758513 / 10.191392 (5.567121) | 0.170427 / 0.680424 (-0.509997) | 0.021421 / 0.534201 (-0.512780) | 0.518623 / 0.579283 (-0.060660) | 0.525887 / 0.434364 (0.091523) | 0.640331 / 0.540337 (0.099993) | 0.766748 / 1.386936 (-0.620188) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.007680 / 0.011353 (-0.003673) | 0.005289 / 0.011008 (-0.005719) | 0.093773 / 0.038508 (0.055265) | 0.054997 / 0.023109 (0.031888) | 0.456277 / 0.275898 (0.180379) | 0.500642 / 0.323480 (0.177162) | 0.005935 / 0.007986 (-0.002050) | 0.004375 / 0.004328 (0.000047) | 0.094131 / 0.004250 (0.089881) | 0.063399 / 0.037052 (0.026347) | 0.470546 / 0.258489 (0.212057) | 0.504989 / 0.293841 (0.211148) | 0.038541 / 0.128546 (-0.090006) | 0.010403 / 0.075646 (-0.065244) | 0.102469 / 0.419271 (-0.316802) | 0.063105 / 0.043533 (0.019572) | 0.466005 / 0.255139 (0.210866) | 0.458677 / 0.283200 (0.175477) | 0.028407 / 0.141683 (-0.113276) | 1.893829 / 1.452155 (0.441675) | 1.917954 / 1.492716 (0.425238) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.272760 / 0.018006 (0.254754) | 0.476159 / 0.000490 (0.475669) | 0.008467 / 0.000200 (0.008267) | 0.000146 / 0.000054 (0.000091) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.035755 / 0.037411 (-0.001656) | 0.145038 / 0.014526 (0.130512) | 0.148322 / 0.176557 (-0.028235) | 0.210193 / 0.737135 (-0.526943) | 0.156547 / 0.296338 (-0.139792) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.541204 / 0.215209 (0.325995) | 5.382746 / 2.077655 (3.305091) | 2.704229 / 1.504120 (1.200109) | 2.468422 / 1.541195 (0.927227) | 2.522672 / 1.468490 (1.054182) | 0.644899 / 4.584777 (-3.939878) | 4.654401 / 3.745712 (0.908689) | 2.159223 / 5.269862 (-3.110638) | 1.280098 / 4.565676 (-3.285578) | 0.080053 / 0.424275 (-0.344222) | 0.014383 / 0.007607 (0.006776) | 0.662770 / 0.226044 (0.436725) | 6.617651 / 2.268929 (4.348722) | 3.234347 / 55.444624 (-52.210277) | 2.861417 / 6.876477 (-4.015059) | 2.888928 / 2.142072 (0.746856) | 0.792854 / 4.805227 (-4.012374) | 0.172553 / 6.500664 (-6.328111) | 0.078402 / 0.075469 (0.002933) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.565351 / 1.841788 (-0.276436) | 18.681916 / 8.074308 (10.607608) | 17.264473 / 10.191392 (7.073081) | 0.168461 / 0.680424 (-0.511963) | 0.021353 / 0.534201 (-0.512848) | 0.517843 / 0.579283 (-0.061440) | 0.519907 / 0.434364 (0.085543) | 0.623687 / 0.540337 (0.083350) | 0.761796 / 1.386936 (-0.625140) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#bbf58747f734a46e75937bdbcbc05b06ade0224a \"CML watermark\")\n", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.006750 / 0.011353 (-0.004603) | 0.004268 / 0.011008 (-0.006741) | 0.098644 / 0.038508 (0.060136) | 0.044643 / 0.023109 (0.021534) | 0.309420 / 0.275898 (0.033522) | 0.379294 / 0.323480 (0.055815) | 0.005729 / 0.007986 (-0.002256) | 0.003615 / 0.004328 (-0.000714) | 0.076086 / 0.004250 (0.071835) | 0.068994 / 0.037052 (0.031942) | 0.325653 / 0.258489 (0.067164) | 0.375187 / 0.293841 (0.081347) | 0.032546 / 0.128546 (-0.096000) | 0.009089 / 0.075646 (-0.066557) | 0.329905 / 0.419271 (-0.089366) | 0.066832 / 0.043533 (0.023300) | 0.299247 / 0.255139 (0.044108) | 0.323460 / 0.283200 (0.040260) | 0.034226 / 0.141683 (-0.107457) | 1.475659 / 1.452155 (0.023505) | 1.556234 / 1.492716 (0.063518) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.292305 / 0.018006 (0.274299) | 0.542584 / 0.000490 (0.542094) | 0.003047 / 0.000200 (0.002847) | 0.000082 / 0.000054 (0.000027) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.030096 / 0.037411 (-0.007315) | 0.112341 / 0.014526 (0.097815) | 0.124965 / 0.176557 (-0.051591) | 0.183159 / 0.737135 (-0.553976) | 0.131885 / 0.296338 (-0.164453) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.426437 / 0.215209 (0.211228) | 4.260984 / 2.077655 (2.183330) | 2.078358 / 1.504120 (0.574238) | 1.877644 / 1.541195 (0.336449) | 2.044036 / 1.468490 (0.575546) | 0.532980 / 4.584777 (-4.051797) | 3.749573 / 3.745712 (0.003860) | 1.944155 / 5.269862 (-3.325706) | 1.090307 / 4.565676 (-3.475370) | 0.065445 / 0.424275 (-0.358830) | 0.011237 / 0.007607 (0.003630) | 0.521448 / 0.226044 (0.295403) | 5.213118 / 2.268929 (2.944189) | 2.507829 / 55.444624 (-52.936795) | 2.177179 / 6.876477 (-4.699297) | 2.351161 / 2.142072 (0.209088) | 0.656775 / 4.805227 (-4.148452) | 0.141207 / 6.500664 (-6.359457) | 0.063286 / 0.075469 (-0.012183) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.190281 / 1.841788 (-0.651506) | 15.327424 / 8.074308 (7.253116) | 13.300695 / 10.191392 (3.109303) | 0.190484 / 0.680424 (-0.489939) | 0.017984 / 0.534201 (-0.516217) | 0.405714 / 0.579283 (-0.173569) | 0.435915 / 0.434364 (0.001551) | 0.494083 / 0.540337 (-0.046254) | 0.600616 / 1.386936 (-0.786320) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.006740 / 0.011353 (-0.004613) | 0.004289 / 0.011008 (-0.006719) | 0.076532 / 0.038508 (0.038024) | 0.043305 / 0.023109 (0.020196) | 0.356111 / 0.275898 (0.080213) | 0.434121 / 0.323480 (0.110641) | 0.005599 / 0.007986 (-0.002387) | 0.003461 / 0.004328 (-0.000868) | 0.077097 / 0.004250 (0.072847) | 0.055369 / 0.037052 (0.018317) | 0.367093 / 0.258489 (0.108604) | 0.418801 / 0.293841 (0.124960) | 0.032057 / 0.128546 (-0.096489) | 0.009048 / 0.075646 (-0.066599) | 0.082897 / 0.419271 (-0.336374) | 0.050287 / 0.043533 (0.006754) | 0.352060 / 0.255139 (0.096921) | 0.376278 / 0.283200 (0.093078) | 0.023924 / 0.141683 (-0.117759) | 1.522780 / 1.452155 (0.070626) | 1.578938 / 1.492716 (0.086222) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.287317 / 0.018006 (0.269311) | 0.508490 / 0.000490 (0.508000) | 0.000431 / 0.000200 (0.000231) | 0.000056 / 0.000054 (0.000002) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.031139 / 0.037411 (-0.006272) | 0.113927 / 0.014526 (0.099401) | 0.128147 / 0.176557 (-0.048409) | 0.179712 / 0.737135 (-0.557424) | 0.134364 / 0.296338 (-0.161975) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.452834 / 0.215209 (0.237625) | 4.507944 / 2.077655 (2.430289) | 2.287758 / 1.504120 (0.783638) | 2.091145 / 1.541195 (0.549951) | 2.196228 / 1.468490 (0.727738) | 0.539306 / 4.584777 (-4.045471) | 3.838941 / 3.745712 (0.093228) | 1.908801 / 5.269862 (-3.361060) | 1.139235 / 4.565676 (-3.426442) | 0.066677 / 0.424275 (-0.357599) | 0.011422 / 0.007607 (0.003815) | 0.562966 / 0.226044 (0.336921) | 5.633712 / 2.268929 (3.364784) | 2.788622 / 55.444624 (-52.656002) | 2.438465 / 6.876477 (-4.438012) | 2.523479 / 2.142072 (0.381407) | 0.668730 / 4.805227 (-4.136498) | 0.143977 / 6.500664 (-6.356687) | 0.064661 / 0.075469 (-0.010808) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.291708 / 1.841788 (-0.550080) | 15.573316 / 8.074308 (7.499008) | 14.435099 / 10.191392 (4.243707) | 0.147745 / 0.680424 (-0.532679) | 0.017602 / 0.534201 (-0.516599) | 0.401560 / 0.579283 (-0.177723) | 0.429861 / 0.434364 (-0.004502) | 0.469800 / 0.540337 (-0.070538) | 0.567515 / 1.386936 (-0.819421) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#79c340f5dcfd06340f180f6c6ea2d5ef81f49d98 \"CML watermark\")\n" ]

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[]

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[ "cc @pcuenca as well. \r\n\r\nNot super urgent btw", "The issue commes from the dataset itself and is not related to the `datasets` lib\r\n\r\nsee https://huggingface.co/datasets/mozilla-foundation/common_voice_6_1/blob/2c475b3b88e0f2e5828f830a4b91618a25ff20b7/common_voice_6_1.py#L148-L152", "Let's remove these lines in the dataset no? cc @anton-l @Vaibhavs10 " ]

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[ "This must be due to DatasetInfo.from_merge which drops them and is used in `concatenate_datasets`.\r\n\r\nAnd you're experiencing this issue because multiprocessing does concatenate the resulting datasets from each process.\r\n\r\nMaybe they should be kept if all the subdatasets share the same values for config_name and split", "That sounds like a clean workaround!" ]

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[ "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.006186 / 0.011353 (-0.005167) | 0.003744 / 0.011008 (-0.007264) | 0.097295 / 0.038508 (0.058787) | 0.037106 / 0.023109 (0.013997) | 0.424154 / 0.275898 (0.148256) | 0.474536 / 0.323480 (0.151057) | 0.003454 / 0.007986 (-0.004532) | 0.003865 / 0.004328 (-0.000463) | 0.077348 / 0.004250 (0.073097) | 0.051728 / 0.037052 (0.014675) | 0.437120 / 0.258489 (0.178631) | 0.478379 / 0.293841 (0.184538) | 0.028939 / 0.128546 (-0.099608) | 0.008376 / 0.075646 (-0.067270) | 0.312002 / 0.419271 (-0.107270) | 0.053723 / 0.043533 (0.010190) | 0.424815 / 0.255139 (0.169676) | 0.446203 / 0.283200 (0.163004) | 0.026553 / 0.141683 (-0.115130) | 1.479983 / 1.452155 (0.027828) | 1.530613 / 1.492716 (0.037896) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.196627 / 0.018006 (0.178620) | 0.422361 / 0.000490 (0.421871) | 0.003442 / 0.000200 (0.003242) | 0.000077 / 0.000054 (0.000023) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.022913 / 0.037411 (-0.014499) | 0.096011 / 0.014526 (0.081485) | 0.104091 / 0.176557 (-0.072466) | 0.163273 / 0.737135 (-0.573862) | 0.109142 / 0.296338 (-0.187197) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.431032 / 0.215209 (0.215823) | 4.314391 / 2.077655 (2.236737) | 2.003812 / 1.504120 (0.499692) | 1.799538 / 1.541195 (0.258344) | 1.830026 / 1.468490 (0.361536) | 0.560131 / 4.584777 (-4.024646) | 3.368997 / 3.745712 (-0.376715) | 1.703032 / 5.269862 (-3.566830) | 1.026949 / 4.565676 (-3.538727) | 0.067507 / 0.424275 (-0.356768) | 0.010910 / 0.007607 (0.003303) | 0.532606 / 0.226044 (0.306562) | 5.345179 / 2.268929 (3.076250) | 2.368077 / 55.444624 (-53.076548) | 2.028913 / 6.876477 (-4.847564) | 2.147621 / 2.142072 (0.005549) | 0.675696 / 4.805227 (-4.129531) | 0.134902 / 6.500664 (-6.365762) | 0.065004 / 0.075469 (-0.010465) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.233412 / 1.841788 (-0.608376) | 13.767465 / 8.074308 (5.693157) | 13.933653 / 10.191392 (3.742261) | 0.129010 / 0.680424 (-0.551414) | 0.016708 / 0.534201 (-0.517493) | 0.362341 / 0.579283 (-0.216942) | 0.390902 / 0.434364 (-0.043462) | 0.429156 / 0.540337 (-0.111182) | 0.521166 / 1.386936 (-0.865770) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.006169 / 0.011353 (-0.005184) | 0.003839 / 0.011008 (-0.007169) | 0.078784 / 0.038508 (0.040276) | 0.040218 / 0.023109 (0.017109) | 0.360439 / 0.275898 (0.084541) | 0.423957 / 0.323480 (0.100477) | 0.003456 / 0.007986 (-0.004529) | 0.002900 / 0.004328 (-0.001428) | 0.078820 / 0.004250 (0.074569) | 0.047240 / 0.037052 (0.010187) | 0.372081 / 0.258489 (0.113592) | 0.424263 / 0.293841 (0.130422) | 0.027977 / 0.128546 (-0.100569) | 0.008400 / 0.075646 (-0.067246) | 0.084399 / 0.419271 (-0.334872) | 0.043303 / 0.043533 (-0.000230) | 0.361583 / 0.255139 (0.106444) | 0.394987 / 0.283200 (0.111787) | 0.020006 / 0.141683 (-0.121677) | 1.520208 / 1.452155 (0.068053) | 1.587335 / 1.492716 (0.094619) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.223847 / 0.018006 (0.205840) | 0.402194 / 0.000490 (0.401704) | 0.000384 / 0.000200 (0.000184) | 0.000057 / 0.000054 (0.000003) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.024902 / 0.037411 (-0.012509) | 0.099076 / 0.014526 (0.084550) | 0.108041 / 0.176557 (-0.068516) | 0.159385 / 0.737135 (-0.577750) | 0.111442 / 0.296338 (-0.184896) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.446232 / 0.215209 (0.231023) | 4.464927 / 2.077655 (2.387272) | 2.155234 / 1.504120 (0.651114) | 1.953645 / 1.541195 (0.412450) | 1.965991 / 1.468490 (0.497501) | 0.553473 / 4.584777 (-4.031304) | 3.321397 / 3.745712 (-0.424315) | 1.693761 / 5.269862 (-3.576101) | 1.006299 / 4.565676 (-3.559378) | 0.067013 / 0.424275 (-0.357262) | 0.011116 / 0.007607 (0.003509) | 0.555014 / 0.226044 (0.328970) | 5.535694 / 2.268929 (3.266765) | 2.598339 / 55.444624 (-52.846285) | 2.249298 / 6.876477 (-4.627179) | 2.243419 / 2.142072 (0.101347) | 0.667603 / 4.805227 (-4.137624) | 0.133322 / 6.500664 (-6.367343) | 0.065473 / 0.075469 (-0.009996) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.293051 / 1.841788 (-0.548737) | 14.103731 / 8.074308 (6.029423) | 14.215204 / 10.191392 (4.023812) | 0.143990 / 0.680424 (-0.536434) | 0.016805 / 0.534201 (-0.517396) | 0.363264 / 0.579283 (-0.216019) | 0.392769 / 0.434364 (-0.041594) | 0.425291 / 0.540337 (-0.115046) | 0.515479 / 1.386936 (-0.871457) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#e03a58f3f5d7e6f07279fb833e62d859a0babaad \"CML watermark\")\n", "_The documentation is not available anymore as the PR was closed or merged._", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.006346 / 0.011353 (-0.005006) | 0.004130 / 0.011008 (-0.006878) | 0.096898 / 0.038508 (0.058390) | 0.042564 / 0.023109 (0.019455) | 0.343748 / 0.275898 (0.067850) | 0.412515 / 0.323480 (0.089035) | 0.006153 / 0.007986 (-0.001833) | 0.003345 / 0.004328 (-0.000984) | 0.075314 / 0.004250 (0.071064) | 0.061478 / 0.037052 (0.024426) | 0.362948 / 0.258489 (0.104459) | 0.401533 / 0.293841 (0.107692) | 0.032363 / 0.128546 (-0.096184) | 0.008780 / 0.075646 (-0.066867) | 0.328691 / 0.419271 (-0.090580) | 0.054253 / 0.043533 (0.010721) | 0.340783 / 0.255139 (0.085644) | 0.360705 / 0.283200 (0.077505) | 0.023183 / 0.141683 (-0.118500) | 1.484078 / 1.452155 (0.031924) | 1.528581 / 1.492716 (0.035865) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.208732 / 0.018006 (0.190726) | 0.452572 / 0.000490 (0.452082) | 0.002936 / 0.000200 (0.002737) | 0.000082 / 0.000054 (0.000028) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.024616 / 0.037411 (-0.012795) | 0.107547 / 0.014526 (0.093021) | 0.114492 / 0.176557 (-0.062065) | 0.171770 / 0.737135 (-0.565365) | 0.122538 / 0.296338 (-0.173800) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.406140 / 0.215209 (0.190930) | 4.062391 / 2.077655 (1.984736) | 1.865962 / 1.504120 (0.361842) | 1.682236 / 1.541195 (0.141041) | 1.738119 / 1.468490 (0.269629) | 0.532244 / 4.584777 (-4.052533) | 3.816421 / 3.745712 (0.070709) | 2.981205 / 5.269862 (-2.288656) | 1.519497 / 4.565676 (-3.046179) | 0.065904 / 0.424275 (-0.358371) | 0.011277 / 0.007607 (0.003670) | 0.512789 / 0.226044 (0.286745) | 5.107618 / 2.268929 (2.838690) | 2.419399 / 55.444624 (-53.025226) | 2.079262 / 6.876477 (-4.797214) | 2.150447 / 2.142072 (0.008375) | 0.696737 / 4.805227 (-4.108490) | 0.142497 / 6.500664 (-6.358167) | 0.063521 / 0.075469 (-0.011949) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.180692 / 1.841788 (-0.661095) | 14.343084 / 8.074308 (6.268776) | 13.303719 / 10.191392 (3.112327) | 0.164234 / 0.680424 (-0.516190) | 0.017439 / 0.534201 (-0.516762) | 0.399712 / 0.579283 (-0.179571) | 0.428248 / 0.434364 (-0.006115) | 0.471909 / 0.540337 (-0.068428) | 0.573853 / 1.386936 (-0.813083) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.006210 / 0.011353 (-0.005143) | 0.004104 / 0.011008 (-0.006905) | 0.075140 / 0.038508 (0.036632) | 0.044647 / 0.023109 (0.021538) | 0.370120 / 0.275898 (0.094222) | 0.452936 / 0.323480 (0.129457) | 0.003943 / 0.007986 (-0.004042) | 0.003285 / 0.004328 (-0.001043) | 0.075267 / 0.004250 (0.071017) | 0.055517 / 0.037052 (0.018465) | 0.396385 / 0.258489 (0.137896) | 0.447870 / 0.293841 (0.154029) | 0.031342 / 0.128546 (-0.097204) | 0.008720 / 0.075646 (-0.066926) | 0.082702 / 0.419271 (-0.336570) | 0.051010 / 0.043533 (0.007477) | 0.350546 / 0.255139 (0.095407) | 0.425395 / 0.283200 (0.142195) | 0.024483 / 0.141683 (-0.117200) | 1.467341 / 1.452155 (0.015186) | 1.537187 / 1.492716 (0.044471) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.218067 / 0.018006 (0.200061) | 0.441603 / 0.000490 (0.441114) | 0.003711 / 0.000200 (0.003512) | 0.000092 / 0.000054 (0.000038) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.028669 / 0.037411 (-0.008742) | 0.112941 / 0.014526 (0.098415) | 0.122584 / 0.176557 (-0.053972) | 0.176494 / 0.737135 (-0.560641) | 0.129369 / 0.296338 (-0.166970) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.434543 / 0.215209 (0.219334) | 4.344056 / 2.077655 (2.266401) | 2.079286 / 1.504120 (0.575166) | 1.887264 / 1.541195 (0.346069) | 1.910386 / 1.468490 (0.441896) | 0.538824 / 4.584777 (-4.045953) | 3.844786 / 3.745712 (0.099074) | 2.902091 / 5.269862 (-2.367770) | 1.270852 / 4.565676 (-3.294824) | 0.066324 / 0.424275 (-0.357951) | 0.011346 / 0.007607 (0.003739) | 0.537122 / 0.226044 (0.311078) | 5.367354 / 2.268929 (3.098426) | 2.533672 / 55.444624 (-52.910952) | 2.203260 / 6.876477 (-4.673217) | 2.224310 / 2.142072 (0.082237) | 0.663806 / 4.805227 (-4.141422) | 0.142758 / 6.500664 (-6.357906) | 0.063870 / 0.075469 (-0.011599) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.260487 / 1.841788 (-0.581301) | 14.800106 / 8.074308 (6.725798) | 13.993488 / 10.191392 (3.802096) | 0.165829 / 0.680424 (-0.514595) | 0.017347 / 0.534201 (-0.516854) | 0.401819 / 0.579283 (-0.177464) | 0.424577 / 0.434364 (-0.009787) | 0.475161 / 0.540337 (-0.065176) | 0.574659 / 1.386936 (-0.812277) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#02e1e9ab6df4720f57b2d08c0b800cecac79a7c8 \"CML watermark\")\n" ]

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[ "Thanks for reporting! \r\n\r\nSpecifying the target features explicitly should avoid this error:\r\n```python\r\ndataset = dataset.map(\r\n batch_process,\r\n batched=True,\r\n batch_size=1,\r\n num_proc=1,\r\n remove_columns=dataset.column_names,\r\n features=datasets.Features({\"texts\": datasets.Sequence(datasets.Value(\"string\"))})\r\n)\r\n```\r\n\r\nThis error stems from our type promotion not handling the nested case. But this promotion/casting allocates memory in most scenarios, which can be problematic for large datasets, so explicitly passing the features is the optimal solution.", "Hi @mariosasko thanks for the context, this is helpful to know. Would it be worth having some logic to generate this explicit feature specification automatically if a type annotation for a .map returns a dataclass that can be inferred?\r\n\r\nFeels like something that would be easy to implement and could save memory / deal with this case in a standardized way.", "> . Would it be worth having some logic to generate this explicit feature specification automatically if a type annotation for a .map returns a dataclass that can be inferred?\r\n\r\nInteresting proposal! Yes, we could consider doing this if the (return) type hint is `TypedDict`, and raise an error that type hints are incorrect if the cast using the inferred types fails.", "@mariosasko Put up an initial PR to implement this proposal. Let me know your thoughts on direction and what else should be in-scope here." ]

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[ "_The documentation is not available anymore as the PR was closed or merged._", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.006795 / 0.011353 (-0.004558) | 0.004170 / 0.011008 (-0.006838) | 0.098698 / 0.038508 (0.060190) | 0.045393 / 0.023109 (0.022284) | 0.309205 / 0.275898 (0.033307) | 0.361333 / 0.323480 (0.037853) | 0.006009 / 0.007986 (-0.001977) | 0.003334 / 0.004328 (-0.000995) | 0.075071 / 0.004250 (0.070821) | 0.062587 / 0.037052 (0.025535) | 0.322395 / 0.258489 (0.063906) | 0.360499 / 0.293841 (0.066659) | 0.032243 / 0.128546 (-0.096303) | 0.008768 / 0.075646 (-0.066878) | 0.329799 / 0.419271 (-0.089472) | 0.062261 / 0.043533 (0.018728) | 0.298112 / 0.255139 (0.042973) | 0.322815 / 0.283200 (0.039615) | 0.032348 / 0.141683 (-0.109335) | 1.445807 / 1.452155 (-0.006347) | 1.528768 / 1.492716 (0.036051) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.195701 / 0.018006 (0.177695) | 0.437042 / 0.000490 (0.436552) | 0.003867 / 0.000200 (0.003667) | 0.000080 / 0.000054 (0.000026) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.026713 / 0.037411 (-0.010698) | 0.109548 / 0.014526 (0.095022) | 0.119216 / 0.176557 (-0.057341) | 0.178947 / 0.737135 (-0.558188) | 0.125224 / 0.296338 (-0.171114) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.400885 / 0.215209 (0.185676) | 3.991223 / 2.077655 (1.913568) | 1.818449 / 1.504120 (0.314329) | 1.609285 / 1.541195 (0.068090) | 1.666675 / 1.468490 (0.198184) | 0.531486 / 4.584777 (-4.053291) | 3.770142 / 3.745712 (0.024430) | 3.057189 / 5.269862 (-2.212673) | 1.517491 / 4.565676 (-3.048186) | 0.065782 / 0.424275 (-0.358493) | 0.011251 / 0.007607 (0.003644) | 0.504277 / 0.226044 (0.278233) | 5.038979 / 2.268929 (2.770050) | 2.254717 / 55.444624 (-53.189908) | 1.929743 / 6.876477 (-4.946734) | 2.080051 / 2.142072 (-0.062022) | 0.656831 / 4.805227 (-4.148396) | 0.142860 / 6.500664 (-6.357804) | 0.063057 / 0.075469 (-0.012412) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.208819 / 1.841788 (-0.632969) | 14.456966 / 8.074308 (6.382658) | 12.839799 / 10.191392 (2.648407) | 0.164361 / 0.680424 (-0.516063) | 0.017330 / 0.534201 (-0.516871) | 0.397384 / 0.579283 (-0.181899) | 0.422704 / 0.434364 (-0.011660) | 0.472065 / 0.540337 (-0.068273) | 0.576960 / 1.386936 (-0.809976) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.006950 / 0.011353 (-0.004403) | 0.004012 / 0.011008 (-0.006997) | 0.076050 / 0.038508 (0.037542) | 0.046646 / 0.023109 (0.023537) | 0.353813 / 0.275898 (0.077915) | 0.417111 / 0.323480 (0.093631) | 0.005422 / 0.007986 (-0.002564) | 0.003356 / 0.004328 (-0.000972) | 0.076662 / 0.004250 (0.072411) | 0.055018 / 0.037052 (0.017966) | 0.371561 / 0.258489 (0.113072) | 0.410471 / 0.293841 (0.116630) | 0.031860 / 0.128546 (-0.096686) | 0.008754 / 0.075646 (-0.066893) | 0.083192 / 0.419271 (-0.336079) | 0.050479 / 0.043533 (0.006946) | 0.351725 / 0.255139 (0.096586) | 0.371596 / 0.283200 (0.088396) | 0.023042 / 0.141683 (-0.118641) | 1.480533 / 1.452155 (0.028379) | 1.545970 / 1.492716 (0.053254) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.220095 / 0.018006 (0.202089) | 0.441550 / 0.000490 (0.441061) | 0.000375 / 0.000200 (0.000175) | 0.000056 / 0.000054 (0.000002) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.029527 / 0.037411 (-0.007884) | 0.111645 / 0.014526 (0.097119) | 0.125732 / 0.176557 (-0.050825) | 0.177322 / 0.737135 (-0.559813) | 0.128620 / 0.296338 (-0.167718) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.432415 / 0.215209 (0.217206) | 4.314381 / 2.077655 (2.236726) | 2.079450 / 1.504120 (0.575331) | 1.893139 / 1.541195 (0.351944) | 1.951363 / 1.468490 (0.482873) | 0.531466 / 4.584777 (-4.053311) | 3.716860 / 3.745712 (-0.028852) | 1.850111 / 5.269862 (-3.419750) | 1.100676 / 4.565676 (-3.465000) | 0.066247 / 0.424275 (-0.358028) | 0.011503 / 0.007607 (0.003896) | 0.537208 / 0.226044 (0.311164) | 5.367560 / 2.268929 (3.098631) | 2.543697 / 55.444624 (-52.900927) | 2.221670 / 6.876477 (-4.654806) | 2.252009 / 2.142072 (0.109937) | 0.658509 / 4.805227 (-4.146718) | 0.142345 / 6.500664 (-6.358319) | 0.064701 / 0.075469 (-0.010768) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.266442 / 1.841788 (-0.575346) | 15.105953 / 8.074308 (7.031645) | 14.288229 / 10.191392 (4.096837) | 0.161182 / 0.680424 (-0.519242) | 0.017074 / 0.534201 (-0.517127) | 0.399464 / 0.579283 (-0.179819) | 0.419459 / 0.434364 (-0.014905) | 0.467553 / 0.540337 (-0.072784) | 0.566337 / 1.386936 (-0.820599) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#53ac2d9662f9e5923ae7c52199eaa620d82f0043 \"CML watermark\")\n" ]

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[ "i got error using method from_spark when using multi-node Spark cluster. seems could only use \"from_spark\" in local?", "@lhoestq ", "cc @maddiedawson it looks like there an issue with `_validate_cache_dir` ?\r\n\r\nIt looks like the function passed to mapPartitions has a reference to the Spark dataset builder, and therefore contains the SparkContext itself.\r\n\r\nI think it can be fixed by defining `create_cache_and_write_probe` outside the Spark dataset builder, and pass a `partial(create_cache_and_write_probe, cache_dir=self._cache_dir)` to `mapPartitions`", "Just saw this; thanks for flagging! Your proposed solution sounds good. I can prepare a PR", "@maddiedawson can you show me the demo ,so i can test in local .before your PR" ]

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[ "Does \"number of shards\" refer to the total number of data?\r\n\r\nmy config:\r\nnproc_per_node=2\r\nds=ds['train'] = load_dataset(streaming=True).take(50000)\r\n\r\nI'm test again: in prepare_data(), data have the same for each GPU\r\n", "The number of shards is `ds.n_shards`. It corresponds generally to the number of files the dataset is made of, to be able to distribute to several nodes.\r\n\r\n**You don't end up with the same data per GPU**. But all the samples are going through your preprocessing function you pass to map. They are just skipped afterwards to only keep 1 sample out of n(GPUs)", "For each GPU, although see the same data in prepare_data(), the actual training data will not be the same in the end. \r\nIs my understanding correct?\r\n\r\nWhere can I print the actual training data for each GPU?", "> For each GPU, although see the same data in prepare_data(), the actual training data will not be the same in the end.\r\nIs my understanding correct?\r\n\r\nYes exactly :)\r\n\r\n> Where can I print the actual training data for each GPU?\r\n\r\nYou should call print in the data_collator", "I print out n_shards, and under multiple GPUs, this value is always 1.\r\nIs this value correct?", "Yes it's correct, and it explains why you always have the same data passed to your map function (the data can't be split).\r\n\r\nBut after being passed to `map`, each GPU keeps one example out of n(GPUs) so that you don't end up with duplicate data across GPUs", "> > For each GPU, although see the same data in prepare_data(), the actual training data will not be the same in the end.\r\n> > Is my understanding correct?\r\n> \r\n> Yes exactly :)\r\n> \r\n> > Where can I print the actual training data for each GPU?\r\n> \r\n> You should call print in the data_collator\r\n\r\nOK, when printing the train data in the data collator, each GPU sees different data.\r\n\r\nThanks for your reply" ]

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[ "Sorry, I solve this by call `evaluate.load('glue_metric.py','sst-2')`\r\n" ]

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[ "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_5958). All of your documentation changes will be reflected on that endpoint.", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.006232 / 0.011353 (-0.005121) | 0.003788 / 0.011008 (-0.007220) | 0.100014 / 0.038508 (0.061506) | 0.036488 / 0.023109 (0.013379) | 0.306255 / 0.275898 (0.030357) | 0.363337 / 0.323480 (0.039857) | 0.004765 / 0.007986 (-0.003221) | 0.002935 / 0.004328 (-0.001394) | 0.078897 / 0.004250 (0.074647) | 0.052221 / 0.037052 (0.015169) | 0.315169 / 0.258489 (0.056680) | 0.353050 / 0.293841 (0.059209) | 0.029059 / 0.128546 (-0.099488) | 0.008599 / 0.075646 (-0.067047) | 0.318770 / 0.419271 (-0.100502) | 0.046631 / 0.043533 (0.003098) | 0.303728 / 0.255139 (0.048589) | 0.332379 / 0.283200 (0.049180) | 0.021164 / 0.141683 (-0.120519) | 1.576963 / 1.452155 (0.124808) | 1.629575 / 1.492716 (0.136859) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.204246 / 0.018006 (0.186240) | 0.426600 / 0.000490 (0.426110) | 0.004336 / 0.000200 (0.004136) | 0.000082 / 0.000054 (0.000027) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.024039 / 0.037411 (-0.013372) | 0.098240 / 0.014526 (0.083715) | 0.108889 / 0.176557 (-0.067668) | 0.170827 / 0.737135 (-0.566308) | 0.111288 / 0.296338 (-0.185051) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.418103 / 0.215209 (0.202894) | 4.190759 / 2.077655 (2.113104) | 1.875978 / 1.504120 (0.371858) | 1.679198 / 1.541195 (0.138003) | 1.737965 / 1.468490 (0.269474) | 0.556660 / 4.584777 (-4.028117) | 3.413800 / 3.745712 (-0.331912) | 3.004999 / 5.269862 (-2.264862) | 1.464030 / 4.565676 (-3.101647) | 0.067338 / 0.424275 (-0.356937) | 0.011486 / 0.007607 (0.003879) | 0.522589 / 0.226044 (0.296544) | 5.214653 / 2.268929 (2.945724) | 2.316903 / 55.444624 (-53.127722) | 1.991941 / 6.876477 (-4.884536) | 2.110601 / 2.142072 (-0.031471) | 0.665400 / 4.805227 (-4.139828) | 0.135755 / 6.500664 (-6.364910) | 0.065980 / 0.075469 (-0.009489) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.197269 / 1.841788 (-0.644519) | 14.085205 / 8.074308 (6.010897) | 14.083360 / 10.191392 (3.891968) | 0.148054 / 0.680424 (-0.532369) | 0.016548 / 0.534201 (-0.517653) | 0.371538 / 0.579283 (-0.207745) | 0.391068 / 0.434364 (-0.043296) | 0.430589 / 0.540337 (-0.109748) | 0.529319 / 1.386936 (-0.857617) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.006214 / 0.011353 (-0.005138) | 0.003846 / 0.011008 (-0.007162) | 0.078559 / 0.038508 (0.040051) | 0.037855 / 0.023109 (0.014745) | 0.437479 / 0.275898 (0.161581) | 0.497588 / 0.323480 (0.174108) | 0.003491 / 0.007986 (-0.004494) | 0.003900 / 0.004328 (-0.000428) | 0.078443 / 0.004250 (0.074193) | 0.048019 / 0.037052 (0.010967) | 0.452076 / 0.258489 (0.193587) | 0.494597 / 0.293841 (0.200756) | 0.028127 / 0.128546 (-0.100419) | 0.008549 / 0.075646 (-0.067098) | 0.082977 / 0.419271 (-0.336295) | 0.043133 / 0.043533 (-0.000400) | 0.441342 / 0.255139 (0.186203) | 0.464339 / 0.283200 (0.181139) | 0.020110 / 0.141683 (-0.121573) | 1.485181 / 1.452155 (0.033026) | 1.532019 / 1.492716 (0.039302) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.228014 / 0.018006 (0.210007) | 0.416887 / 0.000490 (0.416397) | 0.001133 / 0.000200 (0.000933) | 0.000108 / 0.000054 (0.000053) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.026452 / 0.037411 (-0.010960) | 0.104328 / 0.014526 (0.089802) | 0.110045 / 0.176557 (-0.066511) | 0.164725 / 0.737135 (-0.572410) | 0.116348 / 0.296338 (-0.179990) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.483502 / 0.215209 (0.268293) | 4.829814 / 2.077655 (2.752159) | 2.505271 / 1.504120 (1.001151) | 2.305819 / 1.541195 (0.764624) | 2.348633 / 1.468490 (0.880143) | 0.562316 / 4.584777 (-4.022461) | 3.426425 / 3.745712 (-0.319287) | 1.737934 / 5.269862 (-3.531927) | 1.042616 / 4.565676 (-3.523061) | 0.068088 / 0.424275 (-0.356187) | 0.011735 / 0.007607 (0.004128) | 0.586339 / 0.226044 (0.360295) | 5.861283 / 2.268929 (3.592354) | 2.953956 / 55.444624 (-52.490668) | 2.626611 / 6.876477 (-4.249865) | 2.687978 / 2.142072 (0.545906) | 0.672748 / 4.805227 (-4.132479) | 0.137231 / 6.500664 (-6.363433) | 0.068149 / 0.075469 (-0.007320) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.323139 / 1.841788 (-0.518649) | 14.503102 / 8.074308 (6.428794) | 14.092102 / 10.191392 (3.900710) | 0.165395 / 0.680424 (-0.515028) | 0.016898 / 0.534201 (-0.517303) | 0.366905 / 0.579283 (-0.212378) | 0.396671 / 0.434364 (-0.037692) | 0.421831 / 0.540337 (-0.118506) | 0.514075 / 1.386936 (-0.872861) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#9d4238c132dd44b9a6e1dfe7101228bdeb538d57 \"CML watermark\")\n", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.007778 / 0.011353 (-0.003575) | 0.004624 / 0.011008 (-0.006384) | 0.123426 / 0.038508 (0.084918) | 0.052209 / 0.023109 (0.029100) | 0.341084 / 0.275898 (0.065186) | 0.421905 / 0.323480 (0.098425) | 0.005768 / 0.007986 (-0.002217) | 0.003647 / 0.004328 (-0.000682) | 0.085569 / 0.004250 (0.081319) | 0.070473 / 0.037052 (0.033421) | 0.356626 / 0.258489 (0.098136) | 0.407413 / 0.293841 (0.113572) | 0.038800 / 0.128546 (-0.089746) | 0.010289 / 0.075646 (-0.065357) | 0.462707 / 0.419271 (0.043436) | 0.060390 / 0.043533 (0.016858) | 0.349805 / 0.255139 (0.094666) | 0.355288 / 0.283200 (0.072088) | 0.025364 / 0.141683 (-0.116318) | 1.745720 / 1.452155 (0.293565) | 1.852764 / 1.492716 (0.360048) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.290582 / 0.018006 (0.272576) | 0.480044 / 0.000490 (0.479554) | 0.007658 / 0.000200 (0.007458) | 0.000100 / 0.000054 (0.000046) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.031529 / 0.037411 (-0.005882) | 0.130441 / 0.014526 (0.115915) | 0.147653 / 0.176557 (-0.028904) | 0.215935 / 0.737135 (-0.521200) | 0.149871 / 0.296338 (-0.146467) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.461662 / 0.215209 (0.246453) | 4.570353 / 2.077655 (2.492698) | 2.104416 / 1.504120 (0.600297) | 1.936974 / 1.541195 (0.395779) | 2.139167 / 1.468490 (0.670677) | 0.645100 / 4.584777 (-3.939677) | 4.361536 / 3.745712 (0.615824) | 2.155960 / 5.269862 (-3.113902) | 1.207854 / 4.565676 (-3.357822) | 0.080162 / 0.424275 (-0.344113) | 0.014265 / 0.007607 (0.006658) | 0.606294 / 0.226044 (0.380250) | 5.928093 / 2.268929 (3.659165) | 2.701811 / 55.444624 (-52.742813) | 2.344490 / 6.876477 (-4.531987) | 2.435997 / 2.142072 (0.293925) | 0.761020 / 4.805227 (-4.044207) | 0.165860 / 6.500664 (-6.334804) | 0.075666 / 0.075469 (0.000197) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.427318 / 1.841788 (-0.414469) | 17.327468 / 8.074308 (9.253160) | 15.323065 / 10.191392 (5.131673) | 0.178518 / 0.680424 (-0.501905) | 0.020888 / 0.534201 (-0.513313) | 0.497891 / 0.579283 (-0.081393) | 0.487717 / 0.434364 (0.053353) | 0.581430 / 0.540337 (0.041093) | 0.703430 / 1.386936 (-0.683506) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.007954 / 0.011353 (-0.003399) | 0.004442 / 0.011008 (-0.006566) | 0.090950 / 0.038508 (0.052442) | 0.054282 / 0.023109 (0.031173) | 0.424474 / 0.275898 (0.148576) | 0.531770 / 0.323480 (0.208290) | 0.004492 / 0.007986 (-0.003493) | 0.004745 / 0.004328 (0.000416) | 0.088213 / 0.004250 (0.083962) | 0.063967 / 0.037052 (0.026914) | 0.454256 / 0.258489 (0.195767) | 0.502870 / 0.293841 (0.209029) | 0.038203 / 0.128546 (-0.090343) | 0.010327 / 0.075646 (-0.065319) | 0.097809 / 0.419271 (-0.321463) | 0.062136 / 0.043533 (0.018604) | 0.426148 / 0.255139 (0.171009) | 0.467812 / 0.283200 (0.184612) | 0.029148 / 0.141683 (-0.112535) | 1.762307 / 1.452155 (0.310152) | 1.814238 / 1.492716 (0.321521) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.195676 / 0.018006 (0.177670) | 0.475382 / 0.000490 (0.474892) | 0.003070 / 0.000200 (0.002870) | 0.000112 / 0.000054 (0.000057) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.033945 / 0.037411 (-0.003466) | 0.134666 / 0.014526 (0.120140) | 0.147585 / 0.176557 (-0.028971) | 0.209472 / 0.737135 (-0.527664) | 0.154471 / 0.296338 (-0.141867) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.518132 / 0.215209 (0.302923) | 5.103423 / 2.077655 (3.025768) | 2.565207 / 1.504120 (1.061087) | 2.389454 / 1.541195 (0.848259) | 2.391706 / 1.468490 (0.923216) | 0.606463 / 4.584777 (-3.978314) | 4.392227 / 3.745712 (0.646515) | 2.067121 / 5.269862 (-3.202741) | 1.217551 / 4.565676 (-3.348125) | 0.074304 / 0.424275 (-0.349971) | 0.013418 / 0.007607 (0.005811) | 0.623327 / 0.226044 (0.397282) | 6.340233 / 2.268929 (4.071304) | 3.153948 / 55.444624 (-52.290677) | 2.824548 / 6.876477 (-4.051929) | 2.938402 / 2.142072 (0.796329) | 0.774305 / 4.805227 (-4.030922) | 0.170681 / 6.500664 (-6.329983) | 0.075895 / 0.075469 (0.000426) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.473491 / 1.841788 (-0.368296) | 17.372294 / 8.074308 (9.297986) | 15.550201 / 10.191392 (5.358809) | 0.191402 / 0.680424 (-0.489022) | 0.021401 / 0.534201 (-0.512800) | 0.484377 / 0.579283 (-0.094906) | 0.488844 / 0.434364 (0.054480) | 0.563336 / 0.540337 (0.022999) | 0.694210 / 1.386936 (-0.692726) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#b96da7f51d81e52d7b587685f820b5e55f71e07d \"CML watermark\")\n" ]

