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ce48d47543
rather than handling multi-GPU training within a recipe, distributed training should be one of our scheduler offerings. Introduce the DistributedJobScheduler which kicks off a `finetune_handler.py` script using torchrun. This handler processes the training args via argparse and calls the right recipe as `post_training.py` used to do. Torchrun takes care of env variables like world_size, local_rank, etc. Signed-off-by: Charlie Doern <cdoern@redhat.com> |
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| .. | ||
| bedrock | ||
| common | ||
| datasetio | ||
| inference | ||
| kvstore | ||
| memory | ||
| responses | ||
| scoring | ||
| sqlstore | ||
| telemetry | ||
| tools | ||
| __init__.py | ||
| pagination.py | ||
| scheduler.py | ||