Create README.md

README.md

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+---
+task_categories:
+- image-classification
+- unconditional-image-generation
+size_categories:
+- 10K<n<100K
+---
+# MNIST WebDataset PNG
+The MNIST dataset with samples stored as PNG images and compiled into the WebDataset format.
+
+## DALI/JAX Example
+The following code shows how this dataset can be loaded into JAX arrays by DALI.
+```python
+from nvidia.dali import pipeline_def
+import nvidia.dali.fn as fn
+import nvidia.dali.types as types
+from nvidia.dali.plugin.jax import DALIGenericIterator
+from nvidia.dali.plugin.base_iterator import LastBatchPolicy
+
+def get_data_iterator(batch_size, dataset_path):
+    @pipeline_def(batch_size=batch_size, num_threads=4, device_id=0)
+    def wds_pipeline():
+        raw_image, ascii_label = fn.readers.webdataset(
+            paths=dataset_path, 
+            ext=['png', 'cls'], 
+            missing_component_behavior='error',
+        )
+        image = fn.decoders.image(raw_image)
+        ascii_shift = types.Constant(48).uint8()
+        label = ascii_label - ascii_shift
+        return image, label
+
+    data_pipeline = wds_pipeline()
+    data_iterator = DALIGenericIterator(
+        pipelines=[data_pipeline], 
+        output_map=['x', 'y'], 
+        last_batch_policy=LastBatchPolicy.DROP
+    )
+    return data_iterator
+
+data_iterator = get_data_iterator(
+    batch_size=32, 
+    dataset_path='data/mnist_webdataset_numpy_flat_9/data.tar'
+)
+batch = next(data_iterator)
+x = batch['x']
+y = batch['y']
+print('x shape:', x.shape)
+print('y shape:', y.shape)
+print('y:', y[:, 0])
+```
+
+Output:
+```
+x shape: (32, 28, 28, 3)
+y shape: (32, 1)
+y: [5 0 4 1 9 2 1 3 1 4 3 5 3 6 1 7 2 8 6 9 4 0 9 1 1 2 4 3 2 7 3 8]
+```
+
+## Acknowledgements
+- Yann LeCun, Courant Institute, NYU
+- Corinna Cortes, Google Labs, New York
+- Christopher J.C. Burges, Microsoft Research, Redmond