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[ "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.006498 / 0.011353 (-0.004855) | 0.003970 / 0.011008 (-0.007038) | 0.099242 / 0.038508 (0.060734) | 0.044363 / 0.023109 (0.021254) | 0.313900 / 0.275898 (0.038002) | 0.386562 / 0.323480 (0.063082) | 0.003837 / 0.007986 (-0.004149) | 0.004203 / 0.004328 (-0.000125) | 0.076191 / 0.004250 (0.071940) | 0.058823 / 0.037052 (0.021771) | 0.333838 / 0.258489 (0.075349) | 0.368235 / 0.293841 (0.074394) | 0.030774 / 0.128546 (-0.097772) | 0.008787 / 0.075646 (-0.066860) | 0.326474 / 0.419271 (-0.092798) | 0.050903 / 0.043533 (0.007370) | 0.303928 / 0.255139 (0.048789) | 0.321532 / 0.283200 (0.038333) | 0.024162 / 0.141683 (-0.117520) | 1.479662 / 1.452155 (0.027507) | 1.520300 / 1.492716 (0.027584) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.212403 / 0.018006 (0.194397) | 0.448019 / 0.000490 (0.447529) | 0.005465 / 0.000200 (0.005265) | 0.000388 / 0.000054 (0.000334) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.027533 / 0.037411 (-0.009878) | 0.117477 / 0.014526 (0.102952) | 0.121182 / 0.176557 (-0.055374) | 0.181150 / 0.737135 (-0.555985) | 0.128557 / 0.296338 (-0.167782) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.397763 / 0.215209 (0.182554) | 3.959460 / 2.077655 (1.881805) | 1.822057 / 1.504120 (0.317937) | 1.627020 / 1.541195 (0.085826) | 1.695394 / 1.468490 (0.226904) | 0.536848 / 4.584777 (-4.047929) | 3.765205 / 3.745712 (0.019493) | 3.196300 / 5.269862 (-2.073561) | 1.623583 / 4.565676 (-2.942094) | 0.065823 / 0.424275 (-0.358452) | 0.011062 / 0.007607 (0.003455) | 0.500428 / 0.226044 (0.274384) | 5.008816 / 2.268929 (2.739888) | 2.314660 / 55.444624 (-53.129965) | 2.007429 / 6.876477 (-4.869047) | 2.141438 / 2.142072 (-0.000635) | 0.656697 / 4.805227 (-4.148530) | 0.143555 / 6.500664 (-6.357109) | 0.063928 / 0.075469 (-0.011541) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.169038 / 1.841788 (-0.672750) | 15.027186 / 8.074308 (6.952878) | 13.571484 / 10.191392 (3.380092) | 0.166437 / 0.680424 (-0.513986) | 0.017656 / 0.534201 (-0.516545) | 0.397725 / 0.579283 (-0.181558) | 0.451019 / 0.434364 (0.016655) | 0.469134 / 0.540337 (-0.071203) | 0.575885 / 1.386936 (-0.811051) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.006887 / 0.011353 (-0.004465) | 0.004166 / 0.011008 (-0.006842) | 0.077137 / 0.038508 (0.038629) | 0.055631 / 0.023109 (0.032522) | 0.397658 / 0.275898 (0.121760) | 0.473981 / 0.323480 (0.150502) | 0.005365 / 0.007986 (-0.002621) | 0.003401 / 0.004328 (-0.000928) | 0.076481 / 0.004250 (0.072231) | 0.056014 / 0.037052 (0.018961) | 0.415253 / 0.258489 (0.156764) | 0.457620 / 0.293841 (0.163779) | 0.031850 / 0.128546 (-0.096696) | 0.008869 / 0.075646 (-0.066777) | 0.083475 / 0.419271 (-0.335796) | 0.049232 / 0.043533 (0.005699) | 0.392947 / 0.255139 (0.137808) | 0.417243 / 0.283200 (0.134043) | 0.024554 / 0.141683 (-0.117129) | 1.508081 / 1.452155 (0.055926) | 1.541845 / 1.492716 (0.049129) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.228470 / 0.018006 (0.210464) | 0.450933 / 0.000490 (0.450443) | 0.001508 / 0.000200 (0.001308) | 0.000083 / 0.000054 (0.000029) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.030189 / 0.037411 (-0.007222) | 0.118853 / 0.014526 (0.104327) | 0.124809 / 0.176557 (-0.051747) | 0.175066 / 0.737135 (-0.562069) | 0.129819 / 0.296338 (-0.166519) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.451830 / 0.215209 (0.236621) | 4.505352 / 2.077655 (2.427698) | 2.309303 / 1.504120 (0.805183) | 2.120983 / 1.541195 (0.579789) | 2.198808 / 1.468490 (0.730317) | 0.543836 / 4.584777 (-4.040940) | 3.836650 / 3.745712 (0.090938) | 1.872293 / 5.269862 (-3.397568) | 1.122335 / 4.565676 (-3.443342) | 0.067463 / 0.424275 (-0.356812) | 0.012143 / 0.007607 (0.004536) | 0.553674 / 0.226044 (0.327630) | 5.572101 / 2.268929 (3.303173) | 2.772151 / 55.444624 (-52.672473) | 2.451557 / 6.876477 (-4.424920) | 2.521241 / 2.142072 (0.379169) | 0.665799 / 4.805227 (-4.139428) | 0.143842 / 6.500664 (-6.356822) | 0.065373 / 0.075469 (-0.010096) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.271013 / 1.841788 (-0.570775) | 15.290054 / 8.074308 (7.215746) | 14.807044 / 10.191392 (4.615652) | 0.163767 / 0.680424 (-0.516657) | 0.017383 / 0.534201 (-0.516818) | 0.393046 / 0.579283 (-0.186237) | 0.423056 / 0.434364 (-0.011308) | 0.459193 / 0.540337 (-0.081145) | 0.559964 / 1.386936 (-0.826972) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#011b75f044ef7fa6b8981ef3496615296aeb315b \"CML watermark\")\n", "_The documentation is not available anymore as the PR was closed or merged._", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.006112 / 0.011353 (-0.005241) | 0.003712 / 0.011008 (-0.007297) | 0.099996 / 0.038508 (0.061488) | 0.037526 / 0.023109 (0.014417) | 0.305834 / 0.275898 (0.029936) | 0.361368 / 0.323480 (0.037888) | 0.004849 / 0.007986 (-0.003136) | 0.002912 / 0.004328 (-0.001417) | 0.077729 / 0.004250 (0.073479) | 0.053203 / 0.037052 (0.016151) | 0.318088 / 0.258489 (0.059599) | 0.371745 / 0.293841 (0.077904) | 0.029384 / 0.128546 (-0.099162) | 0.008504 / 0.075646 (-0.067142) | 0.318472 / 0.419271 (-0.100799) | 0.046043 / 0.043533 (0.002510) | 0.310418 / 0.255139 (0.055279) | 0.335044 / 0.283200 (0.051844) | 0.020364 / 0.141683 (-0.121319) | 1.503201 / 1.452155 (0.051047) | 1.556408 / 1.492716 (0.063692) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.210245 / 0.018006 (0.192239) | 0.418918 / 0.000490 (0.418428) | 0.002552 / 0.000200 (0.002352) | 0.000084 / 0.000054 (0.000030) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.022295 / 0.037411 (-0.015116) | 0.099534 / 0.014526 (0.085008) | 0.106432 / 0.176557 (-0.070124) | 0.165110 / 0.737135 (-0.572026) | 0.109851 / 0.296338 (-0.186488) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.423947 / 0.215209 (0.208738) | 4.232978 / 2.077655 (2.155323) | 2.004849 / 1.504120 (0.500729) | 1.814345 / 1.541195 (0.273151) | 1.809192 / 1.468490 (0.340702) | 0.561146 / 4.584777 (-4.023631) | 3.385043 / 3.745712 (-0.360669) | 1.708265 / 5.269862 (-3.561597) | 1.030290 / 4.565676 (-3.535387) | 0.067095 / 0.424275 (-0.357180) | 0.011052 / 0.007607 (0.003445) | 0.522416 / 0.226044 (0.296371) | 5.207003 / 2.268929 (2.938075) | 2.367067 / 55.444624 (-53.077558) | 1.998705 / 6.876477 (-4.877772) | 2.068633 / 2.142072 (-0.073439) | 0.672396 / 4.805227 (-4.132831) | 0.135818 / 6.500664 (-6.364846) | 0.065229 / 0.075469 (-0.010240) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.187079 / 1.841788 (-0.654709) | 13.893153 / 8.074308 (5.818845) | 13.951328 / 10.191392 (3.759936) | 0.142519 / 0.680424 (-0.537905) | 0.016546 / 0.534201 (-0.517655) | 0.364008 / 0.579283 (-0.215275) | 0.385957 / 0.434364 (-0.048407) | 0.425218 / 0.540337 (-0.115120) | 0.519586 / 1.386936 (-0.867350) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.005914 / 0.011353 (-0.005439) | 0.003619 / 0.011008 (-0.007389) | 0.077806 / 0.038508 (0.039298) | 0.037254 / 0.023109 (0.014144) | 0.378976 / 0.275898 (0.103078) | 0.433620 / 0.323480 (0.110140) | 0.003291 / 0.007986 (-0.004694) | 0.004523 / 0.004328 (0.000194) | 0.077604 / 0.004250 (0.073353) | 0.047493 / 0.037052 (0.010441) | 0.396027 / 0.258489 (0.137538) | 0.453345 / 0.293841 (0.159504) | 0.028170 / 0.128546 (-0.100376) | 0.008431 / 0.075646 (-0.067215) | 0.083985 / 0.419271 (-0.335286) | 0.045149 / 0.043533 (0.001617) | 0.369364 / 0.255139 (0.114225) | 0.407191 / 0.283200 (0.123991) | 0.024033 / 0.141683 (-0.117649) | 1.516838 / 1.452155 (0.064683) | 1.564260 / 1.492716 (0.071544) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.200848 / 0.018006 (0.182842) | 0.407818 / 0.000490 (0.407328) | 0.003971 / 0.000200 (0.003771) | 0.000077 / 0.000054 (0.000023) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.025033 / 0.037411 (-0.012378) | 0.103585 / 0.014526 (0.089059) | 0.108741 / 0.176557 (-0.067816) | 0.161061 / 0.737135 (-0.576075) | 0.112763 / 0.296338 (-0.183576) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.479913 / 0.215209 (0.264704) | 4.801904 / 2.077655 (2.724249) | 2.511433 / 1.504120 (1.007313) | 2.307523 / 1.541195 (0.766328) | 2.338343 / 1.468490 (0.869853) | 0.557731 / 4.584777 (-4.027046) | 3.386261 / 3.745712 (-0.359451) | 2.999978 / 5.269862 (-2.269883) | 1.463058 / 4.565676 (-3.102619) | 0.067645 / 0.424275 (-0.356630) | 0.011224 / 0.007607 (0.003617) | 0.596854 / 0.226044 (0.370810) | 5.940946 / 2.268929 (3.672017) | 2.980194 / 55.444624 (-52.464430) | 2.634961 / 6.876477 (-4.241516) | 2.648160 / 2.142072 (0.506088) | 0.669728 / 4.805227 (-4.135499) | 0.135536 / 6.500664 (-6.365128) | 0.066865 / 0.075469 (-0.008604) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.287151 / 1.841788 (-0.554637) | 14.491681 / 8.074308 (6.417373) | 14.185752 / 10.191392 (3.994360) | 0.129391 / 0.680424 (-0.551032) | 0.016650 / 0.534201 (-0.517551) | 0.380111 / 0.579283 (-0.199172) | 0.392877 / 0.434364 (-0.041487) | 0.439402 / 0.540337 (-0.100935) | 0.530865 / 1.386936 (-0.856071) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#9aaee6fd0b2bcbe18e4829602084bcd83d669c5e \"CML watermark\")\n", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.011446 / 0.011353 (0.000093) | 0.006623 / 0.011008 (-0.004386) | 0.131915 / 0.038508 (0.093407) | 0.047364 / 0.023109 (0.024255) | 0.369203 / 0.275898 (0.093305) | 0.451509 / 0.323480 (0.128029) | 0.006265 / 0.007986 (-0.001720) | 0.004072 / 0.004328 (-0.000257) | 0.098626 / 0.004250 (0.094375) | 0.079523 / 0.037052 (0.042470) | 0.406038 / 0.258489 (0.147549) | 0.450564 / 0.293841 (0.156723) | 0.050793 / 0.128546 (-0.077753) | 0.014667 / 0.075646 (-0.060979) | 0.401359 / 0.419271 (-0.017913) | 0.072299 / 0.043533 (0.028767) | 0.404456 / 0.255139 (0.149317) | 0.396223 / 0.283200 (0.113023) | 0.037048 / 0.141683 (-0.104635) | 1.869123 / 1.452155 (0.416968) | 1.953621 / 1.492716 (0.460905) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.237246 / 0.018006 (0.219240) | 0.533207 / 0.000490 (0.532717) | 0.007392 / 0.000200 (0.007192) | 0.000117 / 0.000054 (0.000062) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.029458 / 0.037411 (-0.007954) | 0.112438 / 0.014526 (0.097912) | 0.139115 / 0.176557 (-0.037441) | 0.215225 / 0.737135 (-0.521911) | 0.134440 / 0.296338 (-0.161898) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.616783 / 0.215209 (0.401574) | 6.113925 / 2.077655 (4.036270) | 2.403465 / 1.504120 (0.899345) | 1.967523 / 1.541195 (0.426329) | 2.042144 / 1.468490 (0.573654) | 0.927447 / 4.584777 (-3.657330) | 5.280413 / 3.745712 (1.534701) | 2.715335 / 5.269862 (-2.554527) | 1.755640 / 4.565676 (-2.810036) | 0.114370 / 0.424275 (-0.309905) | 0.013583 / 0.007607 (0.005976) | 0.761701 / 0.226044 (0.535657) | 7.466049 / 2.268929 (5.197120) | 3.041943 / 55.444624 (-52.402682) | 2.314477 / 6.876477 (-4.562000) | 2.469285 / 2.142072 (0.327213) | 1.216055 / 4.805227 (-3.589172) | 0.214205 / 6.500664 (-6.286459) | 0.080901 / 0.075469 (0.005432) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.565185 / 1.841788 (-0.276603) | 18.387986 / 8.074308 (10.313678) | 19.665109 / 10.191392 (9.473717) | 0.226670 / 0.680424 (-0.453754) | 0.028430 / 0.534201 (-0.505771) | 0.510526 / 0.579283 (-0.068757) | 0.623178 / 0.434364 (0.188814) | 0.592039 / 0.540337 (0.051702) | 0.728462 / 1.386936 (-0.658474) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.009161 / 0.011353 (-0.002192) | 0.004891 / 0.011008 (-0.006117) | 0.106502 / 0.038508 (0.067994) | 0.048234 / 0.023109 (0.025125) | 0.451173 / 0.275898 (0.175275) | 0.557948 / 0.323480 (0.234468) | 0.005350 / 0.007986 (-0.002635) | 0.004559 / 0.004328 (0.000230) | 0.110393 / 0.004250 (0.106142) | 0.060624 / 0.037052 (0.023572) | 0.459265 / 0.258489 (0.200776) | 0.575302 / 0.293841 (0.281461) | 0.051379 / 0.128546 (-0.077167) | 0.015576 / 0.075646 (-0.060070) | 0.116650 / 0.419271 (-0.302621) | 0.065534 / 0.043533 (0.022001) | 0.461431 / 0.255139 (0.206292) | 0.487677 / 0.283200 (0.204477) | 0.037773 / 0.141683 (-0.103910) | 1.992416 / 1.452155 (0.540261) | 1.991280 / 1.492716 (0.498564) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.233607 / 0.018006 (0.215601) | 0.507539 / 0.000490 (0.507049) | 0.001307 / 0.000200 (0.001107) | 0.000096 / 0.000054 (0.000042) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.032897 / 0.037411 (-0.004514) | 0.126549 / 0.014526 (0.112023) | 0.137893 / 0.176557 (-0.038663) | 0.192124 / 0.737135 (-0.545012) | 0.147300 / 0.296338 (-0.149038) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.679371 / 0.215209 (0.464162) | 6.673249 / 2.077655 (4.595595) | 2.979141 / 1.504120 (1.475022) | 2.568789 / 1.541195 (1.027594) | 2.537540 / 1.468490 (1.069050) | 0.973555 / 4.584777 (-3.611222) | 5.313536 / 3.745712 (1.567824) | 2.693283 / 5.269862 (-2.576579) | 1.819483 / 4.565676 (-2.746194) | 0.111644 / 0.424275 (-0.312631) | 0.013218 / 0.007607 (0.005611) | 0.776114 / 0.226044 (0.550070) | 7.758907 / 2.268929 (5.489978) | 3.417611 / 55.444624 (-52.027013) | 2.859502 / 6.876477 (-4.016975) | 2.927726 / 2.142072 (0.785653) | 1.163671 / 4.805227 (-3.641556) | 0.228636 / 6.500664 (-6.272028) | 0.082077 / 0.075469 (0.006607) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.746150 / 1.841788 (-0.095637) | 17.961955 / 8.074308 (9.887647) | 21.590545 / 10.191392 (11.399153) | 0.210017 / 0.680424 (-0.470406) | 0.028435 / 0.534201 (-0.505766) | 0.509253 / 0.579283 (-0.070030) | 0.606993 / 0.434364 (0.172629) | 0.587189 / 0.540337 (0.046851) | 0.684023 / 1.386936 (-0.702913) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#9aaee6fd0b2bcbe18e4829602084bcd83d669c5e \"CML watermark\")\n" ]

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[ "_The documentation is not available anymore as the PR was closed or merged._", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.005893 / 0.011353 (-0.005460) | 0.003682 / 0.011008 (-0.007327) | 0.098358 / 0.038508 (0.059850) | 0.028130 / 0.023109 (0.005020) | 0.305960 / 0.275898 (0.030062) | 0.334869 / 0.323480 (0.011390) | 0.003522 / 0.007986 (-0.004463) | 0.003683 / 0.004328 (-0.000645) | 0.079418 / 0.004250 (0.075168) | 0.037662 / 0.037052 (0.000609) | 0.310893 / 0.258489 (0.052404) | 0.341347 / 0.293841 (0.047506) | 0.027450 / 0.128546 (-0.101096) | 0.008381 / 0.075646 (-0.067265) | 0.316020 / 0.419271 (-0.103252) | 0.045079 / 0.043533 (0.001546) | 0.307806 / 0.255139 (0.052667) | 0.331804 / 0.283200 (0.048604) | 0.091806 / 0.141683 (-0.049877) | 1.492611 / 1.452155 (0.040457) | 1.551762 / 1.492716 (0.059046) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.201640 / 0.018006 (0.183634) | 0.422776 / 0.000490 (0.422286) | 0.003734 / 0.000200 (0.003535) | 0.000080 / 0.000054 (0.000026) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.025429 / 0.037411 (-0.011982) | 0.104699 / 0.014526 (0.090173) | 0.110505 / 0.176557 (-0.066051) | 0.171252 / 0.737135 (-0.565883) | 0.113131 / 0.296338 (-0.183208) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.419914 / 0.215209 (0.204705) | 4.184414 / 2.077655 (2.106760) | 1.999263 / 1.504120 (0.495143) | 1.828669 / 1.541195 (0.287474) | 1.940366 / 1.468490 (0.471876) | 0.556939 / 4.584777 (-4.027838) | 3.389164 / 3.745712 (-0.356548) | 1.796323 / 5.269862 (-3.473538) | 1.048843 / 4.565676 (-3.516833) | 0.067315 / 0.424275 (-0.356960) | 0.011531 / 0.007607 (0.003923) | 0.517226 / 0.226044 (0.291182) | 5.167255 / 2.268929 (2.898326) | 2.431129 / 55.444624 (-53.013495) | 2.133913 / 6.876477 (-4.742564) | 2.359021 / 2.142072 (0.216948) | 0.666390 / 4.805227 (-4.138838) | 0.135147 / 6.500664 (-6.365517) | 0.064855 / 0.075469 (-0.010614) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.166530 / 1.841788 (-0.675258) | 14.060551 / 8.074308 (5.986242) | 14.171663 / 10.191392 (3.980271) | 0.285821 / 0.680424 (-0.394603) | 0.016867 / 0.534201 (-0.517334) | 0.369102 / 0.579283 (-0.210181) | 0.393580 / 0.434364 (-0.040784) | 0.423721 / 0.540337 (-0.116616) | 0.512559 / 1.386936 (-0.874377) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.006674 / 0.011353 (-0.004679) | 0.004006 / 0.011008 (-0.007002) | 0.080160 / 0.038508 (0.041652) | 0.032508 / 0.023109 (0.009399) | 0.378168 / 0.275898 (0.102270) | 0.417796 / 0.323480 (0.094316) | 0.003706 / 0.007986 (-0.004280) | 0.002995 / 0.004328 (-0.001333) | 0.079275 / 0.004250 (0.075025) | 0.043690 / 0.037052 (0.006638) | 0.377717 / 0.258489 (0.119228) | 0.439801 / 0.293841 (0.145961) | 0.028438 / 0.128546 (-0.100108) | 0.008661 / 0.075646 (-0.066985) | 0.085280 / 0.419271 (-0.333991) | 0.043716 / 0.043533 (0.000183) | 0.370086 / 0.255139 (0.114947) | 0.403763 / 0.283200 (0.120563) | 0.095022 / 0.141683 (-0.046661) | 1.534376 / 1.452155 (0.082221) | 1.597658 / 1.492716 (0.104942) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.240229 / 0.018006 (0.222223) | 0.496281 / 0.000490 (0.495792) | 0.002165 / 0.000200 (0.001965) | 0.000075 / 0.000054 (0.000020) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.025330 / 0.037411 (-0.012081) | 0.102414 / 0.014526 (0.087888) | 0.112733 / 0.176557 (-0.063824) | 0.161181 / 0.737135 (-0.575955) | 0.114196 / 0.296338 (-0.182143) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.456808 / 0.215209 (0.241599) | 4.534937 / 2.077655 (2.457283) | 2.318834 / 1.504120 (0.814714) | 2.074085 / 1.541195 (0.532890) | 2.117409 / 1.468490 (0.648919) | 0.559110 / 4.584777 (-4.025667) | 3.371695 / 3.745712 (-0.374017) | 2.543154 / 5.269862 (-2.726708) | 1.360552 / 4.565676 (-3.205125) | 0.067602 / 0.424275 (-0.356674) | 0.011396 / 0.007607 (0.003789) | 0.561666 / 0.226044 (0.335622) | 5.607666 / 2.268929 (3.338737) | 2.802775 / 55.444624 (-52.641849) | 2.486162 / 6.876477 (-4.390315) | 2.390885 / 2.142072 (0.248813) | 0.667407 / 4.805227 (-4.137820) | 0.135948 / 6.500664 (-6.364717) | 0.067272 / 0.075469 (-0.008197) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.279664 / 1.841788 (-0.562124) | 15.188099 / 8.074308 (7.113791) | 14.380355 / 10.191392 (4.188963) | 0.140344 / 0.680424 (-0.540080) | 0.016832 / 0.534201 (-0.517369) | 0.364631 / 0.579283 (-0.214652) | 0.400306 / 0.434364 (-0.034058) | 0.430793 / 0.540337 (-0.109545) | 0.525923 / 1.386936 (-0.861013) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#48ca19cf1f4d1c99765a1f847c1f6b849496d99d \"CML watermark\")\n", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.008502 / 0.011353 (-0.002851) | 0.005946 / 0.011008 (-0.005062) | 0.131279 / 0.038508 (0.092771) | 0.035400 / 0.023109 (0.012291) | 0.423240 / 0.275898 (0.147342) | 0.470248 / 0.323480 (0.146768) | 0.004949 / 0.007986 (-0.003037) | 0.004544 / 0.004328 (0.000215) | 0.106856 / 0.004250 (0.102605) | 0.046579 / 0.037052 (0.009527) | 0.441135 / 0.258489 (0.182646) | 0.470401 / 0.293841 (0.176561) | 0.047231 / 0.128546 (-0.081315) | 0.017278 / 0.075646 (-0.058368) | 0.401937 / 0.419271 (-0.017335) | 0.067151 / 0.043533 (0.023619) | 0.453908 / 0.255139 (0.198769) | 0.422171 / 0.283200 (0.138971) | 0.123583 / 0.141683 (-0.018100) | 1.852895 / 1.452155 (0.400740) | 1.827282 / 1.492716 (0.334566) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.246419 / 0.018006 (0.228413) | 0.576930 / 0.000490 (0.576440) | 0.007511 / 0.000200 (0.007312) | 0.000165 / 0.000054 (0.000111) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.032732 / 0.037411 (-0.004680) | 0.130266 / 0.014526 (0.115740) | 0.150537 / 0.176557 (-0.026019) | 0.218554 / 0.737135 (-0.518582) | 0.148572 / 0.296338 (-0.147766) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.598611 / 0.215209 (0.383402) | 6.181219 / 2.077655 (4.103564) | 2.473468 / 1.504120 (0.969348) | 2.206374 / 1.541195 (0.665179) | 2.216707 / 1.468490 (0.748217) | 0.981295 / 4.584777 (-3.603482) | 5.716384 / 3.745712 (1.970672) | 5.882327 / 5.269862 (0.612466) | 2.761081 / 4.565676 (-1.804595) | 0.113544 / 0.424275 (-0.310731) | 0.015131 / 0.007607 (0.007524) | 0.850939 / 0.226044 (0.624894) | 8.046611 / 2.268929 (5.777682) | 3.340542 / 55.444624 (-52.104083) | 2.673692 / 6.876477 (-4.202785) | 2.926330 / 2.142072 (0.784257) | 1.176164 / 4.805227 (-3.629064) | 0.226745 / 6.500664 (-6.273919) | 0.085910 / 0.075469 (0.010441) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.483792 / 1.841788 (-0.357995) | 18.895009 / 8.074308 (10.820701) | 20.982461 / 10.191392 (10.791069) | 0.253085 / 0.680424 (-0.427339) | 0.031284 / 0.534201 (-0.502917) | 0.516569 / 0.579283 (-0.062714) | 0.635781 / 0.434364 (0.201417) | 0.604359 / 0.540337 (0.064022) | 0.725278 / 1.386936 (-0.661658) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.009220 / 0.011353 (-0.002133) | 0.005792 / 0.011008 (-0.005216) | 0.099795 / 0.038508 (0.061287) | 0.033812 / 0.023109 (0.010703) | 0.459386 / 0.275898 (0.183488) | 0.518067 / 0.323480 (0.194587) | 0.005083 / 0.007986 (-0.002902) | 0.004145 / 0.004328 (-0.000183) | 0.103506 / 0.004250 (0.099255) | 0.050429 / 0.037052 (0.013377) | 0.478149 / 0.258489 (0.219660) | 0.531280 / 0.293841 (0.237440) | 0.047373 / 0.128546 (-0.081173) | 0.013647 / 0.075646 (-0.061999) | 0.115174 / 0.419271 (-0.304098) | 0.061099 / 0.043533 (0.017566) | 0.455002 / 0.255139 (0.199863) | 0.507765 / 0.283200 (0.224565) | 0.112219 / 0.141683 (-0.029464) | 1.873591 / 1.452155 (0.421436) | 1.952061 / 1.492716 (0.459345) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.283587 / 0.018006 (0.265581) | 0.587562 / 0.000490 (0.587073) | 0.001252 / 0.000200 (0.001052) | 0.000095 / 0.000054 (0.000040) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.032706 / 0.037411 (-0.004705) | 0.137715 / 0.014526 (0.123189) | 0.131932 / 0.176557 (-0.044625) | 0.200042 / 0.737135 (-0.537094) | 0.159327 / 0.296338 (-0.137011) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.624061 / 0.215209 (0.408852) | 6.386235 / 2.077655 (4.308580) | 2.908786 / 1.504120 (1.404666) | 2.589855 / 1.541195 (1.048660) | 2.387988 / 1.468490 (0.919498) | 0.952625 / 4.584777 (-3.632152) | 5.571641 / 3.745712 (1.825929) | 2.711154 / 5.269862 (-2.558708) | 1.788015 / 4.565676 (-2.777662) | 0.104488 / 0.424275 (-0.319787) | 0.015213 / 0.007607 (0.007606) | 0.798446 / 0.226044 (0.572401) | 8.011614 / 2.268929 (5.742686) | 3.711951 / 55.444624 (-51.732673) | 2.896881 / 6.876477 (-3.979595) | 3.172116 / 2.142072 (1.030043) | 1.136816 / 4.805227 (-3.668411) | 0.239254 / 6.500664 (-6.261410) | 0.081136 / 0.075469 (0.005667) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.798246 / 1.841788 (-0.043542) | 19.497108 / 8.074308 (11.422800) | 23.450258 / 10.191392 (13.258866) | 0.250021 / 0.680424 (-0.430403) | 0.029138 / 0.534201 (-0.505063) | 0.532984 / 0.579283 (-0.046299) | 0.638161 / 0.434364 (0.203797) | 0.615720 / 0.540337 (0.075382) | 0.770621 / 1.386936 (-0.616315) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#7d8345c5f8a844ff44cfbb30cbda514ffe89bfd7 \"CML watermark\")\n", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.009120 / 0.011353 (-0.002233) | 0.005381 / 0.011008 (-0.005627) | 0.139719 / 0.038508 (0.101211) | 0.037229 / 0.023109 (0.014120) | 0.414633 / 0.275898 (0.138734) | 0.480313 / 0.323480 (0.156833) | 0.005027 / 0.007986 (-0.002959) | 0.005015 / 0.004328 (0.000687) | 0.108513 / 0.004250 (0.104263) | 0.056167 / 0.037052 (0.019115) | 0.407588 / 0.258489 (0.149099) | 0.518899 / 0.293841 (0.225058) | 0.048857 / 0.128546 (-0.079689) | 0.013694 / 0.075646 (-0.061952) | 0.418035 / 0.419271 (-0.001237) | 0.067755 / 0.043533 (0.024222) | 0.417740 / 0.255139 (0.162601) | 0.478622 / 0.283200 (0.195422) | 0.118290 / 0.141683 (-0.023393) | 1.901473 / 1.452155 (0.449319) | 1.978126 / 1.492716 (0.485409) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.271960 / 0.018006 (0.253954) | 0.602745 / 0.000490 (0.602255) | 0.005371 / 0.000200 (0.005171) | 0.000102 / 0.000054 (0.000048) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.029620 / 0.037411 (-0.007791) | 0.122402 / 0.014526 (0.107877) | 0.132645 / 0.176557 (-0.043911) | 0.212635 / 0.737135 (-0.524500) | 0.136901 / 0.296338 (-0.159438) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.644017 / 0.215209 (0.428808) | 6.597151 / 2.077655 (4.519496) | 2.454471 / 1.504120 (0.950351) | 2.151357 / 1.541195 (0.610163) | 2.290748 / 1.468490 (0.822258) | 0.970194 / 4.584777 (-3.614583) | 5.475275 / 3.745712 (1.729563) | 2.772658 / 5.269862 (-2.497204) | 1.785311 / 4.565676 (-2.780366) | 0.114503 / 0.424275 (-0.309772) | 0.015374 / 0.007607 (0.007767) | 0.768413 / 0.226044 (0.542368) | 7.956219 / 2.268929 (5.687290) | 3.272138 / 55.444624 (-52.172486) | 2.539638 / 6.876477 (-4.336839) | 2.713526 / 2.142072 (0.571454) | 1.181221 / 4.805227 (-3.624006) | 0.236327 / 6.500664 (-6.264337) | 0.089815 / 0.075469 (0.014345) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.521805 / 1.841788 (-0.319983) | 18.196529 / 8.074308 (10.122221) | 20.287324 / 10.191392 (10.095932) | 0.256959 / 0.680424 (-0.423465) | 0.028846 / 0.534201 (-0.505355) | 0.522354 / 0.579283 (-0.056929) | 0.600216 / 0.434364 (0.165852) | 0.607668 / 0.540337 (0.067331) | 0.762101 / 1.386936 (-0.624835) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.009227 / 0.011353 (-0.002126) | 0.005398 / 0.011008 (-0.005610) | 0.094998 / 0.038508 (0.056490) | 0.036633 / 0.023109 (0.013524) | 0.493317 / 0.275898 (0.217419) | 0.517216 / 0.323480 (0.193736) | 0.005510 / 0.007986 (-0.002476) | 0.004249 / 0.004328 (-0.000079) | 0.107936 / 0.004250 (0.103685) | 0.050223 / 0.037052 (0.013171) | 0.580275 / 0.258489 (0.321786) | 0.551477 / 0.293841 (0.257636) | 0.048758 / 0.128546 (-0.079788) | 0.013954 / 0.075646 (-0.061692) | 0.107021 / 0.419271 (-0.312250) | 0.064416 / 0.043533 (0.020884) | 0.485225 / 0.255139 (0.230086) | 0.513862 / 0.283200 (0.230663) | 0.118848 / 0.141683 (-0.022835) | 1.755396 / 1.452155 (0.303241) | 1.970349 / 1.492716 (0.477633) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.290743 / 0.018006 (0.272737) | 0.603293 / 0.000490 (0.602803) | 0.006814 / 0.000200 (0.006614) | 0.000156 / 0.000054 (0.000101) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.029862 / 0.037411 (-0.007550) | 0.136530 / 0.014526 (0.122005) | 0.133728 / 0.176557 (-0.042829) | 0.194709 / 0.737135 (-0.542427) | 0.151080 / 0.296338 (-0.145258) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.649202 / 0.215209 (0.433993) | 6.637578 / 2.077655 (4.559923) | 3.040135 / 1.504120 (1.536015) | 2.671308 / 1.541195 (1.130113) | 2.722412 / 1.468490 (1.253922) | 0.953029 / 4.584777 (-3.631748) | 5.805002 / 3.745712 (2.059290) | 5.049939 / 5.269862 (-0.219922) | 2.284053 / 4.565676 (-2.281623) | 0.130399 / 0.424275 (-0.293876) | 0.014726 / 0.007607 (0.007119) | 0.932570 / 0.226044 (0.706526) | 8.576693 / 2.268929 (6.307765) | 4.032738 / 55.444624 (-51.411886) | 3.274715 / 6.876477 (-3.601762) | 3.513788 / 2.142072 (1.371716) | 1.130624 / 4.805227 (-3.674603) | 0.219597 / 6.500664 (-6.281067) | 0.081425 / 0.075469 (0.005956) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.735312 / 1.841788 (-0.106476) | 18.438587 / 8.074308 (10.364279) | 21.582310 / 10.191392 (11.390918) | 0.224040 / 0.680424 (-0.456384) | 0.027590 / 0.534201 (-0.506611) | 0.503598 / 0.579283 (-0.075685) | 0.624379 / 0.434364 (0.190015) | 0.571911 / 0.540337 (0.031574) | 0.723215 / 1.386936 (-0.663721) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#9e40d28f2b0060a429c70827191fa5ff3ce8cf27 \"CML watermark\")\n" ]

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[ "This is the actual error:\r\n```\r\nFailed to read file '/home/lakala/hjc/code/pycode/glm/temp.json' with error <class 'pyarrow.lib.ArrowInvalid'>: cannot mix list and non-list, non-null values\r\n```\r\nWhich means some samples are incorrectly formatted.\r\n\r\nPyArrow, a storage backend that we use under the hood, requires that all the list elements have the same level of nesting (same number of dimensions) or are `None`.\r\n```python\r\nimport pyarrow as pa\r\npa.array([[1, 2, 3], 2]) # ArrowInvalid: cannot mix list and non-list, non-null values\r\npa.array([[1, 2, 3], [2]]) # works\r\n``` ", "@mariosasko \r\nI used the same operation to check the original data before and after slicing.\r\nThis is reflected in my code.\r\n160000 is not a specific number.\r\nI can also get output using 150000.\r\nThis doesn't seem to align very well with what you said.\r\nBecause if only some sample formats are incorrect.\r\nSo there should be an error in one of the front and back slices.\r\nthank you for your reply.", "Our JSON loader does the following in your case:\r\n\r\n```python\r\nimport json\r\nimport pyarrow as pa\r\n\r\nwith open(file, encoding=\"utf-8\") as f:\r\n dataset = json.load(f)\r\nkeys = set().union(*[row.keys() for row in dataset])\r\nmapping = {col: [row.get(col) for row in dataset] for col in keys}\r\npa_table = pa.Table.from_pydict(mapping) # the ArrowInvalid error comes from here\r\n```\r\n\r\nSo if this code throws an error with correctly-formatted JSON, then this is an Arrow bug and should be reported in their repo.\r\n\r\n> I used the same operation to check the original data before and after slicing.\r\nThis is reflected in my code.\r\n160000 is not a specific number.\r\nI can also get output using 150000.\r\nThis doesn't seem to align very well with what you said.\r\nBecause if only some sample formats are incorrect.\r\nSo there should be an error in one of the front and back slices.\r\n\r\nYou should shuffle the data to make sure that's not the case", "@mariosasko \r\nThank you.\r\nI will try again." ]

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[ "_The documentation is not available anymore as the PR was closed or merged._", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.006374 / 0.011353 (-0.004979) | 0.004100 / 0.011008 (-0.006909) | 0.104031 / 0.038508 (0.065523) | 0.035186 / 0.023109 (0.012076) | 0.328904 / 0.275898 (0.053006) | 0.361409 / 0.323480 (0.037929) | 0.003855 / 0.007986 (-0.004130) | 0.004140 / 0.004328 (-0.000189) | 0.080406 / 0.004250 (0.076156) | 0.045658 / 0.037052 (0.008606) | 0.341133 / 0.258489 (0.082644) | 0.372688 / 0.293841 (0.078847) | 0.032025 / 0.128546 (-0.096521) | 0.008877 / 0.075646 (-0.066769) | 0.354784 / 0.419271 (-0.064488) | 0.068874 / 0.043533 (0.025341) | 0.335441 / 0.255139 (0.080302) | 0.356498 / 0.283200 (0.073298) | 0.113367 / 0.141683 (-0.028316) | 1.522458 / 1.452155 (0.070304) | 1.608046 / 1.492716 (0.115329) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.231653 / 0.018006 (0.213647) | 0.446678 / 0.000490 (0.446188) | 0.003246 / 0.000200 (0.003046) | 0.000085 / 0.000054 (0.000030) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.025299 / 0.037411 (-0.012112) | 0.111440 / 0.014526 (0.096914) | 0.118758 / 0.176557 (-0.057799) | 0.175037 / 0.737135 (-0.562098) | 0.124583 / 0.296338 (-0.171755) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.418694 / 0.215209 (0.203484) | 4.174695 / 2.077655 (2.097041) | 1.890323 / 1.504120 (0.386203) | 1.683300 / 1.541195 (0.142106) | 1.781954 / 1.468490 (0.313464) | 0.546131 / 4.584777 (-4.038645) | 3.768055 / 3.745712 (0.022343) | 1.839878 / 5.269862 (-3.429983) | 1.111877 / 4.565676 (-3.453800) | 0.068568 / 0.424275 (-0.355707) | 0.011950 / 0.007607 (0.004343) | 0.527469 / 0.226044 (0.301425) | 5.274887 / 2.268929 (3.005958) | 2.391274 / 55.444624 (-53.053351) | 2.063837 / 6.876477 (-4.812640) | 2.140627 / 2.142072 (-0.001445) | 0.681508 / 4.805227 (-4.123719) | 0.148203 / 6.500664 (-6.352461) | 0.064456 / 0.075469 (-0.011013) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.221478 / 1.841788 (-0.620310) | 14.713705 / 8.074308 (6.639397) | 14.674184 / 10.191392 (4.482792) | 0.148411 / 0.680424 (-0.532012) | 0.017858 / 0.534201 (-0.516343) | 0.436166 / 0.579283 (-0.143117) | 0.437290 / 0.434364 (0.002926) | 0.521994 / 0.540337 (-0.018343) | 0.635488 / 1.386936 (-0.751448) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.006108 / 0.011353 (-0.005245) | 0.003888 / 0.011008 (-0.007120) | 0.078424 / 0.038508 (0.039916) | 0.033618 / 0.023109 (0.010509) | 0.376284 / 0.275898 (0.100386) | 0.396957 / 0.323480 (0.073477) | 0.003799 / 0.007986 (-0.004187) | 0.003160 / 0.004328 (-0.001168) | 0.078358 / 0.004250 (0.074107) | 0.045597 / 0.037052 (0.008545) | 0.386396 / 0.258489 (0.127907) | 0.412985 / 0.293841 (0.119144) | 0.031610 / 0.128546 (-0.096936) | 0.008720 / 0.075646 (-0.066926) | 0.085944 / 0.419271 (-0.333328) | 0.050780 / 0.043533 (0.007247) | 0.378099 / 0.255139 (0.122960) | 0.381894 / 0.283200 (0.098694) | 0.098926 / 0.141683 (-0.042756) | 1.513842 / 1.452155 (0.061688) | 1.595040 / 1.492716 (0.102323) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.208169 / 0.018006 (0.190163) | 0.431653 / 0.000490 (0.431163) | 0.000935 / 0.000200 (0.000735) | 0.000088 / 0.000054 (0.000034) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.029600 / 0.037411 (-0.007812) | 0.116936 / 0.014526 (0.102410) | 0.125603 / 0.176557 (-0.050953) | 0.177007 / 0.737135 (-0.560129) | 0.130602 / 0.296338 (-0.165736) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.457158 / 0.215209 (0.241949) | 4.563254 / 2.077655 (2.485599) | 2.303549 / 1.504120 (0.799429) | 2.107269 / 1.541195 (0.566074) | 2.130861 / 1.468490 (0.662371) | 0.548931 / 4.584777 (-4.035846) | 3.745578 / 3.745712 (-0.000134) | 1.820372 / 5.269862 (-3.449490) | 1.099316 / 4.565676 (-3.466361) | 0.068218 / 0.424275 (-0.356057) | 0.012336 / 0.007607 (0.004728) | 0.569721 / 0.226044 (0.343676) | 5.691312 / 2.268929 (3.422384) | 2.797483 / 55.444624 (-52.647141) | 2.422621 / 6.876477 (-4.453855) | 2.426187 / 2.142072 (0.284115) | 0.674777 / 4.805227 (-4.130451) | 0.144855 / 6.500664 (-6.355809) | 0.065805 / 0.075469 (-0.009664) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.305078 / 1.841788 (-0.536709) | 14.874315 / 8.074308 (6.800007) | 14.541301 / 10.191392 (4.349909) | 0.175818 / 0.680424 (-0.504606) | 0.018169 / 0.534201 (-0.516032) | 0.435836 / 0.579283 (-0.143447) | 0.458397 / 0.434364 (0.024033) | 0.506232 / 0.540337 (-0.034106) | 0.605306 / 1.386936 (-0.781630) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#7e0c1ceab96821c7c6557482d25a9bd2078d716a \"CML watermark\")\n", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.006138 / 0.011353 (-0.005215) | 0.003792 / 0.011008 (-0.007216) | 0.099417 / 0.038508 (0.060908) | 0.028739 / 0.023109 (0.005630) | 0.302835 / 0.275898 (0.026937) | 0.336397 / 0.323480 (0.012918) | 0.003537 / 0.007986 (-0.004449) | 0.002973 / 0.004328 (-0.001355) | 0.077461 / 0.004250 (0.073211) | 0.039493 / 0.037052 (0.002440) | 0.302367 / 0.258489 (0.043878) | 0.344936 / 0.293841 (0.051095) | 0.027813 / 0.128546 (-0.100733) | 0.008591 / 0.075646 (-0.067055) | 0.318975 / 0.419271 (-0.100297) | 0.045971 / 0.043533 (0.002438) | 0.301672 / 0.255139 (0.046533) | 0.328202 / 0.283200 (0.045003) | 0.091400 / 0.141683 (-0.050282) | 1.487215 / 1.452155 (0.035060) | 1.557730 / 1.492716 (0.065014) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.208343 / 0.018006 (0.190336) | 0.426764 / 0.000490 (0.426275) | 0.001196 / 0.000200 (0.000996) | 0.000069 / 0.000054 (0.000015) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.024332 / 0.037411 (-0.013079) | 0.101861 / 0.014526 (0.087335) | 0.108669 / 0.176557 (-0.067888) | 0.172042 / 0.737135 (-0.565093) | 0.113048 / 0.296338 (-0.183290) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.421419 / 0.215209 (0.206210) | 4.200816 / 2.077655 (2.123162) | 1.913516 / 1.504120 (0.409396) | 1.712167 / 1.541195 (0.170972) | 1.762129 / 1.468490 (0.293639) | 0.561616 / 4.584777 (-4.023161) | 3.398122 / 3.745712 (-0.347590) | 1.744323 / 5.269862 (-3.525538) | 1.036023 / 4.565676 (-3.529653) | 0.067658 / 0.424275 (-0.356617) | 0.011145 / 0.007607 (0.003538) | 0.522803 / 0.226044 (0.296759) | 5.226245 / 2.268929 (2.957317) | 2.355148 / 55.444624 (-53.089476) | 2.014939 / 6.876477 (-4.861538) | 2.140028 / 2.142072 (-0.002044) | 0.695049 / 4.805227 (-4.110178) | 0.138428 / 6.500664 (-6.362236) | 0.066721 / 0.075469 (-0.008748) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.219610 / 1.841788 (-0.622177) | 14.239576 / 8.074308 (6.165268) | 14.381955 / 10.191392 (4.190563) | 0.131208 / 0.680424 (-0.549216) | 0.016698 / 0.534201 (-0.517503) | 0.361373 / 0.579283 (-0.217910) | 0.382560 / 0.434364 (-0.051804) | 0.419427 / 0.540337 (-0.120911) | 0.508314 / 1.386936 (-0.878622) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.006174 / 0.011353 (-0.005179) | 0.003893 / 0.011008 (-0.007115) | 0.079614 / 0.038508 (0.041106) | 0.028685 / 0.023109 (0.005576) | 0.368627 / 0.275898 (0.092729) | 0.411599 / 0.323480 (0.088119) | 0.003573 / 0.007986 (-0.004413) | 0.002989 / 0.004328 (-0.001340) | 0.078653 / 0.004250 (0.074402) | 0.041146 / 0.037052 (0.004094) | 0.362387 / 0.258489 (0.103898) | 0.417234 / 0.293841 (0.123393) | 0.027958 / 0.128546 (-0.100589) | 0.008695 / 0.075646 (-0.066952) | 0.084637 / 0.419271 (-0.334635) | 0.044188 / 0.043533 (0.000655) | 0.358514 / 0.255139 (0.103375) | 0.392314 / 0.283200 (0.109114) | 0.093986 / 0.141683 (-0.047697) | 1.535366 / 1.452155 (0.083212) | 1.605978 / 1.492716 (0.113262) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.196215 / 0.018006 (0.178209) | 0.429403 / 0.000490 (0.428913) | 0.003736 / 0.000200 (0.003536) | 0.000078 / 0.000054 (0.000023) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.025281 / 0.037411 (-0.012130) | 0.104325 / 0.014526 (0.089799) | 0.111548 / 0.176557 (-0.065009) | 0.162326 / 0.737135 (-0.574809) | 0.113853 / 0.296338 (-0.182486) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.447600 / 0.215209 (0.232391) | 4.463422 / 2.077655 (2.385767) | 2.168028 / 1.504120 (0.663908) | 1.968699 / 1.541195 (0.427504) | 2.035531 / 1.468490 (0.567041) | 0.564575 / 4.584777 (-4.020202) | 3.435338 / 3.745712 (-0.310374) | 2.981930 / 5.269862 (-2.287932) | 1.492172 / 4.565676 (-3.073505) | 0.067981 / 0.424275 (-0.356294) | 0.011254 / 0.007607 (0.003647) | 0.544385 / 0.226044 (0.318340) | 5.441694 / 2.268929 (3.172765) | 2.650168 / 55.444624 (-52.794456) | 2.333974 / 6.876477 (-4.542503) | 2.383424 / 2.142072 (0.241351) | 0.669814 / 4.805227 (-4.135414) | 0.135456 / 6.500664 (-6.365209) | 0.067067 / 0.075469 (-0.008402) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.313275 / 1.841788 (-0.528513) | 14.527636 / 8.074308 (6.453328) | 14.470957 / 10.191392 (4.279565) | 0.144361 / 0.680424 (-0.536063) | 0.016847 / 0.534201 (-0.517354) | 0.365158 / 0.579283 (-0.214125) | 0.393809 / 0.434364 (-0.040555) | 0.428527 / 0.540337 (-0.111810) | 0.515816 / 1.386936 (-0.871120) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#7845d4c3c301226b3f8941ac90aaa123bfd7c69e \"CML watermark\")\n" ]

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[ "cc @sanchit-gandhi @Vaibhavs10 @lhoestq - this is mainly for demos that use Common Voice datasets as done here: https://github.com/facebookresearch/fairseq/tree/main/examples/mms#-transformers\r\n", "Hi ! the error for me is\r\n\r\n```\r\nFileNotFoundError: Couldn't find a dataset script at /content/mozilla-foundation/common_voice_13_0/common_voice_13_0.py or any data file in the same directory. Couldn't find 'mozilla-foundation/common_voice_13_0' on the Hugging Face Hub either: FileNotFoundError: Dataset 'mozilla-foundation/common_voice_13_0' doesn't exist on the Hub. If the repo is private or gated, make sure to log in with `huggingface-cli login`.\r\n```\r\n\r\nAnd tbh idk how you managed to get your error. \"n_shards.json\" is not even a thing in `datasets`", "Okay, I am able to reproduce @patrickvonplaten's original error: https://github.com/Vaibhavs10/scratchpad/blob/main/cv13_datasets_test.ipynb\r\n\r\nAlso not sure why it looks for `n_shards.json`", "Ok I see, this file is downloaded from the CV dataset script - let me investigate", "Ok I see: when you log out you no longer have access to the repository.\r\n\r\nTherefore the dataset script is loaded from cache:\r\n```\r\nWARNING:datasets.load:Using the latest cached version of the module from /root/.cache/huggingface/modules/datasets_modules/datasets/mozilla-foundation--common_voice_13_0/22809012aac1fc9803eaffc44122e4149043748e93933935d5ea19898587e4d7 (last modified on Wed Jun 14 10:13:17 2023) since it couldn't be found locally at mozilla-foundation/common_voice_13_0., or remotely on the Hugging Face Hub.\r\n```\r\n\r\nand the script tries to download the n_shards.json but fails", "Is this ok for you https://github.com/huggingface/datasets/pull/5954 ?\r\n\r\nI'll do a release this afternoon", "Cool! ", "this is included in the new release 2.13.0" ]

