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small tweaks to docs and variable names stylistically
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15
train.py
15
train.py
@ -9,10 +9,11 @@ To run with DDP on 4 gpus on 1 node, example:
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$ torchrun --standalone --nproc_per_node=4 train.py
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To run with DDP on 4 gpus across 2 nodes, example:
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- Run on the first (master) node:
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- Run on the first (master) node with example IP 123.456.123.456:
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$ torchrun --nproc_per_node=8 --nnodes=2 --node_rank=0 --master_addr=123.456.123.456 --master_port=1234 train.py
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- Run on the worker node:
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$ torchrun --nproc_per_node=8 --nnodes=2 --node_rank=1 --master_addr=123.456.123.456 --master_port=1234 train.py
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(If your cluster does not have Infiniband interconnect prepend NCCL_IB_DISABLE=1)
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"""
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import os
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@ -79,11 +80,11 @@ config = {k: globals()[k] for k in config_keys} # will be useful for logging
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ddp = int(os.environ.get('RANK', -1)) != -1 # is this a ddp run?
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if ddp:
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init_process_group(backend=backend)
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DDP_RANK = int(os.environ['RANK'])
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DDP_LOCAL_RANK = int(os.environ['LOCAL_RANK'])
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device = f'cuda:{DDP_LOCAL_RANK}'
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master_process = DDP_RANK == 0 # this process will do logging, checkpointing etc.
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seed_offset = DDP_RANK # each process gets a different seed
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ddp_rank = int(os.environ['RANK'])
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ddp_local_rank = int(os.environ['LOCAL_RANK'])
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device = f'cuda:{ddp_local_rank}'
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master_process = ddp_rank == 0 # this process will do logging, checkpointing etc.
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seed_offset = ddp_rank # each process gets a different seed
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else:
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# if not ddp, we are running on a single gpu, and one process
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master_process = True
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@ -181,7 +182,7 @@ if compile:
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# wrap model into DDP container
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if ddp:
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model = DDP(model, device_ids=[DDP_LOCAL_RANK])
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model = DDP(model, device_ids=[ddp_local_rank])
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@torch.no_grad()
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def estimate_loss():
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