compute_environment: LOCAL_MACHINE
deepspeed_config: {}
+distributed_type: MULTI_GPU
downcast_bf16: 'no'
dynamo_backend: 'NO'
fsdp_config: {}
+gpu_ids: all
+machine_rank: 0
main_training_function: main
megatron_lm_config: {}
mixed_precision: 'no'
+num_machines: 1
+num_processes: 4
+rdzv_backend: static
+same_network: true
use_cpu: false

## None ## If the YAML was generated through the `accelerate config` command: ``` accelerate launch {script_name.py} {--arg1} {--arg2} ... ``` If the YAML is saved to a `~/config.yaml` file: ``` accelerate launch --config_file ~/config.yaml {script_name.py} {--arg1} {--arg2} ... ``` Or you can use `accelerate launch` with right configuration parameters and have no `config.yaml` file: ``` accelerate launch --multi_gpu --num_processes=4 {script_name.py} {--arg1} {--arg2} ... ``` ## Launching on multi-GPU instances requires a different launch command than just `python myscript.py`. Accelerate will wrap around the proper launching script to delegate and call, reading in how to set their configuration based on the parameters passed in. It is a passthrough to the `torchrun` command. **Remember that you can always use the `accelerate launch` functionality, even if the code in your script does not use the `Accelerator`** ## To learn more checkout the related documentation: - Launching distributed code - The Command Line