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[ "_The documentation is not available anymore as the PR was closed or merged._", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.006522 / 0.011353 (-0.004831) | 0.004319 / 0.011008 (-0.006690) | 0.099280 / 0.038508 (0.060772) | 0.033117 / 0.023109 (0.010007) | 0.339392 / 0.275898 (0.063494) | 0.366219 / 0.323480 (0.042739) | 0.003896 / 0.007986 (-0.004090) | 0.003412 / 0.004328 (-0.000916) | 0.076655 / 0.004250 (0.072404) | 0.045203 / 0.037052 (0.008150) | 0.355800 / 0.258489 (0.097311) | 0.372533 / 0.293841 (0.078692) | 0.032318 / 0.128546 (-0.096229) | 0.009030 / 0.075646 (-0.066616) | 0.328701 / 0.419271 (-0.090571) | 0.052891 / 0.043533 (0.009358) | 0.341131 / 0.255139 (0.085992) | 0.351593 / 0.283200 (0.068393) | 0.105136 / 0.141683 (-0.036546) | 1.475953 / 1.452155 (0.023798) | 1.566074 / 1.492716 (0.073357) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.216671 / 0.018006 (0.198664) | 0.446952 / 0.000490 (0.446462) | 0.006340 / 0.000200 (0.006140) | 0.000096 / 0.000054 (0.000042) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.028293 / 0.037411 (-0.009118) | 0.112298 / 0.014526 (0.097773) | 0.118634 / 0.176557 (-0.057923) | 0.175542 / 0.737135 (-0.561593) | 0.124773 / 0.296338 (-0.171565) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.435209 / 0.215209 (0.220000) | 4.344361 / 2.077655 (2.266706) | 2.128943 / 1.504120 (0.624823) | 1.945465 / 1.541195 (0.404271) | 2.049932 / 1.468490 (0.581442) | 0.547126 / 4.584777 (-4.037651) | 3.768698 / 3.745712 (0.022986) | 1.924441 / 5.269862 (-3.345420) | 1.146364 / 4.565676 (-3.419312) | 0.067466 / 0.424275 (-0.356809) | 0.011175 / 0.007607 (0.003568) | 0.540978 / 0.226044 (0.314933) | 5.393120 / 2.268929 (3.124191) | 2.639027 / 55.444624 (-52.805597) | 2.327216 / 6.876477 (-4.549261) | 2.500532 / 2.142072 (0.358460) | 0.679120 / 4.805227 (-4.126107) | 0.148824 / 6.500664 (-6.351840) | 0.064195 / 0.075469 (-0.011274) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.158387 / 1.841788 (-0.683401) | 14.880751 / 8.074308 (6.806443) | 14.725249 / 10.191392 (4.533857) | 0.149785 / 0.680424 (-0.530639) | 0.017338 / 0.534201 (-0.516863) | 0.390980 / 0.579283 (-0.188303) | 0.425611 / 0.434364 (-0.008753) | 0.458851 / 0.540337 (-0.081487) | 0.559209 / 1.386936 (-0.827727) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.006835 / 0.011353 (-0.004518) | 0.004318 / 0.011008 (-0.006690) | 0.076715 / 0.038508 (0.038207) | 0.033528 / 0.023109 (0.010419) | 0.411986 / 0.275898 (0.136087) | 0.438752 / 0.323480 (0.115272) | 0.004039 / 0.007986 (-0.003947) | 0.003509 / 0.004328 (-0.000819) | 0.077924 / 0.004250 (0.073673) | 0.049519 / 0.037052 (0.012467) | 0.420595 / 0.258489 (0.162106) | 0.450536 / 0.293841 (0.156695) | 0.032817 / 0.128546 (-0.095729) | 0.008963 / 0.075646 (-0.066684) | 0.083818 / 0.419271 (-0.335454) | 0.057591 / 0.043533 (0.014058) | 0.404605 / 0.255139 (0.149466) | 0.423661 / 0.283200 (0.140462) | 0.110698 / 0.141683 (-0.030984) | 1.512515 / 1.452155 (0.060361) | 1.569207 / 1.492716 (0.076490) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.200795 / 0.018006 (0.182789) | 0.448853 / 0.000490 (0.448363) | 0.003657 / 0.000200 (0.003457) | 0.000102 / 0.000054 (0.000048) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.031612 / 0.037411 (-0.005799) | 0.116712 / 0.014526 (0.102186) | 0.126162 / 0.176557 (-0.050395) | 0.180522 / 0.737135 (-0.556614) | 0.129768 / 0.296338 (-0.166570) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.433797 / 0.215209 (0.218588) | 4.353099 / 2.077655 (2.275444) | 2.117582 / 1.504120 (0.613462) | 1.934487 / 1.541195 (0.393292) | 2.016988 / 1.468490 (0.548498) | 0.531387 / 4.584777 (-4.053390) | 3.843520 / 3.745712 (0.097807) | 1.879560 / 5.269862 (-3.390301) | 1.129445 / 4.565676 (-3.436231) | 0.065952 / 0.424275 (-0.358323) | 0.011566 / 0.007607 (0.003959) | 0.533949 / 0.226044 (0.307904) | 5.327447 / 2.268929 (3.058518) | 2.572202 / 55.444624 (-52.872422) | 2.240723 / 6.876477 (-4.635753) | 2.329290 / 2.142072 (0.187217) | 0.662162 / 4.805227 (-4.143066) | 0.143191 / 6.500664 (-6.357473) | 0.065273 / 0.075469 (-0.010196) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.274945 / 1.841788 (-0.566843) | 15.444511 / 8.074308 (7.370203) | 14.793524 / 10.191392 (4.602132) | 0.175607 / 0.680424 (-0.504817) | 0.017324 / 0.534201 (-0.516877) | 0.396172 / 0.579283 (-0.183111) | 0.437334 / 0.434364 (0.002970) | 0.472621 / 0.540337 (-0.067716) | 0.574888 / 1.386936 (-0.812048) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#b4ab1b3ed7257b0e0ad075d7271a51835f320a5e \"CML watermark\")\n", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.006976 / 0.011353 (-0.004377) | 0.004541 / 0.011008 (-0.006467) | 0.106085 / 0.038508 (0.067577) | 0.029148 / 0.023109 (0.006039) | 0.306386 / 0.275898 (0.030488) | 0.351474 / 0.323480 (0.027994) | 0.003924 / 0.007986 (-0.004062) | 0.004588 / 0.004328 (0.000260) | 0.090479 / 0.004250 (0.086229) | 0.041195 / 0.037052 (0.004142) | 0.346020 / 0.258489 (0.087531) | 0.362526 / 0.293841 (0.068685) | 0.041020 / 0.128546 (-0.087526) | 0.012536 / 0.075646 (-0.063110) | 0.333247 / 0.419271 (-0.086024) | 0.059786 / 0.043533 (0.016253) | 0.318094 / 0.255139 (0.062955) | 0.343879 / 0.283200 (0.060679) | 0.110083 / 0.141683 (-0.031600) | 1.514027 / 1.452155 (0.061872) | 1.551435 / 1.492716 (0.058719) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.235401 / 0.018006 (0.217395) | 0.544292 / 0.000490 (0.543803) | 0.005284 / 0.000200 (0.005084) | 0.000112 / 0.000054 (0.000057) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.025008 / 0.037411 (-0.012403) | 0.102235 / 0.014526 (0.087709) | 0.105523 / 0.176557 (-0.071034) | 0.180846 / 0.737135 (-0.556289) | 0.107078 / 0.296338 (-0.189261) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.502374 / 0.215209 (0.287165) | 5.224254 / 2.077655 (3.146600) | 1.987193 / 1.504120 (0.483073) | 1.694680 / 1.541195 (0.153485) | 1.663907 / 1.468490 (0.195417) | 0.786470 / 4.584777 (-3.798307) | 4.977895 / 3.745712 (1.232183) | 4.713451 / 5.269862 (-0.556410) | 2.298763 / 4.565676 (-2.266913) | 0.090225 / 0.424275 (-0.334051) | 0.011427 / 0.007607 (0.003820) | 0.640686 / 0.226044 (0.414641) | 6.351727 / 2.268929 (4.082798) | 2.636912 / 55.444624 (-52.807712) | 2.075566 / 6.876477 (-4.800911) | 2.080260 / 2.142072 (-0.061812) | 0.952727 / 4.805227 (-3.852500) | 0.188651 / 6.500664 (-6.312013) | 0.068997 / 0.075469 (-0.006472) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.258878 / 1.841788 (-0.582910) | 15.444724 / 8.074308 (7.370416) | 17.521918 / 10.191392 (7.330526) | 0.189732 / 0.680424 (-0.490692) | 0.031084 / 0.534201 (-0.503117) | 0.445150 / 0.579283 (-0.134133) | 0.575844 / 0.434364 (0.141480) | 0.498162 / 0.540337 (-0.042176) | 0.635885 / 1.386936 (-0.751051) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.007402 / 0.011353 (-0.003951) | 0.005058 / 0.011008 (-0.005950) | 0.077659 / 0.038508 (0.039151) | 0.034934 / 0.023109 (0.011825) | 0.373139 / 0.275898 (0.097241) | 0.411857 / 0.323480 (0.088377) | 0.003751 / 0.007986 (-0.004235) | 0.003634 / 0.004328 (-0.000695) | 0.075914 / 0.004250 (0.071663) | 0.037555 / 0.037052 (0.000503) | 0.387482 / 0.258489 (0.128993) | 0.434407 / 0.293841 (0.140566) | 0.040540 / 0.128546 (-0.088006) | 0.013458 / 0.075646 (-0.062189) | 0.096129 / 0.419271 (-0.323143) | 0.055369 / 0.043533 (0.011836) | 0.386564 / 0.255139 (0.131425) | 0.410417 / 0.283200 (0.127218) | 0.093265 / 0.141683 (-0.048418) | 1.432841 / 1.452155 (-0.019314) | 1.533180 / 1.492716 (0.040463) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.281051 / 0.018006 (0.263045) | 0.547635 / 0.000490 (0.547146) | 0.004434 / 0.000200 (0.004234) | 0.000105 / 0.000054 (0.000050) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.026409 / 0.037411 (-0.011002) | 0.098586 / 0.014526 (0.084060) | 0.109223 / 0.176557 (-0.067334) | 0.165958 / 0.737135 (-0.571177) | 0.111751 / 0.296338 (-0.184587) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.542717 / 0.215209 (0.327508) | 5.530075 / 2.077655 (3.452420) | 2.351141 / 1.504120 (0.847022) | 2.021659 / 1.541195 (0.480464) | 1.964900 / 1.468490 (0.496410) | 0.819698 / 4.584777 (-3.765079) | 4.917412 / 3.745712 (1.171700) | 2.425149 / 5.269862 (-2.844712) | 1.561953 / 4.565676 (-3.003724) | 0.098417 / 0.424275 (-0.325858) | 0.012594 / 0.007607 (0.004986) | 0.717212 / 0.226044 (0.491168) | 6.994833 / 2.268929 (4.725904) | 2.997347 / 55.444624 (-52.447277) | 2.388366 / 6.876477 (-4.488111) | 2.502913 / 2.142072 (0.360841) | 1.030545 / 4.805227 (-3.774682) | 0.184844 / 6.500664 (-6.315820) | 0.076889 / 0.075469 (0.001420) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.371647 / 1.841788 (-0.470141) | 15.522995 / 8.074308 (7.448687) | 17.349823 / 10.191392 (7.158431) | 0.229709 / 0.680424 (-0.450714) | 0.023303 / 0.534201 (-0.510898) | 0.413874 / 0.579283 (-0.165409) | 0.567552 / 0.434364 (0.133188) | 0.491722 / 0.540337 (-0.048615) | 0.590640 / 1.386936 (-0.796296) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#f1911ffa5d1f58f509d04fe1ddeb9d00a63f94d5 \"CML watermark\")\n" ]

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[ "Thanks for opening https://huggingface.co/datasets/discofuse/discussions/3, let's continue the discussion over there if you don't mind", "I have posted there also sir, please check\r\n@lhoestq" ]

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[ { "id": 1935892871, "node_id": "MDU6TGFiZWwxOTM1ODkyODcx", "url": "https://api.github.com/repos/huggingface/datasets/labels/enhancement", "name": "enhancement", "color": "a2eeef", "default": true, "description": "New feature or request" } ]

[]

[ "Hi ! We use the Arrow columnar format under the hood, which doesn't support such dictionaries: each field must have a fixed type and exist in each sample.\r\n\r\nInstead you can restructure your data like\r\n```\r\n{\r\n \"index\": 0,\r\n \"keys\": [\"2 * x + y >= 3\"],\r\n \"values\": [[\"2 * x + y >= 3\", \"4 * x + 2 * y >= 6\"]],\r\n }\r\n},\r\n...\r\n{\r\n \"index\": 9999,\r\n \"keys\": [\"x >= 6\"],\r\n \"values\": [[\"x >= 6\", \"x >= 0\", \"x >= -1\"]],\r\n},\r\n...\r\n```" ]

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[ "_The documentation is not available anymore as the PR was closed or merged._", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.006635 / 0.011353 (-0.004718) | 0.004439 / 0.011008 (-0.006570) | 0.107831 / 0.038508 (0.069323) | 0.035664 / 0.023109 (0.012555) | 0.393733 / 0.275898 (0.117835) | 0.418336 / 0.323480 (0.094856) | 0.005739 / 0.007986 (-0.002247) | 0.005737 / 0.004328 (0.001408) | 0.079820 / 0.004250 (0.075569) | 0.045402 / 0.037052 (0.008349) | 0.396108 / 0.258489 (0.137619) | 0.422951 / 0.293841 (0.129110) | 0.030506 / 0.128546 (-0.098040) | 0.009785 / 0.075646 (-0.065861) | 0.375302 / 0.419271 (-0.043969) | 0.054355 / 0.043533 (0.010823) | 0.399652 / 0.255139 (0.144513) | 0.410825 / 0.283200 (0.127625) | 0.109238 / 0.141683 (-0.032445) | 1.687532 / 1.452155 (0.235378) | 1.736829 / 1.492716 (0.244113) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.226514 / 0.018006 (0.208508) | 0.487010 / 0.000490 (0.486520) | 0.006436 / 0.000200 (0.006236) | 0.000102 / 0.000054 (0.000048) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.029097 / 0.037411 (-0.008315) | 0.122979 / 0.014526 (0.108453) | 0.129454 / 0.176557 (-0.047103) | 0.194006 / 0.737135 (-0.543129) | 0.137968 / 0.296338 (-0.158370) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.466425 / 0.215209 (0.251216) | 4.627307 / 2.077655 (2.549652) | 2.108840 / 1.504120 (0.604720) | 1.882547 / 1.541195 (0.341353) | 1.891077 / 1.468490 (0.422587) | 0.590646 / 4.584777 (-3.994131) | 4.176918 / 3.745712 (0.431205) | 2.071475 / 5.269862 (-3.198386) | 1.173815 / 4.565676 (-3.391862) | 0.075330 / 0.424275 (-0.348945) | 0.012944 / 0.007607 (0.005337) | 0.587080 / 0.226044 (0.361036) | 5.827053 / 2.268929 (3.558125) | 2.694258 / 55.444624 (-52.750366) | 2.276997 / 6.876477 (-4.599480) | 2.329678 / 2.142072 (0.187605) | 0.721860 / 4.805227 (-4.083367) | 0.159238 / 6.500664 (-6.341426) | 0.073013 / 0.075469 (-0.002456) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.345396 / 1.841788 (-0.496391) | 16.619283 / 8.074308 (8.544975) | 14.754754 / 10.191392 (4.563362) | 0.180784 / 0.680424 (-0.499639) | 0.020376 / 0.534201 (-0.513825) | 0.451010 / 0.579283 (-0.128273) | 0.481524 / 0.434364 (0.047160) | 0.564777 / 0.540337 (0.024440) | 0.683232 / 1.386936 (-0.703704) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.007243 / 0.011353 (-0.004110) | 0.005262 / 0.011008 (-0.005746) | 0.084090 / 0.038508 (0.045581) | 0.037429 / 0.023109 (0.014320) | 0.404038 / 0.275898 (0.128140) | 0.445040 / 0.323480 (0.121560) | 0.006220 / 0.007986 (-0.001766) | 0.004256 / 0.004328 (-0.000072) | 0.083794 / 0.004250 (0.079544) | 0.052655 / 0.037052 (0.015603) | 0.414083 / 0.258489 (0.155594) | 0.458190 / 0.293841 (0.164349) | 0.032719 / 0.128546 (-0.095828) | 0.010063 / 0.075646 (-0.065583) | 0.092281 / 0.419271 (-0.326990) | 0.053888 / 0.043533 (0.010355) | 0.407813 / 0.255139 (0.152674) | 0.431692 / 0.283200 (0.148493) | 0.119799 / 0.141683 (-0.021884) | 1.709853 / 1.452155 (0.257698) | 1.771592 / 1.492716 (0.278876) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.246540 / 0.018006 (0.228534) | 0.483199 / 0.000490 (0.482709) | 0.002514 / 0.000200 (0.002315) | 0.000096 / 0.000054 (0.000042) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.031576 / 0.037411 (-0.005835) | 0.130020 / 0.014526 (0.115495) | 0.140285 / 0.176557 (-0.036272) | 0.196164 / 0.737135 (-0.540972) | 0.143924 / 0.296338 (-0.152414) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.488549 / 0.215209 (0.273340) | 4.888055 / 2.077655 (2.810400) | 2.389163 / 1.504120 (0.885043) | 2.184626 / 1.541195 (0.643431) | 2.260227 / 1.468490 (0.791737) | 0.601331 / 4.584777 (-3.983446) | 4.386159 / 3.745712 (0.640447) | 3.345814 / 5.269862 (-1.924048) | 1.734360 / 4.565676 (-2.831317) | 0.073199 / 0.424275 (-0.351076) | 0.012397 / 0.007607 (0.004790) | 0.601411 / 0.226044 (0.375366) | 6.135000 / 2.268929 (3.866072) | 2.930169 / 55.444624 (-52.514456) | 2.532631 / 6.876477 (-4.343845) | 2.619351 / 2.142072 (0.477279) | 0.740954 / 4.805227 (-4.064274) | 0.162936 / 6.500664 (-6.337728) | 0.073885 / 0.075469 (-0.001585) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.502493 / 1.841788 (-0.339294) | 17.026756 / 8.074308 (8.952448) | 15.880958 / 10.191392 (5.689566) | 0.167261 / 0.680424 (-0.513163) | 0.020347 / 0.534201 (-0.513854) | 0.452902 / 0.579283 (-0.126381) | 0.481614 / 0.434364 (0.047250) | 0.539893 / 0.540337 (-0.000445) | 0.653401 / 1.386936 (-0.733535) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#6a5781212e968e2515afdf29370a6eab6f657120 \"CML watermark\")\n", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.008268 / 0.011353 (-0.003084) | 0.005538 / 0.011008 (-0.005470) | 0.126136 / 0.038508 (0.087628) | 0.046100 / 0.023109 (0.022991) | 0.366882 / 0.275898 (0.090984) | 0.408912 / 0.323480 (0.085432) | 0.007090 / 0.007986 (-0.000895) | 0.004820 / 0.004328 (0.000491) | 0.091432 / 0.004250 (0.087181) | 0.058390 / 0.037052 (0.021338) | 0.368787 / 0.258489 (0.110298) | 0.419429 / 0.293841 (0.125588) | 0.034958 / 0.128546 (-0.093588) | 0.010526 / 0.075646 (-0.065120) | 0.463063 / 0.419271 (0.043791) | 0.070544 / 0.043533 (0.027011) | 0.366182 / 0.255139 (0.111043) | 0.390851 / 0.283200 (0.107652) | 0.128377 / 0.141683 (-0.013306) | 1.819385 / 1.452155 (0.367231) | 1.928834 / 1.492716 (0.436117) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.228413 / 0.018006 (0.210407) | 0.485511 / 0.000490 (0.485021) | 0.005395 / 0.000200 (0.005195) | 0.000119 / 0.000054 (0.000064) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.035209 / 0.037411 (-0.002203) | 0.144492 / 0.014526 (0.129967) | 0.150467 / 0.176557 (-0.026089) | 0.223861 / 0.737135 (-0.513274) | 0.156363 / 0.296338 (-0.139975) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.517751 / 0.215209 (0.302542) | 5.150438 / 2.077655 (3.072783) | 2.483601 / 1.504120 (0.979481) | 2.279786 / 1.541195 (0.738592) | 2.374510 / 1.468490 (0.906020) | 0.637547 / 4.584777 (-3.947230) | 4.845393 / 3.745712 (1.099681) | 2.241554 / 5.269862 (-3.028307) | 1.290105 / 4.565676 (-3.275572) | 0.079791 / 0.424275 (-0.344484) | 0.014915 / 0.007607 (0.007308) | 0.640468 / 0.226044 (0.414423) | 6.394810 / 2.268929 (4.125881) | 3.012748 / 55.444624 (-52.431876) | 2.625565 / 6.876477 (-4.250912) | 2.792435 / 2.142072 (0.650363) | 0.782284 / 4.805227 (-4.022944) | 0.171628 / 6.500664 (-6.329036) | 0.081714 / 0.075469 (0.006245) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.592411 / 1.841788 (-0.249377) | 18.999604 / 8.074308 (10.925295) | 18.469946 / 10.191392 (8.278554) | 0.200878 / 0.680424 (-0.479546) | 0.021595 / 0.534201 (-0.512606) | 0.519247 / 0.579283 (-0.060036) | 0.534940 / 0.434364 (0.100576) | 0.656325 / 0.540337 (0.115987) | 0.789658 / 1.386936 (-0.597278) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.008093 / 0.011353 (-0.003260) | 0.005524 / 0.011008 (-0.005484) | 0.092339 / 0.038508 (0.053831) | 0.045619 / 0.023109 (0.022510) | 0.449376 / 0.275898 (0.173478) | 0.478587 / 0.323480 (0.155107) | 0.006978 / 0.007986 (-0.001007) | 0.004622 / 0.004328 (0.000294) | 0.090618 / 0.004250 (0.086368) | 0.059321 / 0.037052 (0.022269) | 0.450989 / 0.258489 (0.192500) | 0.491652 / 0.293841 (0.197811) | 0.033308 / 0.128546 (-0.095238) | 0.010677 / 0.075646 (-0.064969) | 0.099836 / 0.419271 (-0.319435) | 0.055937 / 0.043533 (0.012404) | 0.440560 / 0.255139 (0.185421) | 0.475305 / 0.283200 (0.192105) | 0.130829 / 0.141683 (-0.010854) | 1.857943 / 1.452155 (0.405789) | 1.989534 / 1.492716 (0.496818) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.244715 / 0.018006 (0.226709) | 0.482866 / 0.000490 (0.482377) | 0.001100 / 0.000200 (0.000900) | 0.000095 / 0.000054 (0.000041) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.036288 / 0.037411 (-0.001124) | 0.147903 / 0.014526 (0.133377) | 0.154141 / 0.176557 (-0.022416) | 0.221863 / 0.737135 (-0.515272) | 0.162319 / 0.296338 (-0.134019) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.536972 / 0.215209 (0.321763) | 5.382866 / 2.077655 (3.305211) | 2.719575 / 1.504120 (1.215456) | 2.516596 / 1.541195 (0.975401) | 2.699602 / 1.468490 (1.231112) | 0.639886 / 4.584777 (-3.944891) | 5.109746 / 3.745712 (1.364034) | 2.260206 / 5.269862 (-3.009656) | 1.305506 / 4.565676 (-3.260170) | 0.080262 / 0.424275 (-0.344013) | 0.014801 / 0.007607 (0.007194) | 0.661228 / 0.226044 (0.435184) | 6.596485 / 2.268929 (4.327557) | 3.226114 / 55.444624 (-52.218510) | 2.859776 / 6.876477 (-4.016701) | 3.059355 / 2.142072 (0.917282) | 0.793413 / 4.805227 (-4.011814) | 0.176521 / 6.500664 (-6.324143) | 0.084062 / 0.075469 (0.008593) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.642085 / 1.841788 (-0.199703) | 20.355459 / 8.074308 (12.281151) | 17.979620 / 10.191392 (7.788228) | 0.229329 / 0.680424 (-0.451094) | 0.025681 / 0.534201 (-0.508520) | 0.534142 / 0.579283 (-0.045141) | 0.623439 / 0.434364 (0.189075) | 0.621938 / 0.540337 (0.081601) | 0.759038 / 1.386936 (-0.627898) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#6a98ff43225df344139023a5b7eb9caef610b677 \"CML watermark\")\n", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.007703 / 0.011353 (-0.003649) | 0.005362 / 0.011008 (-0.005646) | 0.113111 / 0.038508 (0.074602) | 0.038891 / 0.023109 (0.015782) | 0.348938 / 0.275898 (0.073040) | 0.398079 / 0.323480 (0.074599) | 0.006707 / 0.007986 (-0.001278) | 0.004489 / 0.004328 (0.000160) | 0.087194 / 0.004250 (0.082943) | 0.054268 / 0.037052 (0.017216) | 0.359949 / 0.258489 (0.101460) | 0.402959 / 0.293841 (0.109118) | 0.032508 / 0.128546 (-0.096038) | 0.010224 / 0.075646 (-0.065422) | 0.387007 / 0.419271 (-0.032264) | 0.058971 / 0.043533 (0.015439) | 0.345085 / 0.255139 (0.089946) | 0.384306 / 0.283200 (0.101107) | 0.122253 / 0.141683 (-0.019430) | 1.706353 / 1.452155 (0.254199) | 1.840780 / 1.492716 (0.348063) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.254374 / 0.018006 (0.236368) | 0.497387 / 0.000490 (0.496897) | 0.012294 / 0.000200 (0.012094) | 0.000108 / 0.000054 (0.000054) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.030902 / 0.037411 (-0.006509) | 0.132098 / 0.014526 (0.117573) | 0.140311 / 0.176557 (-0.036245) | 0.205887 / 0.737135 (-0.531249) | 0.143992 / 0.296338 (-0.152347) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.467367 / 0.215209 (0.252158) | 4.669936 / 2.077655 (2.592281) | 2.155358 / 1.504120 (0.651238) | 1.984132 / 1.541195 (0.442937) | 2.102352 / 1.468490 (0.633861) | 0.607014 / 4.584777 (-3.977763) | 4.396479 / 3.745712 (0.650767) | 4.666056 / 5.269862 (-0.603806) | 2.176649 / 4.565676 (-2.389028) | 0.072657 / 0.424275 (-0.351619) | 0.012367 / 0.007607 (0.004759) | 0.569706 / 0.226044 (0.343661) | 5.749083 / 2.268929 (3.480154) | 2.640824 / 55.444624 (-52.803801) | 2.310253 / 6.876477 (-4.566224) | 2.486748 / 2.142072 (0.344676) | 0.737891 / 4.805227 (-4.067336) | 0.163507 / 6.500664 (-6.337157) | 0.075776 / 0.075469 (0.000307) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.362710 / 1.841788 (-0.479078) | 17.010705 / 8.074308 (8.936396) | 15.084231 / 10.191392 (4.892839) | 0.218274 / 0.680424 (-0.462150) | 0.019555 / 0.534201 (-0.514646) | 0.456013 / 0.579283 (-0.123270) | 0.502772 / 0.434364 (0.068408) | 0.581480 / 0.540337 (0.041142) | 0.686952 / 1.386936 (-0.699984) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.007976 / 0.011353 (-0.003377) | 0.005141 / 0.011008 (-0.005868) | 0.086629 / 0.038508 (0.048121) | 0.039553 / 0.023109 (0.016444) | 0.433028 / 0.275898 (0.157130) | 0.463444 / 0.323480 (0.139964) | 0.006967 / 0.007986 (-0.001018) | 0.005814 / 0.004328 (0.001485) | 0.086266 / 0.004250 (0.082015) | 0.055384 / 0.037052 (0.018332) | 0.428733 / 0.258489 (0.170243) | 0.475670 / 0.293841 (0.181829) | 0.032872 / 0.128546 (-0.095674) | 0.010664 / 0.075646 (-0.064983) | 0.094357 / 0.419271 (-0.324915) | 0.058386 / 0.043533 (0.014854) | 0.431114 / 0.255139 (0.175975) | 0.441728 / 0.283200 (0.158528) | 0.131942 / 0.141683 (-0.009740) | 1.782214 / 1.452155 (0.330060) | 1.843185 / 1.492716 (0.350469) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.247047 / 0.018006 (0.229041) | 0.488931 / 0.000490 (0.488441) | 0.002657 / 0.000200 (0.002457) | 0.000106 / 0.000054 (0.000052) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.033893 / 0.037411 (-0.003518) | 0.131021 / 0.014526 (0.116495) | 0.142892 / 0.176557 (-0.033665) | 0.200955 / 0.737135 (-0.536180) | 0.151329 / 0.296338 (-0.145010) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.521138 / 0.215209 (0.305929) | 5.085207 / 2.077655 (3.007552) | 2.652901 / 1.504120 (1.148781) | 2.401545 / 1.541195 (0.860350) | 2.553461 / 1.468490 (1.084971) | 0.615347 / 4.584777 (-3.969430) | 4.448038 / 3.745712 (0.702326) | 2.049997 / 5.269862 (-3.219865) | 1.190602 / 4.565676 (-3.375075) | 0.073356 / 0.424275 (-0.350919) | 0.013685 / 0.007607 (0.006078) | 0.626705 / 0.226044 (0.400660) | 6.391941 / 2.268929 (4.123012) | 3.218864 / 55.444624 (-52.225760) | 2.858808 / 6.876477 (-4.017669) | 3.005808 / 2.142072 (0.863736) | 0.740725 / 4.805227 (-4.064502) | 0.161904 / 6.500664 (-6.338760) | 0.073727 / 0.075469 (-0.001742) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.488623 / 1.841788 (-0.353164) | 17.584367 / 8.074308 (9.510059) | 16.281818 / 10.191392 (6.090426) | 0.164482 / 0.680424 (-0.515942) | 0.020197 / 0.534201 (-0.514003) | 0.456750 / 0.579283 (-0.122533) | 0.501156 / 0.434364 (0.066792) | 0.549779 / 0.540337 (0.009442) | 0.650156 / 1.386936 (-0.736780) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#2b6cc63b868ea4ee60502845ebec68abb943958b \"CML watermark\")\n", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.008337 / 0.011353 (-0.003016) | 0.005911 / 0.011008 (-0.005097) | 0.129037 / 0.038508 (0.090529) | 0.046071 / 0.023109 (0.022962) | 0.418657 / 0.275898 (0.142759) | 0.490340 / 0.323480 (0.166860) | 0.006387 / 0.007986 (-0.001598) | 0.004724 / 0.004328 (0.000396) | 0.097953 / 0.004250 (0.093702) | 0.069025 / 0.037052 (0.031972) | 0.431178 / 0.258489 (0.172689) | 0.458363 / 0.293841 (0.164522) | 0.049341 / 0.128546 (-0.079205) | 0.014637 / 0.075646 (-0.061009) | 0.439800 / 0.419271 (0.020529) | 0.069905 / 0.043533 (0.026373) | 0.406775 / 0.255139 (0.151636) | 0.441989 / 0.283200 (0.158790) | 0.046009 / 0.141683 (-0.095674) | 1.847630 / 1.452155 (0.395475) | 1.904067 / 1.492716 (0.411351) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.288305 / 0.018006 (0.270299) | 0.594547 / 0.000490 (0.594058) | 0.005600 / 0.000200 (0.005400) | 0.000106 / 0.000054 (0.000052) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.033847 / 0.037411 (-0.003564) | 0.125139 / 0.014526 (0.110613) | 0.147982 / 0.176557 (-0.028574) | 0.208396 / 0.737135 (-0.528739) | 0.144005 / 0.296338 (-0.152334) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.669175 / 0.215209 (0.453966) | 6.605289 / 2.077655 (4.527634) | 2.720468 / 1.504120 (1.216348) | 2.341355 / 1.541195 (0.800160) | 2.402069 / 1.468490 (0.933578) | 0.939303 / 4.584777 (-3.645474) | 5.718545 / 3.745712 (1.972833) | 2.856235 / 5.269862 (-2.413627) | 1.821555 / 4.565676 (-2.744121) | 0.105473 / 0.424275 (-0.318802) | 0.014490 / 0.007607 (0.006883) | 0.774349 / 0.226044 (0.548305) | 8.065048 / 2.268929 (5.796120) | 3.508482 / 55.444624 (-51.936143) | 2.822881 / 6.876477 (-4.053596) | 2.962947 / 2.142072 (0.820875) | 1.138944 / 4.805227 (-3.666284) | 0.248414 / 6.500664 (-6.252250) | 0.095665 / 0.075469 (0.020196) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.688231 / 1.841788 (-0.153557) | 18.673305 / 8.074308 (10.598997) | 22.768663 / 10.191392 (12.577271) | 0.211238 / 0.680424 (-0.469186) | 0.031380 / 0.534201 (-0.502821) | 0.517175 / 0.579283 (-0.062108) | 0.626437 / 0.434364 (0.192073) | 0.624225 / 0.540337 (0.083888) | 0.743746 / 1.386936 (-0.643191) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.008888 / 0.011353 (-0.002464) | 0.005491 / 0.011008 (-0.005517) | 0.105013 / 0.038508 (0.066505) | 0.049456 / 0.023109 (0.026347) | 0.528989 / 0.275898 (0.253091) | 0.651871 / 0.323480 (0.328391) | 0.006683 / 0.007986 (-0.001302) | 0.004365 / 0.004328 (0.000037) | 0.098161 / 0.004250 (0.093911) | 0.075615 / 0.037052 (0.038563) | 0.543746 / 0.258489 (0.285257) | 0.650855 / 0.293841 (0.357014) | 0.050220 / 0.128546 (-0.078327) | 0.014471 / 0.075646 (-0.061175) | 0.115903 / 0.419271 (-0.303368) | 0.065925 / 0.043533 (0.022392) | 0.527797 / 0.255139 (0.272658) | 0.543834 / 0.283200 (0.260634) | 0.043005 / 0.141683 (-0.098678) | 1.842846 / 1.452155 (0.390691) | 1.970615 / 1.492716 (0.477899) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.287350 / 0.018006 (0.269343) | 0.591139 / 0.000490 (0.590649) | 0.006423 / 0.000200 (0.006223) | 0.000107 / 0.000054 (0.000052) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.034594 / 0.037411 (-0.002818) | 0.137155 / 0.014526 (0.122629) | 0.154662 / 0.176557 (-0.021894) | 0.217834 / 0.737135 (-0.519301) | 0.159642 / 0.296338 (-0.136696) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.664288 / 0.215209 (0.449079) | 6.926912 / 2.077655 (4.849257) | 3.028957 / 1.504120 (1.524837) | 2.625178 / 1.541195 (1.083983) | 2.725316 / 1.468490 (1.256826) | 1.015715 / 4.584777 (-3.569062) | 5.834694 / 3.745712 (2.088982) | 5.105269 / 5.269862 (-0.164593) | 2.316194 / 4.565676 (-2.249483) | 0.113802 / 0.424275 (-0.310473) | 0.014079 / 0.007607 (0.006472) | 0.893727 / 0.226044 (0.667683) | 8.577701 / 2.268929 (6.308772) | 3.706907 / 55.444624 (-51.737717) | 3.087530 / 6.876477 (-3.788947) | 3.295004 / 2.142072 (1.152931) | 1.204172 / 4.805227 (-3.601055) | 0.248720 / 6.500664 (-6.251944) | 0.107208 / 0.075469 (0.031739) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.800058 / 1.841788 (-0.041730) | 19.253646 / 8.074308 (11.179338) | 22.590804 / 10.191392 (12.399412) | 0.270687 / 0.680424 (-0.409737) | 0.028678 / 0.534201 (-0.505522) | 0.534670 / 0.579283 (-0.044613) | 0.642881 / 0.434364 (0.208518) | 0.615521 / 0.540337 (0.075184) | 0.723733 / 1.386936 (-0.663203) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#2591cd45a002a06bd551343ec785abf16f1433e2 \"CML watermark\")\n", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.017236 / 0.011353 (0.005883) | 0.005341 / 0.011008 (-0.005667) | 0.131471 / 0.038508 (0.092963) | 0.048868 / 0.023109 (0.025758) | 0.448942 / 0.275898 (0.173044) | 0.498721 / 0.323480 (0.175241) | 0.006825 / 0.007986 (-0.001161) | 0.004587 / 0.004328 (0.000259) | 0.104142 / 0.004250 (0.099891) | 0.075521 / 0.037052 (0.038469) | 0.439538 / 0.258489 (0.181049) | 0.498720 / 0.293841 (0.204879) | 0.051352 / 0.128546 (-0.077194) | 0.015070 / 0.075646 (-0.060576) | 0.441752 / 0.419271 (0.022480) | 0.089166 / 0.043533 (0.045633) | 0.428909 / 0.255139 (0.173770) | 0.446648 / 0.283200 (0.163448) | 0.042371 / 0.141683 (-0.099312) | 1.993948 / 1.452155 (0.541793) | 2.065756 / 1.492716 (0.573039) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.257279 / 0.018006 (0.239273) | 0.575453 / 0.000490 (0.574964) | 0.004120 / 0.000200 (0.003920) | 0.000114 / 0.000054 (0.000060) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.034012 / 0.037411 (-0.003399) | 0.141737 / 0.014526 (0.127211) | 0.145241 / 0.176557 (-0.031316) | 0.226196 / 0.737135 (-0.510939) | 0.149526 / 0.296338 (-0.146813) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.665762 / 0.215209 (0.450553) | 6.683737 / 2.077655 (4.606083) | 2.869485 / 1.504120 (1.365365) | 2.462808 / 1.541195 (0.921613) | 2.526808 / 1.468490 (1.058318) | 0.957518 / 4.584777 (-3.627259) | 5.926261 / 3.745712 (2.180548) | 5.027822 / 5.269862 (-0.242040) | 2.643185 / 4.565676 (-1.922491) | 0.117014 / 0.424275 (-0.307261) | 0.015142 / 0.007607 (0.007535) | 0.835694 / 0.226044 (0.609650) | 8.427356 / 2.268929 (6.158427) | 3.649597 / 55.444624 (-51.795027) | 2.989607 / 6.876477 (-3.886870) | 3.043160 / 2.142072 (0.901088) | 1.158872 / 4.805227 (-3.646355) | 0.240456 / 6.500664 (-6.260208) | 0.089196 / 0.075469 (0.013726) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.689361 / 1.841788 (-0.152427) | 18.842158 / 8.074308 (10.767850) | 22.604249 / 10.191392 (12.412857) | 0.248487 / 0.680424 (-0.431936) | 0.029668 / 0.534201 (-0.504533) | 0.536283 / 0.579283 (-0.043001) | 0.663253 / 0.434364 (0.228890) | 0.622973 / 0.540337 (0.082635) | 0.735297 / 1.386936 (-0.651639) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.009296 / 0.011353 (-0.002057) | 0.005955 / 0.011008 (-0.005053) | 0.105723 / 0.038508 (0.067215) | 0.051184 / 0.023109 (0.028074) | 0.527095 / 0.275898 (0.251197) | 0.631697 / 0.323480 (0.308217) | 0.006577 / 0.007986 (-0.001408) | 0.004452 / 0.004328 (0.000124) | 0.105921 / 0.004250 (0.101670) | 0.071951 / 0.037052 (0.034899) | 0.572518 / 0.258489 (0.314029) | 0.623957 / 0.293841 (0.330116) | 0.050861 / 0.128546 (-0.077686) | 0.014897 / 0.075646 (-0.060749) | 0.122013 / 0.419271 (-0.297258) | 0.067194 / 0.043533 (0.023661) | 0.530352 / 0.255139 (0.275213) | 0.563912 / 0.283200 (0.280712) | 0.034756 / 0.141683 (-0.106927) | 1.961580 / 1.452155 (0.509425) | 2.052412 / 1.492716 (0.559696) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.304996 / 0.018006 (0.286990) | 0.584899 / 0.000490 (0.584409) | 0.010444 / 0.000200 (0.010244) | 0.000134 / 0.000054 (0.000080) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.032540 / 0.037411 (-0.004871) | 0.137349 / 0.014526 (0.122823) | 0.146233 / 0.176557 (-0.030323) | 0.206978 / 0.737135 (-0.530157) | 0.154380 / 0.296338 (-0.141959) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.705438 / 0.215209 (0.490229) | 7.042159 / 2.077655 (4.964504) | 3.285501 / 1.504120 (1.781381) | 2.904710 / 1.541195 (1.363515) | 2.952838 / 1.468490 (1.484348) | 0.987784 / 4.584777 (-3.596993) | 5.949550 / 3.745712 (2.203838) | 2.927148 / 5.269862 (-2.342714) | 1.870054 / 4.565676 (-2.695622) | 0.119548 / 0.424275 (-0.304727) | 0.014565 / 0.007607 (0.006958) | 0.858311 / 0.226044 (0.632266) | 8.721679 / 2.268929 (6.452750) | 4.100825 / 55.444624 (-51.343800) | 3.358093 / 6.876477 (-3.518383) | 3.499637 / 2.142072 (1.357564) | 1.208932 / 4.805227 (-3.596295) | 0.232961 / 6.500664 (-6.267703) | 0.089727 / 0.075469 (0.014258) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.780143 / 1.841788 (-0.061645) | 19.074991 / 8.074308 (11.000683) | 21.218487 / 10.191392 (11.027095) | 0.258690 / 0.680424 (-0.421734) | 0.029514 / 0.534201 (-0.504687) | 0.541764 / 0.579283 (-0.037519) | 0.640603 / 0.434364 (0.206239) | 0.635336 / 0.540337 (0.094999) | 0.756309 / 1.386936 (-0.630627) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#1b525c199e6352aa8aac55f1dcddeb55a80db373 \"CML watermark\")\n", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.009619 / 0.011353 (-0.001734) | 0.005683 / 0.011008 (-0.005325) | 0.136971 / 0.038508 (0.098463) | 0.051607 / 0.023109 (0.028497) | 0.439716 / 0.275898 (0.163818) | 0.486193 / 0.323480 (0.162713) | 0.006304 / 0.007986 (-0.001681) | 0.004489 / 0.004328 (0.000160) | 0.103837 / 0.004250 (0.099587) | 0.082954 / 0.037052 (0.045901) | 0.447286 / 0.258489 (0.188797) | 0.495434 / 0.293841 (0.201593) | 0.049244 / 0.128546 (-0.079302) | 0.015176 / 0.075646 (-0.060470) | 0.444406 / 0.419271 (0.025134) | 0.074766 / 0.043533 (0.031233) | 0.438585 / 0.255139 (0.183446) | 0.438232 / 0.283200 (0.155032) | 0.043372 / 0.141683 (-0.098311) | 2.057286 / 1.452155 (0.605131) | 2.049540 / 1.492716 (0.556824) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.298038 / 0.018006 (0.280031) | 0.630771 / 0.000490 (0.630281) | 0.008287 / 0.000200 (0.008087) | 0.000123 / 0.000054 (0.000068) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.033637 / 0.037411 (-0.003775) | 0.128327 / 0.014526 (0.113801) | 0.150672 / 0.176557 (-0.025885) | 0.228521 / 0.737135 (-0.508614) | 0.142733 / 0.296338 (-0.153606) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.629072 / 0.215209 (0.413863) | 6.612047 / 2.077655 (4.534392) | 2.715594 / 1.504120 (1.211474) | 2.327823 / 1.541195 (0.786628) | 2.417508 / 1.468490 (0.949018) | 0.959134 / 4.584777 (-3.625643) | 5.669921 / 3.745712 (1.924209) | 2.977920 / 5.269862 (-2.291941) | 1.814564 / 4.565676 (-2.751112) | 0.120233 / 0.424275 (-0.304042) | 0.015859 / 0.007607 (0.008252) | 0.822618 / 0.226044 (0.596574) | 8.440306 / 2.268929 (6.171377) | 3.721611 / 55.444624 (-51.723013) | 2.954867 / 6.876477 (-3.921610) | 3.135364 / 2.142072 (0.993292) | 1.226475 / 4.805227 (-3.578752) | 0.246658 / 6.500664 (-6.254006) | 0.093920 / 0.075469 (0.018451) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.665631 / 1.841788 (-0.176157) | 19.136369 / 8.074308 (11.062061) | 23.659564 / 10.191392 (13.468172) | 0.273430 / 0.680424 (-0.406994) | 0.028180 / 0.534201 (-0.506021) | 0.559588 / 0.579283 (-0.019695) | 0.649203 / 0.434364 (0.214840) | 0.647113 / 0.540337 (0.106776) | 0.737978 / 1.386936 (-0.648958) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.009104 / 0.011353 (-0.002249) | 0.006838 / 0.011008 (-0.004171) | 0.104516 / 0.038508 (0.066008) | 0.047986 / 0.023109 (0.024877) | 0.521849 / 0.275898 (0.245951) | 0.586281 / 0.323480 (0.262801) | 0.006225 / 0.007986 (-0.001760) | 0.005713 / 0.004328 (0.001384) | 0.111507 / 0.004250 (0.107257) | 0.072320 / 0.037052 (0.035267) | 0.551061 / 0.258489 (0.292572) | 0.628034 / 0.293841 (0.334193) | 0.055417 / 0.128546 (-0.073129) | 0.019613 / 0.075646 (-0.056034) | 0.123958 / 0.419271 (-0.295314) | 0.066132 / 0.043533 (0.022600) | 0.504461 / 0.255139 (0.249322) | 0.560428 / 0.283200 (0.277229) | 0.036098 / 0.141683 (-0.105585) | 1.927398 / 1.452155 (0.475243) | 2.015952 / 1.492716 (0.523235) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.313065 / 0.018006 (0.295059) | 0.609174 / 0.000490 (0.608684) | 0.008755 / 0.000200 (0.008555) | 0.000120 / 0.000054 (0.000066) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.040042 / 0.037411 (0.002630) | 0.136053 / 0.014526 (0.121527) | 0.143406 / 0.176557 (-0.033150) | 0.213080 / 0.737135 (-0.524055) | 0.154730 / 0.296338 (-0.141609) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.692706 / 0.215209 (0.477497) | 6.952968 / 2.077655 (4.875314) | 3.232023 / 1.504120 (1.727903) | 2.835450 / 1.541195 (1.294256) | 2.933821 / 1.468490 (1.465331) | 0.984712 / 4.584777 (-3.600065) | 6.127651 / 3.745712 (2.381939) | 2.956781 / 5.269862 (-2.313081) | 1.879928 / 4.565676 (-2.685748) | 0.111069 / 0.424275 (-0.313206) | 0.014598 / 0.007607 (0.006991) | 0.871486 / 0.226044 (0.645442) | 8.588500 / 2.268929 (6.319572) | 3.910740 / 55.444624 (-51.533885) | 3.115781 / 6.876477 (-3.760695) | 3.222367 / 2.142072 (1.080294) | 1.229680 / 4.805227 (-3.575547) | 0.232092 / 6.500664 (-6.268572) | 0.097717 / 0.075469 (0.022248) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.774193 / 1.841788 (-0.067595) | 19.863087 / 8.074308 (11.788779) | 24.058856 / 10.191392 (13.867464) | 0.214917 / 0.680424 (-0.465507) | 0.028771 / 0.534201 (-0.505430) | 0.544548 / 0.579283 (-0.034735) | 0.655882 / 0.434364 (0.221518) | 0.629110 / 0.540337 (0.088773) | 0.749246 / 1.386936 (-0.637690) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#f4a5ea6a42dcfef1577288b51beeccc0eb124cee \"CML watermark\")\n", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.007075 / 0.011353 (-0.004278) | 0.005195 / 0.011008 (-0.005813) | 0.113043 / 0.038508 (0.074535) | 0.038442 / 0.023109 (0.015333) | 0.336310 / 0.275898 (0.060412) | 0.381888 / 0.323480 (0.058409) | 0.005990 / 0.007986 (-0.001996) | 0.003893 / 0.004328 (-0.000435) | 0.093123 / 0.004250 (0.088872) | 0.058449 / 0.037052 (0.021397) | 0.359463 / 0.258489 (0.100974) | 0.427485 / 0.293841 (0.133644) | 0.041454 / 0.128546 (-0.087092) | 0.013016 / 0.075646 (-0.062630) | 0.372849 / 0.419271 (-0.046422) | 0.059386 / 0.043533 (0.015853) | 0.381398 / 0.255139 (0.126259) | 0.367603 / 0.283200 (0.084403) | 0.033907 / 0.141683 (-0.107775) | 1.628903 / 1.452155 (0.176749) | 1.764131 / 1.492716 (0.271415) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.298329 / 0.018006 (0.280322) | 0.593030 / 0.000490 (0.592540) | 0.007653 / 0.000200 (0.007453) | 0.000091 / 0.000054 (0.000036) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.025445 / 0.037411 (-0.011966) | 0.112062 / 0.014526 (0.097536) | 0.119863 / 0.176557 (-0.056693) | 0.178389 / 0.737135 (-0.558746) | 0.129934 / 0.296338 (-0.166404) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.532834 / 0.215209 (0.317625) | 5.250908 / 2.077655 (3.173253) | 2.086920 / 1.504120 (0.582800) | 1.799745 / 1.541195 (0.258550) | 1.909648 / 1.468490 (0.441158) | 0.825382 / 4.584777 (-3.759395) | 5.268304 / 3.745712 (1.522592) | 2.533347 / 5.269862 (-2.736515) | 1.730187 / 4.565676 (-2.835490) | 0.099824 / 0.424275 (-0.324451) | 0.012969 / 0.007607 (0.005362) | 0.732234 / 0.226044 (0.506189) | 6.989066 / 2.268929 (4.720138) | 2.873486 / 55.444624 (-52.571138) | 2.274351 / 6.876477 (-4.602125) | 2.311060 / 2.142072 (0.168987) | 1.125366 / 4.805227 (-3.679861) | 0.214522 / 6.500664 (-6.286142) | 0.077579 / 0.075469 (0.002110) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.670950 / 1.841788 (-0.170838) | 18.131528 / 8.074308 (10.057220) | 21.277823 / 10.191392 (11.086431) | 0.238807 / 0.680424 (-0.441617) | 0.032251 / 0.534201 (-0.501950) | 0.503859 / 0.579283 (-0.075424) | 0.604825 / 0.434364 (0.170461) | 0.555623 / 0.540337 (0.015286) | 0.647301 / 1.386936 (-0.739635) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.010857 / 0.011353 (-0.000496) | 0.005581 / 0.011008 (-0.005427) | 0.094346 / 0.038508 (0.055838) | 0.053084 / 0.023109 (0.029975) | 0.457586 / 0.275898 (0.181688) | 0.545475 / 0.323480 (0.221995) | 0.006761 / 0.007986 (-0.001225) | 0.005094 / 0.004328 (0.000765) | 0.095509 / 0.004250 (0.091258) | 0.077182 / 0.037052 (0.040130) | 0.498717 / 0.258489 (0.240228) | 0.542433 / 0.293841 (0.248592) | 0.051547 / 0.128546 (-0.076999) | 0.014633 / 0.075646 (-0.061014) | 0.106843 / 0.419271 (-0.312428) | 0.068459 / 0.043533 (0.024926) | 0.435793 / 0.255139 (0.180654) | 0.475484 / 0.283200 (0.192285) | 0.039495 / 0.141683 (-0.102188) | 1.684906 / 1.452155 (0.232751) | 1.798693 / 1.492716 (0.305976) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.279853 / 0.018006 (0.261847) | 0.601016 / 0.000490 (0.600526) | 0.002055 / 0.000200 (0.001855) | 0.000219 / 0.000054 (0.000165) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.030935 / 0.037411 (-0.006477) | 0.121197 / 0.014526 (0.106671) | 0.143360 / 0.176557 (-0.033197) | 0.200862 / 0.737135 (-0.536274) | 0.138656 / 0.296338 (-0.157683) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.613904 / 0.215209 (0.398695) | 6.155422 / 2.077655 (4.077767) | 2.777238 / 1.504120 (1.273118) | 2.473045 / 1.541195 (0.931851) | 2.604470 / 1.468490 (1.135980) | 0.898871 / 4.584777 (-3.685906) | 5.739666 / 3.745712 (1.993954) | 4.719822 / 5.269862 (-0.550040) | 2.727354 / 4.565676 (-1.838322) | 0.108232 / 0.424275 (-0.316043) | 0.013632 / 0.007607 (0.006025) | 0.771802 / 0.226044 (0.545757) | 7.987466 / 2.268929 (5.718537) | 3.609856 / 55.444624 (-51.834768) | 2.974421 / 6.876477 (-3.902056) | 2.956567 / 2.142072 (0.814495) | 1.093792 / 4.805227 (-3.711435) | 0.213369 / 6.500664 (-6.287295) | 0.084486 / 0.075469 (0.009017) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.693855 / 1.841788 (-0.147933) | 18.055027 / 8.074308 (9.980719) | 21.397964 / 10.191392 (11.206571) | 0.240549 / 0.680424 (-0.439875) | 0.031212 / 0.534201 (-0.502989) | 0.513657 / 0.579283 (-0.065626) | 0.651348 / 0.434364 (0.216985) | 0.603740 / 0.540337 (0.063402) | 0.752287 / 1.386936 (-0.634649) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#6f3f38d00dd40a444ae54c18caa28304ae36b9c3 \"CML watermark\")\n" ]

