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Training with Multiple GPUs

This part of tutorial shows how you can train models with multiple GPUs.

single machine & multi gpus

python -m torch.distributed.launch --nproc_per_node=${NUMBER_GPUS} --master_port=${MASTER_PORT} scripts/train.py -c ${cfg_file}
  • nproc_per_node (int): Number of GPUs in the current machine, for example --nproc_per_node=8.
  • master_port (int): Master port, for example --master_port=29527.

multi machines & multi gpus

For example, we have 2 nodes.

Node 1:

python -m torch.distributed.launch --nproc_per_node=${NUMBER_GPUS} --nnodes=2 --node_rank=0 --master_addr=${MASTER_IP_ADDRESS} --master_port=${MASTER_PORT} scripts/train.py -c ${cfg_file}

Node 2:

python -m torch.distributed.launch --nproc_per_node=${NUMBER_GPUS} --nnodes=2 --node_rank=1 --master_addr=${MASTER_IP_ADDRESS} --master_port=${MASTER_PORT} scripts/train.py -c ${cfg_file}
  • nproc_per_node(int): Number of GPUs in the current machine, for example --nproc_per_node=8.
  • nnodes(int): Number of nodes.
  • node_rank(int): Rank of current node, starting from 0.
  • master_addr(int): Master IP, for example --master_addr=192.168.1.1.
  • master_port(int): Master port, for example --master_port=29527.

[Optional] Modify configuration file

Add this script to the end of your configuration file when RuntimeError occurs.

parallel:
  type: DistributedDataParallel
  find_unused_parameters: true