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[ "_The documentation is not available anymore as the PR was closed or merged._", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.007220 / 0.011353 (-0.004133) | 0.004558 / 0.011008 (-0.006451) | 0.116647 / 0.038508 (0.078139) | 0.046845 / 0.023109 (0.023736) | 0.352429 / 0.275898 (0.076531) | 0.429739 / 0.323480 (0.106259) | 0.006620 / 0.007986 (-0.001366) | 0.003731 / 0.004328 (-0.000597) | 0.088683 / 0.004250 (0.084433) | 0.070583 / 0.037052 (0.033530) | 0.366699 / 0.258489 (0.108210) | 0.420730 / 0.293841 (0.126889) | 0.037342 / 0.128546 (-0.091204) | 0.010041 / 0.075646 (-0.065605) | 0.383477 / 0.419271 (-0.035795) | 0.060279 / 0.043533 (0.016746) | 0.349988 / 0.255139 (0.094849) | 0.371423 / 0.283200 (0.088224) | 0.026725 / 0.141683 (-0.114958) | 1.736886 / 1.452155 (0.284731) | 1.812874 / 1.492716 (0.320157) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.253256 / 0.018006 (0.235250) | 0.563470 / 0.000490 (0.562980) | 0.010475 / 0.000200 (0.010275) | 0.000164 / 0.000054 (0.000110) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.030518 / 0.037411 (-0.006893) | 0.133324 / 0.014526 (0.118798) | 0.137095 / 0.176557 (-0.039461) | 0.202227 / 0.737135 (-0.534909) | 0.144195 / 0.296338 (-0.152143) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.480870 / 0.215209 (0.265661) | 4.822713 / 2.077655 (2.745058) | 2.124183 / 1.504120 (0.620064) | 1.910733 / 1.541195 (0.369538) | 1.970266 / 1.468490 (0.501776) | 0.624695 / 4.584777 (-3.960082) | 4.459659 / 3.745712 (0.713947) | 2.210123 / 5.269862 (-3.059739) | 1.300520 / 4.565676 (-3.265157) | 0.077096 / 0.424275 (-0.347180) | 0.013333 / 0.007607 (0.005726) | 0.596841 / 0.226044 (0.370797) | 5.917397 / 2.268929 (3.648469) | 2.699397 / 55.444624 (-52.745228) | 2.274833 / 6.876477 (-4.601644) | 2.525376 / 2.142072 (0.383304) | 0.755718 / 4.805227 (-4.049510) | 0.163587 / 6.500664 (-6.337077) | 0.072817 / 0.075469 (-0.002653) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.524306 / 1.841788 (-0.317481) | 18.843312 / 8.074308 (10.769004) | 15.694644 / 10.191392 (5.503252) | 0.177400 / 0.680424 (-0.503024) | 0.020104 / 0.534201 (-0.514097) | 0.466421 / 0.579283 (-0.112862) | 0.537274 / 0.434364 (0.102910) | 0.576920 / 0.540337 (0.036583) | 0.718889 / 1.386936 (-0.668047) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.007671 / 0.011353 (-0.003682) | 0.004850 / 0.011008 (-0.006158) | 0.090085 / 0.038508 (0.051576) | 0.052023 / 0.023109 (0.028914) | 0.508575 / 0.275898 (0.232677) | 0.590024 / 0.323480 (0.266544) | 0.004564 / 0.007986 (-0.003422) | 0.005345 / 0.004328 (0.001017) | 0.087904 / 0.004250 (0.083653) | 0.064446 / 0.037052 (0.027394) | 0.525625 / 0.258489 (0.267136) | 0.584307 / 0.293841 (0.290466) | 0.037221 / 0.128546 (-0.091325) | 0.010588 / 0.075646 (-0.065059) | 0.098612 / 0.419271 (-0.320659) | 0.059597 / 0.043533 (0.016064) | 0.488064 / 0.255139 (0.232925) | 0.522330 / 0.283200 (0.239131) | 0.030004 / 0.141683 (-0.111679) | 1.732512 / 1.452155 (0.280357) | 1.809027 / 1.492716 (0.316310) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.218741 / 0.018006 (0.200735) | 0.494946 / 0.000490 (0.494456) | 0.004580 / 0.000200 (0.004380) | 0.000104 / 0.000054 (0.000049) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.034916 / 0.037411 (-0.002495) | 0.133695 / 0.014526 (0.119169) | 0.147964 / 0.176557 (-0.028592) | 0.213210 / 0.737135 (-0.523926) | 0.148850 / 0.296338 (-0.147488) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.508855 / 0.215209 (0.293646) | 5.065088 / 2.077655 (2.987433) | 2.473110 / 1.504120 (0.968990) | 2.259765 / 1.541195 (0.718570) | 2.359189 / 1.468490 (0.890699) | 0.639082 / 4.584777 (-3.945695) | 4.768195 / 3.745712 (1.022482) | 2.253803 / 5.269862 (-3.016059) | 1.442996 / 4.565676 (-3.122680) | 0.078761 / 0.424275 (-0.345514) | 0.013936 / 0.007607 (0.006329) | 0.625977 / 0.226044 (0.399933) | 6.260817 / 2.268929 (3.991888) | 3.149640 / 55.444624 (-52.294985) | 2.753555 / 6.876477 (-4.122921) | 2.831872 / 2.142072 (0.689799) | 0.781294 / 4.805227 (-4.023933) | 0.169109 / 6.500664 (-6.331555) | 0.075810 / 0.075469 (0.000341) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.533282 / 1.841788 (-0.308506) | 19.460579 / 8.074308 (11.386271) | 17.250424 / 10.191392 (7.059032) | 0.193485 / 0.680424 (-0.486939) | 0.020650 / 0.534201 (-0.513551) | 0.472110 / 0.579283 (-0.107173) | 0.532276 / 0.434364 (0.097912) | 0.613152 / 0.540337 (0.072814) | 0.684684 / 1.386936 (-0.702252) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#650a86ee122209d4a8c8e8068c01ebfd3ba553f5 \"CML watermark\")\n" ]

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[ "Hi ! The audio data don't always exist as files on disk - the blobs are often stored in the Arrow files. For now I'd suggest disabling decoding with `.cast_column(\"audio\", Audio(decode=False))` and apply your own decoding that handles corrupted files (maybe to filter them out ?)\r\n\r\ncc @sanchit-gandhi since it's related to our discussion about allowing users to make decoding return `None` and show a warning when there are corrupted files", "Thanks @lhoestq, I wasn't aware of the decode flag. It makes more sense as you say to show a warning when there are corrupted files together with some metadata of the file that allows to filter them from the dataset.\r\n\r\nMy workaround was to catch the LibsndfileError and generate a dummy audio with an unsual sample rate to filter it later. However returning `None` seems better. \r\n\r\n`try:\r\n array, sampling_rate = sf.read(file)\r\nexcept sf.LibsndfileError:\r\n print(\"bad file\")\r\n array = np.array([0.0])\r\n sampling_rate = 99.000` \r\n\r\n" ]

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[ "https://colab.research.google.com/#scrollTo=AQ_HCYruWIHU&fileId=https%3A//huggingface.co/dfurman/falcon-40b-chat-oasst1/blob/main/finetune_falcon40b_oasst1_with_bnb_peft.ipynb\r\n\r\nI ran the same administration exactly the same but got the same error", "Looks related to https://discuss.huggingface.co/t/indexerror-invalid-key-16-is-out-of-bounds-for-size-0/14298/4?u=lhoestq", "> Looks related to https://discuss.huggingface.co/t/indexerror-invalid-key-16-is-out-of-bounds-for-size-0/14298/4?u=lhoestq\n\nThe problem has not been solved, I have tried this before, but the problem is the same", "> \r\n\r\n@syngokhan did u solve it? \r\nI am desperate ", "data = data[\"train\"].shuffle().map(generate_and_tokenize_prompt, batched = False) # change this line to -\r\n\r\ndata[\"train\"] = data[\"train\"].shuffle().map(generate_and_tokenize_prompt, batched = False)\r\nAfter doing this change you code should run fine.", "> > \r\n> \r\n> @syngokhan did u solve it? I am desperate\r\n\r\nrefer to my earlier comment. you will find the solution." ]

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[ "Hi ! Feel free to re-run your code later, it will resume automatically where you left", "Tried many times in the last 2 weeks, problem remains.", "Alternatively you can save your dataset in parquet files locally and upload them to the hub manually\r\n\r\n```python\r\nfrom tqdm import tqdm\r\nnum_shards = 60\r\nfor index in tqdm(range(num_shards)):\r\n ds.shard(num_shards=num_shards, index=index, contiguous=True).to_parquet(f\"{index:05d}.parquet\")\r\n````" ]

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[ "_The documentation is not available anymore as the PR was closed or merged._", "@lhoestq tips applied. Thanks for a review. :smile: It's a lot of fun to improve this project. ", "Let's add some documentation in a subsequent PR :)\r\n\r\nIn particular @mariosasko and I think it's important to note to users that local arrow data are copied to cache according to the way load_dataset works, but if they want they can use Dataset.from_file instead", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.006384 / 0.011353 (-0.004969) | 0.003788 / 0.011008 (-0.007220) | 0.098524 / 0.038508 (0.060016) | 0.031786 / 0.023109 (0.008677) | 0.307799 / 0.275898 (0.031901) | 0.337329 / 0.323480 (0.013849) | 0.003650 / 0.007986 (-0.004336) | 0.003731 / 0.004328 (-0.000598) | 0.076816 / 0.004250 (0.072566) | 0.041888 / 0.037052 (0.004835) | 0.310702 / 0.258489 (0.052213) | 0.343846 / 0.293841 (0.050005) | 0.027841 / 0.128546 (-0.100705) | 0.008312 / 0.075646 (-0.067334) | 0.320230 / 0.419271 (-0.099042) | 0.047378 / 0.043533 (0.003845) | 0.308683 / 0.255139 (0.053544) | 0.335129 / 0.283200 (0.051930) | 0.096294 / 0.141683 (-0.045389) | 1.485521 / 1.452155 (0.033366) | 1.559868 / 1.492716 (0.067152) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.197376 / 0.018006 (0.179370) | 0.430461 / 0.000490 (0.429972) | 0.004152 / 0.000200 (0.003953) | 0.000068 / 0.000054 (0.000014) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.023660 / 0.037411 (-0.013751) | 0.103128 / 0.014526 (0.088602) | 0.107549 / 0.176557 (-0.069008) | 0.175934 / 0.737135 (-0.561201) | 0.112210 / 0.296338 (-0.184129) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.415804 / 0.215209 (0.200595) | 4.216333 / 2.077655 (2.138679) | 1.910354 / 1.504120 (0.406234) | 1.712689 / 1.541195 (0.171494) | 1.754705 / 1.468490 (0.286215) | 0.554647 / 4.584777 (-4.030130) | 3.393592 / 3.745712 (-0.352120) | 1.737504 / 5.269862 (-3.532358) | 1.021213 / 4.565676 (-3.544464) | 0.066908 / 0.424275 (-0.357367) | 0.011446 / 0.007607 (0.003839) | 0.524630 / 0.226044 (0.298585) | 5.243005 / 2.268929 (2.974077) | 2.349685 / 55.444624 (-53.094939) | 2.027457 / 6.876477 (-4.849020) | 2.131053 / 2.142072 (-0.011020) | 0.669070 / 4.805227 (-4.136157) | 0.136317 / 6.500664 (-6.364347) | 0.065924 / 0.075469 (-0.009545) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.254102 / 1.841788 (-0.587686) | 13.790492 / 8.074308 (5.716184) | 14.197772 / 10.191392 (4.006380) | 0.143989 / 0.680424 (-0.536434) | 0.016577 / 0.534201 (-0.517624) | 0.375437 / 0.579283 (-0.203846) | 0.398995 / 0.434364 (-0.035369) | 0.445287 / 0.540337 (-0.095050) | 0.538632 / 1.386936 (-0.848304) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.006251 / 0.011353 (-0.005101) | 0.004019 / 0.011008 (-0.006989) | 0.077985 / 0.038508 (0.039477) | 0.028705 / 0.023109 (0.005596) | 0.417360 / 0.275898 (0.141462) | 0.463964 / 0.323480 (0.140484) | 0.003489 / 0.007986 (-0.004497) | 0.003032 / 0.004328 (-0.001296) | 0.077953 / 0.004250 (0.073702) | 0.040104 / 0.037052 (0.003051) | 0.405242 / 0.258489 (0.146753) | 0.475029 / 0.293841 (0.181188) | 0.028113 / 0.128546 (-0.100433) | 0.008610 / 0.075646 (-0.067036) | 0.084847 / 0.419271 (-0.334424) | 0.048227 / 0.043533 (0.004694) | 0.417235 / 0.255139 (0.162096) | 0.450470 / 0.283200 (0.167270) | 0.096978 / 0.141683 (-0.044705) | 1.514688 / 1.452155 (0.062533) | 1.560205 / 1.492716 (0.067488) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.235125 / 0.018006 (0.217119) | 0.409904 / 0.000490 (0.409414) | 0.002474 / 0.000200 (0.002275) | 0.000074 / 0.000054 (0.000020) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.025152 / 0.037411 (-0.012259) | 0.103517 / 0.014526 (0.088991) | 0.110154 / 0.176557 (-0.066402) | 0.161431 / 0.737135 (-0.575704) | 0.114891 / 0.296338 (-0.181448) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.456077 / 0.215209 (0.240868) | 4.541171 / 2.077655 (2.463517) | 2.297912 / 1.504120 (0.793792) | 2.079337 / 1.541195 (0.538143) | 2.121291 / 1.468490 (0.652801) | 0.560172 / 4.584777 (-4.024605) | 3.421122 / 3.745712 (-0.324590) | 1.764675 / 5.269862 (-3.505186) | 1.043482 / 4.565676 (-3.522195) | 0.067652 / 0.424275 (-0.356623) | 0.011181 / 0.007607 (0.003574) | 0.557232 / 0.226044 (0.331188) | 5.607851 / 2.268929 (3.338922) | 2.783715 / 55.444624 (-52.660909) | 2.380943 / 6.876477 (-4.495534) | 2.378316 / 2.142072 (0.236244) | 0.674356 / 4.805227 (-4.130871) | 0.135912 / 6.500664 (-6.364752) | 0.067009 / 0.075469 (-0.008460) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.309002 / 1.841788 (-0.532786) | 14.464073 / 8.074308 (6.389765) | 14.418727 / 10.191392 (4.227335) | 0.148486 / 0.680424 (-0.531938) | 0.016650 / 0.534201 (-0.517551) | 0.368786 / 0.579283 (-0.210497) | 0.395026 / 0.434364 (-0.039338) | 0.433565 / 0.540337 (-0.106772) | 0.526603 / 1.386936 (-0.860333) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#443fc92700b4f9e12421e8082e205535314a67d5 \"CML watermark\")\n" ]

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[ "Hi ! you can speed it up using multiprocessing by passing `num_proc=` to `load_dataset()`", "already did，but not useful for step Generating train split，it works in step \"Resolving data files\" & \"Downloading data files\" ", "@mariosasko some advice ， thanks！", "I met the same problem, terrible experience", "@mariosasko ", "We need more info about the issue to provide help. \r\n\r\nCan you interrupt the process (with `num_proc=None`) after the `load_dataset` call when the slowdown occurs? So we can know what part of the code is causing it.\r\n\r\nThe `audiofolder` \\ `imagefolder` with metadata is not performant for large datasets. Luckily, we can make them much faster if drop the nested metadata files feature (not that useful). I plan to work on this soon.\r\n\r\nIn the meantime, it's better to use `Dataset.from_generator` (requires replacing the `load_dataset` calls in the transformers script with `Dataset.from_generator`) or write a dataset loading script for large datasets.", "Can you interrupt the process (with num_proc=None) after the load_dataset call when the slowdown occurs? So we can know what part of the code is causing it.\r\n（I'll try this operation）\r\nThe audiofolder \\ imagefolder with metadata is not performant for large datasets. Luckily, we can make them much faster if drop the nested metadata files feature (not that useful). I plan to work on this soon.\r\n(My data is indeed a bit large, exceeding 10000 hours of audio data. Looking forward to your improvement work very much)\r\n\r\nIn the meantime, it's better to use Dataset.from_generator (requires replacing the load_dataset calls in the transformers script with Dataset.from_generator) or write a dataset loading script for large datasets.\r\n（I want to use Dataset.from_generator instead of load_dataset ，where can i found sample code to load audio&label dataset， I was to do asr task）", "Can you interrupt the process (with num_proc=None) after the load_dataset call when the slowdown occurs? So we can know what part of the code is causing it.\r\n================================================================================\r\nHere is the log：\r\n[load_dataset.log](https://github.com/huggingface/datasets/files/12169362/load_dataset.log)\r\n（The larger my training data, the slower it loads）\r\n\r\n\r\n", "In the meantime, it's better to use Dataset.from_generator (requires replacing the load_dataset calls in the transformers script with Dataset.from_generator) or write a dataset loading script for large datasets.\r\n================================================================================\r\nI tried ‘Dataset. from_generator’ implements data loading, but the testing results show no improvement" ]

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[ "Hi @AntreasAntoniou , sorry to know you are facing this issue. To help debugging it, could you tell me:\r\n- What is the total dataset size?\r\n- Is it always failing on the same shard or is the hanging problem happening randomly?\r\n- Were you able to save the dataset as parquet locally? This would help us determine if the problem comes from the upload or the file generation.\r\n\r\nI'm cc-ing @lhoestq who might have some insights from a `datasets` perspective.", "One trick that can also help is to check the traceback when you kill your python process: it will show where in the code it was hanging", "Right. So I did the trick @lhoestq suggested. Here is where things seem to hang\r\n\r\n```\r\nError while uploading 'data/train-00120-of-00195-466c2dbab2eb9989.parquet' to the Hub. \r\nPushing split train to the Hub. \r\nCreating parquet from Arrow format: 100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 3/3 [00:03<00:00, 1.15s/ba]\r\nUpload 1 LFS files: 100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 1/1 [00:52<00:00, 52.12s/it]\r\nCreating parquet from Arrow format: 100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 3/3 [00:03<00:00, 1.08s/ba]\r\nUpload 1 LFS files: 100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 1/1 [00:45<00:00, 45.54s/it]\r\nCreating parquet from Arrow format: 100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 3/3 [00:03<00:00, 1.08s/ba]\r\nCreating parquet from Arrow format: 100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 3/3 [00:03<00:00, 1.03s/ba^Upload 1 LFS files: 0%| | 0/1 [\r\n21:27:35<?, ?it/s] \r\nPushing dataset shards to the dataset hub: 63%|█████████████████████████████████████████████████████████████▎ | 122/195 [23:37:11<14:07:59, 696.98s/it]\r\n^CError in sys.excepthook: \r\nTraceback (most recent call last): \r\n File \"/opt/conda/envs/main/lib/python3.10/site-packages/rich/console.py\", line 1699, in print \r\n extend(render(renderable, render_options)) \r\n File \"/opt/conda/envs/main/lib/python3.10/site-packages/rich/console.py\", line 1335, in render \r\n yield from self.render(render_output, _options) \r\n File \"/opt/conda/envs/main/lib/python3.10/site-packages/rich/console.py\", line 1331, in render \r\n for render_output in iter_render: \r\n File \"/opt/conda/envs/main/lib/python3.10/site-packages/rich/constrain.py\", line 29, in __rich_console__ \r\n yield from console.render(self.renderable, child_options) \r\n File \"/opt/conda/envs/main/lib/python3.10/site-packages/rich/console.py\", line 1331, in render \r\n for render_output in iter_render: \r\n File \"/opt/conda/envs/main/lib/python3.10/site-packages/rich/panel.py\", line 220, in __rich_console__ \r\n lines = console.render_lines(renderable, child_options, style=style) \r\n File \"/opt/conda/envs/main/lib/python3.10/site-packages/rich/console.py\", line 1371, in render_lines \r\n lines = list( \r\n File \"/opt/conda/envs/main/lib/python3.10/site-packages/rich/segment.py\", line 292, in split_and_crop_lines \r\n for segment in segments: \r\n File \"/opt/conda/envs/main/lib/python3.10/site-packages/rich/console.py\", line 1331, in render \r\n for render_output in iter_render: \r\n File \"/opt/conda/envs/main/lib/python3.10/site-packages/rich/padding.py\", line 97, in __rich_console__ \r\n lines = console.render_lines( \r\n File \"/opt/conda/envs/main/lib/python3.10/site-packages/rich/console.py\", line 1371, in render_lines \r\n lines = list( \r\n File \"/opt/conda/envs/main/lib/python3.10/site-packages/rich/segment.py\", line 292, in split_and_crop_lines \r\n for segment in segments: \r\n File \"/opt/conda/envs/main/lib/python3.10/site-packages/rich/console.py\", line 1335, in render \r\n yield from self.render(render_output, _options) \r\n File \"/opt/conda/envs/main/lib/python3.10/site-packages/rich/console.py\", line 1331, in render \r\n for render_output in iter_render: \r\n File \"/opt/conda/envs/main/lib/python3.10/site-packages/rich/syntax.py\", line 611, in __rich_console__ \r\n segments = Segments(self._get_syntax(console, options)) \r\n File \"/opt/conda/envs/main/lib/python3.10/site-packages/rich/segment.py\", line 668, in __init__ \r\n self.segments = list(segments) \r\n File \"/opt/conda/envs/main/lib/python3.10/site-packages/rich/syntax.py\", line 674, in _get_syntax \r\n lines: Union[List[Text], Lines] = text.split(\"\\n\", allow_blank=ends_on_nl) \r\n File \"/opt/conda/envs/main/lib/python3.10/site-packages/rich/text.py\", line 1042, in split \r\n lines = Lines( \r\n File \"/opt/conda/envs/main/lib/python3.10/site-packages/rich/containers.py\", line 70, in __init__ \r\n self._lines: List[\"Text\"] = list(lines) \r\n File \"/opt/conda/envs/main/lib/python3.10/site-packages/rich/text.py\", line 1043, in <genexpr> \r\n line for line in self.divide(flatten_spans()) if line.plain != separator \r\n File \"/opt/conda/envs/main/lib/python3.10/site-packages/rich/text.py\", line 385, in plain \r\n if len(self._text) != 1: \r\nKeyboardInterrupt \r\n \r\nOriginal exception was: \r\nTraceback (most recent call last): \r\n File \"/opt/conda/envs/main/lib/python3.10/site-packages/tqdm/contrib/concurrent.py\", line 51, in _executor_map \r\n return list(tqdm_class(ex.map(fn, *iterables, chunksize=chunksize), **kwargs)) \r\n File \"/opt/conda/envs/main/lib/python3.10/site-packages/tqdm/std.py\", line 1178, in __iter__ \r\n for obj in iterable: \r\n File \"/opt/conda/envs/main/lib/python3.10/concurrent/futures/_base.py\", line 621, in result_iterator \r\n yield _result_or_cancel(fs.pop()) \r\n File \"/opt/conda/envs/main/lib/python3.10/concurrent/futures/_base.py\", line 319, in _result_or_cancel \r\n return fut.result(timeout) \r\n File \"/opt/conda/envs/main/lib/python3.10/concurrent/futures/_base.py\", line 453, in result \r\n self._condition.wait(timeout) \r\n File \"/opt/conda/envs/main/lib/python3.10/threading.py\", line 320, in wait \r\n waiter.acquire() \r\nKeyboardInterrupt \r\n \r\nDuring handling of the above exception, another exception occurred: \r\n \r\nTraceback (most recent call last): \r\n File \"/TALI/tali/scripts/validate_dataset.py\", line 127, in <module> \r\n train_dataset.push_to_hub(repo_id=\"Antreas/TALI-base\", max_shard_size=\"5GB\") \r\n File \"/opt/conda/envs/main/lib/python3.10/site-packages/datasets/dataset_dict.py\", line 1583, in push_to_hub \r\n repo_id, split, uploaded_size, dataset_nbytes, _, _ = self[split]._push_parquet_shards_to_hub( \r\n File \"/opt/conda/envs/main/lib/python3.10/site-packages/datasets/arrow_dataset.py\", line 5275, in _push_parquet_shards_to_hub \r\n _retry( \r\n File \"/opt/conda/envs/main/lib/python3.10/site-packages/datasets/utils/file_utils.py\", line 282, in _retry \r\n return func(*func_args, **func_kwargs) \r\n File \"/opt/conda/envs/main/lib/python3.10/site-packages/huggingface_hub/utils/_validators.py\", line 118, in _inner_fn \r\n return fn(*args, **kwargs) \r\n File \"/opt/conda/envs/main/lib/python3.10/site-packages/huggingface_hub/hf_api.py\", line 826, in _inner \r\n return fn(self, *args, **kwargs) \r\n File \"/opt/conda/envs/main/lib/python3.10/site-packages/huggingface_hub/hf_api.py\", line 3205, in upload_file \r\n commit_info = self.create_commit( \r\n File \"/opt/conda/envs/main/lib/python3.10/site-packages/huggingface_hub/utils/_validators.py\", line 118, in _inner_fn \r\n return fn(*args, **kwargs) \r\n File \"/opt/conda/envs/main/lib/python3.10/site-packages/huggingface_hub/hf_api.py\", line 826, in _inner \r\n return fn(self, *args, **kwargs) \r\n File \"/opt/conda/envs/main/lib/python3.10/site-packages/huggingface_hub/hf_api.py\", line 2680, in create_commit \r\n upload_lfs_files( \r\n File \"/opt/conda/envs/main/lib/python3.10/site-packages/huggingface_hub/utils/_validators.py\", line 118, in _inner_fn \r\n return fn(*args, **kwargs) \r\n File \"/opt/conda/envs/main/lib/python3.10/site-packages/huggingface_hub/_commit_api.py\", line 353, in upload_lfs_files \r\n thread_map( \r\n File \"/opt/conda/envs/main/lib/python3.10/site-packages/tqdm/contrib/concurrent.py\", line 69, in thread_map \r\n return _executor_map(ThreadPoolExecutor, fn, *iterables, **tqdm_kwargs) \r\n File \"/opt/conda/envs/main/lib/python3.10/site-packages/tqdm/contrib/concurrent.py\", line 49, in _executor_map \r\n with PoolExecutor(max_workers=max_workers, initializer=tqdm_class.set_lock, \r\n File \"/opt/conda/envs/main/lib/python3.10/concurrent/futures/_base.py\", line 649, in __exit__ \r\n self.shutdown(wait=True) \r\n File \"/opt/conda/envs/main/lib/python3.10/concurrent/futures/thread.py\", line 235, in shutdown \r\n t.join() \r\n File \"/opt/conda/envs/main/lib/python3.10/threading.py\", line 1096, in join \r\n self._wait_for_tstate_lock() \r\n File \"/opt/conda/envs/main/lib/python3.10/threading.py\", line 1116, in _wait_for_tstate_lock \r\n if lock.acquire(block, timeout): \r\nKeyboardInterrupt \r\n```", "@Wauplin \r\n\r\n>What is the total dataset size?\r\n\r\nThere are three variants, and the random hanging happens on all three. The sizes are 2TB, 1TB, and 200GB. \r\n\r\n>Is it always failing on the same shard or is the hanging problem happening randomly?\r\n\r\nIt seems to be very much random, as restarting can help move past the previous hang, only to find a new one, or not. \r\n\r\n>Were you able to save the dataset as parquet locally? This would help us determine if the problem comes from the upload or the file generation.\r\n\r\nYes. The dataset seems to be locally stored as parquet. ", "Hmm it looks like an issue with TQDM lock. Maybe you can try updating TQDM ?", "I am using the latest version of tqdm\r\n\r\n```\r\n⬢ [Docker] ❯ pip install tqdm --upgrade\r\nRequirement already satisfied: tqdm in /opt/conda/envs/main/lib/python3.10/site-packages (4.65.0)\r\nWARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv\r\n```", "I tried trying to catch the hanging issue in action again\r\n\r\n```\r\nPushing dataset shards to the dataset hub: 65%|█████████████████████████████████████████████████████████████████▊ | 127/195 [2:28:02<1:19:15, 69.94s/it] \r\nError while uploading 'data/train-00127-of-00195-3f8d036ade107c27.parquet' to the Hub. \r\nPushing split train to the Hub. \r\nPushing dataset shards to the dataset hub: 64%|████████████████████████████████████████████████████████████████▏ | 124/195 [2:06:10<1:12:14, 61.05s/it]C^[^C^C^C \r\n╭─────────────────────────────── Traceback (most recent call last) ────────────────────────────────╮ \r\n│ /TALI/tali/scripts/validate_dataset.py:127 in <module> │ \r\n│ │ \r\n│ 124 │ │ \r\n│ 125 │ while not succesful_competion: │ \r\n│ 126 │ │ try: │ \r\n│ ❱ 127 │ │ │ train_dataset.push_to_hub(repo_id=\"Antreas/TALI-base\", max_shard_size=\"5GB\") │ \r\n│ 128 │ │ │ succesful_competion = True │ \r\n│ 129 │ │ except Exception as e: │ \r\n│ 130 │ │ │ print(e) │ \r\n│ │ \r\n│ /opt/conda/envs/main/lib/python3.10/site-packages/datasets/dataset_dict.py:1583 in push_to_hub │ \r\n│ │ \r\n│ 1580 │ │ for split in self.keys(): │ \r\n│ 1581 │ │ │ logger.warning(f\"Pushing split {split} to the Hub.\") │ \r\n│ 1582 │ │ │ # The split=key needs to be removed before merging │ \r\n│ ❱ 1583 │ │ │ repo_id, split, uploaded_size, dataset_nbytes, _, _ = self[split]._push_parq │ \r\n│ 1584 │ │ │ │ repo_id, │ \r\n│ 1585 │ │ │ │ split=split, │ \r\n│ 1586 │ │ │ │ private=private, │ \r\n│ │ \r\n│ /opt/conda/envs/main/lib/python3.10/site-packages/datasets/arrow_dataset.py:5263 in │ \r\n│ _push_parquet_shards_to_hub │ \r\n│ │ \r\n│ 5260 │ │ │ \r\n│ 5261 │ │ uploaded_size = 0 │ \r\n│ 5262 │ │ shards_path_in_repo = [] │ \r\n│ ❱ 5263 │ │ for index, shard in logging.tqdm( │ \r\n│ 5264 │ │ │ enumerate(itertools.chain([first_shard], shards_iter)), │ \r\n│ 5265 │ │ │ desc=\"Pushing dataset shards to the dataset hub\", │ \r\n│ 5266 │ │ │ total=num_shards, │ \r\n│ │ \r\n│ /opt/conda/envs/main/lib/python3.10/site-packages/tqdm/std.py:1178 in __iter__ │ \r\n│ │ \r\n│ 1175 │ │ time = self._time │ \r\n│ 1176 │ │ │ \r\n│ 1177 │ │ try: │\r\n│ ❱ 1178 │ │ │ for obj in iterable: │\r\n│ 1179 │ │ │ │ yield obj │\r\n│ 1180 │ │ │ │ # Update and possibly print the progressbar. │\r\n│ 1181 │ │ │ │ # Note: does not call self.update(1) for speed optimisation. │\r\n│ │\r\n│ /opt/conda/envs/main/lib/python3.10/site-packages/datasets/arrow_dataset.py:5238 in │\r\n│ shards_with_embedded_external_files │\r\n│ │\r\n│ 5235 │ │ │ │ for shard in shards: │\r\n│ 5236 │ │ │ │ │ format = shard.format │\r\n│ 5237 │ │ │ │ │ shard = shard.with_format(\"arrow\") │\r\n│ ❱ 5238 │ │ │ │ │ shard = shard.map( │\r\n│ 5239 │ │ │ │ │ │ embed_table_storage, │\r\n│ 5240 │ │ │ │ │ │ batched=True, │\r\n│ 5241 │ │ │ │ │ │ batch_size=1000, │\r\n│ │\r\n│ /opt/conda/envs/main/lib/python3.10/site-packages/datasets/arrow_dataset.py:578 in wrapper │\r\n│ │\r\n│ 575 │ │ else: │\r\n│ 576 │ │ │ self: \"Dataset\" = kwargs.pop(\"self\") │\r\n│ 577 │ │ # apply actual function │\r\n│ ❱ 578 │ │ out: Union[\"Dataset\", \"DatasetDict\"] = func(self, *args, **kwargs) │ \r\n│ 579 │ │ datasets: List[\"Dataset\"] = list(out.values()) if isinstance(out, dict) else [ou │ \r\n│ 580 │ │ for dataset in datasets: │ \r\n│ 581 │ │ │ # Remove task templates if a column mapping of the template is no longer val │ \r\n│ │ \r\n│ /opt/conda/envs/main/lib/python3.10/site-packages/datasets/arrow_dataset.py:543 in wrapper │ \r\n│ │ \r\n│ 540 │ │ │ \"output_all_columns\": self._output_all_columns, │ \r\n│ 541 │ │ } │ \r\n│ 542 │ │ # apply actual function │ \r\n│ ❱ 543 │ │ out: Union[\"Dataset\", \"DatasetDict\"] = func(self, *args, **kwargs) │ \r\n│ 544 │ │ datasets: List[\"Dataset\"] = list(out.values()) if isinstance(out, dict) else [ou │ \r\n│ 545 │ │ # re-apply format to the output │ \r\n│ 546 │ │ for dataset in datasets: │ \r\n│ │ \r\n│ /opt/conda/envs/main/lib/python3.10/site-packages/datasets/arrow_dataset.py:3073 in map │ \r\n│ │ \r\n│ 3070 │ │ │ │ │ leave=False, │ \r\n│ 3071 │ │ │ │ │ desc=desc or \"Map\", │ \r\n│ 3072 │ │ │ │ ) as pbar: │ \r\n│ ❱ 3073 │ │ │ │ │ for rank, done, content in Dataset._map_single(**dataset_kwargs): │ \r\n│ 3074 │ │ │ │ │ │ if done: │ \r\n│ 3075 │ │ │ │ │ │ │ shards_done += 1 │ \r\n│ 3076 │ │ │ │ │ │ │ logger.debug(f\"Finished processing shard number {rank} of {n │ \r\n│ │ \r\n│ /opt/conda/envs/main/lib/python3.10/site-packages/datasets/arrow_dataset.py:3464 in _map_single │ \r\n│ │ \r\n│ 3461 │ │ │ │ │ │ │ │ buf_writer, writer, tmp_file = init_buffer_and_writer() │ \r\n│ 3462 │ │ │ │ │ │ │ │ stack.enter_context(writer) │ \r\n│ 3463 │ │ │ │ │ │ │ if isinstance(batch, pa.Table): │ \r\n│ ❱ 3464 │ │ │ │ │ │ │ │ writer.write_table(batch) │ \r\n│ 3465 │ │ │ │ │ │ │ else: │ \r\n│ 3466 │ │ │ │ │ │ │ │ writer.write_batch(batch) │ \r\n│ 3467 │ │ │ │ │ │ num_examples_progress_update += num_examples_in_batch │ \r\n│ │ \r\n│ /opt/conda/envs/main/lib/python3.10/site-packages/datasets/arrow_writer.py:567 in write_table │ \r\n│ │ \r\n│ 564 │ │ │ writer_batch_size = self.writer_batch_size │ \r\n│ 565 │ │ if self.pa_writer is None: │ \r\n│ 566 │ │ │ self._build_writer(inferred_schema=pa_table.schema) │ \r\n│ ❱ 567 │ │ pa_table = pa_table.combine_chunks() │ \r\n│ 568 │ │ pa_table = table_cast(pa_table, self._schema) │ \r\n│ 569 │ │ if self.embed_local_files: │ \r\n│ 570 │ │ │ pa_table = embed_table_storage(pa_table) │ \r\n╰──────────────────────────────────────────────────────────────────────────────────────────────────╯ \r\nKeyboardInterrupt \r\n```", "I'm on my phone so can't help that much. What I'd advice to do is to [save_to_disk](https://huggingface.co/docs/datasets/package_reference/main_classes#save_to_disk) if it's not already done and then upload the files/folder to the Hub separately. You can find what you need in the [upload guide](https://huggingface.co/docs/huggingface_hub/guides/upload). It might not help finding the exact issue for now but at least it can unblock you. ", "In your last stacktrace it interrupted while embedding external content - in case your dataset in made of images or audio files that live on your disk. Is it the case ?", "Yeah, the dataset has images, audio, video and text. ", "It's maybe related to https://github.com/apache/arrow/issues/34455: are you using ArrayND features ?\r\n\r\nAlso what's your `pyarrow` version ? Could you try updating to >= 12.0.1 ?", "I was using pyarrow == 12.0.0\r\n\r\nI am not explicitly using ArrayND features, unless the hub API automatically converts my files to such. ", "I have now updated to pyarrow == 12.0.1 and retrying", "You can also try to reduce the `max_shard_size` - Sometimes parquet has a hard time working with data bigger than 2GB", "So, updating the pyarrow seems to help. It can still throw errors here and there but I can retry when that happens. It's better than hanging. \r\n\r\nHowever, I am a bit confused about something. I have uploaded my datasets, but while earlier I could see all three sets, now I can only see 1. What's going on? \r\nhttps://huggingface.co/datasets/Antreas/TALI-base\r\n\r\nI have seen this happen before as well, so I deleted and reuploaded, but this dataset is way too large for me to do this. ", "It's a bug on our side, I'll update the dataset viewer ;)\r\n\r\nThanks for reporting !", "Apparently this happened because of bad modifications in the README.md split metadata.\r\n\r\nI fixed them in this PR: https://huggingface.co/datasets/Antreas/TALI-base/discussions/1", "@lhoestq It's a bit odd that when uploading a dataset, one set at a time \"train\", \"val\", \"test\", the push_to_hub function overwrites the readme and removes differently named sets from previous commits. i.e., you push \"val\", all is well. Then you push \"test\", and the \"val\" entry disappears from the readme, while the data remain intact. ", "Also, just found another related issue. One of the many that make things hang or fail when pushing to hub. \r\n\r\nIn the following code:\r\n\r\n```python\r\ntrain_generator = lambda: data_generator(\"train\", percentage=1.0)\r\n val_generator = lambda: data_generator(\"val\")\r\n test_generator = lambda: data_generator(\"test\")\r\n\r\n train_data = datasets.Dataset.from_generator(\r\n train_generator,\r\n num_proc=mp.cpu_count(),\r\n writer_batch_size=5000,\r\n cache_dir=tali_dataset_dir,\r\n )\r\n\r\n val_data = datasets.Dataset.from_generator(\r\n val_generator,\r\n writer_batch_size=5000,\r\n num_proc=mp.cpu_count(),\r\n cache_dir=tali_dataset_dir,\r\n )\r\n\r\n test_data = datasets.Dataset.from_generator(\r\n test_generator,\r\n writer_batch_size=5000,\r\n num_proc=mp.cpu_count(),\r\n cache_dir=tali_dataset_dir,\r\n )\r\n\r\n print(f\"Pushing TALI-large to hub\")\r\n\r\n dataset = datasets.DatasetDict(\r\n {\"train\": train_data, \"val\": val_data, \"test\": test_data}\r\n )\r\n succesful_competion = False\r\n\r\n while not succesful_competion:\r\n try:\r\n dataset.push_to_hub(repo_id=\"Antreas/TALI-large\", max_shard_size=\"2GB\")\r\n succesful_competion = True\r\n except Exception as e:\r\n print(e)\r\n ```\r\n \r\n \r\n Things keep failing in the push_to_repo step, at random places, with the following error:\r\n \r\n ```bash\r\n Pushing dataset shards to the dataset hub: 7%|██████████▋ | 67/950 [42:41<9:22:37, 38.23s/it]\r\nError while uploading 'data/train-00067-of-00950-a4d179ed5a593486.parquet' to the Hub.\r\nPushing split train to the Hub.\r\nCreating parquet from Arrow format: 100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 2/2 [00:01<00:00, 1.81ba/s]\r\nUpload 1 LFS files: 100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 1/1 [00:11<00:00, 11.20s/it]\r\nCreating parquet from Arrow format: 100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 2/2 [00:00<00:00, 2.48ba/s]\r\nUpload 1 LFS files: 100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 1/1 [00:15<00:00, 15.30s/it]\r\nCreating parquet from Arrow format: 100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 2/2 [00:00<00:00, 2.39ba/s]\r\nUpload 1 LFS files: 100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 1/1 [00:11<00:00, 11.52s/it]\r\nCreating parquet from Arrow format: 100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 2/2 [00:00<00:00, 2.47ba/s]\r\nUpload 1 LFS files: 100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 1/1 [00:10<00:00, 10.39s/it]\r\nCreating parquet from Arrow format: 100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 2/2 [00:00<00:00, 2.26ba/s]\r\nUpload 1 LFS files: 0%| | 0/1 [16:38<?, ?it/s]\r\nPushing dataset shards to the dataset hub: 7%|███████████▎ | 71/950 [44:37<9:12:28, 37.71s/it]\r\nError while uploading 'data/train-00071-of-00950-72bab6e5cb223aee.parquet' to the Hub.\r\nPushing split train to the Hub.\r\nCreating parquet from Arrow format: 100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 2/2 [00:00<00:00, 2.18ba/s]\r\nUpload 1 LFS files: 100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 1/1 [00:10<00:00, 10.94s/it]\r\nCreating parquet from Arrow format: 100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 2/2 [00:00<00:00, 2.36ba/s]\r\nUpload 1 LFS files: 100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 1/1 [00:10<00:00, 10.67s/it]\r\nCreating parquet from Arrow format: 100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 2/2 [00:00<00:00, 2.57ba/s]\r\nUpload 1 LFS files: 100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 1/1 [00:10<00:00, 10.16s/it]\r\nCreating parquet from Arrow format: 100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 2/2 [00:00<00:00, 2.68ba/s]\r\nUpload 1 LFS files: 100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 1/1 [00:09<00:00, 9.63s/it]\r\nCreating parquet from Arrow format: 100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 2/2 [00:00<00:00, 2.36ba/s]\r\nUpload 1 LFS files: 100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 1/1 [00:10<00:00, 10.67s/it]\r\nCreating parquet from Arrow format: 100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 2/2 [00:00<00:00, 2.37ba/s]\r\nUpload 1 LFS files: 0%| | 0/1 [16:39<?, ?it/s]\r\nPushing dataset shards to the dataset hub: 8%|████████████ | 76/950 [46:21<8:53:08, 36.60s/it]\r\nError while uploading 'data/train-00076-of-00950-b90e4e3b433db179.parquet' to the Hub.\r\nPushing split train to the Hub.\r\nCreating parquet from Arrow format: 100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 2/2 [00:00<00:00, 2.21ba/s]\r\nUpload 1 LFS files: 100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 1/1 [00:25<00:00, 25.40s/it]\r\nCreating parquet from Arrow format: 100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 2/2 [00:01<00:00, 1.56ba/s]\r\nUpload 1 LFS files: 100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 1/1 [00:10<00:00, 10.40s/it]\r\nCreating parquet from Arrow format: 100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 2/2 [00:00<00:00, 2.49ba/s]\r\nUpload 1 LFS files: 100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 1/1 [00:23<00:00, 23.53s/it]\r\nCreating parquet from Arrow format: 100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 2/2 [00:00<00:00, 2.27ba/s]\r\nUpload 1 LFS files: 100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 1/1 [00:10<00:00, 10.25s/it]\r\nCreating parquet from Arrow format: 100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 2/2 [00:00<00:00, 2.42ba/s]\r\nUpload 1 LFS files: 100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 1/1 [00:11<00:00, 11.03s/it]\r\nCreating parquet from Arrow format: 100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 2/2 [00:00<00:00, 2.39ba/s]\r\nUpload 1 LFS files: 0%| | 0/1 [16:39<?, ?it/s]\r\nPushing dataset shards to the dataset hub: 9%|████████████▊ | 81/950 [48:30<8:40:22, 35.93s/it]\r\nError while uploading 'data/train-00081-of-00950-84b0450a1df093a9.parquet' to the Hub.\r\nPushing split train to the Hub.\r\nCreating parquet from Arrow format: 100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 2/2 [00:00<00:00, 2.18ba/s]\r\nUpload 1 LFS files: 100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 1/1 [00:11<00:00, 11.65s/it]\r\nCreating parquet from Arrow format: 100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 2/2 [00:01<00:00, 1.92ba/s]\r\nUpload 1 LFS files: 0%| | 0/1 [16:38<?, ?it/s]\r\nPushing dataset shards to the dataset hub: 9%|█████████████ | 82/950 [48:55<8:37:57, 35.80s/it]\r\nError while uploading 'data/train-00082-of-00950-0a1f52da35653e08.parquet' to the Hub.\r\nPushing split train to the Hub.\r\nCreating parquet from Arrow format: 100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 2/2 [00:00<00:00, 2.31ba/s]\r\nUpload 1 LFS files: 100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 1/1 [00:26<00:00, 26.29s/it]\r\nCreating parquet from Arrow format: 100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 2/2 [00:00<00:00, 2.42ba/s]\r\nUpload 1 LFS files: 100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 1/1 [00:10<00:00, 10.57s/it]\r\nCreating parquet from Arrow format: 100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 2/2 [00:00<00:00, 2.64ba/s]\r\nUpload 1 LFS files: 100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 1/1 [00:10<00:00, 10.35s/it]\r\nCreating parquet from Arrow format: 100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 2/2 [00:00<00:00, 2.64ba/s]\r\nUpload 1 LFS files: 100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 1/1 [00:11<00:00, 11.74s/it]\r\nCreating parquet from Arrow format: 100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 2/2 [00:00<00:00, 2.31ba/s]\r\nUpload 1 LFS files: 0%| | 0/1 [16:40<?, ?it/s]\r\nPushing dataset shards to the dataset hub: 9%|█████████████▋ | 86/950 [50:48<8:30:25, 35.45s/it]\r\nError while uploading 'data/train-00086-of-00950-e1cc80dd17191b20.parquet' to the Hub.\r\n```\r\n\r\nI have a while loop that forces retries, but it seems that the progress itself is randomly getting lost as well. Any ideas on how to improve this? It has been blocking me for way too long. \r\n\r\nShould I build the parquet manually and then push manually as well? If I do things manually, how can I ensure my dataset works properly with \"stream=True\"? \r\n\r\nThank you for your help and time. ", "> @lhoestq It's a bit odd that when uploading a dataset, one set at a time \"train\", \"val\", \"test\", the push_to_hub function overwrites the readme and removes differently named sets from previous commits. i.e., you push \"val\", all is well. Then you push \"test\", and the \"val\" entry disappears from the readme, while the data remain intact.\r\n\r\nHmm this shouldn't happen. What code did you run exactly ? Using which version of `datasets` ?", "> I have a while loop that forces retries, but it seems that the progress itself is randomly getting lost as well. Any ideas on how to improve this? It has been blocking me for way too long.\r\n\r\nCould you also print the cause of the error (`e.__cause__`) ? Or show the full stack trace when the error happens ?\r\nThis would give more details about why it failed and would help investigate.", "> Should I build the parquet manually and then push manually as well? If I do things manually, how can I ensure my dataset works properly with \"stream=True\"?\r\n\r\nParquet is supported out of the box ^^\r\n\r\nIf you want to make sure it works as expected you can try locally first:\r\n```python\r\nds = load_dataset(\"path/to/local\", streaming=True)\r\n```", "@lhoestq @AntreasAntoniou I transferred this issue to the `datasets` repository as the questions and answers are more related to this repo. Hope it can help other users find the bug and fixes more easily (like updating [tqdm](https://github.com/huggingface/datasets/issues/5990#issuecomment-1607120204) and [pyarrow](https://github.com/huggingface/datasets/issues/5990#issuecomment-1607120278) or [setting a lower `max_shard_size`](https://github.com/huggingface/datasets/issues/5990#issuecomment-1607120328)).\r\n\r\n~For the initial \"pushing large dataset consistently hangs\"-issue, I still think it's best to try to `save_to_disk` first and then upload it manually/with a script (see [upload_folder](https://huggingface.co/docs/huggingface_hub/guides/upload#upload-a-folder)). It's not the most satisfying solution but at least it would confirm from where the problem comes from.~\r\n\r\n**EDIT:** removed suggestion about saving to disk first (see https://github.com/huggingface/datasets/issues/5990#issuecomment-1607186914).", "> @lhoestq @AntreasAntoniou I transferred this issue to the datasets repository as the questions and answers are more related to this repo. Hope it can help other users find the bug and fixes more easily (like updating https://github.com/huggingface/datasets/issues/5990#issuecomment-1607120204 and https://github.com/huggingface/datasets/issues/5990#issuecomment-1607120278 or https://github.com/huggingface/datasets/issues/5990#issuecomment-1607120328).\r\n\r\nthanks :)\r\n\r\n> For the initial \"pushing large dataset consistently hangs\"-issue, I still think it's best to try to save_to_disk first and then upload it manually/with a script (see [upload_folder](https://huggingface.co/docs/huggingface_hub/guides/upload#upload-a-folder)). It's not the most satisfying solution but at least it would confirm from where the problem comes from.\r\n\r\nAs I've already said in other discussions, I would not recommend pushing files saved with `save_to_disk` to the Hub but save to parquet shards and upload them instead. The Hub does not support datasets saved with `save_to_disk`, which is meant for disk only.", "> As I've already said in other discussions, I would not recommend pushing files saved with save_to_disk to the Hub but save to parquet shards and upload them instead. The Hub does not support datasets saved with save_to_disk, which is meant for disk only.\r\n\r\nWell noted, thanks. That part was not clear to me :)", "Sorry for not replying in a few days, I was on leave. :) \r\n\r\nSo, here are more information as to the error that causes some of the delay\r\n\r\n```bash\r\nPushing Antreas/TALI-tiny to hub\r\nAttempting to push to hub\r\nPushing split train to the Hub.\r\nCreating parquet from Arrow format: 100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 6/6 [00:24<00:00, 4.06s/ba]\r\nCreating parquet from Arrow format: 100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 6/6 [00:24<00:00, 4.15s/ba]\r\nCreating parquet from Arrow format: 100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 6/6 [00:26<00:00, 4.45s/ba]\r\n/opt/conda/envs/main/lib/python3.10/site-packages/huggingface_hub/lfs.py:310: UserWarning: hf_transfer is enabled but does not support uploading from bytes or BinaryIO, falling back to regular upload\r\n warnings.warn(\r\nCreating parquet from Arrow format: 100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 6/6 [00:25<00:00, 4.26s/ba]\r\nCreating parquet from Arrow format: 100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 6/6 [00:27<00:00, 4.58s/ba]\r\nCreating parquet from Arrow format: 100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 6/6 [00:24<00:00, 4.10s/ba]\r\nPushing dataset shards to the dataset hub: 22%|████████████████████████▎ | 5/23 [52:23<3:08:37, 628.74s/it]\r\nException: Error while uploading 'data/train-00005-of-00023-e224d901fd65e062.parquet' to the Hub., with stacktrace: <traceback object at 0x7f745458d0c0>, and type: <class 'RuntimeError'>, and \r\ncause: HTTPSConnectionPool(host='s3.us-east-1.amazonaws.com', port=443): Max retries exceeded with url: \r\n/lfs.huggingface.co/repos/7c/d3/7cd385d9324302dc13e3986331d72d9be6fa0174c63dcfe0e08cd474f7f1e8b7/3415166ae28c0beccbbc692f38742b8dea2c197f5c805321104e888d21d7eb90?X-Amz-Algorithm=AWS4-HMAC-SHA256\r\n&X-Amz-Content-Sha256=UNSIGNED-PAYLOAD&X-Amz-Credential=AKIA4N7VTDGO27GPWFUO%2F20230627%2Fus-east-1%2Fs3%2Faws4_request&X-Amz-Date=20230627T003349Z&X-Amz-Expires=86400&X-Amz-Signature=5a12ff96f2\r\n91f644134170992a6628e5f3c4e7b2e7fc3e940b4378fe11ae5390&X-Amz-SignedHeaders=host&partNumber=1&uploadId=JSsK8r63XSF.VlKQx3Vf8OW4DEVp5YIIY7LPnuapNIegsxs5EHgM1p4u0.Nn6_wlPlQnvxm8HKMxZhczKE9KB74t0etB\r\noLcxqBIvsgey3uXBTZMAEGwU6y7CDUADiEIO&x-id=UploadPart (Caused by SSLError(SSLEOFError(8, 'EOF occurred in violation of protocol (_ssl.c:2426)')))\r\nPush failed, retrying\r\nAttempting to push to hub\r\nPushing split train to the Hub.\r\n```\r\n\r\nOne issue is that the uploading does not continue from the chunk it failed off. It often continues from a very old chunk. e.g. if it failed on chunk 192/250, it will continue from say 53/250, and this behaviour appears almost random. ", "Are you using a proxy of some sort ?", "I am using a kubernetes cluster built into a university VPN. ", "So, other than the random connection drops here and there, any idea why the progress does not continue where it left off?\r\n\r\n```bash\r\nPushing split train to the Hub.\r\nCreating parquet from Arrow format: 100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 28/28 [00:02<00:00, 10.79ba/s]\r\nCreating parquet from Arrow format: 100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 28/28 [00:02<00:00, 13.65ba/s]\r\nCreating parquet from Arrow format: 100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 28/28 [00:02<00:00, 13.39ba/s]\r\nCreating parquet from Arrow format: 100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 28/28 [00:02<00:00, 13.04ba/s]\r\nCreating parquet from Arrow format: 100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 28/28 [00:02<00:00, 13.52ba/s]\r\nCreating parquet from Arrow format: 100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 28/28 [00:02<00:00, 12.28ba/s]\r\nPushing dataset shards to the dataset hub: 20%|██████████████████████ | 75/381 [1:34:39<6:26:11, 75.72s/it]\r\nException: Error while uploading 'data/train-00075-of-00381-1614bc251b778766.parquet' to the Hub., with stacktrace: <traceback object at 0x7fab6d9a4980>, and type: <class 'RuntimeError'>, and \r\ncause: HTTPSConnectionPool(host='s3.us-east-1.amazonaws.com', port=443): Max retries exceeded with url: \r\n/lfs.huggingface.co/repos/3b/31/3b311464573d8d63b137fcd5b40af1e7a5b1306843c88e80372d0117157504e5/ed8dae933fb79ae1ef5fb1f698f5125d3e1c02977ac69438631f152bb3bfdd1e?X-Amz-Algorithm=AWS4-HMAC-SHA256&X-\r\nAmz-Content-Sha256=UNSIGNED-PAYLOAD&X-Amz-Credential=AKIA4N7VTDGO27GPWFUO%2F20230629%2Fus-east-1%2Fs3%2Faws4_request&X-Amz-Date=20230629T053004Z&X-Amz-Expires=86400&X-Amz-Signature=da2b26270edfd6d0\r\nd069c015a5a432031107a8664c3f0917717e5e40c688183c&X-Amz-SignedHeaders=host&partNumber=1&uploadId=2erWGHTh3ICqBLU_QvHfnygZ2tkMWbL0rEqpJdYohCKHUHnfwMjvoBIg0TI_KSGn4rSKxUxOyqSIzFUFSRSzixZeLeneaXJOw.Qx8\r\nzLKSV5xV7HRQDj4RBesNve6cSoo&x-id=UploadPart (Caused by SSLError(SSLEOFError(8, 'EOF occurred in violation of protocol (_ssl.c:2426)')))\r\nPush failed, retrying\r\nAttempting to push to hub\r\nPushing split train to the Hub.\r\nCreating parquet from Arrow format: 100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 28/28 [00:02<00:00, 12.09ba/s]\r\nCreating parquet from Arrow format: 100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 28/28 [00:02<00:00, 11.51ba/s]\r\nCreating parquet from Arrow format: 100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 28/28 [00:02<00:00, 10.77ba/s]\r\nPushing dataset shards to the dataset hub: 20%|██████████████████████▋ | 77/381 [1:32:50<6:06:34, 72.35s/it]\r\nException: Error while uploading 'data/train-00077-of-00381-368b2327a9908aab.parquet' to the Hub., with stacktrace: <traceback object at 0x7fab45b27f80>, and type: <class 'RuntimeError'>, and \r\ncause: HTTPSConnectionPool(host='s3.us-east-1.amazonaws.com', port=443): Max retries exceeded with url: \r\n/lfs.huggingface.co/repos/3b/31/3b311464573d8d63b137fcd5b40af1e7a5b1306843c88e80372d0117157504e5/9462ff2c5e61283b53b091984a22de2f41a2f6e37b681171e2eca4a998f979cb?X-Amz-Algorithm=AWS4-HMAC-SHA256&X-\r\nAmz-Content-Sha256=UNSIGNED-PAYLOAD&X-Amz-Credential=AKIA4N7VTDGO27GPWFUO%2F20230629%2Fus-east-1%2Fs3%2Faws4_request&X-Amz-Date=20230629T070510Z&X-Amz-Expires=86400&X-Amz-Signature=9ab8487b93d443cd\r\n21f05476405855d46051a0771b4986bbb20f770ded21b1a4&X-Amz-SignedHeaders=host&partNumber=1&uploadId=UiHX1B.DcoAO2QmIHpWpCuNPwhXU_o1dsTkTGPqZt1P51o9k0yz.EsFD9eKpQMwgAST3jOatRG78I_JWRBeLBDYYVNp8r0TpIdeSg\r\neUg8uwPZOCPw9y5mWOw8MWJrnBo&x-id=UploadPart (Caused by SSLError(SSLEOFError(8, 'EOF occurred in violation of protocol (_ssl.c:2426)')))\r\nPush failed, retrying\r\nAttempting to push to hub\r\nPushing split train to the Hub.\r\nPushing dataset shards to the dataset hub: 8%|████████▋ | 29/381 [27:39<5:50:03, 59.67s/it]\r\nMap: 36%|████████████████████████████████████████████████████ | 1000/2764 [00:35<00:34, 51.63 examples/Map: 72%|████████████████████████████████████████████████████████████████████████████████████████████████████████▏ | 2000/2764 [00:40<00:15, 49.06 examples/Map: 72%|████████████████████████████████████████████████████████████████████████████████████████████████████████▏ | 2000/2764 [00:55<00:15, 49.06 examples/Map: 100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 2764/2764 [00:56<00:00, 48.82 examples/Pushing dataset shards to the dataset hub: 8%|████████▉ | 30/381 [28:35<5:43:03, 58.64s/iPushing dataset shards to the dataset hub: 8%|█████████▎ | 31/381 [29:40<5:52:18, 60.40s/iPushing dataset shards to the dataset hub: 8%|█████████▌ | 32/381 [30:46<6:02:20, 62.29s/it] \r\nMap: 36%|███████████████████████████████████████████████████▎ \r\n```\r\n\r\nThis is actually the issue that wastes the most time for me, and I need it fixed. Please advice on how I can go about it.\r\n\r\nNotice how the progress goes from \r\n| 77/381 to 30/381", "If the any shard is missing on the Hub, it will re-upload it. It looks like the 30th shard was missing on the Hub in your case. \r\n\r\nIt also means that the other files up to the 77th that were successfully uploaded won't be uploaded again.\r\n\r\ncc @mariosasko who might know better" ]

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[ "_The documentation is not available anymore as the PR was closed or merged._", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.007241 / 0.011353 (-0.004112) | 0.004574 / 0.011008 (-0.006434) | 0.120481 / 0.038508 (0.081973) | 0.040492 / 0.023109 (0.017383) | 0.391399 / 0.275898 (0.115501) | 0.422844 / 0.323480 (0.099365) | 0.004441 / 0.007986 (-0.003545) | 0.004544 / 0.004328 (0.000216) | 0.089482 / 0.004250 (0.085231) | 0.052939 / 0.037052 (0.015887) | 0.393649 / 0.258489 (0.135160) | 0.433852 / 0.293841 (0.140011) | 0.035882 / 0.128546 (-0.092664) | 0.010172 / 0.075646 (-0.065474) | 0.410331 / 0.419271 (-0.008940) | 0.061481 / 0.043533 (0.017948) | 0.405066 / 0.255139 (0.149927) | 0.417732 / 0.283200 (0.134532) | 0.121647 / 0.141683 (-0.020035) | 1.790624 / 1.452155 (0.338469) | 1.863398 / 1.492716 (0.370681) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.250650 / 0.018006 (0.232644) | 0.489044 / 0.000490 (0.488554) | 0.010421 / 0.000200 (0.010222) | 0.000106 / 0.000054 (0.000051) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.030340 / 0.037411 (-0.007071) | 0.128318 / 0.014526 (0.113792) | 0.140463 / 0.176557 (-0.036093) | 0.205762 / 0.737135 (-0.531373) | 0.147996 / 0.296338 (-0.148342) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.493158 / 0.215209 (0.277949) | 4.858346 / 2.077655 (2.780691) | 2.242942 / 1.504120 (0.738822) | 2.010092 / 1.541195 (0.468897) | 2.076765 / 1.468490 (0.608275) | 0.636669 / 4.584777 (-3.948108) | 4.478027 / 3.745712 (0.732314) | 2.157843 / 5.269862 (-3.112019) | 1.305133 / 4.565676 (-3.260543) | 0.079220 / 0.424275 (-0.345055) | 0.013858 / 0.007607 (0.006251) | 0.604501 / 0.226044 (0.378457) | 5.950071 / 2.268929 (3.681143) | 2.738373 / 55.444624 (-52.706251) | 2.380275 / 6.876477 (-4.496201) | 2.517108 / 2.142072 (0.375035) | 0.772249 / 4.805227 (-4.032979) | 0.169874 / 6.500664 (-6.330790) | 0.078026 / 0.075469 (0.002557) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.450200 / 1.841788 (-0.391588) | 17.810965 / 8.074308 (9.736657) | 15.518998 / 10.191392 (5.327606) | 0.200469 / 0.680424 (-0.479954) | 0.020777 / 0.534201 (-0.513424) | 0.504556 / 0.579283 (-0.074727) | 0.518493 / 0.434364 (0.084129) | 0.615335 / 0.540337 (0.074998) | 0.754065 / 1.386936 (-0.632871) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.007224 / 0.011353 (-0.004129) | 0.004663 / 0.011008 (-0.006345) | 0.092151 / 0.038508 (0.053643) | 0.038359 / 0.023109 (0.015250) | 0.486413 / 0.275898 (0.210515) | 0.521596 / 0.323480 (0.198116) | 0.004207 / 0.007986 (-0.003778) | 0.003745 / 0.004328 (-0.000583) | 0.089840 / 0.004250 (0.085589) | 0.050996 / 0.037052 (0.013943) | 0.498090 / 0.258489 (0.239601) | 0.533647 / 0.293841 (0.239806) | 0.035151 / 0.128546 (-0.093395) | 0.010293 / 0.075646 (-0.065354) | 0.099056 / 0.419271 (-0.320215) | 0.057365 / 0.043533 (0.013833) | 0.470652 / 0.255139 (0.215513) | 0.509801 / 0.283200 (0.226602) | 0.115650 / 0.141683 (-0.026033) | 1.810860 / 1.452155 (0.358705) | 1.896775 / 1.492716 (0.404059) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.261887 / 0.018006 (0.243880) | 0.489919 / 0.000490 (0.489430) | 0.006117 / 0.000200 (0.005917) | 0.000134 / 0.000054 (0.000079) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.035033 / 0.037411 (-0.002378) | 0.141093 / 0.014526 (0.126567) | 0.152613 / 0.176557 (-0.023943) | 0.218351 / 0.737135 (-0.518785) | 0.158366 / 0.296338 (-0.137972) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.542219 / 0.215209 (0.327010) | 5.479358 / 2.077655 (3.401703) | 2.749586 / 1.504120 (1.245466) | 2.537686 / 1.541195 (0.996491) | 2.582351 / 1.468490 (1.113861) | 0.636750 / 4.584777 (-3.948027) | 4.537501 / 3.745712 (0.791789) | 2.141392 / 5.269862 (-3.128469) | 1.279711 / 4.565676 (-3.285965) | 0.079227 / 0.424275 (-0.345048) | 0.014141 / 0.007607 (0.006534) | 0.662070 / 0.226044 (0.436025) | 6.572144 / 2.268929 (4.303215) | 3.321349 / 55.444624 (-52.123275) | 2.928219 / 6.876477 (-3.948258) | 3.002732 / 2.142072 (0.860659) | 0.773808 / 4.805227 (-4.031419) | 0.166017 / 6.500664 (-6.334647) | 0.076424 / 0.075469 (0.000955) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.584325 / 1.841788 (-0.257463) | 18.359247 / 8.074308 (10.284938) | 16.977875 / 10.191392 (6.786483) | 0.195381 / 0.680424 (-0.485043) | 0.021048 / 0.534201 (-0.513153) | 0.512237 / 0.579283 (-0.067047) | 0.511435 / 0.434364 (0.077071) | 0.592856 / 0.540337 (0.052518) | 0.711905 / 1.386936 (-0.675031) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#d536e37b21a6dd5c122b6d8113994ec50846c5b5 \"CML watermark\")\n" ]

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[ "_The documentation is not available anymore as the PR was closed or merged._", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.006157 / 0.011353 (-0.005196) | 0.003790 / 0.011008 (-0.007219) | 0.097889 / 0.038508 (0.059381) | 0.029038 / 0.023109 (0.005929) | 0.306918 / 0.275898 (0.031020) | 0.339637 / 0.323480 (0.016157) | 0.003526 / 0.007986 (-0.004460) | 0.003102 / 0.004328 (-0.001227) | 0.076908 / 0.004250 (0.072658) | 0.039254 / 0.037052 (0.002201) | 0.309197 / 0.258489 (0.050708) | 0.345635 / 0.293841 (0.051794) | 0.027954 / 0.128546 (-0.100593) | 0.008510 / 0.075646 (-0.067136) | 0.314674 / 0.419271 (-0.104598) | 0.057102 / 0.043533 (0.013569) | 0.307495 / 0.255139 (0.052356) | 0.329501 / 0.283200 (0.046302) | 0.098450 / 0.141683 (-0.043233) | 1.480102 / 1.452155 (0.027948) | 1.550554 / 1.492716 (0.057838) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.207440 / 0.018006 (0.189434) | 0.426560 / 0.000490 (0.426071) | 0.003250 / 0.000200 (0.003050) | 0.000074 / 0.000054 (0.000019) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.023777 / 0.037411 (-0.013634) | 0.103905 / 0.014526 (0.089379) | 0.108324 / 0.176557 (-0.068233) | 0.167223 / 0.737135 (-0.569913) | 0.113529 / 0.296338 (-0.182810) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.426770 / 0.215209 (0.211561) | 4.251806 / 2.077655 (2.174151) | 2.010426 / 1.504120 (0.506306) | 1.858630 / 1.541195 (0.317435) | 1.941318 / 1.468490 (0.472828) | 0.558056 / 4.584777 (-4.026721) | 3.399107 / 3.745712 (-0.346606) | 1.758386 / 5.269862 (-3.511476) | 1.036305 / 4.565676 (-3.529372) | 0.067094 / 0.424275 (-0.357182) | 0.011167 / 0.007607 (0.003560) | 0.526705 / 0.226044 (0.300661) | 5.250319 / 2.268929 (2.981390) | 2.496723 / 55.444624 (-52.947902) | 2.154013 / 6.876477 (-4.722464) | 2.394724 / 2.142072 (0.252652) | 0.669723 / 4.805227 (-4.135504) | 0.136367 / 6.500664 (-6.364297) | 0.067080 / 0.075469 (-0.008389) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.269700 / 1.841788 (-0.572088) | 14.099775 / 8.074308 (6.025467) | 14.422936 / 10.191392 (4.231544) | 0.132344 / 0.680424 (-0.548080) | 0.016744 / 0.534201 (-0.517457) | 0.378286 / 0.579283 (-0.200997) | 0.392282 / 0.434364 (-0.042082) | 0.437648 / 0.540337 (-0.102689) | 0.528554 / 1.386936 (-0.858382) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.006086 / 0.011353 (-0.005267) | 0.003769 / 0.011008 (-0.007239) | 0.077414 / 0.038508 (0.038906) | 0.027806 / 0.023109 (0.004697) | 0.360333 / 0.275898 (0.084434) | 0.404725 / 0.323480 (0.081245) | 0.003443 / 0.007986 (-0.004543) | 0.004434 / 0.004328 (0.000106) | 0.077309 / 0.004250 (0.073059) | 0.040441 / 0.037052 (0.003388) | 0.358627 / 0.258489 (0.100138) | 0.415246 / 0.293841 (0.121405) | 0.027718 / 0.128546 (-0.100829) | 0.008495 / 0.075646 (-0.067151) | 0.082874 / 0.419271 (-0.336397) | 0.042323 / 0.043533 (-0.001210) | 0.354895 / 0.255139 (0.099756) | 0.390032 / 0.283200 (0.106832) | 0.092377 / 0.141683 (-0.049306) | 1.492817 / 1.452155 (0.040662) | 1.551859 / 1.492716 (0.059143) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.198921 / 0.018006 (0.180915) | 0.417699 / 0.000490 (0.417209) | 0.001349 / 0.000200 (0.001149) | 0.000071 / 0.000054 (0.000016) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.026349 / 0.037411 (-0.011062) | 0.105712 / 0.014526 (0.091186) | 0.111792 / 0.176557 (-0.064765) | 0.163677 / 0.737135 (-0.573459) | 0.116864 / 0.296338 (-0.179474) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.447532 / 0.215209 (0.232323) | 4.468770 / 2.077655 (2.391116) | 2.403820 / 1.504120 (0.899700) | 2.273640 / 1.541195 (0.732445) | 2.337505 / 1.468490 (0.869015) | 0.560729 / 4.584777 (-4.024048) | 3.389165 / 3.745712 (-0.356547) | 2.697614 / 5.269862 (-2.572247) | 1.351909 / 4.565676 (-3.213768) | 0.068089 / 0.424275 (-0.356186) | 0.011639 / 0.007607 (0.004032) | 0.555277 / 0.226044 (0.329233) | 5.559291 / 2.268929 (3.290363) | 2.657609 / 55.444624 (-52.787015) | 2.346667 / 6.876477 (-4.529809) | 2.615823 / 2.142072 (0.473751) | 0.668662 / 4.805227 (-4.136566) | 0.136593 / 6.500664 (-6.364071) | 0.068384 / 0.075469 (-0.007085) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.312089 / 1.841788 (-0.529699) | 14.477510 / 8.074308 (6.403202) | 14.231432 / 10.191392 (4.040040) | 0.132015 / 0.680424 (-0.548409) | 0.016908 / 0.534201 (-0.517293) | 0.368315 / 0.579283 (-0.210968) | 0.397964 / 0.434364 (-0.036400) | 0.432446 / 0.540337 (-0.107891) | 0.526349 / 1.386936 (-0.860587) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#78b4d55c3cfc60e309eb033d3ed0aba5e796b6ce \"CML watermark\")\n" ]

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[ "Related, `float16` is the only dtype not supported by `Array2D` (probably by every `ArrayND`):\r\n\r\n```python\r\nfrom datasets import Array2D, Features, Dataset\r\n\r\nimport numpy as np\r\n\r\nfor dtype in [\r\n \"bool\", # ok\r\n \"int8\", # ok\r\n \"int16\", # ok\r\n \"int32\", # ok\r\n \"int64\", # ok\r\n \"uint8\", # ok\r\n \"uint16\", # ok\r\n \"uint32\", # ok\r\n \"uint64\", # ok\r\n \"float16\", # failed\r\n \"float32\", # ok\r\n \"float64\", # ok\r\n]:\r\n features = Features({\"foo\": Array2D(dtype=dtype, shape=(3, 4))})\r\n array = np.zeros((3, 4), dtype=dtype)\r\n try:\r\n dataset = Dataset.from_dict({\"foo\": [array]}, features=features)\r\n except Exception as e:\r\n print(f\"Failed for dtype={dtype}\")\r\n```", "Here's something I can't explain:\r\n\r\nWhen an array is encoded in the `from_dict` method, the numpy array is converted to a list (thus losing the original dtype, which is transfromed to the nearest builtin Python type)\r\n\r\nhttps://github.com/huggingface/datasets/blob/6ee61e6e695b1df9f232d47faf3a5e2b30b33737/src/datasets/features/features.py#L524-L525\r\n\r\nHowever, later on, this same data is written to memory, and it seems authorized that the data is an array (or in this case, a list of arrays). \r\n\r\nhttps://github.com/huggingface/datasets/blob/6ee61e6e695b1df9f232d47faf3a5e2b30b33737/src/datasets/arrow_writer.py#L185-L186\r\n\r\nSo the question is: why convert it to a Python list? This seems to be quite expensive both in terms of write time (all data is copied) and memory (e.g., an int8 is converted to an int64).\r\n\r\nFinally, if I try to remove this step, it solves all the previous problems, and it seems to me that it doesn't break anything (the CI passes without problem).", "Arrow only support 1d numpy arrays, so we convert multidim arrays to lists of 1s arrays (and keep the dtype).\r\n\r\nThough you noticed that it's concerting to lists and lose the dtype. If it's the case then it's a bug.", "Ok the conversion to list shouldn't be there indeed ! Could you open a PR to remove it ?" ]

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[ "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.007489 / 0.011353 (-0.003864) | 0.004914 / 0.011008 (-0.006095) | 0.111626 / 0.038508 (0.073117) | 0.037920 / 0.023109 (0.014811) | 0.350571 / 0.275898 (0.074673) | 0.389667 / 0.323480 (0.066187) | 0.006309 / 0.007986 (-0.001676) | 0.005488 / 0.004328 (0.001160) | 0.083962 / 0.004250 (0.079712) | 0.050728 / 0.037052 (0.013675) | 0.360997 / 0.258489 (0.102508) | 0.392736 / 0.293841 (0.098895) | 0.031975 / 0.128546 (-0.096571) | 0.009941 / 0.075646 (-0.065705) | 0.379840 / 0.419271 (-0.039432) | 0.056522 / 0.043533 (0.012989) | 0.359379 / 0.255139 (0.104240) | 0.384487 / 0.283200 (0.101287) | 0.117523 / 0.141683 (-0.024160) | 1.683639 / 1.452155 (0.231485) | 1.791645 / 1.492716 (0.298929) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.236862 / 0.018006 (0.218856) | 0.481208 / 0.000490 (0.480719) | 0.007455 / 0.000200 (0.007255) | 0.000111 / 0.000054 (0.000057) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.030854 / 0.037411 (-0.006557) | 0.126892 / 0.014526 (0.112367) | 0.139207 / 0.176557 (-0.037350) | 0.206447 / 0.737135 (-0.530689) | 0.143095 / 0.296338 (-0.153244) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.474677 / 0.215209 (0.259468) | 4.699534 / 2.077655 (2.621879) | 2.152102 / 1.504120 (0.647983) | 1.934815 / 1.541195 (0.393620) | 1.986448 / 1.468490 (0.517958) | 0.607184 / 4.584777 (-3.977593) | 4.480385 / 3.745712 (0.734673) | 2.074729 / 5.269862 (-3.195132) | 1.182383 / 4.565676 (-3.383294) | 0.075624 / 0.424275 (-0.348651) | 0.014046 / 0.007607 (0.006439) | 0.598859 / 0.226044 (0.372814) | 5.959551 / 2.268929 (3.690622) | 2.700851 / 55.444624 (-52.743773) | 2.303775 / 6.876477 (-4.572702) | 2.456441 / 2.142072 (0.314369) | 0.747185 / 4.805227 (-4.058042) | 0.165787 / 6.500664 (-6.334878) | 0.075817 / 0.075469 (0.000348) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.411859 / 1.841788 (-0.429928) | 17.375495 / 8.074308 (9.301187) | 15.187098 / 10.191392 (4.995706) | 0.169953 / 0.680424 (-0.510471) | 0.020204 / 0.534201 (-0.513997) | 0.461424 / 0.579283 (-0.117859) | 0.494443 / 0.434364 (0.060080) | 0.544583 / 0.540337 (0.004246) | 0.648231 / 1.386936 (-0.738705) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.007785 / 0.011353 (-0.003568) | 0.005314 / 0.011008 (-0.005694) | 0.087273 / 0.038508 (0.048765) | 0.037810 / 0.023109 (0.014701) | 0.425473 / 0.275898 (0.149575) | 0.459976 / 0.323480 (0.136497) | 0.007270 / 0.007986 (-0.000716) | 0.004631 / 0.004328 (0.000303) | 0.087063 / 0.004250 (0.082812) | 0.052630 / 0.037052 (0.015578) | 0.432384 / 0.258489 (0.173895) | 0.500291 / 0.293841 (0.206450) | 0.033144 / 0.128546 (-0.095402) | 0.010101 / 0.075646 (-0.065545) | 0.096068 / 0.419271 (-0.323204) | 0.062750 / 0.043533 (0.019217) | 0.419308 / 0.255139 (0.164169) | 0.437099 / 0.283200 (0.153900) | 0.122289 / 0.141683 (-0.019394) | 1.737829 / 1.452155 (0.285674) | 1.851481 / 1.492716 (0.358765) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.014277 / 0.018006 (-0.003729) | 0.489835 / 0.000490 (0.489345) | 0.008423 / 0.000200 (0.008223) | 0.000188 / 0.000054 (0.000134) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.032966 / 0.037411 (-0.004445) | 0.130069 / 0.014526 (0.115544) | 0.144372 / 0.176557 (-0.032185) | 0.200400 / 0.737135 (-0.536735) | 0.149384 / 0.296338 (-0.146954) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.511542 / 0.215209 (0.296333) | 5.093879 / 2.077655 (3.016225) | 2.572088 / 1.504120 (1.067968) | 2.339118 / 1.541195 (0.797923) | 2.441637 / 1.468490 (0.973147) | 0.614818 / 4.584777 (-3.969959) | 4.724441 / 3.745712 (0.978729) | 5.431978 / 5.269862 (0.162116) | 2.257794 / 4.565676 (-2.307883) | 0.078109 / 0.424275 (-0.346166) | 0.013821 / 0.007607 (0.006214) | 0.639232 / 0.226044 (0.413188) | 6.424623 / 2.268929 (4.155694) | 3.163018 / 55.444624 (-52.281606) | 2.756786 / 6.876477 (-4.119690) | 2.808655 / 2.142072 (0.666583) | 0.745843 / 4.805227 (-4.059385) | 0.165562 / 6.500664 (-6.335102) | 0.076610 / 0.075469 (0.001141) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.738630 / 1.841788 (-0.103158) | 18.073573 / 8.074308 (9.999265) | 16.482820 / 10.191392 (6.291428) | 0.213233 / 0.680424 (-0.467191) | 0.022839 / 0.534201 (-0.511362) | 0.487043 / 0.579283 (-0.092240) | 0.512518 / 0.434364 (0.078154) | 0.549365 / 0.540337 (0.009028) | 0.656612 / 1.386936 (-0.730324) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#288e92b03bd4ec91c10c8a529b32631cfaba9fb7 \"CML watermark\")\n", "Good idea!\r\n\r\nI was wondering: if we want to optimize the balance between the size of downloading a row group, and the number of rows in the group, would it make sense to compute the row group size by checking the average size of the rows?\r\n\r\neg. 32x32 images could have a larger row group size than full HD images, no? Relying on the size would even remove the need to check the column types.\r\n\r\n(in this proposal, we could use the computed row group size, eg 837, or use the nearest row group size in a list of values: 10, 100, 1000, 10000)", "Probably, but I would go for a simpler solution first :p", "Sure! I wanted to understand if the idea made sense or not, but it's not for this PR.", "I think it will be more useful for people who use the viewer and won't impact sequential io that much.", "DuckDB [paragraph](https://duckdb.org/docs/data/parquet/tips.html#selecting-a-row_group_size) that explains how to choose the `row_group_size`. Our default shard size is 500 MB in `push_to_hub`, so, ideally, we should aim for 64 MB row groups (and make this part configurable for power users 🙂).\r\n\r\nSo, before merging this PR, let's add a TODO or open an issue as a reminder that this can be improved.", "I moved the config values, improved the features check and mentioned the improvements we could do in the docstring :)", "_The documentation is not available anymore as the PR was closed or merged._", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.006211 / 0.011353 (-0.005141) | 0.004244 / 0.011008 (-0.006764) | 0.097941 / 0.038508 (0.059433) | 0.028564 / 0.023109 (0.005455) | 0.299651 / 0.275898 (0.023753) | 0.340694 / 0.323480 (0.017214) | 0.005161 / 0.007986 (-0.002824) | 0.004764 / 0.004328 (0.000435) | 0.075505 / 0.004250 (0.071255) | 0.039656 / 0.037052 (0.002603) | 0.309242 / 0.258489 (0.050753) | 0.350783 / 0.293841 (0.056942) | 0.025145 / 0.128546 (-0.103401) | 0.008498 / 0.075646 (-0.067148) | 0.317657 / 0.419271 (-0.101615) | 0.043926 / 0.043533 (0.000394) | 0.305915 / 0.255139 (0.050776) | 0.331630 / 0.283200 (0.048430) | 0.088564 / 0.141683 (-0.053119) | 1.533175 / 1.452155 (0.081021) | 1.581017 / 1.492716 (0.088301) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.206032 / 0.018006 (0.188025) | 0.433446 / 0.000490 (0.432956) | 0.003955 / 0.000200 (0.003755) | 0.000095 / 0.000054 (0.000040) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.023468 / 0.037411 (-0.013943) | 0.103292 / 0.014526 (0.088766) | 0.107234 / 0.176557 (-0.069322) | 0.168525 / 0.737135 (-0.568610) | 0.113218 / 0.296338 (-0.183120) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.431085 / 0.215209 (0.215875) | 4.302082 / 2.077655 (2.224427) | 2.068290 / 1.504120 (0.564171) | 1.850718 / 1.541195 (0.309523) | 1.964261 / 1.468490 (0.495771) | 0.547562 / 4.584777 (-4.037215) | 3.410739 / 3.745712 (-0.334974) | 1.779640 / 5.269862 (-3.490221) | 1.005466 / 4.565676 (-3.560210) | 0.066250 / 0.424275 (-0.358025) | 0.011877 / 0.007607 (0.004270) | 0.525185 / 0.226044 (0.299141) | 5.234786 / 2.268929 (2.965857) | 2.398045 / 55.444624 (-53.046580) | 2.073020 / 6.876477 (-4.803457) | 2.210753 / 2.142072 (0.068680) | 0.654897 / 4.805227 (-4.150331) | 0.134639 / 6.500664 (-6.366025) | 0.067050 / 0.075469 (-0.008419) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.180210 / 1.841788 (-0.661577) | 13.613091 / 8.074308 (5.538783) | 13.441837 / 10.191392 (3.250445) | 0.146048 / 0.680424 (-0.534376) | 0.016505 / 0.534201 (-0.517696) | 0.363210 / 0.579283 (-0.216073) | 0.405484 / 0.434364 (-0.028880) | 0.428712 / 0.540337 (-0.111625) | 0.522300 / 1.386936 (-0.864636) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.006147 / 0.011353 (-0.005206) | 0.004161 / 0.011008 (-0.006847) | 0.075861 / 0.038508 (0.037353) | 0.027948 / 0.023109 (0.004839) | 0.362466 / 0.275898 (0.086568) | 0.398227 / 0.323480 (0.074747) | 0.005014 / 0.007986 (-0.002972) | 0.004772 / 0.004328 (0.000444) | 0.075674 / 0.004250 (0.071423) | 0.039158 / 0.037052 (0.002106) | 0.363567 / 0.258489 (0.105078) | 0.410378 / 0.293841 (0.116537) | 0.025510 / 0.128546 (-0.103036) | 0.008528 / 0.075646 (-0.067118) | 0.081803 / 0.419271 (-0.337468) | 0.040954 / 0.043533 (-0.002579) | 0.358492 / 0.255139 (0.103353) | 0.381345 / 0.283200 (0.098145) | 0.092347 / 0.141683 (-0.049336) | 1.567695 / 1.452155 (0.115540) | 1.668412 / 1.492716 (0.175696) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.203367 / 0.018006 (0.185360) | 0.424642 / 0.000490 (0.424152) | 0.002451 / 0.000200 (0.002251) | 0.000071 / 0.000054 (0.000017) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.026129 / 0.037411 (-0.011282) | 0.102564 / 0.014526 (0.088039) | 0.110583 / 0.176557 (-0.065973) | 0.164332 / 0.737135 (-0.572804) | 0.115706 / 0.296338 (-0.180632) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.468925 / 0.215209 (0.253716) | 4.657266 / 2.077655 (2.579612) | 2.423280 / 1.504120 (0.919160) | 2.236284 / 1.541195 (0.695089) | 2.323019 / 1.468490 (0.854529) | 0.548120 / 4.584777 (-4.036657) | 3.455602 / 3.745712 (-0.290110) | 1.730421 / 5.269862 (-3.539441) | 1.006089 / 4.565676 (-3.559588) | 0.067478 / 0.424275 (-0.356797) | 0.011465 / 0.007607 (0.003857) | 0.574235 / 0.226044 (0.348190) | 5.744404 / 2.268929 (3.475475) | 2.882225 / 55.444624 (-52.562400) | 2.618246 / 6.876477 (-4.258231) | 2.642920 / 2.142072 (0.500847) | 0.661441 / 4.805227 (-4.143787) | 0.137358 / 6.500664 (-6.363306) | 0.070372 / 0.075469 (-0.005097) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.333815 / 1.841788 (-0.507973) | 14.689667 / 8.074308 (6.615359) | 14.362294 / 10.191392 (4.170902) | 0.152011 / 0.680424 (-0.528413) | 0.016869 / 0.534201 (-0.517332) | 0.370433 / 0.579283 (-0.208851) | 0.399642 / 0.434364 (-0.034722) | 0.433759 / 0.540337 (-0.106578) | 0.525443 / 1.386936 (-0.861493) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#09e9f9a88edd9055b5c540e3d83b5a11d48f8ba8 \"CML watermark\")\n", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.006564 / 0.011353 (-0.004789) | 0.004350 / 0.011008 (-0.006658) | 0.096277 / 0.038508 (0.057769) | 0.032956 / 0.023109 (0.009847) | 0.303675 / 0.275898 (0.027777) | 0.336384 / 0.323480 (0.012904) | 0.005789 / 0.007986 (-0.002197) | 0.003957 / 0.004328 (-0.000371) | 0.073990 / 0.004250 (0.069740) | 0.050974 / 0.037052 (0.013922) | 0.321754 / 0.258489 (0.063265) | 0.349489 / 0.293841 (0.055648) | 0.031138 / 0.128546 (-0.097409) | 0.009000 / 0.075646 (-0.066646) | 0.325445 / 0.419271 (-0.093826) | 0.070173 / 0.043533 (0.026640) | 0.304706 / 0.255139 (0.049567) | 0.321803 / 0.283200 (0.038603) | 0.109405 / 0.141683 (-0.032278) | 1.489812 / 1.452155 (0.037657) | 1.577729 / 1.492716 (0.085013) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.287187 / 0.018006 (0.269181) | 0.527625 / 0.000490 (0.527135) | 0.006533 / 0.000200 (0.006333) | 0.000090 / 0.000054 (0.000036) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.026659 / 0.037411 (-0.010752) | 0.106236 / 0.014526 (0.091710) | 0.118615 / 0.176557 (-0.057941) | 0.173156 / 0.737135 (-0.563979) | 0.122883 / 0.296338 (-0.173456) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.407189 / 0.215209 (0.191980) | 4.055732 / 2.077655 (1.978078) | 1.865594 / 1.504120 (0.361474) | 1.664325 / 1.541195 (0.123130) | 1.668961 / 1.468490 (0.200471) | 0.521207 / 4.584777 (-4.063570) | 3.740424 / 3.745712 (-0.005288) | 3.431973 / 5.269862 (-1.837889) | 1.636669 / 4.565676 (-2.929008) | 0.065271 / 0.424275 (-0.359005) | 0.012151 / 0.007607 (0.004544) | 0.514233 / 0.226044 (0.288189) | 5.110150 / 2.268929 (2.841222) | 2.264340 / 55.444624 (-53.180284) | 1.940428 / 6.876477 (-4.936049) | 2.042286 / 2.142072 (-0.099787) | 0.639200 / 4.805227 (-4.166028) | 0.139537 / 6.500664 (-6.361127) | 0.063195 / 0.075469 (-0.012274) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.179501 / 1.841788 (-0.662286) | 14.600133 / 8.074308 (6.525825) | 14.902137 / 10.191392 (4.710745) | 0.144509 / 0.680424 (-0.535915) | 0.017449 / 0.534201 (-0.516752) | 0.393135 / 0.579283 (-0.186148) | 0.413103 / 0.434364 (-0.021261) | 0.459897 / 0.540337 (-0.080440) | 0.552602 / 1.386936 (-0.834334) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.006891 / 0.011353 (-0.004462) | 0.004633 / 0.011008 (-0.006375) | 0.073093 / 0.038508 (0.034585) | 0.032509 / 0.023109 (0.009399) | 0.348332 / 0.275898 (0.072434) | 0.381920 / 0.323480 (0.058440) | 0.005978 / 0.007986 (-0.002007) | 0.005360 / 0.004328 (0.001032) | 0.074307 / 0.004250 (0.070056) | 0.049668 / 0.037052 (0.012615) | 0.354713 / 0.258489 (0.096224) | 0.398521 / 0.293841 (0.104681) | 0.032013 / 0.128546 (-0.096534) | 0.008890 / 0.075646 (-0.066756) | 0.080013 / 0.419271 (-0.339259) | 0.051820 / 0.043533 (0.008288) | 0.349730 / 0.255139 (0.094591) | 0.369267 / 0.283200 (0.086067) | 0.103874 / 0.141683 (-0.037809) | 1.484148 / 1.452155 (0.031993) | 1.573927 / 1.492716 (0.081211) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.009699 / 0.018006 (-0.008307) | 0.511176 / 0.000490 (0.510686) | 0.002938 / 0.000200 (0.002738) | 0.000109 / 0.000054 (0.000054) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.027847 / 0.037411 (-0.009564) | 0.111565 / 0.014526 (0.097039) | 0.120625 / 0.176557 (-0.055932) | 0.172130 / 0.737135 (-0.565006) | 0.125949 / 0.296338 (-0.170389) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.430634 / 0.215209 (0.215424) | 4.315377 / 2.077655 (2.237722) | 2.070764 / 1.504120 (0.566644) | 1.881962 / 1.541195 (0.340767) | 1.904053 / 1.468490 (0.435563) | 0.524973 / 4.584777 (-4.059804) | 3.718359 / 3.745712 (-0.027353) | 3.415344 / 5.269862 (-1.854518) | 1.224568 / 4.565676 (-3.341108) | 0.065593 / 0.424275 (-0.358682) | 0.011643 / 0.007607 (0.004036) | 0.537050 / 0.226044 (0.311006) | 5.352155 / 2.268929 (3.083226) | 2.557361 / 55.444624 (-52.887263) | 2.217770 / 6.876477 (-4.658707) | 2.194975 / 2.142072 (0.052902) | 0.635142 / 4.805227 (-4.170085) | 0.140642 / 6.500664 (-6.360022) | 0.064690 / 0.075469 (-0.010779) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.266125 / 1.841788 (-0.575663) | 14.836413 / 8.074308 (6.762105) | 14.446870 / 10.191392 (4.255478) | 0.191545 / 0.680424 (-0.488878) | 0.017433 / 0.534201 (-0.516768) | 0.392296 / 0.579283 (-0.186987) | 0.420698 / 0.434364 (-0.013666) | 0.463225 / 0.540337 (-0.077112) | 0.556127 / 1.386936 (-0.830809) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#7fcbe5b1575c8d162b65b9397b3dfda995a4e048 \"CML watermark\")\n" ]

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[ "I've addressed this as part of #6019, so feel free to close this PR. ", "Thanks !" ]

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[ "I just added the same test with dynamic shape", "_The documentation is not available anymore as the PR was closed or merged._", "Awesome ! I'm merging now if you don't mind :)\r\nWe should probably give you permissions to merge your own PRs when you have an approval", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.009980 / 0.011353 (-0.001373) | 0.005709 / 0.011008 (-0.005300) | 0.132185 / 0.038508 (0.093677) | 0.039299 / 0.023109 (0.016190) | 0.400168 / 0.275898 (0.124270) | 0.470582 / 0.323480 (0.147102) | 0.007753 / 0.007986 (-0.000233) | 0.005196 / 0.004328 (0.000868) | 0.093698 / 0.004250 (0.089448) | 0.052631 / 0.037052 (0.015579) | 0.430347 / 0.258489 (0.171858) | 0.460162 / 0.293841 (0.166321) | 0.057511 / 0.128546 (-0.071035) | 0.013944 / 0.075646 (-0.061702) | 0.459008 / 0.419271 (0.039737) | 0.075532 / 0.043533 (0.031999) | 0.405165 / 0.255139 (0.150026) | 0.456142 / 0.283200 (0.172942) | 0.117309 / 0.141683 (-0.024374) | 1.945787 / 1.452155 (0.493633) | 2.067162 / 1.492716 (0.574446) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.285755 / 0.018006 (0.267749) | 0.619965 / 0.000490 (0.619476) | 0.005071 / 0.000200 (0.004871) | 0.000114 / 0.000054 (0.000059) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.031112 / 0.037411 (-0.006299) | 0.128514 / 0.014526 (0.113988) | 0.137161 / 0.176557 (-0.039396) | 0.211363 / 0.737135 (-0.525772) | 0.151045 / 0.296338 (-0.145293) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.609361 / 0.215209 (0.394152) | 6.124844 / 2.077655 (4.047189) | 2.440757 / 1.504120 (0.936637) | 2.034495 / 1.541195 (0.493300) | 2.047192 / 1.468490 (0.578702) | 0.883171 / 4.584777 (-3.701606) | 5.470552 / 3.745712 (1.724840) | 4.401696 / 5.269862 (-0.868165) | 2.378674 / 4.565676 (-2.187003) | 0.108065 / 0.424275 (-0.316210) | 0.013239 / 0.007607 (0.005632) | 0.830957 / 0.226044 (0.604913) | 8.090659 / 2.268929 (5.821731) | 3.289203 / 55.444624 (-52.155422) | 2.500777 / 6.876477 (-4.375700) | 2.561440 / 2.142072 (0.419367) | 1.064893 / 4.805227 (-3.740334) | 0.220486 / 6.500664 (-6.280178) | 0.079507 / 0.075469 (0.004038) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.544334 / 1.841788 (-0.297454) | 17.878997 / 8.074308 (9.804689) | 18.952191 / 10.191392 (8.760799) | 0.245166 / 0.680424 (-0.435258) | 0.028022 / 0.534201 (-0.506179) | 0.517828 / 0.579283 (-0.061455) | 0.618988 / 0.434364 (0.184624) | 0.589742 / 0.540337 (0.049405) | 0.670902 / 1.386936 (-0.716034) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.009616 / 0.011353 (-0.001737) | 0.006098 / 0.011008 (-0.004911) | 0.100301 / 0.038508 (0.061793) | 0.037792 / 0.023109 (0.014683) | 0.484667 / 0.275898 (0.208769) | 0.519286 / 0.323480 (0.195806) | 0.007427 / 0.007986 (-0.000558) | 0.007172 / 0.004328 (0.002844) | 0.104429 / 0.004250 (0.100179) | 0.056567 / 0.037052 (0.019515) | 0.502641 / 0.258489 (0.244152) | 0.549629 / 0.293841 (0.255788) | 0.049574 / 0.128546 (-0.078972) | 0.015223 / 0.075646 (-0.060424) | 0.113947 / 0.419271 (-0.305324) | 0.064585 / 0.043533 (0.021053) | 0.512962 / 0.255139 (0.257823) | 0.507218 / 0.283200 (0.224019) | 0.122194 / 0.141683 (-0.019488) | 1.927821 / 1.452155 (0.475667) | 2.051161 / 1.492716 (0.558445) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.291350 / 0.018006 (0.273344) | 0.588099 / 0.000490 (0.587610) | 0.001368 / 0.000200 (0.001168) | 0.000153 / 0.000054 (0.000099) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.030604 / 0.037411 (-0.006807) | 0.126810 / 0.014526 (0.112285) | 0.139309 / 0.176557 (-0.037248) | 0.208030 / 0.737135 (-0.529105) | 0.138985 / 0.296338 (-0.157353) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.681254 / 0.215209 (0.466045) | 6.753856 / 2.077655 (4.676201) | 2.780704 / 1.504120 (1.276585) | 2.475205 / 1.541195 (0.934010) | 2.486784 / 1.468490 (1.018294) | 0.879223 / 4.584777 (-3.705554) | 5.662294 / 3.745712 (1.916582) | 2.698705 / 5.269862 (-2.571156) | 1.660620 / 4.565676 (-2.905057) | 0.112218 / 0.424275 (-0.312057) | 0.014211 / 0.007607 (0.006604) | 0.796957 / 0.226044 (0.570913) | 8.180897 / 2.268929 (5.911969) | 3.540419 / 55.444624 (-51.904205) | 2.899467 / 6.876477 (-3.977010) | 2.870306 / 2.142072 (0.728233) | 1.069537 / 4.805227 (-3.735690) | 0.211281 / 6.500664 (-6.289383) | 0.078898 / 0.075469 (0.003429) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.666790 / 1.841788 (-0.174998) | 18.302127 / 8.074308 (10.227819) | 21.317546 / 10.191392 (11.126153) | 0.242795 / 0.680424 (-0.437629) | 0.026754 / 0.534201 (-0.507447) | 0.493375 / 0.579283 (-0.085908) | 0.605400 / 0.434364 (0.171036) | 0.586888 / 0.540337 (0.046550) | 0.722809 / 1.386936 (-0.664127) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#ce2328e7b1d62998b22510492530af55d4493b73 \"CML watermark\")\n" ]

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[ "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.008499 / 0.011353 (-0.002854) | 0.006155 / 0.011008 (-0.004853) | 0.124032 / 0.038508 (0.085524) | 0.037337 / 0.023109 (0.014228) | 0.389274 / 0.275898 (0.113376) | 0.427736 / 0.323480 (0.104257) | 0.006929 / 0.007986 (-0.001057) | 0.005017 / 0.004328 (0.000689) | 0.096356 / 0.004250 (0.092105) | 0.055694 / 0.037052 (0.018642) | 0.391417 / 0.258489 (0.132928) | 0.448098 / 0.293841 (0.154257) | 0.042442 / 0.128546 (-0.086105) | 0.013456 / 0.075646 (-0.062190) | 0.423502 / 0.419271 (0.004230) | 0.062919 / 0.043533 (0.019386) | 0.384317 / 0.255139 (0.129178) | 0.410851 / 0.283200 (0.127652) | 0.112807 / 0.141683 (-0.028875) | 1.746050 / 1.452155 (0.293895) | 1.977974 / 1.492716 (0.485257) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.306382 / 0.018006 (0.288375) | 0.620310 / 0.000490 (0.619820) | 0.009309 / 0.000200 (0.009109) | 0.000106 / 0.000054 (0.000052) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.026900 / 0.037411 (-0.010511) | 0.140125 / 0.014526 (0.125599) | 0.136295 / 0.176557 (-0.040261) | 0.207721 / 0.737135 (-0.529414) | 0.146328 / 0.296338 (-0.150011) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.616712 / 0.215209 (0.401503) | 6.237820 / 2.077655 (4.160166) | 2.503809 / 1.504120 (0.999689) | 2.129739 / 1.541195 (0.588544) | 2.160768 / 1.468490 (0.692277) | 0.971273 / 4.584777 (-3.613504) | 5.687161 / 3.745712 (1.941449) | 2.738148 / 5.269862 (-2.531713) | 1.692695 / 4.565676 (-2.872981) | 0.113701 / 0.424275 (-0.310574) | 0.014809 / 0.007607 (0.007202) | 0.774795 / 0.226044 (0.548750) | 7.660012 / 2.268929 (5.391083) | 3.253036 / 55.444624 (-52.191588) | 2.607498 / 6.876477 (-4.268979) | 2.681678 / 2.142072 (0.539606) | 1.095275 / 4.805227 (-3.709952) | 0.239078 / 6.500664 (-6.261586) | 0.081034 / 0.075469 (0.005565) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.574547 / 1.841788 (-0.267240) | 18.323566 / 8.074308 (10.249258) | 19.274482 / 10.191392 (9.083090) | 0.210275 / 0.680424 (-0.470149) | 0.031843 / 0.534201 (-0.502358) | 0.514843 / 0.579283 (-0.064440) | 0.633782 / 0.434364 (0.199418) | 0.588569 / 0.540337 (0.048232) | 0.721401 / 1.386936 (-0.665535) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.008866 / 0.011353 (-0.002487) | 0.006460 / 0.011008 (-0.004548) | 0.121337 / 0.038508 (0.082829) | 0.033896 / 0.023109 (0.010786) | 0.455702 / 0.275898 (0.179804) | 0.509685 / 0.323480 (0.186205) | 0.007650 / 0.007986 (-0.000336) | 0.005578 / 0.004328 (0.001250) | 0.098505 / 0.004250 (0.094255) | 0.056122 / 0.037052 (0.019069) | 0.478483 / 0.258489 (0.219994) | 0.560008 / 0.293841 (0.266167) | 0.044926 / 0.128546 (-0.083620) | 0.014562 / 0.075646 (-0.061085) | 0.115027 / 0.419271 (-0.304244) | 0.066494 / 0.043533 (0.022961) | 0.463434 / 0.255139 (0.208296) | 0.513856 / 0.283200 (0.230656) | 0.126436 / 0.141683 (-0.015247) | 1.874729 / 1.452155 (0.422575) | 1.925080 / 1.492716 (0.432364) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.012672 / 0.018006 (-0.005334) | 0.615797 / 0.000490 (0.615307) | 0.001606 / 0.000200 (0.001406) | 0.000118 / 0.000054 (0.000064) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.031104 / 0.037411 (-0.006307) | 0.130107 / 0.014526 (0.115581) | 0.140587 / 0.176557 (-0.035970) | 0.205081 / 0.737135 (-0.532054) | 0.144068 / 0.296338 (-0.152270) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.646549 / 0.215209 (0.431340) | 6.403962 / 2.077655 (4.326307) | 2.812594 / 1.504120 (1.308474) | 2.478480 / 1.541195 (0.937285) | 2.552385 / 1.468490 (1.083895) | 0.991987 / 4.584777 (-3.592790) | 5.777917 / 3.745712 (2.032205) | 5.697830 / 5.269862 (0.427969) | 2.370583 / 4.565676 (-2.195094) | 0.109905 / 0.424275 (-0.314370) | 0.013801 / 0.007607 (0.006193) | 0.799932 / 0.226044 (0.573888) | 8.155672 / 2.268929 (5.886743) | 3.711662 / 55.444624 (-51.732963) | 3.042164 / 6.876477 (-3.834312) | 3.073549 / 2.142072 (0.931477) | 1.137515 / 4.805227 (-3.667712) | 0.231266 / 6.500664 (-6.269398) | 0.080893 / 0.075469 (0.005424) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.669210 / 1.841788 (-0.172577) | 18.747144 / 8.074308 (10.672836) | 21.084589 / 10.191392 (10.893197) | 0.241379 / 0.680424 (-0.439045) | 0.029473 / 0.534201 (-0.504728) | 0.524605 / 0.579283 (-0.054678) | 0.622852 / 0.434364 (0.188488) | 0.604941 / 0.540337 (0.064604) | 0.715978 / 1.386936 (-0.670958) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#142484a60b1330359d7713e906fc9e5e30aa9f64 \"CML watermark\")\n", "Cool ! what about `.github/workflows/build_pr_documentation.yml` and `.github/workflows/delete_doc_comment.yml` ?", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.005973 / 0.011353 (-0.005380) | 0.004389 / 0.011008 (-0.006620) | 0.096076 / 0.038508 (0.057568) | 0.031569 / 0.023109 (0.008460) | 0.328300 / 0.275898 (0.052402) | 0.359356 / 0.323480 (0.035876) | 0.005378 / 0.007986 (-0.002607) | 0.003703 / 0.004328 (-0.000625) | 0.075251 / 0.004250 (0.071000) | 0.042340 / 0.037052 (0.005287) | 0.346103 / 0.258489 (0.087614) | 0.379896 / 0.293841 (0.086055) | 0.027493 / 0.128546 (-0.101053) | 0.009033 / 0.075646 (-0.066613) | 0.327829 / 0.419271 (-0.091442) | 0.064074 / 0.043533 (0.020541) | 0.337703 / 0.255139 (0.082564) | 0.355335 / 0.283200 (0.072136) | 0.101179 / 0.141683 (-0.040504) | 1.471738 / 1.452155 (0.019584) | 1.539031 / 1.492716 (0.046315) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.194097 / 0.018006 (0.176091) | 0.434190 / 0.000490 (0.433701) | 0.005730 / 0.000200 (0.005530) | 0.000088 / 0.000054 (0.000033) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.025634 / 0.037411 (-0.011778) | 0.105080 / 0.014526 (0.090555) | 0.116508 / 0.176557 (-0.060049) | 0.173867 / 0.737135 (-0.563269) | 0.117749 / 0.296338 (-0.178590) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.401566 / 0.215209 (0.186357) | 4.003558 / 2.077655 (1.925903) | 1.802756 / 1.504120 (0.298636) | 1.604222 / 1.541195 (0.063027) | 1.656617 / 1.468490 (0.188127) | 0.523385 / 4.584777 (-4.061392) | 3.744292 / 3.745712 (-0.001420) | 1.794295 / 5.269862 (-3.475567) | 1.044690 / 4.565676 (-3.520987) | 0.064992 / 0.424275 (-0.359284) | 0.011542 / 0.007607 (0.003935) | 0.507830 / 0.226044 (0.281785) | 5.061574 / 2.268929 (2.792645) | 2.252896 / 55.444624 (-53.191729) | 1.912551 / 6.876477 (-4.963926) | 2.073510 / 2.142072 (-0.068562) | 0.642148 / 4.805227 (-4.163079) | 0.140151 / 6.500664 (-6.360513) | 0.062623 / 0.075469 (-0.012846) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.180367 / 1.841788 (-0.661421) | 14.263475 / 8.074308 (6.189167) | 12.917251 / 10.191392 (2.725859) | 0.143815 / 0.680424 (-0.536608) | 0.017286 / 0.534201 (-0.516915) | 0.388411 / 0.579283 (-0.190872) | 0.430512 / 0.434364 (-0.003851) | 0.466595 / 0.540337 (-0.073742) | 0.564545 / 1.386936 (-0.822391) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.006059 / 0.011353 (-0.005294) | 0.004419 / 0.011008 (-0.006590) | 0.074206 / 0.038508 (0.035697) | 0.031180 / 0.023109 (0.008071) | 0.380031 / 0.275898 (0.104133) | 0.410373 / 0.323480 (0.086893) | 0.005397 / 0.007986 (-0.002589) | 0.003952 / 0.004328 (-0.000376) | 0.074426 / 0.004250 (0.070176) | 0.046256 / 0.037052 (0.009203) | 0.385543 / 0.258489 (0.127054) | 0.430724 / 0.293841 (0.136883) | 0.028052 / 0.128546 (-0.100494) | 0.008810 / 0.075646 (-0.066836) | 0.080749 / 0.419271 (-0.338522) | 0.046746 / 0.043533 (0.003214) | 0.380325 / 0.255139 (0.125186) | 0.398901 / 0.283200 (0.115701) | 0.099607 / 0.141683 (-0.042076) | 1.433343 / 1.452155 (-0.018812) | 1.520447 / 1.492716 (0.027730) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.202232 / 0.018006 (0.184225) | 0.431342 / 0.000490 (0.430852) | 0.001020 / 0.000200 (0.000820) | 0.000089 / 0.000054 (0.000035) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.028762 / 0.037411 (-0.008649) | 0.111777 / 0.014526 (0.097251) | 0.119283 / 0.176557 (-0.057273) | 0.168151 / 0.737135 (-0.568985) | 0.126093 / 0.296338 (-0.170245) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.442689 / 0.215209 (0.227480) | 4.369202 / 2.077655 (2.291547) | 2.167703 / 1.504120 (0.663583) | 1.960580 / 1.541195 (0.419385) | 2.001459 / 1.468490 (0.532969) | 0.527169 / 4.584777 (-4.057608) | 3.738987 / 3.745712 (-0.006726) | 1.819002 / 5.269862 (-3.450860) | 1.082786 / 4.565676 (-3.482891) | 0.066209 / 0.424275 (-0.358066) | 0.011549 / 0.007607 (0.003942) | 0.545959 / 0.226044 (0.319915) | 5.466655 / 2.268929 (3.197727) | 2.671448 / 55.444624 (-52.773176) | 2.340968 / 6.876477 (-4.535509) | 2.358805 / 2.142072 (0.216733) | 0.649456 / 4.805227 (-4.155771) | 0.142009 / 6.500664 (-6.358655) | 0.064199 / 0.075469 (-0.011270) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.259819 / 1.841788 (-0.581969) | 14.456988 / 8.074308 (6.382680) | 14.478982 / 10.191392 (4.287590) | 0.163156 / 0.680424 (-0.517268) | 0.017090 / 0.534201 (-0.517111) | 0.391339 / 0.579283 (-0.187944) | 0.422021 / 0.434364 (-0.012343) | 0.465340 / 0.540337 (-0.074997) | 0.564517 / 1.386936 (-0.822419) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#97358c88f996a65f49923ec215358044e4146a95 \"CML watermark\")\n", "> .github/workflows/delete_doc_comment.yml \r\n\r\nis already updated https://github.com/huggingface/datasets/pull/5932/files\r\n\r\n> .github/workflows/build_pr_documentation.yml\r\n\r\nindeed no changes are needed" ]

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[ "This can happen when a map transform cannot be hashed deterministically (e.g., an object referenced by the transform changes its state after the first call - an issue with fast tokenizers). The solution is to provide `cache_file_name` in the `map` call to check this file for the cached result instead of relying on the default caching mechanism." ]

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[ "This issue seems to have been resolved, so I'm closing it." ]

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[ "Hi! The times match when I run this code locally or on Colab.\r\n\r\nAlso, we use `multiprocess`, not `multiprocessing`, for parallelization, and torch's `__init__.py` (executed on `import torch` ) slightly modifies the latter.", "Hey Mariosasko,\r\n\r\nThanks for looking into it. We further did some investigations after your comment and figured out it's only affecting some hardware/software configurations with the `pytorch` installation of `conda-forge`. Based on this we found the following issue in PyTorch: https://github.com/pytorch/pytorch/issues/102269 with a quick fix for now.\r\n\r\nSince it seems to be a deeper issue with forking processes, the difference between`multiprocess` and `multiprocessing` didn't make a difference.\r\n\r\nClosing this, since the issue comes from `pytorch` not `dataset`. \r\n" ]

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[ "_The documentation is not available anymore as the PR was closed or merged._", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.006693 / 0.011353 (-0.004660) | 0.004331 / 0.011008 (-0.006677) | 0.098022 / 0.038508 (0.059514) | 0.032764 / 0.023109 (0.009654) | 0.295812 / 0.275898 (0.019914) | 0.325029 / 0.323480 (0.001550) | 0.005779 / 0.007986 (-0.002206) | 0.005381 / 0.004328 (0.001052) | 0.075785 / 0.004250 (0.071535) | 0.048759 / 0.037052 (0.011707) | 0.308986 / 0.258489 (0.050497) | 0.348000 / 0.293841 (0.054159) | 0.027686 / 0.128546 (-0.100860) | 0.008839 / 0.075646 (-0.066807) | 0.328389 / 0.419271 (-0.090883) | 0.062173 / 0.043533 (0.018640) | 0.312257 / 0.255139 (0.057119) | 0.325024 / 0.283200 (0.041824) | 0.103886 / 0.141683 (-0.037797) | 1.440215 / 1.452155 (-0.011940) | 1.528665 / 1.492716 (0.035948) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.210082 / 0.018006 (0.192076) | 0.442480 / 0.000490 (0.441990) | 0.006559 / 0.000200 (0.006359) | 0.000092 / 0.000054 (0.000038) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.026774 / 0.037411 (-0.010637) | 0.108362 / 0.014526 (0.093837) | 0.117631 / 0.176557 (-0.058926) | 0.176657 / 0.737135 (-0.560478) | 0.124154 / 0.296338 (-0.172184) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.428136 / 0.215209 (0.212927) | 4.270287 / 2.077655 (2.192632) | 2.014728 / 1.504120 (0.510608) | 1.806772 / 1.541195 (0.265577) | 1.946284 / 1.468490 (0.477794) | 0.525542 / 4.584777 (-4.059235) | 3.667025 / 3.745712 (-0.078687) | 1.878751 / 5.269862 (-3.391111) | 1.048321 / 4.565676 (-3.517356) | 0.065550 / 0.424275 (-0.358725) | 0.011881 / 0.007607 (0.004274) | 0.529873 / 0.226044 (0.303829) | 5.289641 / 2.268929 (3.020712) | 2.489403 / 55.444624 (-52.955221) | 2.141037 / 6.876477 (-4.735440) | 2.230735 / 2.142072 (0.088662) | 0.639781 / 4.805227 (-4.165447) | 0.141410 / 6.500664 (-6.359254) | 0.064374 / 0.075469 (-0.011095) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.159462 / 1.841788 (-0.682325) | 14.524730 / 8.074308 (6.450422) | 13.578070 / 10.191392 (3.386678) | 0.152138 / 0.680424 (-0.528286) | 0.017255 / 0.534201 (-0.516946) | 0.387607 / 0.579283 (-0.191676) | 0.413652 / 0.434364 (-0.020712) | 0.453644 / 0.540337 (-0.086693) | 0.550051 / 1.386936 (-0.836885) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.006668 / 0.011353 (-0.004685) | 0.004677 / 0.011008 (-0.006331) | 0.075950 / 0.038508 (0.037442) | 0.032439 / 0.023109 (0.009329) | 0.381839 / 0.275898 (0.105941) | 0.419411 / 0.323480 (0.095931) | 0.005813 / 0.007986 (-0.002172) | 0.004090 / 0.004328 (-0.000238) | 0.075052 / 0.004250 (0.070802) | 0.048453 / 0.037052 (0.011401) | 0.388076 / 0.258489 (0.129587) | 0.431793 / 0.293841 (0.137952) | 0.028408 / 0.128546 (-0.100138) | 0.009028 / 0.075646 (-0.066618) | 0.082569 / 0.419271 (-0.336702) | 0.046772 / 0.043533 (0.003239) | 0.380182 / 0.255139 (0.125043) | 0.401828 / 0.283200 (0.118629) | 0.105388 / 0.141683 (-0.036294) | 1.453356 / 1.452155 (0.001201) | 1.561483 / 1.492716 (0.068767) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.008922 / 0.018006 (-0.009084) | 0.444112 / 0.000490 (0.443623) | 0.002756 / 0.000200 (0.002556) | 0.000104 / 0.000054 (0.000050) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.030408 / 0.037411 (-0.007003) | 0.112924 / 0.014526 (0.098399) | 0.124625 / 0.176557 (-0.051932) | 0.176915 / 0.737135 (-0.560220) | 0.129141 / 0.296338 (-0.167198) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.448197 / 0.215209 (0.232987) | 4.476548 / 2.077655 (2.398893) | 2.243977 / 1.504120 (0.739857) | 2.054060 / 1.541195 (0.512865) | 2.130680 / 1.468490 (0.662190) | 0.526815 / 4.584777 (-4.057962) | 3.759312 / 3.745712 (0.013600) | 3.333618 / 5.269862 (-1.936244) | 1.579611 / 4.565676 (-2.986065) | 0.065714 / 0.424275 (-0.358561) | 0.011939 / 0.007607 (0.004332) | 0.550313 / 0.226044 (0.324269) | 5.476946 / 2.268929 (3.208018) | 2.726521 / 55.444624 (-52.718104) | 2.364977 / 6.876477 (-4.511499) | 2.450624 / 2.142072 (0.308551) | 0.647174 / 4.805227 (-4.158053) | 0.141265 / 6.500664 (-6.359399) | 0.065493 / 0.075469 (-0.009976) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.249702 / 1.841788 (-0.592085) | 15.205647 / 8.074308 (7.131338) | 14.678310 / 10.191392 (4.486918) | 0.141539 / 0.680424 (-0.538884) | 0.017323 / 0.534201 (-0.516878) | 0.387602 / 0.579283 (-0.191681) | 0.415106 / 0.434364 (-0.019258) | 0.458146 / 0.540337 (-0.082192) | 0.553318 / 1.386936 (-0.833618) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#55127d7bf399fd2f3a8713db9822e8cb47cdbbed \"CML watermark\")\n", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.008567 / 0.011353 (-0.002786) | 0.005245 / 0.011008 (-0.005763) | 0.115074 / 0.038508 (0.076566) | 0.032567 / 0.023109 (0.009458) | 0.352297 / 0.275898 (0.076399) | 0.393403 / 0.323480 (0.069923) | 0.006402 / 0.007986 (-0.001583) | 0.004353 / 0.004328 (0.000025) | 0.087903 / 0.004250 (0.083653) | 0.048424 / 0.037052 (0.011372) | 0.370078 / 0.258489 (0.111588) | 0.410192 / 0.293841 (0.116351) | 0.042396 / 0.128546 (-0.086150) | 0.014426 / 0.075646 (-0.061220) | 0.411358 / 0.419271 (-0.007914) | 0.059546 / 0.043533 (0.016013) | 0.364721 / 0.255139 (0.109582) | 0.385100 / 0.283200 (0.101901) | 0.100572 / 0.141683 (-0.041111) | 1.741457 / 1.452155 (0.289302) | 1.933134 / 1.492716 (0.440418) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.217177 / 0.018006 (0.199171) | 0.510399 / 0.000490 (0.509909) | 0.005542 / 0.000200 (0.005342) | 0.000120 / 0.000054 (0.000065) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.026852 / 0.037411 (-0.010559) | 0.125580 / 0.014526 (0.111054) | 0.132164 / 0.176557 (-0.044392) | 0.189073 / 0.737135 (-0.548063) | 0.135980 / 0.296338 (-0.160358) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.601924 / 0.215209 (0.386715) | 5.891397 / 2.077655 (3.813743) | 2.389494 / 1.504120 (0.885375) | 2.044013 / 1.541195 (0.502818) | 2.019367 / 1.468490 (0.550877) | 0.883807 / 4.584777 (-3.700970) | 5.141349 / 3.745712 (1.395636) | 2.607415 / 5.269862 (-2.662446) | 1.567268 / 4.565676 (-2.998409) | 0.102738 / 0.424275 (-0.321537) | 0.013480 / 0.007607 (0.005873) | 0.744979 / 0.226044 (0.518934) | 7.404182 / 2.268929 (5.135254) | 2.983406 / 55.444624 (-52.461219) | 2.331847 / 6.876477 (-4.544630) | 2.465119 / 2.142072 (0.323047) | 1.106725 / 4.805227 (-3.698502) | 0.205779 / 6.500664 (-6.294885) | 0.081019 / 0.075469 (0.005550) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.527840 / 1.841788 (-0.313947) | 16.989487 / 8.074308 (8.915179) | 18.016123 / 10.191392 (7.824731) | 0.216157 / 0.680424 (-0.464266) | 0.025393 / 0.534201 (-0.508808) | 0.496743 / 0.579283 (-0.082540) | 0.575365 / 0.434364 (0.141002) | 0.559978 / 0.540337 (0.019641) | 0.677474 / 1.386936 (-0.709462) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.008913 / 0.011353 (-0.002440) | 0.005540 / 0.011008 (-0.005469) | 0.100001 / 0.038508 (0.061493) | 0.034432 / 0.023109 (0.011323) | 0.419824 / 0.275898 (0.143926) | 0.443566 / 0.323480 (0.120086) | 0.006372 / 0.007986 (-0.001614) | 0.004405 / 0.004328 (0.000077) | 0.094927 / 0.004250 (0.090677) | 0.050300 / 0.037052 (0.013248) | 0.424806 / 0.258489 (0.166317) | 0.480793 / 0.293841 (0.186952) | 0.050869 / 0.128546 (-0.077677) | 0.015899 / 0.075646 (-0.059747) | 0.111413 / 0.419271 (-0.307859) | 0.058093 / 0.043533 (0.014560) | 0.430575 / 0.255139 (0.175436) | 0.483786 / 0.283200 (0.200586) | 0.106878 / 0.141683 (-0.034805) | 1.763576 / 1.452155 (0.311422) | 1.837750 / 1.492716 (0.345033) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.011565 / 0.018006 (-0.006441) | 0.484411 / 0.000490 (0.483922) | 0.004869 / 0.000200 (0.004669) | 0.000111 / 0.000054 (0.000057) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.030706 / 0.037411 (-0.006706) | 0.126901 / 0.014526 (0.112375) | 0.130367 / 0.176557 (-0.046190) | 0.206568 / 0.737135 (-0.530567) | 0.146505 / 0.296338 (-0.149834) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.627266 / 0.215209 (0.412057) | 6.314049 / 2.077655 (4.236394) | 2.582920 / 1.504120 (1.078800) | 2.249401 / 1.541195 (0.708206) | 2.244960 / 1.468490 (0.776470) | 0.907770 / 4.584777 (-3.677007) | 5.349622 / 3.745712 (1.603910) | 4.591244 / 5.269862 (-0.678618) | 2.301612 / 4.565676 (-2.264064) | 0.108813 / 0.424275 (-0.315462) | 0.013187 / 0.007607 (0.005580) | 0.806071 / 0.226044 (0.580027) | 7.843903 / 2.268929 (5.574974) | 3.405968 / 55.444624 (-52.038656) | 2.564301 / 6.876477 (-4.312176) | 2.652208 / 2.142072 (0.510135) | 1.168142 / 4.805227 (-3.637086) | 0.218551 / 6.500664 (-6.282113) | 0.078120 / 0.075469 (0.002651) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.562517 / 1.841788 (-0.279271) | 17.519325 / 8.074308 (9.445017) | 20.727083 / 10.191392 (10.535691) | 0.207135 / 0.680424 (-0.473288) | 0.028208 / 0.534201 (-0.505993) | 0.496157 / 0.579283 (-0.083126) | 0.569239 / 0.434364 (0.134875) | 0.566137 / 0.540337 (0.025799) | 0.704208 / 1.386936 (-0.682728) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#8eb3f34d876da98e722d866be90d7f26135ea9e3 \"CML watermark\")\n" ]

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[ "Easy fix would be to add:\r\n\r\n```python\r\nnull_indices -= np.arange(len(null_indices))\r\n```\r\n\r\nbefore L279, but I'm not sure it's the most intuitive way to fix it.", "Same issue here:\r\n\r\nhttps://github.com/huggingface/datasets/blob/7fcbe5b1575c8d162b65b9397b3dfda995a4e048/src/datasets/features/features.py#L1398\r\n\r\nFixed in #5948 " ]

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[ "Thanks for reporting, @severo.\r\n\r\nThis is a known issue with `fsspec`:\r\n- #5862\r\n- https://github.com/fsspec/filesystem_spec/issues/1265" ]

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[ "Hi @lhoestq, I added the `parallel` part according to the discussion we had. Could you take a look to see if this is aligned with your proposal?\r\n\r\nMeanwhile I'm working on adding a `parallel_backend` parameter to `load_datasets` so that it can be used like:\r\n```python\r\nwith parallel_backend('spark', steps=['downloading']) as backend:\r\n ds = load_dataset(..., parallel_backend=backend)\r\n```\r\nwhere `parallel_backend` is a `ParallelBackend` class.", "_The documentation is not available anymore as the PR was closed or merged._", "@lhoestq Thanks for the comments!\r\nWith your suggestion, no changes made to `load_dataset` and I validated that downloading with spark is working now with this:\r\n```py\r\nwith parallel_backend('spark', steps=[\"download\"]):\r\n dataset = load_dataset(..., num_proc=2)\r\n```", "@lhoestq Can a maintainer help trigger the tests again?\r\n> One idea is to decorate the download method to set the current global step to \"download\", and then only use joblib if the current step is one of the steps provided in parallel_backend.\r\n\r\nYes I think this is doable in a subsequent PR.\r\nFor throwing `NotImplementedError` I also think it can be done in a subsequent PR, because I'm not sure if `Dataset.map` is the only function that a user would expect to run using `with parallel_backend`.", "Just triggered the tests :)\r\n\r\n> Yes I think this is doable in a subsequent PR.\r\nFor throwing NotImplementedError I also think it can be done in a subsequent PR, because I'm not sure if Dataset.map is the only function that a user would expect to run using with parallel_backend.\r\n\r\nI think any Dataset method that has a `num_proc` argument: Dataset.map (the other methods like filter or cast or based on map), and later we can see for the to_xxx methods (to_csv, to_parquet, etc.)", "Hi maintainers, I've just addressed most of the comments, please take another look, thank you.", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.008422 / 0.011353 (-0.002931) | 0.005658 / 0.011008 (-0.005350) | 0.135372 / 0.038508 (0.096864) | 0.044766 / 0.023109 (0.021657) | 0.417876 / 0.275898 (0.141978) | 0.462785 / 0.323480 (0.139305) | 0.005485 / 0.007986 (-0.002501) | 0.005640 / 0.004328 (0.001311) | 0.105020 / 0.004250 (0.100770) | 0.049114 / 0.037052 (0.012062) | 0.490450 / 0.258489 (0.231961) | 0.467693 / 0.293841 (0.173852) | 0.050929 / 0.128546 (-0.077617) | 0.014644 / 0.075646 (-0.061002) | 0.452373 / 0.419271 (0.033101) | 0.074897 / 0.043533 (0.031364) | 0.425816 / 0.255139 (0.170677) | 0.420415 / 0.283200 (0.137215) | 0.134121 / 0.141683 (-0.007561) | 1.927744 / 1.452155 (0.475589) | 2.014417 / 1.492716 (0.521701) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.254811 / 0.018006 (0.236805) | 0.550011 / 0.000490 (0.549521) | 0.004913 / 0.000200 (0.004714) | 0.000117 / 0.000054 (0.000062) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.032644 / 0.037411 (-0.004768) | 0.135672 / 0.014526 (0.121146) | 0.158984 / 0.176557 (-0.017572) | 0.218267 / 0.737135 (-0.518869) | 0.150348 / 0.296338 (-0.145991) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.625723 / 0.215209 (0.410514) | 6.247559 / 2.077655 (4.169905) | 2.626785 / 1.504120 (1.122666) | 2.195224 / 1.541195 (0.654030) | 2.232140 / 1.468490 (0.763650) | 0.943082 / 4.584777 (-3.641695) | 5.799262 / 3.745712 (2.053550) | 2.849411 / 5.269862 (-2.420450) | 1.744160 / 4.565676 (-2.821516) | 0.119056 / 0.424275 (-0.305219) | 0.014233 / 0.007607 (0.006626) | 0.795238 / 0.226044 (0.569194) | 7.569586 / 2.268929 (5.300657) | 3.179481 / 55.444624 (-52.265143) | 2.519772 / 6.876477 (-4.356704) | 2.714570 / 2.142072 (0.572498) | 1.107197 / 4.805227 (-3.698030) | 0.229986 / 6.500664 (-6.270678) | 0.087993 / 0.075469 (0.012524) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.535610 / 1.841788 (-0.306178) | 18.639369 / 8.074308 (10.565061) | 21.081844 / 10.191392 (10.890452) | 0.253247 / 0.680424 (-0.427177) | 0.026711 / 0.534201 (-0.507490) | 0.503790 / 0.579283 (-0.075493) | 0.600124 / 0.434364 (0.165760) | 0.617944 / 0.540337 (0.077607) | 0.766947 / 1.386936 (-0.619989) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.007885 / 0.011353 (-0.003468) | 0.004761 / 0.011008 (-0.006248) | 0.097995 / 0.038508 (0.059487) | 0.033624 / 0.023109 (0.010515) | 0.504307 / 0.275898 (0.228409) | 0.534803 / 0.323480 (0.211323) | 0.006048 / 0.007986 (-0.001937) | 0.005042 / 0.004328 (0.000714) | 0.102288 / 0.004250 (0.098038) | 0.048695 / 0.037052 (0.011643) | 0.559086 / 0.258489 (0.300597) | 0.553233 / 0.293841 (0.259392) | 0.044596 / 0.128546 (-0.083950) | 0.013696 / 0.075646 (-0.061950) | 0.109875 / 0.419271 (-0.309397) | 0.059993 / 0.043533 (0.016460) | 0.485579 / 0.255139 (0.230440) | 0.519835 / 0.283200 (0.236635) | 0.123504 / 0.141683 (-0.018179) | 1.820506 / 1.452155 (0.368351) | 1.963448 / 1.492716 (0.470732) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.292663 / 0.018006 (0.274656) | 0.557783 / 0.000490 (0.557293) | 0.001330 / 0.000200 (0.001130) | 0.000112 / 0.000054 (0.000057) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.036890 / 0.037411 (-0.000522) | 0.140373 / 0.014526 (0.125847) | 0.140176 / 0.176557 (-0.036381) | 0.237378 / 0.737135 (-0.499757) | 0.160186 / 0.296338 (-0.136152) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.673599 / 0.215209 (0.458390) | 6.510280 / 2.077655 (4.432625) | 2.981617 / 1.504120 (1.477497) | 2.684664 / 1.541195 (1.143469) | 2.760471 / 1.468490 (1.291981) | 0.975413 / 4.584777 (-3.609364) | 5.708933 / 3.745712 (1.963220) | 2.772069 / 5.269862 (-2.497793) | 1.763627 / 4.565676 (-2.802049) | 0.111632 / 0.424275 (-0.312643) | 0.013223 / 0.007607 (0.005616) | 0.791545 / 0.226044 (0.565500) | 8.063287 / 2.268929 (5.794359) | 3.671920 / 55.444624 (-51.772704) | 3.057248 / 6.876477 (-3.819229) | 3.083569 / 2.142072 (0.941497) | 1.118136 / 4.805227 (-3.687092) | 0.214655 / 6.500664 (-6.286009) | 0.083074 / 0.075469 (0.007605) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.761731 / 1.841788 (-0.080056) | 18.874200 / 8.074308 (10.799892) | 22.383693 / 10.191392 (12.192301) | 0.240292 / 0.680424 (-0.440132) | 0.028850 / 0.534201 (-0.505351) | 0.557334 / 0.579283 (-0.021949) | 0.627732 / 0.434364 (0.193369) | 0.634484 / 0.540337 (0.094146) | 0.767372 / 1.386936 (-0.619564) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#accaaf2e69fbb5dc5e50229d2eb1591b8ad982b6 \"CML watermark\")\n" ]

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[ "Based on https://github.com/rapidsai/cudf/issues/10187, this probably means your `pyarrow` installation is not compatible with `datasets`.\r\n\r\nCan you please execute the following commands in the terminal and paste the output here?\r\n```\r\nconda list | grep arrow\r\n``` \r\n```\r\npython -c \"import pyarrow; print(pyarrow.__file__)\"\r\n```\r\n\r\n\r\n", "> Based on [rapidsai/cudf#10187](https://github.com/rapidsai/cudf/issues/10187), this probably means your `pyarrow` installation is not compatible with `datasets`.\r\n> \r\n> Can you please execute the following commands in the terminal and paste the output here?\r\n> \r\n> ```\r\n> conda list | grep arrow\r\n> ```\r\n> \r\n> ```\r\n> python -c \"import pyarrow; print(pyarrow.__file__)\"\r\n> ```\r\n\r\n\r\nHere is the output to the first command:\r\n```\r\narrow-cpp 11.0.0 py39h7f74497_0 \r\npyarrow 12.0.0 pypi_0 pypi\r\n```\r\nand the second:\r\n```\r\n/Users/edward/opt/anaconda3/envs/cs235/lib/python3.9/site-packages/pyarrow/__init__.py\r\n```\r\nThanks!\r\n\r\n\r\n\r\n", "after installing pytesseract 0.3.10, I got the above error. FYI ", "RuntimeError: Failed to import transformers.trainer because of the following error (look up to see its traceback):\r\npyarrow.lib.IpcWriteOptions size changed, may indicate binary incompatibility. Expected 88 from C header, got 72 from PyObject", "I got the same error, pyarrow 12.0.0 released May/2023 (https://pypi.org/project/pyarrow/) is not compatible, running `pip install pyarrow==11.0.0` to force install the previous version solved the problem.\r\n\r\nDo we need to update dependencies? ", "Please note that our CI properly passes all tests with `pyarrow-12.0.0`, for Python 3.7 and Python 3.10, for Ubuntu and Windows: see for example https://github.com/huggingface/datasets/actions/runs/5157324334/jobs/9289582291", "For conda with python3.8.16 this solved my problem! thanks!\r\n\r\n> I got the same error, pyarrow 12.0.0 released May/2023 (https://pypi.org/project/pyarrow/) is not compatible, running `pip install pyarrow==11.0.0` to force install the previous version solved the problem.\r\n> \r\n> Do we need to update dependencies? I can work on that if no one else is working on it.\r\n\r\n", "Thanks for replying. I am not sure about those environments but it seems like pyarrow-12.0.0 does not work for conda with python 3.8.16. \r\n\r\n> Please note that our CI properly passes all tests with `pyarrow-12.0.0`, for Python 3.7 and Python 3.10, for Ubuntu and Windows: see for example https://github.com/huggingface/datasets/actions/runs/5157324334/jobs/9289582291\r\n\r\n", "Got the same error with:\r\n\r\n```\r\narrow-cpp 11.0.0 py310h7516544_0 \r\npyarrow 12.0.0 pypi_0 pypi\r\n\r\npython 3.10.11 h7a1cb2a_2 \r\n\r\ndatasets 2.13.0 pyhd8ed1ab_0 conda-forge\r\n```", "> I got the same error, pyarrow 12.0.0 released May/2023 (https://pypi.org/project/pyarrow/) is not compatible, running `pip install pyarrow==11.0.0` to force install the previous version solved the problem.\r\n> \r\n> Do we need to update dependencies?\r\n\r\nThis solved the issue for me as well.", "> I got the same error, pyarrow 12.0.0 released May/2023 (https://pypi.org/project/pyarrow/) is not compatible, running `pip install pyarrow==11.0.0` to force install the previous version solved the problem.\r\n> \r\n> Do we need to update dependencies?\r\n\r\nSolved it for me also", "> 基于 [rapidsai/cudf#10187](https://github.com/rapidsai/cudf/issues/10187)，这可能意味着您的安装与 不兼容。`pyarrow``datasets`\r\n> \r\n> 您能否在终端中执行以下命令并将输出粘贴到此处？\r\n> \r\n> ```\r\n> conda list | grep arrow\r\n> ```\r\n> \r\n> ```\r\n> python -c \"import pyarrow; print(pyarrow.__file__)\"\r\n> ```\r\n\r\narrow-cpp 11.0.0 py310h7516544_0 \r\npyarrow 12.0.1 pypi_0 pypi\r\n\r\n/root/miniconda3/lib/python3.10/site-packages/pyarrow/__init__.py", "Got the same problem with\r\n\r\narrow-cpp 11.0.0 py310h1fc3239_0 \r\npyarrow 12.0.1 pypi_0 pypi\r\n\r\nminiforge3/envs/mlp/lib/python3.10/site-packages/pyarrow/__init__.py\r\n\r\nReverting back to pyarrow 11 solved the problem.\r\n" ]

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[ "As already replied by @lhoestq (private channel):\r\n> `.train_test_split` (as well as `.shard`, `.select`) doesn't create a new arrow table to save time and disk space. Instead, it uses an indices mapping on top of the table that locate which examples are part of train or test.", "This is an optimization that we don't plan to \"fix\", so I'm closing this issue." ]

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[ "_The documentation is not available anymore as the PR was closed or merged._", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.007088 / 0.011353 (-0.004265) | 0.005216 / 0.011008 (-0.005793) | 0.097572 / 0.038508 (0.059064) | 0.036510 / 0.023109 (0.013401) | 0.316885 / 0.275898 (0.040987) | 0.348541 / 0.323480 (0.025061) | 0.006513 / 0.007986 (-0.001473) | 0.004579 / 0.004328 (0.000251) | 0.073779 / 0.004250 (0.069529) | 0.057500 / 0.037052 (0.020448) | 0.329840 / 0.258489 (0.071351) | 0.357530 / 0.293841 (0.063690) | 0.028515 / 0.128546 (-0.100031) | 0.009156 / 0.075646 (-0.066491) | 0.328340 / 0.419271 (-0.090932) | 0.068400 / 0.043533 (0.024867) | 0.313692 / 0.255139 (0.058553) | 0.329170 / 0.283200 (0.045971) | 0.111969 / 0.141683 (-0.029714) | 1.422096 / 1.452155 (-0.030059) | 1.550042 / 1.492716 (0.057326) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.285113 / 0.018006 (0.267107) | 0.546788 / 0.000490 (0.546298) | 0.006992 / 0.000200 (0.006792) | 0.000097 / 0.000054 (0.000043) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.026841 / 0.037411 (-0.010570) | 0.108413 / 0.014526 (0.093887) | 0.118375 / 0.176557 (-0.058181) | 0.174889 / 0.737135 (-0.562246) | 0.122781 / 0.296338 (-0.173558) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.404187 / 0.215209 (0.188978) | 4.039673 / 2.077655 (1.962019) | 1.894616 / 1.504120 (0.390496) | 1.729182 / 1.541195 (0.187987) | 1.772917 / 1.468490 (0.304427) | 0.524046 / 4.584777 (-4.060731) | 3.628111 / 3.745712 (-0.117601) | 1.866075 / 5.269862 (-3.403787) | 1.026435 / 4.565676 (-3.539242) | 0.065328 / 0.424275 (-0.358947) | 0.012717 / 0.007607 (0.005110) | 0.505821 / 0.226044 (0.279777) | 5.049518 / 2.268929 (2.780589) | 2.338486 / 55.444624 (-53.106139) | 2.002874 / 6.876477 (-4.873602) | 2.193049 / 2.142072 (0.050976) | 0.664638 / 4.805227 (-4.140589) | 0.151323 / 6.500664 (-6.349341) | 0.063774 / 0.075469 (-0.011695) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.168168 / 1.841788 (-0.673620) | 15.289200 / 8.074308 (7.214891) | 13.614249 / 10.191392 (3.422857) | 0.167950 / 0.680424 (-0.512474) | 0.017522 / 0.534201 (-0.516679) | 0.393480 / 0.579283 (-0.185803) | 0.420549 / 0.434364 (-0.013815) | 0.461425 / 0.540337 (-0.078912) | 0.563583 / 1.386936 (-0.823353) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.006859 / 0.011353 (-0.004493) | 0.004864 / 0.011008 (-0.006144) | 0.075084 / 0.038508 (0.036576) | 0.033989 / 0.023109 (0.010880) | 0.372512 / 0.275898 (0.096614) | 0.394725 / 0.323480 (0.071246) | 0.006382 / 0.007986 (-0.001604) | 0.004521 / 0.004328 (0.000193) | 0.076422 / 0.004250 (0.072172) | 0.055383 / 0.037052 (0.018331) | 0.400974 / 0.258489 (0.142485) | 0.411570 / 0.293841 (0.117729) | 0.028264 / 0.128546 (-0.100282) | 0.009123 / 0.075646 (-0.066523) | 0.081257 / 0.419271 (-0.338015) | 0.048147 / 0.043533 (0.004614) | 0.390735 / 0.255139 (0.135596) | 0.376426 / 0.283200 (0.093226) | 0.108164 / 0.141683 (-0.033518) | 1.429667 / 1.452155 (-0.022488) | 1.556291 / 1.492716 (0.063575) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.289514 / 0.018006 (0.271508) | 0.532860 / 0.000490 (0.532370) | 0.003810 / 0.000200 (0.003611) | 0.000121 / 0.000054 (0.000066) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.031292 / 0.037411 (-0.006119) | 0.116530 / 0.014526 (0.102005) | 0.127624 / 0.176557 (-0.048932) | 0.178276 / 0.737135 (-0.558859) | 0.133742 / 0.296338 (-0.162597) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.431505 / 0.215209 (0.216296) | 4.309206 / 2.077655 (2.231551) | 2.174779 / 1.504120 (0.670659) | 1.998122 / 1.541195 (0.456927) | 2.126478 / 1.468490 (0.657988) | 0.528971 / 4.584777 (-4.055806) | 3.797608 / 3.745712 (0.051895) | 1.876275 / 5.269862 (-3.393586) | 1.087458 / 4.565676 (-3.478218) | 0.066940 / 0.424275 (-0.357335) | 0.012432 / 0.007607 (0.004825) | 0.538346 / 0.226044 (0.312301) | 5.370968 / 2.268929 (3.102039) | 2.613718 / 55.444624 (-52.830906) | 2.246585 / 6.876477 (-4.629892) | 2.375695 / 2.142072 (0.233622) | 0.652227 / 4.805227 (-4.153001) | 0.143246 / 6.500664 (-6.357418) | 0.066163 / 0.075469 (-0.009306) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.291263 / 1.841788 (-0.550524) | 16.532281 / 8.074308 (8.457973) | 15.038471 / 10.191392 (4.847079) | 0.168139 / 0.680424 (-0.512285) | 0.017724 / 0.534201 (-0.516477) | 0.391636 / 0.579283 (-0.187648) | 0.429690 / 0.434364 (-0.004674) | 0.474941 / 0.540337 (-0.065396) | 0.579461 / 1.386936 (-0.807475) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#db690affa0373b08f7cef04e25fe2113ee831ef5 \"CML watermark\")\n", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.006083 / 0.011353 (-0.005269) | 0.004085 / 0.011008 (-0.006923) | 0.098337 / 0.038508 (0.059829) | 0.027573 / 0.023109 (0.004464) | 0.305688 / 0.275898 (0.029790) | 0.341767 / 0.323480 (0.018287) | 0.005143 / 0.007986 (-0.002842) | 0.003396 / 0.004328 (-0.000932) | 0.076925 / 0.004250 (0.072674) | 0.041027 / 0.037052 (0.003975) | 0.307877 / 0.258489 (0.049388) | 0.346559 / 0.293841 (0.052718) | 0.025183 / 0.128546 (-0.103363) | 0.008575 / 0.075646 (-0.067071) | 0.319449 / 0.419271 (-0.099823) | 0.043378 / 0.043533 (-0.000154) | 0.304563 / 0.255139 (0.049424) | 0.332019 / 0.283200 (0.048819) | 0.087725 / 0.141683 (-0.053958) | 1.484904 / 1.452155 (0.032749) | 1.582780 / 1.492716 (0.090064) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.197503 / 0.018006 (0.179497) | 0.410370 / 0.000490 (0.409880) | 0.003840 / 0.000200 (0.003640) | 0.000067 / 0.000054 (0.000013) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.024179 / 0.037411 (-0.013232) | 0.098876 / 0.014526 (0.084350) | 0.106189 / 0.176557 (-0.070367) | 0.168964 / 0.737135 (-0.568171) | 0.109723 / 0.296338 (-0.186616) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.429453 / 0.215209 (0.214244) | 4.295584 / 2.077655 (2.217929) | 2.014330 / 1.504120 (0.510210) | 1.841119 / 1.541195 (0.299924) | 1.928378 / 1.468490 (0.459888) | 0.554571 / 4.584777 (-4.030206) | 3.431769 / 3.745712 (-0.313943) | 1.716204 / 5.269862 (-3.553658) | 0.995054 / 4.565676 (-3.570622) | 0.067374 / 0.424275 (-0.356902) | 0.012557 / 0.007607 (0.004950) | 0.533785 / 0.226044 (0.307740) | 5.363360 / 2.268929 (3.094431) | 2.535190 / 55.444624 (-52.909434) | 2.191646 / 6.876477 (-4.684831) | 2.400799 / 2.142072 (0.258727) | 0.663961 / 4.805227 (-4.141266) | 0.135992 / 6.500664 (-6.364672) | 0.067378 / 0.075469 (-0.008092) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.235110 / 1.841788 (-0.606678) | 13.820695 / 8.074308 (5.746387) | 13.667202 / 10.191392 (3.475810) | 0.143025 / 0.680424 (-0.537399) | 0.016757 / 0.534201 (-0.517444) | 0.356262 / 0.579283 (-0.223021) | 0.401871 / 0.434364 (-0.032493) | 0.423928 / 0.540337 (-0.116410) | 0.514598 / 1.386936 (-0.872338) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.006260 / 0.011353 (-0.005093) | 0.004159 / 0.011008 (-0.006850) | 0.076780 / 0.038508 (0.038272) | 0.027899 / 0.023109 (0.004789) | 0.412756 / 0.275898 (0.136858) | 0.455145 / 0.323480 (0.131665) | 0.005029 / 0.007986 (-0.002956) | 0.003482 / 0.004328 (-0.000847) | 0.076148 / 0.004250 (0.071898) | 0.038969 / 0.037052 (0.001917) | 0.429975 / 0.258489 (0.171486) | 0.465880 / 0.293841 (0.172039) | 0.025555 / 0.128546 (-0.102991) | 0.008612 / 0.075646 (-0.067034) | 0.082604 / 0.419271 (-0.336667) | 0.039690 / 0.043533 (-0.003842) | 0.403644 / 0.255139 (0.148505) | 0.440438 / 0.283200 (0.157238) | 0.090984 / 0.141683 (-0.050699) | 1.465915 / 1.452155 (0.013760) | 1.564227 / 1.492716 (0.071511) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.010502 / 0.018006 (-0.007504) | 0.410573 / 0.000490 (0.410083) | 0.000384 / 0.000200 (0.000184) | 0.000059 / 0.000054 (0.000004) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.025726 / 0.037411 (-0.011686) | 0.101760 / 0.014526 (0.087235) | 0.110102 / 0.176557 (-0.066454) | 0.161321 / 0.737135 (-0.575815) | 0.112507 / 0.296338 (-0.183832) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.469925 / 0.215209 (0.254716) | 4.718740 / 2.077655 (2.641085) | 2.466272 / 1.504120 (0.962152) | 2.267357 / 1.541195 (0.726162) | 2.331343 / 1.468490 (0.862853) | 0.553448 / 4.584777 (-4.031329) | 3.464228 / 3.745712 (-0.281484) | 3.060957 / 5.269862 (-2.208905) | 1.387261 / 4.565676 (-3.178415) | 0.067989 / 0.424275 (-0.356286) | 0.012349 / 0.007607 (0.004741) | 0.575046 / 0.226044 (0.349001) | 5.740322 / 2.268929 (3.471394) | 2.925666 / 55.444624 (-52.518958) | 2.606535 / 6.876477 (-4.269942) | 2.658144 / 2.142072 (0.516072) | 0.655157 / 4.805227 (-4.150071) | 0.138520 / 6.500664 (-6.362144) | 0.069442 / 0.075469 (-0.006027) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.306523 / 1.841788 (-0.535265) | 14.400380 / 8.074308 (6.326072) | 14.231519 / 10.191392 (4.040127) | 0.146194 / 0.680424 (-0.534230) | 0.016632 / 0.534201 (-0.517569) | 0.361151 / 0.579283 (-0.218132) | 0.388838 / 0.434364 (-0.045526) | 0.419337 / 0.540337 (-0.121001) | 0.500483 / 1.386936 (-0.886453) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#c0429e9806bf7065d03dc5858c039a30c5af716c \"CML watermark\")\n", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.009430 / 0.011353 (-0.001923) | 0.006673 / 0.011008 (-0.004335) | 0.125151 / 0.038508 (0.086643) | 0.038258 / 0.023109 (0.015149) | 0.426383 / 0.275898 (0.150485) | 0.432327 / 0.323480 (0.108847) | 0.006964 / 0.007986 (-0.001022) | 0.005140 / 0.004328 (0.000811) | 0.100767 / 0.004250 (0.096517) | 0.058663 / 0.037052 (0.021610) | 0.424709 / 0.258489 (0.166220) | 0.453049 / 0.293841 (0.159208) | 0.051042 / 0.128546 (-0.077505) | 0.015291 / 0.075646 (-0.060355) | 0.456549 / 0.419271 (0.037278) | 0.067106 / 0.043533 (0.023573) | 0.408959 / 0.255139 (0.153820) | 0.445067 / 0.283200 (0.161867) | 0.115590 / 0.141683 (-0.026092) | 1.929439 / 1.452155 (0.477284) | 2.045709 / 1.492716 (0.552992) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.250726 / 0.018006 (0.232720) | 0.598976 / 0.000490 (0.598486) | 0.007542 / 0.000200 (0.007342) | 0.000101 / 0.000054 (0.000046) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.030317 / 0.037411 (-0.007094) | 0.133177 / 0.014526 (0.118651) | 0.152761 / 0.176557 (-0.023795) | 0.233708 / 0.737135 (-0.503428) | 0.147303 / 0.296338 (-0.149036) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.633562 / 0.215209 (0.418353) | 6.235021 / 2.077655 (4.157366) | 2.652573 / 1.504120 (1.148454) | 2.223363 / 1.541195 (0.682168) | 2.231022 / 1.468490 (0.762531) | 0.942218 / 4.584777 (-3.642559) | 6.068661 / 3.745712 (2.322949) | 2.778604 / 5.269862 (-2.491257) | 1.787939 / 4.565676 (-2.777737) | 0.117749 / 0.424275 (-0.306526) | 0.015613 / 0.007607 (0.008006) | 0.810222 / 0.226044 (0.584177) | 7.931509 / 2.268929 (5.662581) | 3.260679 / 55.444624 (-52.183945) | 2.609085 / 6.876477 (-4.267391) | 2.867838 / 2.142072 (0.725766) | 1.144672 / 4.805227 (-3.660555) | 0.224379 / 6.500664 (-6.276285) | 0.084490 / 0.075469 (0.009021) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.650608 / 1.841788 (-0.191179) | 18.919748 / 8.074308 (10.845440) | 20.163162 / 10.191392 (9.971770) | 0.229427 / 0.680424 (-0.450997) | 0.033090 / 0.534201 (-0.501111) | 0.535549 / 0.579283 (-0.043734) | 0.658629 / 0.434364 (0.224265) | 0.631526 / 0.540337 (0.091189) | 0.748701 / 1.386936 (-0.638235) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.009157 / 0.011353 (-0.002196) | 0.006153 / 0.011008 (-0.004856) | 0.106294 / 0.038508 (0.067786) | 0.040947 / 0.023109 (0.017837) | 0.493242 / 0.275898 (0.217344) | 0.563525 / 0.323480 (0.240045) | 0.007256 / 0.007986 (-0.000730) | 0.006757 / 0.004328 (0.002429) | 0.105151 / 0.004250 (0.100901) | 0.056262 / 0.037052 (0.019209) | 0.573341 / 0.258489 (0.314852) | 0.591125 / 0.293841 (0.297284) | 0.047935 / 0.128546 (-0.080611) | 0.015385 / 0.075646 (-0.060262) | 0.119457 / 0.419271 (-0.299814) | 0.066510 / 0.043533 (0.022977) | 0.485622 / 0.255139 (0.230483) | 0.540929 / 0.283200 (0.257730) | 0.132619 / 0.141683 (-0.009064) | 1.916905 / 1.452155 (0.464750) | 2.152722 / 1.492716 (0.660006) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.294823 / 0.018006 (0.276817) | 0.569371 / 0.000490 (0.568882) | 0.000642 / 0.000200 (0.000442) | 0.000091 / 0.000054 (0.000036) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.034321 / 0.037411 (-0.003090) | 0.134165 / 0.014526 (0.119639) | 0.157871 / 0.176557 (-0.018685) | 0.210753 / 0.737135 (-0.526382) | 0.152961 / 0.296338 (-0.143377) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.686810 / 0.215209 (0.471601) | 6.890432 / 2.077655 (4.812778) | 3.182875 / 1.504120 (1.678755) | 2.770836 / 1.541195 (1.229641) | 2.790785 / 1.468490 (1.322295) | 0.938145 / 4.584777 (-3.646632) | 5.861093 / 3.745712 (2.115381) | 2.719862 / 5.269862 (-2.550000) | 1.760834 / 4.565676 (-2.804842) | 0.111317 / 0.424275 (-0.312958) | 0.015722 / 0.007607 (0.008115) | 0.863032 / 0.226044 (0.636988) | 8.482433 / 2.268929 (6.213504) | 3.892621 / 55.444624 (-51.552003) | 3.207370 / 6.876477 (-3.669106) | 3.344412 / 2.142072 (1.202339) | 1.133903 / 4.805227 (-3.671324) | 0.223456 / 6.500664 (-6.277209) | 0.084335 / 0.075469 (0.008866) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.794116 / 1.841788 (-0.047672) | 19.077447 / 8.074308 (11.003139) | 23.102309 / 10.191392 (12.910917) | 0.268806 / 0.680424 (-0.411617) | 0.027709 / 0.534201 (-0.506492) | 0.540488 / 0.579283 (-0.038796) | 0.658478 / 0.434364 (0.224114) | 0.604769 / 0.540337 (0.064431) | 0.722768 / 1.386936 (-0.664168) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#7e52021c66666e6953d5be0bd45a079e3ddb8c3f \"CML watermark\")\n" ]

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[ "_The documentation is not available anymore as the PR was closed or merged._", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.007439 / 0.011353 (-0.003914) | 0.004884 / 0.011008 (-0.006124) | 0.098750 / 0.038508 (0.060242) | 0.040723 / 0.023109 (0.017613) | 0.347242 / 0.275898 (0.071344) | 0.381202 / 0.323480 (0.057722) | 0.006814 / 0.007986 (-0.001171) | 0.004543 / 0.004328 (0.000215) | 0.075338 / 0.004250 (0.071088) | 0.058976 / 0.037052 (0.021924) | 0.344746 / 0.258489 (0.086257) | 0.406761 / 0.293841 (0.112920) | 0.028961 / 0.128546 (-0.099585) | 0.009531 / 0.075646 (-0.066115) | 0.337324 / 0.419271 (-0.081947) | 0.051071 / 0.043533 (0.007538) | 0.341251 / 0.255139 (0.086112) | 0.362773 / 0.283200 (0.079573) | 0.109423 / 0.141683 (-0.032260) | 1.457420 / 1.452155 (0.005266) | 1.588824 / 1.492716 (0.096108) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.288620 / 0.018006 (0.270614) | 0.568975 / 0.000490 (0.568485) | 0.003350 / 0.000200 (0.003150) | 0.000088 / 0.000054 (0.000034) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.028732 / 0.037411 (-0.008680) | 0.117820 / 0.014526 (0.103294) | 0.120180 / 0.176557 (-0.056376) | 0.178736 / 0.737135 (-0.558399) | 0.126399 / 0.296338 (-0.169939) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.428357 / 0.215209 (0.213148) | 4.251989 / 2.077655 (2.174334) | 2.005239 / 1.504120 (0.501119) | 1.784009 / 1.541195 (0.242815) | 1.883763 / 1.468490 (0.415272) | 0.555429 / 4.584777 (-4.029348) | 3.868146 / 3.745712 (0.122434) | 2.081896 / 5.269862 (-3.187965) | 1.126047 / 4.565676 (-3.439629) | 0.069496 / 0.424275 (-0.354779) | 0.012926 / 0.007607 (0.005318) | 0.536989 / 0.226044 (0.310944) | 5.256052 / 2.268929 (2.987124) | 2.526802 / 55.444624 (-52.917822) | 2.233346 / 6.876477 (-4.643131) | 2.389063 / 2.142072 (0.246990) | 0.677107 / 4.805227 (-4.128120) | 0.147212 / 6.500664 (-6.353452) | 0.067061 / 0.075469 (-0.008408) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.210651 / 1.841788 (-0.631137) | 17.236898 / 8.074308 (9.162589) | 14.427301 / 10.191392 (4.235909) | 0.207194 / 0.680424 (-0.473229) | 0.018079 / 0.534201 (-0.516122) | 0.398355 / 0.579283 (-0.180929) | 0.462453 / 0.434364 (0.028089) | 0.484544 / 0.540337 (-0.055794) | 0.590119 / 1.386936 (-0.796817) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.007392 / 0.011353 (-0.003961) | 0.005614 / 0.011008 (-0.005394) | 0.075587 / 0.038508 (0.037079) | 0.040429 / 0.023109 (0.017320) | 0.389901 / 0.275898 (0.114003) | 0.429466 / 0.323480 (0.105986) | 0.006790 / 0.007986 (-0.001196) | 0.006627 / 0.004328 (0.002299) | 0.075227 / 0.004250 (0.070976) | 0.060298 / 0.037052 (0.023246) | 0.391905 / 0.258489 (0.133416) | 0.449385 / 0.293841 (0.155544) | 0.028794 / 0.128546 (-0.099753) | 0.009461 / 0.075646 (-0.066185) | 0.083386 / 0.419271 (-0.335886) | 0.057968 / 0.043533 (0.014435) | 0.377327 / 0.255139 (0.122188) | 0.402825 / 0.283200 (0.119626) | 0.125477 / 0.141683 (-0.016206) | 1.462986 / 1.452155 (0.010832) | 1.595959 / 1.492716 (0.103243) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.304179 / 0.018006 (0.286173) | 0.543113 / 0.000490 (0.542623) | 0.004136 / 0.000200 (0.003936) | 0.000109 / 0.000054 (0.000054) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.032617 / 0.037411 (-0.004794) | 0.123596 / 0.014526 (0.109070) | 0.128714 / 0.176557 (-0.047842) | 0.176344 / 0.737135 (-0.560792) | 0.132525 / 0.296338 (-0.163813) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.446041 / 0.215209 (0.230832) | 4.438799 / 2.077655 (2.361144) | 2.210815 / 1.504120 (0.706695) | 2.052025 / 1.541195 (0.510830) | 2.204687 / 1.468490 (0.736197) | 0.535219 / 4.584777 (-4.049558) | 3.858407 / 3.745712 (0.112695) | 3.826043 / 5.269862 (-1.443819) | 1.334149 / 4.565676 (-3.231527) | 0.067454 / 0.424275 (-0.356821) | 0.012566 / 0.007607 (0.004958) | 0.551597 / 0.226044 (0.325553) | 5.520054 / 2.268929 (3.251126) | 2.817976 / 55.444624 (-52.626649) | 2.528074 / 6.876477 (-4.348403) | 2.622391 / 2.142072 (0.480319) | 0.657632 / 4.805227 (-4.147595) | 0.147039 / 6.500664 (-6.353625) | 0.069603 / 0.075469 (-0.005866) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.300140 / 1.841788 (-0.541648) | 17.303907 / 8.074308 (9.229599) | 15.657887 / 10.191392 (5.466495) | 0.168991 / 0.680424 (-0.511433) | 0.021332 / 0.534201 (-0.512869) | 0.487261 / 0.579283 (-0.092022) | 0.450073 / 0.434364 (0.015709) | 0.465865 / 0.540337 (-0.074473) | 0.565501 / 1.386936 (-0.821435) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#f1723ab75a6b3a5e156ea0a41651e80e91fa9cc6 \"CML watermark\")\n", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.006536 / 0.011353 (-0.004817) | 0.004254 / 0.011008 (-0.006755) | 0.095387 / 0.038508 (0.056878) | 0.032885 / 0.023109 (0.009776) | 0.298580 / 0.275898 (0.022682) | 0.319771 / 0.323480 (-0.003709) | 0.005510 / 0.007986 (-0.002476) | 0.003891 / 0.004328 (-0.000437) | 0.073763 / 0.004250 (0.069513) | 0.041625 / 0.037052 (0.004573) | 0.294896 / 0.258489 (0.036407) | 0.341308 / 0.293841 (0.047467) | 0.027898 / 0.128546 (-0.100648) | 0.008837 / 0.075646 (-0.066809) | 0.325055 / 0.419271 (-0.094216) | 0.050652 / 0.043533 (0.007119) | 0.298756 / 0.255139 (0.043617) | 0.318261 / 0.283200 (0.035061) | 0.098927 / 0.141683 (-0.042756) | 1.450356 / 1.452155 (-0.001798) | 1.508034 / 1.492716 (0.015318) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.209009 / 0.018006 (0.191003) | 0.439154 / 0.000490 (0.438665) | 0.004299 / 0.000200 (0.004099) | 0.000142 / 0.000054 (0.000087) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.025938 / 0.037411 (-0.011473) | 0.105954 / 0.014526 (0.091429) | 0.113858 / 0.176557 (-0.062698) | 0.168887 / 0.737135 (-0.568249) | 0.121292 / 0.296338 (-0.175046) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.402050 / 0.215209 (0.186841) | 4.002310 / 2.077655 (1.924655) | 1.816190 / 1.504120 (0.312070) | 1.634404 / 1.541195 (0.093209) | 1.713632 / 1.468490 (0.245142) | 0.519633 / 4.584777 (-4.065144) | 3.740291 / 3.745712 (-0.005421) | 1.787602 / 5.269862 (-3.482260) | 1.038844 / 4.565676 (-3.526833) | 0.064973 / 0.424275 (-0.359302) | 0.012475 / 0.007607 (0.004868) | 0.498152 / 0.226044 (0.272108) | 4.970941 / 2.268929 (2.702013) | 2.287429 / 55.444624 (-53.157195) | 1.998050 / 6.876477 (-4.878427) | 2.091903 / 2.142072 (-0.050169) | 0.630363 / 4.805227 (-4.174864) | 0.138623 / 6.500664 (-6.362041) | 0.063293 / 0.075469 (-0.012176) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.201802 / 1.841788 (-0.639986) | 14.073836 / 8.074308 (5.999528) | 12.968665 / 10.191392 (2.777273) | 0.144653 / 0.680424 (-0.535771) | 0.017613 / 0.534201 (-0.516588) | 0.392067 / 0.579283 (-0.187216) | 0.416955 / 0.434364 (-0.017409) | 0.471492 / 0.540337 (-0.068845) | 0.554576 / 1.386936 (-0.832360) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.006408 / 0.011353 (-0.004945) | 0.004452 / 0.011008 (-0.006556) | 0.073648 / 0.038508 (0.035140) | 0.032536 / 0.023109 (0.009427) | 0.358546 / 0.275898 (0.082648) | 0.387330 / 0.323480 (0.063850) | 0.005542 / 0.007986 (-0.002444) | 0.003882 / 0.004328 (-0.000447) | 0.073867 / 0.004250 (0.069617) | 0.044798 / 0.037052 (0.007746) | 0.362303 / 0.258489 (0.103814) | 0.400496 / 0.293841 (0.106655) | 0.028244 / 0.128546 (-0.100302) | 0.008931 / 0.075646 (-0.066715) | 0.080617 / 0.419271 (-0.338654) | 0.046575 / 0.043533 (0.003043) | 0.364283 / 0.255139 (0.109145) | 0.373215 / 0.283200 (0.090015) | 0.100080 / 0.141683 (-0.041603) | 1.430047 / 1.452155 (-0.022108) | 1.530957 / 1.492716 (0.038240) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.221061 / 0.018006 (0.203055) | 0.441753 / 0.000490 (0.441263) | 0.003626 / 0.000200 (0.003426) | 0.000088 / 0.000054 (0.000033) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.029509 / 0.037411 (-0.007902) | 0.109578 / 0.014526 (0.095053) | 0.121009 / 0.176557 (-0.055548) | 0.168950 / 0.737135 (-0.568185) | 0.124475 / 0.296338 (-0.171864) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.431355 / 0.215209 (0.216146) | 4.295507 / 2.077655 (2.217852) | 2.167514 / 1.504120 (0.663394) | 2.013073 / 1.541195 (0.471879) | 1.973730 / 1.468490 (0.505240) | 0.529778 / 4.584777 (-4.054999) | 3.794702 / 3.745712 (0.048989) | 3.062940 / 5.269862 (-2.206922) | 1.503426 / 4.565676 (-3.062251) | 0.066692 / 0.424275 (-0.357583) | 0.011682 / 0.007607 (0.004075) | 0.539311 / 0.226044 (0.313266) | 5.406342 / 2.268929 (3.137414) | 2.652709 / 55.444624 (-52.791916) | 2.260066 / 6.876477 (-4.616410) | 2.295752 / 2.142072 (0.153680) | 0.647199 / 4.805227 (-4.158029) | 0.142981 / 6.500664 (-6.357683) | 0.065082 / 0.075469 (-0.010387) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.279788 / 1.841788 (-0.562000) | 14.982845 / 8.074308 (6.908536) | 14.277166 / 10.191392 (4.085774) | 0.145082 / 0.680424 (-0.535342) | 0.017885 / 0.534201 (-0.516316) | 0.392071 / 0.579283 (-0.187212) | 0.420425 / 0.434364 (-0.013939) | 0.461244 / 0.540337 (-0.079093) | 0.559956 / 1.386936 (-0.826980) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#651d96c1c4083a206c65f11602712d75f1f0453d \"CML watermark\")\n" ]

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[ "hi @lhoestq,\r\nI saw some errors in my test and found all the failed reasons are `FileNotFoundError` about `test_load_streaming_private_dataset_with_zipped_data` and `test_load_dataset_private_zipped_images` in `test_load.py `, I run pytest on my own Wins and Ubuntu system all the test in `test_load.py ` are succeed. could you help me to check the test environment of our server?\r\n\r\n`2023-06-08T16:50:48.0828281Z FAILED tests/test_load.py::test_load_streaming_private_dataset_with_zipped_data - FileNotFoundError: Couldn't find a dataset script at D:\\a\\datasets\\datasets\\__DUMMY_TRANSFORMERS_USER__\\repo_zipped_txt_data-16862429577813\\repo_zipped_txt_data-16862429577813.py or any data file in the same directory. Couldn't find '__DUMMY_TRANSFORMERS_USER__/repo_zipped_txt_data-16862429577813' on the Hugging Face Hub either: FileNotFoundError: No (supported) data files or dataset script found in __DUMMY_TRANSFORMERS_USER__/repo_zipped_txt_data-16862429577813`\r\n`2023-06-08T16:50:48.0830602Z FAILED tests/test_load.py::test_load_dataset_private_zipped_images[False-False] - FileNotFoundError: Couldn't find a dataset script at D:\\a\\datasets\\datasets\\__DUMMY_TRANSFORMERS_USER__\\repo_zipped_img_data-16862429594168\\repo_zipped_img_data-16862429594168.py or any data file in the same directory. Couldn't find '__DUMMY_TRANSFORMERS_USER__/repo_zipped_img_data-16862429594168' on the Hugging Face Hub either: FileNotFoundError: No (supported) data files or dataset script found in __DUMMY_TRANSFORMERS_USER__/repo_zipped_img_data-16862429594168`", "I just re-ran the CI, hopefully it's fixed", "_The documentation is not available anymore as the PR was closed or merged._", "> I just re-ran the CI, hopefully it's fixed\r\n\r\nI just checked, still has the same error, maybe need someone to fix it", "I think the issue comes from this PR somehow, since the CI fail is related to loading private repositories and this PR touches authentication related code. Let me check what's the issue, and I'll also review your PR later (sorry I don't have a ton of bandwidth atm)", "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_5919). All of your documentation changes will be reflected on that endpoint.", "@lhoestq Hi sorry to bother you, the CI check_code_quality failed and it said `would reformat /home/runner/work/datasets/datasets/src/datasets/download/streaming_download_manager.py` but I cant see any changes when I run `python3 -m black --check tests src benchmarks metrics` and `python3 -m ruff tests src benchmarks metrics` on my own computer, is there any version requirements on the tools? I didn't specific the version.", "I just ran `make style` and pushed the changes.\r\nYou can install the right versions of black and ruff using `pip install -e .[quality]` ;)", "I am working on this issue right now https://github.com/huggingface/datasets/issues/6017 which is strongly connected to your PR, and I might end up cherry-picking some of your commits (keeping attribution of course !). Would you be ok with that ?", "it's totally ok for me, I just wish the S3 File system could support streaming too.\r\n", "\r\nI already adjust the code and test on my local Mac, you can check it now, and you can make any changes to it.", "Closing this PR in favor of https://github.com/huggingface/datasets/pull/6028 which includes your contribution :)" ]

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[ "load_dataset () did not work for loading local files either " ]

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[ "_The documentation is not available anymore as the PR was closed or merged._", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.008358 / 0.011353 (-0.002995) | 0.005673 / 0.011008 (-0.005335) | 0.124034 / 0.038508 (0.085526) | 0.037550 / 0.023109 (0.014441) | 0.331301 / 0.275898 (0.055403) | 0.383542 / 0.323480 (0.060062) | 0.006940 / 0.007986 (-0.001046) | 0.005959 / 0.004328 (0.001631) | 0.084670 / 0.004250 (0.080419) | 0.054214 / 0.037052 (0.017162) | 0.359897 / 0.258489 (0.101408) | 0.383260 / 0.293841 (0.089419) | 0.047642 / 0.128546 (-0.080904) | 0.013902 / 0.075646 (-0.061744) | 0.380232 / 0.419271 (-0.039040) | 0.077790 / 0.043533 (0.034257) | 0.376648 / 0.255139 (0.121509) | 0.387536 / 0.283200 (0.104336) | 0.104644 / 0.141683 (-0.037038) | 1.618560 / 1.452155 (0.166406) | 1.742569 / 1.492716 (0.249853) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.257218 / 0.018006 (0.239212) | 0.636801 / 0.000490 (0.636311) | 0.000634 / 0.000200 (0.000434) | 0.000101 / 0.000054 (0.000047) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.037874 / 0.037411 (0.000462) | 0.107454 / 0.014526 (0.092928) | 0.117855 / 0.176557 (-0.058702) | 0.204067 / 0.737135 (-0.533068) | 0.134029 / 0.296338 (-0.162310) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.583657 / 0.215209 (0.368447) | 5.761289 / 2.077655 (3.683635) | 2.280201 / 1.504120 (0.776081) | 2.033442 / 1.541195 (0.492247) | 2.035343 / 1.468490 (0.566853) | 0.868122 / 4.584777 (-3.716655) | 5.352591 / 3.745712 (1.606879) | 2.432814 / 5.269862 (-2.837047) | 1.560765 / 4.565676 (-3.004911) | 0.098793 / 0.424275 (-0.325482) | 0.017327 / 0.007607 (0.009720) | 0.734676 / 0.226044 (0.508631) | 7.070318 / 2.268929 (4.801390) | 2.972701 / 55.444624 (-52.471924) | 2.442189 / 6.876477 (-4.434288) | 2.604379 / 2.142072 (0.462307) | 1.028853 / 4.805227 (-3.776374) | 0.210390 / 6.500664 (-6.290274) | 0.069329 / 0.075469 (-0.006140) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.469586 / 1.841788 (-0.372202) | 16.570305 / 8.074308 (8.495997) | 19.187845 / 10.191392 (8.996453) | 0.219162 / 0.680424 (-0.461262) | 0.026356 / 0.534201 (-0.507845) | 0.447370 / 0.579283 (-0.131913) | 0.555893 / 0.434364 (0.121529) | 0.574958 / 0.540337 (0.034621) | 0.639166 / 1.386936 (-0.747770) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.008166 / 0.011353 (-0.003187) | 0.005577 / 0.011008 (-0.005431) | 0.103578 / 0.038508 (0.065070) | 0.040563 / 0.023109 (0.017454) | 0.441996 / 0.275898 (0.166098) | 0.483594 / 0.323480 (0.160114) | 0.007329 / 0.007986 (-0.000657) | 0.004546 / 0.004328 (0.000218) | 0.090471 / 0.004250 (0.086220) | 0.052740 / 0.037052 (0.015688) | 0.442197 / 0.258489 (0.183708) | 0.524310 / 0.293841 (0.230469) | 0.042487 / 0.128546 (-0.086060) | 0.012917 / 0.075646 (-0.062730) | 0.103992 / 0.419271 (-0.315280) | 0.060570 / 0.043533 (0.017037) | 0.441956 / 0.255139 (0.186817) | 0.477084 / 0.283200 (0.193885) | 0.103815 / 0.141683 (-0.037868) | 1.696963 / 1.452155 (0.244809) | 1.747849 / 1.492716 (0.255132) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.292465 / 0.018006 (0.274458) | 0.571518 / 0.000490 (0.571028) | 0.000476 / 0.000200 (0.000276) | 0.000077 / 0.000054 (0.000022) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.028697 / 0.037411 (-0.008714) | 0.111671 / 0.014526 (0.097145) | 0.138826 / 0.176557 (-0.037731) | 0.189697 / 0.737135 (-0.547439) | 0.125454 / 0.296338 (-0.170884) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.619273 / 0.215209 (0.404064) | 6.138669 / 2.077655 (4.061015) | 2.558622 / 1.504120 (1.054502) | 2.201550 / 1.541195 (0.660356) | 2.279034 / 1.468490 (0.810544) | 0.850752 / 4.584777 (-3.734025) | 5.438185 / 3.745712 (1.692473) | 2.529343 / 5.269862 (-2.740518) | 1.572178 / 4.565676 (-2.993499) | 0.100768 / 0.424275 (-0.323507) | 0.013902 / 0.007607 (0.006295) | 0.726660 / 0.226044 (0.500616) | 7.794918 / 2.268929 (5.525990) | 3.311695 / 55.444624 (-52.132930) | 2.729167 / 6.876477 (-4.147310) | 2.630984 / 2.142072 (0.488911) | 1.018534 / 4.805227 (-3.786693) | 0.194602 / 6.500664 (-6.306062) | 0.070876 / 0.075469 (-0.004593) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.573005 / 1.841788 (-0.268783) | 17.042710 / 8.074308 (8.968401) | 19.615320 / 10.191392 (9.423928) | 0.229405 / 0.680424 (-0.451019) | 0.027560 / 0.534201 (-0.506641) | 0.447984 / 0.579283 (-0.131299) | 0.598392 / 0.434364 (0.164028) | 0.571769 / 0.540337 (0.031431) | 0.653025 / 1.386936 (-0.733911) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#9dca2ff89a8589595313e9535d16597ce10e3700 \"CML watermark\")\n" ]

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[ "_The documentation is not available anymore as the PR was closed or merged._", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.006113 / 0.011353 (-0.005239) | 0.004195 / 0.011008 (-0.006813) | 0.098103 / 0.038508 (0.059595) | 0.027970 / 0.023109 (0.004860) | 0.300992 / 0.275898 (0.025094) | 0.335402 / 0.323480 (0.011922) | 0.005079 / 0.007986 (-0.002906) | 0.003516 / 0.004328 (-0.000813) | 0.077311 / 0.004250 (0.073061) | 0.037863 / 0.037052 (0.000810) | 0.302638 / 0.258489 (0.044149) | 0.346554 / 0.293841 (0.052713) | 0.025218 / 0.128546 (-0.103328) | 0.008630 / 0.075646 (-0.067017) | 0.319748 / 0.419271 (-0.099523) | 0.049182 / 0.043533 (0.005650) | 0.306233 / 0.255139 (0.051094) | 0.331040 / 0.283200 (0.047840) | 0.089203 / 0.141683 (-0.052480) | 1.496104 / 1.452155 (0.043949) | 1.567878 / 1.492716 (0.075162) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.215774 / 0.018006 (0.197768) | 0.436810 / 0.000490 (0.436320) | 0.000307 / 0.000200 (0.000107) | 0.000059 / 0.000054 (0.000005) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.024102 / 0.037411 (-0.013310) | 0.095459 / 0.014526 (0.080933) | 0.106564 / 0.176557 (-0.069992) | 0.169894 / 0.737135 (-0.567241) | 0.109152 / 0.296338 (-0.187186) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.429066 / 0.215209 (0.213857) | 4.297385 / 2.077655 (2.219730) | 2.054854 / 1.504120 (0.550734) | 1.846844 / 1.541195 (0.305649) | 1.840807 / 1.468490 (0.372317) | 0.553193 / 4.584777 (-4.031584) | 3.366788 / 3.745712 (-0.378924) | 1.727337 / 5.269862 (-3.542525) | 0.994357 / 4.565676 (-3.571319) | 0.067790 / 0.424275 (-0.356485) | 0.012002 / 0.007607 (0.004395) | 0.533335 / 0.226044 (0.307291) | 5.341341 / 2.268929 (3.072412) | 2.543581 / 55.444624 (-52.901043) | 2.220374 / 6.876477 (-4.656103) | 2.321656 / 2.142072 (0.179583) | 0.654408 / 4.805227 (-4.150819) | 0.134693 / 6.500664 (-6.365971) | 0.066926 / 0.075469 (-0.008544) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.209463 / 1.841788 (-0.632325) | 13.568221 / 8.074308 (5.493913) | 13.965418 / 10.191392 (3.774026) | 0.145049 / 0.680424 (-0.535375) | 0.016936 / 0.534201 (-0.517265) | 0.371587 / 0.579283 (-0.207696) | 0.386363 / 0.434364 (-0.048001) | 0.437137 / 0.540337 (-0.103201) | 0.514779 / 1.386936 (-0.872157) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.006245 / 0.011353 (-0.005108) | 0.004232 / 0.011008 (-0.006776) | 0.075682 / 0.038508 (0.037174) | 0.027858 / 0.023109 (0.004749) | 0.425325 / 0.275898 (0.149427) | 0.466732 / 0.323480 (0.143253) | 0.005240 / 0.007986 (-0.002745) | 0.003506 / 0.004328 (-0.000823) | 0.075294 / 0.004250 (0.071044) | 0.041677 / 0.037052 (0.004624) | 0.426552 / 0.258489 (0.168063) | 0.469452 / 0.293841 (0.175611) | 0.025443 / 0.128546 (-0.103104) | 0.008526 / 0.075646 (-0.067120) | 0.082190 / 0.419271 (-0.337081) | 0.040906 / 0.043533 (-0.002626) | 0.428406 / 0.255139 (0.173267) | 0.446795 / 0.283200 (0.163595) | 0.093837 / 0.141683 (-0.047846) | 1.518639 / 1.452155 (0.066484) | 1.620214 / 1.492716 (0.127498) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.223259 / 0.018006 (0.205253) | 0.425077 / 0.000490 (0.424588) | 0.001980 / 0.000200 (0.001780) | 0.000077 / 0.000054 (0.000023) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.025813 / 0.037411 (-0.011599) | 0.103062 / 0.014526 (0.088536) | 0.108958 / 0.176557 (-0.067598) | 0.161591 / 0.737135 (-0.575544) | 0.112130 / 0.296338 (-0.184209) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.472843 / 0.215209 (0.257634) | 4.713281 / 2.077655 (2.635626) | 2.458216 / 1.504120 (0.954096) | 2.272467 / 1.541195 (0.731273) | 2.324456 / 1.468490 (0.855965) | 0.554686 / 4.584777 (-4.030091) | 3.445079 / 3.745712 (-0.300634) | 3.451896 / 5.269862 (-1.817966) | 1.431065 / 4.565676 (-3.134612) | 0.067868 / 0.424275 (-0.356407) | 0.012093 / 0.007607 (0.004486) | 0.573571 / 0.226044 (0.347526) | 5.820452 / 2.268929 (3.551523) | 2.934858 / 55.444624 (-52.509767) | 2.602719 / 6.876477 (-4.273758) | 2.645999 / 2.142072 (0.503927) | 0.660688 / 4.805227 (-4.144540) | 0.137490 / 6.500664 (-6.363174) | 0.068311 / 0.075469 (-0.007158) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.321709 / 1.841788 (-0.520079) | 14.592346 / 8.074308 (6.518038) | 14.520748 / 10.191392 (4.329356) | 0.132689 / 0.680424 (-0.547735) | 0.016422 / 0.534201 (-0.517779) | 0.370071 / 0.579283 (-0.209212) | 0.397091 / 0.434364 (-0.037273) | 0.431979 / 0.540337 (-0.108358) | 0.509965 / 1.386936 (-0.876971) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#8bcd061ab2082a0862f30329bc52f6e0d321805c \"CML watermark\")\n", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.006182 / 0.011353 (-0.005171) | 0.004153 / 0.011008 (-0.006855) | 0.095715 / 0.038508 (0.057207) | 0.032457 / 0.023109 (0.009347) | 0.314961 / 0.275898 (0.039063) | 0.353696 / 0.323480 (0.030216) | 0.005256 / 0.007986 (-0.002729) | 0.004870 / 0.004328 (0.000541) | 0.072442 / 0.004250 (0.068192) | 0.046102 / 0.037052 (0.009050) | 0.324410 / 0.258489 (0.065921) | 0.366861 / 0.293841 (0.073020) | 0.027088 / 0.128546 (-0.101458) | 0.008572 / 0.075646 (-0.067075) | 0.325988 / 0.419271 (-0.093284) | 0.049494 / 0.043533 (0.005961) | 0.311221 / 0.255139 (0.056082) | 0.359720 / 0.283200 (0.076521) | 0.095101 / 0.141683 (-0.046581) | 1.472821 / 1.452155 (0.020667) | 1.516157 / 1.492716 (0.023441) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.210456 / 0.018006 (0.192450) | 0.439440 / 0.000490 (0.438950) | 0.003764 / 0.000200 (0.003564) | 0.000087 / 0.000054 (0.000033) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.024076 / 0.037411 (-0.013335) | 0.104886 / 0.014526 (0.090360) | 0.114164 / 0.176557 (-0.062393) | 0.167289 / 0.737135 (-0.569847) | 0.116457 / 0.296338 (-0.179882) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.400039 / 0.215209 (0.184830) | 3.973243 / 2.077655 (1.895588) | 1.801991 / 1.504120 (0.297871) | 1.592017 / 1.541195 (0.050822) | 1.612564 / 1.468490 (0.144074) | 0.527475 / 4.584777 (-4.057302) | 3.676246 / 3.745712 (-0.069466) | 1.806423 / 5.269862 (-3.463438) | 1.176921 / 4.565676 (-3.388756) | 0.065902 / 0.424275 (-0.358373) | 0.012245 / 0.007607 (0.004638) | 0.490883 / 0.226044 (0.264838) | 4.905270 / 2.268929 (2.636341) | 2.218694 / 55.444624 (-53.225930) | 1.903074 / 6.876477 (-4.973403) | 1.979505 / 2.142072 (-0.162567) | 0.644415 / 4.805227 (-4.160812) | 0.142433 / 6.500664 (-6.358231) | 0.063564 / 0.075469 (-0.011905) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.193756 / 1.841788 (-0.648032) | 14.673103 / 8.074308 (6.598795) | 13.410951 / 10.191392 (3.219559) | 0.159175 / 0.680424 (-0.521249) | 0.017076 / 0.534201 (-0.517125) | 0.388880 / 0.579283 (-0.190403) | 0.409974 / 0.434364 (-0.024390) | 0.454494 / 0.540337 (-0.085844) | 0.556873 / 1.386936 (-0.830063) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.006107 / 0.011353 (-0.005246) | 0.004433 / 0.011008 (-0.006575) | 0.073892 / 0.038508 (0.035384) | 0.032386 / 0.023109 (0.009277) | 0.370339 / 0.275898 (0.094441) | 0.388996 / 0.323480 (0.065516) | 0.005438 / 0.007986 (-0.002548) | 0.003875 / 0.004328 (-0.000454) | 0.073867 / 0.004250 (0.069617) | 0.048350 / 0.037052 (0.011298) | 0.380328 / 0.258489 (0.121839) | 0.411373 / 0.293841 (0.117532) | 0.028183 / 0.128546 (-0.100363) | 0.008924 / 0.075646 (-0.066723) | 0.082484 / 0.419271 (-0.336787) | 0.047321 / 0.043533 (0.003788) | 0.371702 / 0.255139 (0.116563) | 0.380535 / 0.283200 (0.097335) | 0.100772 / 0.141683 (-0.040911) | 1.475038 / 1.452155 (0.022883) | 1.564293 / 1.492716 (0.071577) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.214589 / 0.018006 (0.196583) | 0.437193 / 0.000490 (0.436703) | 0.003676 / 0.000200 (0.003476) | 0.000094 / 0.000054 (0.000040) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.027991 / 0.037411 (-0.009421) | 0.111154 / 0.014526 (0.096628) | 0.120365 / 0.176557 (-0.056191) | 0.173601 / 0.737135 (-0.563535) | 0.126244 / 0.296338 (-0.170094) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.442848 / 0.215209 (0.227639) | 4.398336 / 2.077655 (2.320681) | 2.217058 / 1.504120 (0.712938) | 2.011155 / 1.541195 (0.469960) | 2.123086 / 1.468490 (0.654596) | 0.525857 / 4.584777 (-4.058920) | 3.730191 / 3.745712 (-0.015521) | 3.517680 / 5.269862 (-1.752181) | 1.557940 / 4.565676 (-3.007736) | 0.066309 / 0.424275 (-0.357967) | 0.011788 / 0.007607 (0.004181) | 0.548506 / 0.226044 (0.322462) | 5.483615 / 2.268929 (3.214687) | 2.663784 / 55.444624 (-52.780840) | 2.325744 / 6.876477 (-4.550732) | 2.344179 / 2.142072 (0.202106) | 0.644217 / 4.805227 (-4.161010) | 0.141546 / 6.500664 (-6.359118) | 0.063730 / 0.075469 (-0.011739) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.296032 / 1.841788 (-0.545756) | 14.903729 / 8.074308 (6.829421) | 14.505409 / 10.191392 (4.314017) | 0.170478 / 0.680424 (-0.509946) | 0.017876 / 0.534201 (-0.516325) | 0.401047 / 0.579283 (-0.178236) | 0.417855 / 0.434364 (-0.016509) | 0.472138 / 0.540337 (-0.068200) | 0.570859 / 1.386936 (-0.816077) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#5a4d530965eb35c66955ef89df79210c66b7f5e6 \"CML watermark\")\n", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.008495 / 0.011353 (-0.002858) | 0.005322 / 0.011008 (-0.005686) | 0.125471 / 0.038508 (0.086962) | 0.034604 / 0.023109 (0.011495) | 0.419831 / 0.275898 (0.143933) | 0.415707 / 0.323480 (0.092227) | 0.007471 / 0.007986 (-0.000515) | 0.005441 / 0.004328 (0.001112) | 0.095412 / 0.004250 (0.091162) | 0.053865 / 0.037052 (0.016812) | 0.375257 / 0.258489 (0.116768) | 0.438114 / 0.293841 (0.144273) | 0.046183 / 0.128546 (-0.082363) | 0.013663 / 0.075646 (-0.061984) | 0.438317 / 0.419271 (0.019045) | 0.065665 / 0.043533 (0.022133) | 0.387640 / 0.255139 (0.132501) | 0.431350 / 0.283200 (0.148150) | 0.112841 / 0.141683 (-0.028842) | 1.778639 / 1.452155 (0.326484) | 1.891948 / 1.492716 (0.399232) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.284371 / 0.018006 (0.266365) | 0.598247 / 0.000490 (0.597758) | 0.013674 / 0.000200 (0.013474) | 0.000483 / 0.000054 (0.000428) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.032437 / 0.037411 (-0.004974) | 0.120547 / 0.014526 (0.106021) | 0.129845 / 0.176557 (-0.046711) | 0.203455 / 0.737135 (-0.533680) | 0.140039 / 0.296338 (-0.156300) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.596549 / 0.215209 (0.381340) | 6.138766 / 2.077655 (4.061111) | 2.515506 / 1.504120 (1.011386) | 2.124472 / 1.541195 (0.583277) | 2.160812 / 1.468490 (0.692322) | 0.898965 / 4.584777 (-3.685812) | 5.588152 / 3.745712 (1.842440) | 2.717580 / 5.269862 (-2.552282) | 1.683641 / 4.565676 (-2.882036) | 0.108045 / 0.424275 (-0.316230) | 0.014089 / 0.007607 (0.006481) | 0.749567 / 0.226044 (0.523523) | 7.518051 / 2.268929 (5.249123) | 3.198238 / 55.444624 (-52.246386) | 2.575156 / 6.876477 (-4.301321) | 2.725818 / 2.142072 (0.583745) | 1.149338 / 4.805227 (-3.655889) | 0.220443 / 6.500664 (-6.280221) | 0.081452 / 0.075469 (0.005983) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.624462 / 1.841788 (-0.217325) | 18.204963 / 8.074308 (10.130655) | 21.379169 / 10.191392 (11.187777) | 0.248520 / 0.680424 (-0.431903) | 0.030121 / 0.534201 (-0.504080) | 0.499542 / 0.579283 (-0.079741) | 0.599783 / 0.434364 (0.165419) | 0.597642 / 0.540337 (0.057305) | 0.681948 / 1.386936 (-0.704988) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.008431 / 0.011353 (-0.002921) | 0.006143 / 0.011008 (-0.004865) | 0.107531 / 0.038508 (0.069023) | 0.036308 / 0.023109 (0.013199) | 0.480555 / 0.275898 (0.204657) | 0.556407 / 0.323480 (0.232927) | 0.007614 / 0.007986 (-0.000372) | 0.004749 / 0.004328 (0.000421) | 0.105734 / 0.004250 (0.101484) | 0.051619 / 0.037052 (0.014567) | 0.514821 / 0.258489 (0.256332) | 0.562143 / 0.293841 (0.268302) | 0.042957 / 0.128546 (-0.085589) | 0.015142 / 0.075646 (-0.060505) | 0.143161 / 0.419271 (-0.276111) | 0.061910 / 0.043533 (0.018377) | 0.496923 / 0.255139 (0.241784) | 0.556302 / 0.283200 (0.273102) | 0.136700 / 0.141683 (-0.004983) | 1.886184 / 1.452155 (0.434029) | 2.004087 / 1.492716 (0.511371) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.235530 / 0.018006 (0.217523) | 0.600796 / 0.000490 (0.600306) | 0.009074 / 0.000200 (0.008874) | 0.000203 / 0.000054 (0.000149) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.036345 / 0.037411 (-0.001066) | 0.126112 / 0.014526 (0.111586) | 0.143369 / 0.176557 (-0.033188) | 0.211381 / 0.737135 (-0.525755) | 0.151095 / 0.296338 (-0.145243) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.695022 / 0.215209 (0.479813) | 6.685981 / 2.077655 (4.608326) | 3.104521 / 1.504120 (1.600401) | 2.758323 / 1.541195 (1.217128) | 2.706286 / 1.468490 (1.237796) | 0.941182 / 4.584777 (-3.643595) | 5.715839 / 3.745712 (1.970127) | 5.089636 / 5.269862 (-0.180226) | 2.594739 / 4.565676 (-1.970937) | 0.112621 / 0.424275 (-0.311655) | 0.014001 / 0.007607 (0.006394) | 0.812990 / 0.226044 (0.586945) | 8.060890 / 2.268929 (5.791961) | 3.832506 / 55.444624 (-51.612119) | 3.148051 / 6.876477 (-3.728425) | 3.110096 / 2.142072 (0.968023) | 1.105050 / 4.805227 (-3.700178) | 0.219835 / 6.500664 (-6.280829) | 0.078600 / 0.075469 (0.003131) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.707551 / 1.841788 (-0.134237) | 19.238194 / 8.074308 (11.163885) | 22.167076 / 10.191392 (11.975684) | 0.233458 / 0.680424 (-0.446966) | 0.025131 / 0.534201 (-0.509070) | 0.525241 / 0.579283 (-0.054042) | 0.649666 / 0.434364 (0.215303) | 0.602941 / 0.540337 (0.062603) | 0.718472 / 1.386936 (-0.668464) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#ac3a42c525d91cb630273702a0c110a71c9bf54b \"CML watermark\")\n" ]

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[ "_The documentation is not available anymore as the PR was closed or merged._", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.006416 / 0.011353 (-0.004937) | 0.004278 / 0.011008 (-0.006731) | 0.097562 / 0.038508 (0.059054) | 0.029488 / 0.023109 (0.006379) | 0.308648 / 0.275898 (0.032750) | 0.339879 / 0.323480 (0.016399) | 0.005288 / 0.007986 (-0.002697) | 0.005033 / 0.004328 (0.000704) | 0.074666 / 0.004250 (0.070416) | 0.034888 / 0.037052 (-0.002164) | 0.309960 / 0.258489 (0.051471) | 0.344276 / 0.293841 (0.050435) | 0.025564 / 0.128546 (-0.102982) | 0.008579 / 0.075646 (-0.067067) | 0.319796 / 0.419271 (-0.099476) | 0.044786 / 0.043533 (0.001253) | 0.308888 / 0.255139 (0.053749) | 0.334001 / 0.283200 (0.050802) | 0.089917 / 0.141683 (-0.051766) | 1.456696 / 1.452155 (0.004541) | 1.542273 / 1.492716 (0.049557) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.213236 / 0.018006 (0.195230) | 0.425139 / 0.000490 (0.424650) | 0.008831 / 0.000200 (0.008631) | 0.000209 / 0.000054 (0.000155) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.023990 / 0.037411 (-0.013421) | 0.096787 / 0.014526 (0.082261) | 0.105783 / 0.176557 (-0.070774) | 0.167182 / 0.737135 (-0.569954) | 0.108896 / 0.296338 (-0.187442) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.419844 / 0.215209 (0.204635) | 4.201909 / 2.077655 (2.124254) | 1.910784 / 1.504120 (0.406664) | 1.685183 / 1.541195 (0.143988) | 1.716927 / 1.468490 (0.248437) | 0.548261 / 4.584777 (-4.036516) | 3.414168 / 3.745712 (-0.331544) | 1.695446 / 5.269862 (-3.574415) | 0.989668 / 4.565676 (-3.576008) | 0.067328 / 0.424275 (-0.356948) | 0.012084 / 0.007607 (0.004477) | 0.523799 / 0.226044 (0.297754) | 5.240589 / 2.268929 (2.971661) | 2.331618 / 55.444624 (-53.113007) | 1.996094 / 6.876477 (-4.880383) | 2.105450 / 2.142072 (-0.036623) | 0.654614 / 4.805227 (-4.150613) | 0.134721 / 6.500664 (-6.365943) | 0.066227 / 0.075469 (-0.009242) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.196266 / 1.841788 (-0.645521) | 13.990045 / 8.074308 (5.915737) | 13.928126 / 10.191392 (3.736734) | 0.142600 / 0.680424 (-0.537824) | 0.016462 / 0.534201 (-0.517739) | 0.363113 / 0.579283 (-0.216170) | 0.428590 / 0.434364 (-0.005773) | 0.452594 / 0.540337 (-0.087743) | 0.551678 / 1.386936 (-0.835258) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.005992 / 0.011353 (-0.005361) | 0.004161 / 0.011008 (-0.006847) | 0.076098 / 0.038508 (0.037589) | 0.028559 / 0.023109 (0.005450) | 0.411696 / 0.275898 (0.135798) | 0.444519 / 0.323480 (0.121040) | 0.004965 / 0.007986 (-0.003021) | 0.003452 / 0.004328 (-0.000876) | 0.075107 / 0.004250 (0.070857) | 0.037305 / 0.037052 (0.000252) | 0.429728 / 0.258489 (0.171239) | 0.444313 / 0.293841 (0.150472) | 0.025278 / 0.128546 (-0.103268) | 0.008527 / 0.075646 (-0.067120) | 0.081502 / 0.419271 (-0.337770) | 0.041237 / 0.043533 (-0.002296) | 0.417848 / 0.255139 (0.162709) | 0.426615 / 0.283200 (0.143415) | 0.094641 / 0.141683 (-0.047041) | 1.525141 / 1.452155 (0.072987) | 1.615608 / 1.492716 (0.122892) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.192867 / 0.018006 (0.174861) | 0.414979 / 0.000490 (0.414490) | 0.000815 / 0.000200 (0.000615) | 0.000068 / 0.000054 (0.000013) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.025354 / 0.037411 (-0.012058) | 0.102085 / 0.014526 (0.087559) | 0.107930 / 0.176557 (-0.068626) | 0.160483 / 0.737135 (-0.576652) | 0.112341 / 0.296338 (-0.183997) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.446938 / 0.215209 (0.231728) | 4.480057 / 2.077655 (2.402402) | 2.154825 / 1.504120 (0.650705) | 1.942774 / 1.541195 (0.401580) | 1.996418 / 1.468490 (0.527928) | 0.556728 / 4.584777 (-4.028049) | 3.441228 / 3.745712 (-0.304484) | 3.004179 / 5.269862 (-2.265683) | 1.314104 / 4.565676 (-3.251573) | 0.068670 / 0.424275 (-0.355606) | 0.011972 / 0.007607 (0.004365) | 0.556604 / 0.226044 (0.330560) | 5.561783 / 2.268929 (3.292855) | 2.631262 / 55.444624 (-52.813363) | 2.262143 / 6.876477 (-4.614333) | 2.364243 / 2.142072 (0.222170) | 0.660621 / 4.805227 (-4.144607) | 0.137371 / 6.500664 (-6.363293) | 0.069104 / 0.075469 (-0.006365) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.305706 / 1.841788 (-0.536081) | 14.015932 / 8.074308 (5.941624) | 14.353580 / 10.191392 (4.162187) | 0.146172 / 0.680424 (-0.534251) | 0.016699 / 0.534201 (-0.517502) | 0.357970 / 0.579283 (-0.221313) | 0.389067 / 0.434364 (-0.045297) | 0.415470 / 0.540337 (-0.124867) | 0.501359 / 1.386936 (-0.885577) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#b2b837b4e7267db9e32d2613d8bf8d70d2ce0b47 \"CML watermark\")\n", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.006800 / 0.011353 (-0.004552) | 0.004721 / 0.011008 (-0.006287) | 0.097760 / 0.038508 (0.059252) | 0.034192 / 0.023109 (0.011083) | 0.298240 / 0.275898 (0.022342) | 0.331119 / 0.323480 (0.007639) | 0.005826 / 0.007986 (-0.002160) | 0.003968 / 0.004328 (-0.000360) | 0.073833 / 0.004250 (0.069582) | 0.046288 / 0.037052 (0.009236) | 0.303018 / 0.258489 (0.044529) | 0.342163 / 0.293841 (0.048322) | 0.028504 / 0.128546 (-0.100042) | 0.009031 / 0.075646 (-0.066615) | 0.331617 / 0.419271 (-0.087655) | 0.060911 / 0.043533 (0.017379) | 0.304044 / 0.255139 (0.048905) | 0.328959 / 0.283200 (0.045759) | 0.113174 / 0.141683 (-0.028509) | 1.424652 / 1.452155 (-0.027502) | 1.531392 / 1.492716 (0.038676) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.206175 / 0.018006 (0.188169) | 0.435916 / 0.000490 (0.435426) | 0.002587 / 0.000200 (0.002387) | 0.000083 / 0.000054 (0.000029) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.026996 / 0.037411 (-0.010415) | 0.106722 / 0.014526 (0.092196) | 0.117655 / 0.176557 (-0.058902) | 0.176969 / 0.737135 (-0.560166) | 0.122577 / 0.296338 (-0.173762) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.396086 / 0.215209 (0.180877) | 3.972465 / 2.077655 (1.894811) | 1.800798 / 1.504120 (0.296678) | 1.616747 / 1.541195 (0.075552) | 1.680711 / 1.468490 (0.212221) | 0.526479 / 4.584777 (-4.058298) | 3.791528 / 3.745712 (0.045816) | 2.989518 / 5.269862 (-2.280344) | 1.463221 / 4.565676 (-3.102455) | 0.065649 / 0.424275 (-0.358626) | 0.012155 / 0.007607 (0.004548) | 0.500241 / 0.226044 (0.274197) | 5.008895 / 2.268929 (2.739966) | 2.315288 / 55.444624 (-53.129336) | 1.959409 / 6.876477 (-4.917067) | 2.102371 / 2.142072 (-0.039701) | 0.639611 / 4.805227 (-4.165617) | 0.140101 / 6.500664 (-6.360563) | 0.063599 / 0.075469 (-0.011870) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.206729 / 1.841788 (-0.635059) | 15.127250 / 8.074308 (7.052942) | 14.397228 / 10.191392 (4.205836) | 0.148802 / 0.680424 (-0.531622) | 0.017628 / 0.534201 (-0.516573) | 0.396150 / 0.579283 (-0.183133) | 0.435826 / 0.434364 (0.001462) | 0.471215 / 0.540337 (-0.069122) | 0.559413 / 1.386936 (-0.827523) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.006479 / 0.011353 (-0.004874) | 0.004520 / 0.011008 (-0.006488) | 0.074395 / 0.038508 (0.035887) | 0.033400 / 0.023109 (0.010291) | 0.388411 / 0.275898 (0.112513) | 0.396714 / 0.323480 (0.073234) | 0.005736 / 0.007986 (-0.002250) | 0.004038 / 0.004328 (-0.000291) | 0.073595 / 0.004250 (0.069345) | 0.045207 / 0.037052 (0.008155) | 0.378096 / 0.258489 (0.119607) | 0.417830 / 0.293841 (0.123989) | 0.028365 / 0.128546 (-0.100181) | 0.008887 / 0.075646 (-0.066760) | 0.080766 / 0.419271 (-0.338505) | 0.046923 / 0.043533 (0.003390) | 0.376190 / 0.255139 (0.121051) | 0.385875 / 0.283200 (0.102675) | 0.107542 / 0.141683 (-0.034141) | 1.409257 / 1.452155 (-0.042898) | 1.518475 / 1.492716 (0.025759) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.223299 / 0.018006 (0.205292) | 0.440640 / 0.000490 (0.440150) | 0.000397 / 0.000200 (0.000197) | 0.000056 / 0.000054 (0.000002) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.031388 / 0.037411 (-0.006024) | 0.113078 / 0.014526 (0.098552) | 0.124398 / 0.176557 (-0.052159) | 0.173802 / 0.737135 (-0.563333) | 0.129555 / 0.296338 (-0.166783) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.440220 / 0.215209 (0.225011) | 4.398052 / 2.077655 (2.320398) | 2.188396 / 1.504120 (0.684276) | 1.997811 / 1.541195 (0.456616) | 2.093338 / 1.468490 (0.624847) | 0.519597 / 4.584777 (-4.065180) | 3.885795 / 3.745712 (0.140083) | 2.896327 / 5.269862 (-2.373534) | 1.245785 / 4.565676 (-3.319891) | 0.065675 / 0.424275 (-0.358600) | 0.011729 / 0.007607 (0.004121) | 0.541526 / 0.226044 (0.315482) | 5.406763 / 2.268929 (3.137834) | 2.722914 / 55.444624 (-52.721711) | 2.471111 / 6.876477 (-4.405366) | 2.541488 / 2.142072 (0.399415) | 0.633566 / 4.805227 (-4.171661) | 0.139622 / 6.500664 (-6.361042) | 0.064220 / 0.075469 (-0.011249) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.296097 / 1.841788 (-0.545690) | 15.095320 / 8.074308 (7.021012) | 14.300821 / 10.191392 (4.109429) | 0.145470 / 0.680424 (-0.534954) | 0.017496 / 0.534201 (-0.516705) | 0.400589 / 0.579283 (-0.178694) | 0.423091 / 0.434364 (-0.011273) | 0.468258 / 0.540337 (-0.072079) | 0.570873 / 1.386936 (-0.816063) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#aee6c67034d6ff298b2153a2fcdab97f14ee6d66 \"CML watermark\")\n", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.005918 / 0.011353 (-0.005435) | 0.004393 / 0.011008 (-0.006615) | 0.091677 / 0.038508 (0.053169) | 0.033546 / 0.023109 (0.010437) | 0.344682 / 0.275898 (0.068784) | 0.388906 / 0.323480 (0.065426) | 0.005412 / 0.007986 (-0.002574) | 0.004909 / 0.004328 (0.000580) | 0.082589 / 0.004250 (0.078339) | 0.045242 / 0.037052 (0.008190) | 0.339191 / 0.258489 (0.080702) | 0.349673 / 0.293841 (0.055832) | 0.026805 / 0.128546 (-0.101742) | 0.007529 / 0.075646 (-0.068117) | 0.319108 / 0.419271 (-0.100164) | 0.049482 / 0.043533 (0.005949) | 0.320013 / 0.255139 (0.064874) | 0.342059 / 0.283200 (0.058859) | 0.096623 / 0.141683 (-0.045060) | 1.458204 / 1.452155 (0.006049) | 1.571172 / 1.492716 (0.078455) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.235171 / 0.018006 (0.217165) | 0.479678 / 0.000490 (0.479188) | 0.006627 / 0.000200 (0.006427) | 0.000257 / 0.000054 (0.000202) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.025716 / 0.037411 (-0.011696) | 0.107730 / 0.014526 (0.093204) | 0.111595 / 0.176557 (-0.064962) | 0.171316 / 0.737135 (-0.565819) | 0.118962 / 0.296338 (-0.177377) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.376318 / 0.215209 (0.161109) | 4.039484 / 2.077655 (1.961829) | 1.811548 / 1.504120 (0.307428) | 1.646728 / 1.541195 (0.105533) | 1.688071 / 1.468490 (0.219581) | 0.551256 / 4.584777 (-4.033520) | 4.153931 / 3.745712 (0.408218) | 3.424154 / 5.269862 (-1.845707) | 1.734860 / 4.565676 (-2.830816) | 0.067753 / 0.424275 (-0.356522) | 0.012699 / 0.007607 (0.005092) | 0.505722 / 0.226044 (0.279677) | 4.997321 / 2.268929 (2.728392) | 2.258755 / 55.444624 (-53.185869) | 1.954382 / 6.876477 (-4.922095) | 1.967545 / 2.142072 (-0.174527) | 0.630489 / 4.805227 (-4.174738) | 0.138738 / 6.500664 (-6.361926) | 0.064907 / 0.075469 (-0.010562) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.209634 / 1.841788 (-0.632154) | 15.055062 / 8.074308 (6.980754) | 12.721606 / 10.191392 (2.530214) | 0.164908 / 0.680424 (-0.515516) | 0.019528 / 0.534201 (-0.514673) | 0.400136 / 0.579283 (-0.179147) | 0.451640 / 0.434364 (0.017276) | 0.466272 / 0.540337 (-0.074065) | 0.553258 / 1.386936 (-0.833679) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.006341 / 0.011353 (-0.005011) | 0.004617 / 0.011008 (-0.006391) | 0.077953 / 0.038508 (0.039445) | 0.031104 / 0.023109 (0.007995) | 0.360328 / 0.275898 (0.084430) | 0.408403 / 0.323480 (0.084923) | 0.005704 / 0.007986 (-0.002282) | 0.003588 / 0.004328 (-0.000741) | 0.071441 / 0.004250 (0.067190) | 0.043520 / 0.037052 (0.006468) | 0.375798 / 0.258489 (0.117309) | 0.400955 / 0.293841 (0.107114) | 0.028166 / 0.128546 (-0.100381) | 0.008578 / 0.075646 (-0.067068) | 0.086673 / 0.419271 (-0.332598) | 0.046424 / 0.043533 (0.002891) | 0.367276 / 0.255139 (0.112137) | 0.414550 / 0.283200 (0.131351) | 0.097355 / 0.141683 (-0.044328) | 1.465191 / 1.452155 (0.013036) | 1.555028 / 1.492716 (0.062312) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.196642 / 0.018006 (0.178636) | 0.464221 / 0.000490 (0.463731) | 0.002726 / 0.000200 (0.002526) | 0.000110 / 0.000054 (0.000055) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.028078 / 0.037411 (-0.009333) | 0.110762 / 0.014526 (0.096236) | 0.122212 / 0.176557 (-0.054344) | 0.164758 / 0.737135 (-0.572377) | 0.133969 / 0.296338 (-0.162370) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.448134 / 0.215209 (0.232925) | 4.339335 / 2.077655 (2.261680) | 2.129209 / 1.504120 (0.625089) | 1.957805 / 1.541195 (0.416611) | 1.994038 / 1.468490 (0.525548) | 0.497101 / 4.584777 (-4.087676) | 4.114432 / 3.745712 (0.368720) | 3.437305 / 5.269862 (-1.832556) | 1.692810 / 4.565676 (-2.872866) | 0.071077 / 0.424275 (-0.353198) | 0.012735 / 0.007607 (0.005128) | 0.534393 / 0.226044 (0.308348) | 5.217445 / 2.268929 (2.948517) | 2.594858 / 55.444624 (-52.849766) | 2.317464 / 6.876477 (-4.559012) | 2.337974 / 2.142072 (0.195902) | 0.622291 / 4.805227 (-4.182936) | 0.144934 / 6.500664 (-6.355730) | 0.068524 / 0.075469 (-0.006945) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.310601 / 1.841788 (-0.531187) | 15.771527 / 8.074308 (7.697219) | 13.952032 / 10.191392 (3.760640) | 0.212473 / 0.680424 (-0.467951) | 0.017963 / 0.534201 (-0.516238) | 0.400755 / 0.579283 (-0.178528) | 0.439817 / 0.434364 (0.005453) | 0.472614 / 0.540337 (-0.067724) | 0.558410 / 1.386936 (-0.828526) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#1b51429d02a0da1ff798873afe655309136c5689 \"CML watermark\")\n" ]

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[ "Thanks for reporting, @cjt222.\r\n\r\nWhat is the structure of your JSON files. Please note that it is normally simpler if the data file format is JSON-Lines instead. ", "> Thanks for reporting, @cjt222.\r\n> \r\n> What is the structure of your JSON files. Please note that it is normally simpler if the data file format is JSON-Lines instead.\r\n\r\nThanks! I have encountered similar problems. I modify the json format from list to line and works!" ]

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[ "Hi ! in your code batching is **only used within** `map`, to process examples in batch. The dataset itself however is not batched and returns elements one by one.\r\n\r\nTo iterate on batches, you can do\r\n```python\r\nfor batch in dataset.iter(batch_size=8):\r\n ...\r\n```" ]

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[ "Currently, it's not possible to chain `set_format`/`set_transform` calls (plus, this is a breaking change if we decide to implement it), so I see two possible solutions:\r\n* using `set_format`/`set_transform` for the 1st transform and then passing the transformed example/batch to the 2nd transform\r\n* implementing and registering a custom formatter (the relevant code is [here](https://github.com/huggingface/datasets/tree/main/src/datasets/formatting))\r\n\r\nBtw, your example requires a single `set_format` call:\r\n```python\r\nds.set_format(\"torch\", columns=[\"image\"], output_all_columns=True, dtype=torch.double)\r\n```", "Hey Mario,\r\nThanks, for getting back to me. the toDouble was just an example my real life case requires many more transforms.\r\n\r\nWhat do you mean by:\r\n> using set_format/set_transform for the 1st transform and then passing the transformed example/batch to the 2nd transform\r\n\r\nHow would that go, I thought you can't chain them?\r\n\r\nAs for the custom formatter, is it possible to reference an existing formatter, in my case `torch_formatter` inside of my custom formatter?\r\n\r\nmaybe I can inherit from it and just call `super.recursive_tensorize()`?", "> How would that go, I thought you can't chain them?\r\n\r\nYes, they cannot be chained. This is what I meant:\r\n```python\r\nds.set_transform(first_transform)\r\n# calling the 2nd transform on each accessed batch\r\nsecond_transform(ds[2:3])\r\n```\r\n\r\n> As for the custom formatter, is it possible to reference an existing formatter, in my case torch_formatter inside of my custom formatter?\r\n>\r\n>maybe I can inherit from it and just call super.recursive_tensorize()?\r\n\r\nYes, subclassing makes the most sense.", "Great, thank you for the details.", "https://github.com/huggingface/datasets/issues/6012" ]

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[ "_The documentation is not available anymore as the PR was closed or merged._", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.008156 / 0.011353 (-0.003197) | 0.005563 / 0.011008 (-0.005445) | 0.118319 / 0.038508 (0.079810) | 0.044305 / 0.023109 (0.021195) | 0.366221 / 0.275898 (0.090323) | 0.407585 / 0.323480 (0.084105) | 0.006961 / 0.007986 (-0.001024) | 0.004841 / 0.004328 (0.000513) | 0.089949 / 0.004250 (0.085698) | 0.062197 / 0.037052 (0.025144) | 0.360721 / 0.258489 (0.102232) | 0.415332 / 0.293841 (0.121491) | 0.035709 / 0.128546 (-0.092837) | 0.010617 / 0.075646 (-0.065030) | 0.397454 / 0.419271 (-0.021817) | 0.063490 / 0.043533 (0.019958) | 0.374289 / 0.255139 (0.119150) | 0.382827 / 0.283200 (0.099628) | 0.121014 / 0.141683 (-0.020669) | 1.729933 / 1.452155 (0.277779) | 1.896222 / 1.492716 (0.403506) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.254030 / 0.018006 (0.236023) | 0.491225 / 0.000490 (0.490736) | 0.018933 / 0.000200 (0.018734) | 0.000413 / 0.000054 (0.000358) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.033085 / 0.037411 (-0.004327) | 0.132837 / 0.014526 (0.118311) | 0.143275 / 0.176557 (-0.033282) | 0.215800 / 0.737135 (-0.521335) | 0.149802 / 0.296338 (-0.146536) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.474688 / 0.215209 (0.259479) | 4.743223 / 2.077655 (2.665569) | 2.163107 / 1.504120 (0.658988) | 1.946396 / 1.541195 (0.405201) | 2.057538 / 1.468490 (0.589047) | 0.618836 / 4.584777 (-3.965941) | 4.605934 / 3.745712 (0.860222) | 2.201537 / 5.269862 (-3.068324) | 1.275758 / 4.565676 (-3.289919) | 0.077782 / 0.424275 (-0.346493) | 0.014830 / 0.007607 (0.007223) | 0.593372 / 0.226044 (0.367328) | 5.927000 / 2.268929 (3.658072) | 2.687293 / 55.444624 (-52.757331) | 2.301797 / 6.876477 (-4.574679) | 2.489928 / 2.142072 (0.347856) | 0.756779 / 4.805227 (-4.048449) | 0.168065 / 6.500664 (-6.332600) | 0.077276 / 0.075469 (0.001807) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.608169 / 1.841788 (-0.233619) | 19.048790 / 8.074308 (10.974482) | 16.100228 / 10.191392 (5.908836) | 0.215346 / 0.680424 (-0.465077) | 0.022293 / 0.534201 (-0.511907) | 0.535899 / 0.579283 (-0.043384) | 0.533729 / 0.434364 (0.099365) | 0.562697 / 0.540337 (0.022360) | 0.764082 / 1.386936 (-0.622854) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.010087 / 0.011353 (-0.001266) | 0.005357 / 0.011008 (-0.005651) | 0.092678 / 0.038508 (0.054170) | 0.041207 / 0.023109 (0.018098) | 0.437464 / 0.275898 (0.161566) | 0.527867 / 0.323480 (0.204387) | 0.006861 / 0.007986 (-0.001125) | 0.006131 / 0.004328 (0.001802) | 0.093741 / 0.004250 (0.089490) | 0.064142 / 0.037052 (0.027090) | 0.433577 / 0.258489 (0.175088) | 0.537148 / 0.293841 (0.243307) | 0.035339 / 0.128546 (-0.093207) | 0.010432 / 0.075646 (-0.065214) | 0.102838 / 0.419271 (-0.316434) | 0.057905 / 0.043533 (0.014372) | 0.437956 / 0.255139 (0.182817) | 0.509562 / 0.283200 (0.226362) | 0.120620 / 0.141683 (-0.021063) | 1.798686 / 1.452155 (0.346531) | 2.013290 / 1.492716 (0.520574) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.249067 / 0.018006 (0.231061) | 0.462219 / 0.000490 (0.461729) | 0.000476 / 0.000200 (0.000276) | 0.000068 / 0.000054 (0.000013) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.033988 / 0.037411 (-0.003424) | 0.135863 / 0.014526 (0.121337) | 0.144082 / 0.176557 (-0.032474) | 0.201715 / 0.737135 (-0.535421) | 0.152079 / 0.296338 (-0.144259) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.522820 / 0.215209 (0.307611) | 5.216723 / 2.077655 (3.139068) | 2.582355 / 1.504120 (1.078235) | 2.352799 / 1.541195 (0.811604) | 2.451943 / 1.468490 (0.983453) | 0.620381 / 4.584777 (-3.964396) | 4.537841 / 3.745712 (0.792129) | 2.206431 / 5.269862 (-3.063431) | 1.269865 / 4.565676 (-3.295811) | 0.078744 / 0.424275 (-0.345531) | 0.014375 / 0.007607 (0.006768) | 0.648215 / 0.226044 (0.422171) | 6.482809 / 2.268929 (4.213881) | 3.210670 / 55.444624 (-52.233954) | 2.847485 / 6.876477 (-4.028992) | 2.820946 / 2.142072 (0.678873) | 0.762711 / 4.805227 (-4.042516) | 0.171235 / 6.500664 (-6.329429) | 0.080230 / 0.075469 (0.004761) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.646840 / 1.841788 (-0.194948) | 19.400451 / 8.074308 (11.326142) | 16.758845 / 10.191392 (6.567453) | 0.171377 / 0.680424 (-0.509046) | 0.020400 / 0.534201 (-0.513801) | 0.467675 / 0.579283 (-0.111608) | 0.529745 / 0.434364 (0.095381) | 0.605989 / 0.540337 (0.065652) | 0.694659 / 1.386936 (-0.692277) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#006bf33ac5c308f9c70f4df4868abd539eb6c366 \"CML watermark\")\n", "It's faster because all the items are the same object, but this also means modifying one of them will alter each unless these items are immutable, and they are in this case (tuples). So we should be careful when using this idiom." ]

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[ "Hi ! `shard` currently returns a slow dataset by default, with examples evenly distributed in the dataset.\r\n\r\nYou can get a fast dataset using `contiguous=True` (which should be the default imo):\r\n\r\n```python\r\ndataset = dataset.shard(10, 0, contiguous=True)\r\n```\r\n\r\nThis way you don't need to flatten_indices() and sort should be fast as well", "@lhoestq \r\n\r\n> contiguous=True (which should be the default imo)\r\n\r\nFor `IterableDataset`, it's not possible to implement contiguous sharding without knowing the number of examples in advance, so setting the default value to `contiguous=True` would result in an inconsistency between `Dataset` and `IterableDataset` (when we add `IterableDataset.shard`)", "Actually sharded iterable datasets are made of sub iterables that generally yield contiguous data no ? So in a way it's possible to shard an iterable dataset contiguously.\r\n\r\nIf the dataset is made of one shard it's indeed not possible to shard it contiguously though", "> Actually sharded iterable datasets are made of sub iterables that generally yield contiguous data no ? So in a way it's possible to shard an iterable dataset contiguously.\r\n\r\nBut sharding an iterable dataset by sharding its `gen_kwargs` would still yield approximate shards(not equal to `Dataset.shard`), no? ", "Yes indeed !", "I understand the issue doesn't exist with non-mapped datasets, but if flattening is so much more efficient than sorting the indices, that's an issue in itself.\n\nThere are plenty of issues people posted for which the root cause turns out to be the same. It seems like mapped datasets are terribly inefficient. I think I saw some issue like that somewhere (about the mapped datasets in general), but can't find it now.\n\nMaybe indices should be flattened before any additional processing, then." ]

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[ "_The documentation is not available anymore as the PR was closed or merged._", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.006192 / 0.011353 (-0.005161) | 0.004410 / 0.011008 (-0.006598) | 0.095990 / 0.038508 (0.057482) | 0.032662 / 0.023109 (0.009553) | 0.322827 / 0.275898 (0.046929) | 0.352542 / 0.323480 (0.029062) | 0.005398 / 0.007986 (-0.002588) | 0.003926 / 0.004328 (-0.000403) | 0.075131 / 0.004250 (0.070880) | 0.046205 / 0.037052 (0.009153) | 0.330957 / 0.258489 (0.072468) | 0.360166 / 0.293841 (0.066325) | 0.027880 / 0.128546 (-0.100666) | 0.008813 / 0.075646 (-0.066833) | 0.327316 / 0.419271 (-0.091955) | 0.050071 / 0.043533 (0.006539) | 0.319939 / 0.255139 (0.064800) | 0.331593 / 0.283200 (0.048393) | 0.096745 / 0.141683 (-0.044938) | 1.445165 / 1.452155 (-0.006990) | 1.515538 / 1.492716 (0.022821) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.209365 / 0.018006 (0.191358) | 0.437007 / 0.000490 (0.436518) | 0.003207 / 0.000200 (0.003007) | 0.000088 / 0.000054 (0.000033) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.027261 / 0.037411 (-0.010151) | 0.105101 / 0.014526 (0.090575) | 0.117163 / 0.176557 (-0.059394) | 0.176237 / 0.737135 (-0.560898) | 0.122559 / 0.296338 (-0.173779) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.406792 / 0.215209 (0.191583) | 4.060831 / 2.077655 (1.983176) | 1.829691 / 1.504120 (0.325571) | 1.633155 / 1.541195 (0.091960) | 1.704817 / 1.468490 (0.236327) | 0.525325 / 4.584777 (-4.059452) | 3.752907 / 3.745712 (0.007194) | 1.857513 / 5.269862 (-3.412349) | 1.222237 / 4.565676 (-3.343439) | 0.065941 / 0.424275 (-0.358334) | 0.012498 / 0.007607 (0.004891) | 0.495009 / 0.226044 (0.268965) | 4.968074 / 2.268929 (2.699145) | 2.277898 / 55.444624 (-53.166727) | 1.936656 / 6.876477 (-4.939821) | 1.970698 / 2.142072 (-0.171374) | 0.635221 / 4.805227 (-4.170006) | 0.140539 / 6.500664 (-6.360125) | 0.064111 / 0.075469 (-0.011358) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.238151 / 1.841788 (-0.603637) | 14.681262 / 8.074308 (6.606954) | 13.405525 / 10.191392 (3.214133) | 0.163225 / 0.680424 (-0.517199) | 0.017282 / 0.534201 (-0.516918) | 0.395526 / 0.579283 (-0.183757) | 0.429156 / 0.434364 (-0.005208) | 0.470806 / 0.540337 (-0.069531) | 0.571290 / 1.386936 (-0.815646) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.006444 / 0.011353 (-0.004909) | 0.004388 / 0.011008 (-0.006621) | 0.075004 / 0.038508 (0.036496) | 0.032904 / 0.023109 (0.009795) | 0.375360 / 0.275898 (0.099462) | 0.413684 / 0.323480 (0.090204) | 0.005854 / 0.007986 (-0.002132) | 0.005504 / 0.004328 (0.001175) | 0.075049 / 0.004250 (0.070799) | 0.047973 / 0.037052 (0.010920) | 0.377943 / 0.258489 (0.119454) | 0.427039 / 0.293841 (0.133198) | 0.028248 / 0.128546 (-0.100298) | 0.008972 / 0.075646 (-0.066674) | 0.081848 / 0.419271 (-0.337424) | 0.047935 / 0.043533 (0.004402) | 0.377980 / 0.255139 (0.122841) | 0.407856 / 0.283200 (0.124656) | 0.103454 / 0.141683 (-0.038229) | 1.469051 / 1.452155 (0.016896) | 1.590657 / 1.492716 (0.097941) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.192380 / 0.018006 (0.174374) | 0.440995 / 0.000490 (0.440505) | 0.004082 / 0.000200 (0.003882) | 0.000096 / 0.000054 (0.000042) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.029584 / 0.037411 (-0.007828) | 0.110051 / 0.014526 (0.095525) | 0.121196 / 0.176557 (-0.055361) | 0.172249 / 0.737135 (-0.564886) | 0.125380 / 0.296338 (-0.170958) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.435218 / 0.215209 (0.220009) | 4.354811 / 2.077655 (2.277156) | 2.102050 / 1.504120 (0.597930) | 1.913454 / 1.541195 (0.372260) | 1.974624 / 1.468490 (0.506134) | 0.529975 / 4.584777 (-4.054802) | 3.801605 / 3.745712 (0.055893) | 3.162408 / 5.269862 (-2.107454) | 1.599576 / 4.565676 (-2.966101) | 0.066710 / 0.424275 (-0.357565) | 0.012158 / 0.007607 (0.004551) | 0.549187 / 0.226044 (0.323142) | 5.489930 / 2.268929 (3.221002) | 2.646787 / 55.444624 (-52.797837) | 2.311915 / 6.876477 (-4.564562) | 2.335645 / 2.142072 (0.193572) | 0.641067 / 4.805227 (-4.164160) | 0.142227 / 6.500664 (-6.358437) | 0.065303 / 0.075469 (-0.010166) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.283209 / 1.841788 (-0.558579) | 15.241809 / 8.074308 (7.167501) | 14.131471 / 10.191392 (3.940079) | 0.143921 / 0.680424 (-0.536503) | 0.017497 / 0.534201 (-0.516704) | 0.402236 / 0.579283 (-0.177047) | 0.418917 / 0.434364 (-0.015447) | 0.461745 / 0.540337 (-0.078593) | 0.560212 / 1.386936 (-0.826724) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#7098922130cabfbfa6b8a3885ff2e6f032d6203d \"CML watermark\")\n" ]

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[ "We plan to improve this eventually (see https://github.com/huggingface/datasets/issues/5454 and https://github.com/huggingface/datasets/issues/5380).\r\n\r\n> Is it possible to lazily load samples of a mapped dataset ? I'm used to [dataset scripts](https://huggingface.co/docs/datasets/dataset_script), maybe something can be done there.\r\nIf not, I could do it using a plain Pytorch dataset. Then I would need to convert it to a datasets' dataset to get all the features of datasets. Is it something possible ?\r\n\r\nYes, by creating a mapped dataset that stores audio URLs. Indexing a dataset in such format only downloads and decodes the bytes of the accessed samples (without storing them on disk).\r\n\r\nYou can do the following to create this dataset:\r\n```python\r\n\r\ndef gen():\r\n # Generator that yields (audio URL, text) pairs as dict\r\n ...\r\n yield {\"audio\": \"audio_url\", \"text\": \"some text\"}\r\n\r\nfeatures = Features({\"audio\": datasets.Audio(), \"text\": datasets.Value(\"string\")})\r\nds = Dataset.from_generator(gen, features=features)\r\nds[2:5] # downloads and decodes the samples each time they are accessed\r\n```" ]

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[ "_The documentation is not available anymore as the PR was closed or merged._", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.007401 / 0.011353 (-0.003952) | 0.005198 / 0.011008 (-0.005810) | 0.112317 / 0.038508 (0.073809) | 0.038406 / 0.023109 (0.015297) | 0.358008 / 0.275898 (0.082110) | 0.395350 / 0.323480 (0.071870) | 0.006201 / 0.007986 (-0.001785) | 0.004368 / 0.004328 (0.000039) | 0.087718 / 0.004250 (0.083467) | 0.055299 / 0.037052 (0.018247) | 0.350481 / 0.258489 (0.091992) | 0.419876 / 0.293841 (0.126035) | 0.032459 / 0.128546 (-0.096087) | 0.010635 / 0.075646 (-0.065011) | 0.383282 / 0.419271 (-0.035989) | 0.059241 / 0.043533 (0.015708) | 0.365101 / 0.255139 (0.109962) | 0.378144 / 0.283200 (0.094944) | 0.114287 / 0.141683 (-0.027396) | 1.680870 / 1.452155 (0.228715) | 1.788183 / 1.492716 (0.295467) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.242919 / 0.018006 (0.224913) | 0.489850 / 0.000490 (0.489360) | 0.011408 / 0.000200 (0.011208) | 0.000444 / 0.000054 (0.000389) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.030742 / 0.037411 (-0.006669) | 0.123092 / 0.014526 (0.108566) | 0.138246 / 0.176557 (-0.038311) | 0.207299 / 0.737135 (-0.529836) | 0.142647 / 0.296338 (-0.153691) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.472553 / 0.215209 (0.257344) | 4.671763 / 2.077655 (2.594108) | 2.119986 / 1.504120 (0.615866) | 1.891851 / 1.541195 (0.350656) | 1.979094 / 1.468490 (0.510604) | 0.617956 / 4.584777 (-3.966821) | 4.969418 / 3.745712 (1.223706) | 4.672083 / 5.269862 (-0.597779) | 2.119049 / 4.565676 (-2.446627) | 0.077466 / 0.424275 (-0.346809) | 0.014434 / 0.007607 (0.006827) | 0.580746 / 0.226044 (0.354701) | 5.805458 / 2.268929 (3.536530) | 2.622498 / 55.444624 (-52.822126) | 2.259499 / 6.876477 (-4.616978) | 2.362078 / 2.142072 (0.220006) | 0.719911 / 4.805227 (-4.085317) | 0.164939 / 6.500664 (-6.335725) | 0.074762 / 0.075469 (-0.000707) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.496709 / 1.841788 (-0.345079) | 18.247499 / 8.074308 (10.173191) | 15.397075 / 10.191392 (5.205683) | 0.181163 / 0.680424 (-0.499261) | 0.022604 / 0.534201 (-0.511597) | 0.462791 / 0.579283 (-0.116492) | 0.504473 / 0.434364 (0.070109) | 0.582254 / 0.540337 (0.041917) | 0.673849 / 1.386936 (-0.713087) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.007633 / 0.011353 (-0.003720) | 0.004859 / 0.011008 (-0.006149) | 0.091194 / 0.038508 (0.052686) | 0.038255 / 0.023109 (0.015146) | 0.460972 / 0.275898 (0.185074) | 0.470441 / 0.323480 (0.146961) | 0.006482 / 0.007986 (-0.001504) | 0.004500 / 0.004328 (0.000172) | 0.089998 / 0.004250 (0.085748) | 0.055470 / 0.037052 (0.018418) | 0.459188 / 0.258489 (0.200699) | 0.491255 / 0.293841 (0.197414) | 0.032200 / 0.128546 (-0.096346) | 0.010372 / 0.075646 (-0.065274) | 0.097429 / 0.419271 (-0.321843) | 0.052469 / 0.043533 (0.008936) | 0.452492 / 0.255139 (0.197353) | 0.475210 / 0.283200 (0.192010) | 0.116976 / 0.141683 (-0.024707) | 1.752742 / 1.452155 (0.300587) | 1.849535 / 1.492716 (0.356819) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.229822 / 0.018006 (0.211816) | 0.472259 / 0.000490 (0.471770) | 0.000455 / 0.000200 (0.000255) | 0.000067 / 0.000054 (0.000013) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.033796 / 0.037411 (-0.003615) | 0.136151 / 0.014526 (0.121625) | 0.144015 / 0.176557 (-0.032542) | 0.199337 / 0.737135 (-0.537798) | 0.150024 / 0.296338 (-0.146315) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.522737 / 0.215209 (0.307528) | 5.165223 / 2.077655 (3.087568) | 2.630334 / 1.504120 (1.126214) | 2.392383 / 1.541195 (0.851188) | 2.488966 / 1.468490 (1.020476) | 0.608981 / 4.584777 (-3.975796) | 4.711545 / 3.745712 (0.965833) | 2.121537 / 5.269862 (-3.148325) | 1.205477 / 4.565676 (-3.360199) | 0.078277 / 0.424275 (-0.345998) | 0.014175 / 0.007607 (0.006568) | 0.640720 / 0.226044 (0.414675) | 6.391173 / 2.268929 (4.122245) | 3.265131 / 55.444624 (-52.179493) | 2.939188 / 6.876477 (-3.937289) | 2.919217 / 2.142072 (0.777145) | 0.745095 / 4.805227 (-4.060132) | 0.164065 / 6.500664 (-6.336599) | 0.076993 / 0.075469 (0.001524) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.539971 / 1.841788 (-0.301817) | 18.597296 / 8.074308 (10.522988) | 16.899330 / 10.191392 (6.707938) | 0.169005 / 0.680424 (-0.511419) | 0.020447 / 0.534201 (-0.513754) | 0.465862 / 0.579283 (-0.113421) | 0.522819 / 0.434364 (0.088455) | 0.547111 / 0.540337 (0.006773) | 0.657777 / 1.386936 (-0.729159) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#56aff9ecb4e565eb95faad525558914648cc22f1 \"CML watermark\")\n" ]

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[ "Also this could be extended to the rest of the GitHub Action `yml` files, so let me know whether you want me to have a look into it! 🤗", "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_5903). All of your documentation changes will be reflected on that endpoint." ]

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