dist_launch.py

# codebase: torch.distributed.launch.py

import sys
import subprocess
import os
from argparse import ArgumentParser, REMAINDER


def parse_args():
    """
    Helper function parsing the command line options
    @retval ArgumentParser
    """
    parser = ArgumentParser(description="PyTorch distributed training launch "
                                        "helper utility that will spawn up "
                                        "multiple distributed processes")

    # Optional arguments for the launch helper
    parser.add_argument("--nnodes", type=int, default=1,
                        help="The number of nodes to use for distributed "
                             "training")
    parser.add_argument("--node_rank", type=int, default=0,
                        help="The rank of the node for multi-node distributed "
                             "training")
    parser.add_argument("--nproc_per_node", type=int, default=1,
                        help="The number of processes to launch on each node, "
                             "for GPU training, this is recommended to be set "
                             "to the number of GPUs in your system so that "
                             "each process can be bound to a single GPU.")
    parser.add_argument("--master_addr", default="127.0.0.1", type=str,
                        help="Master node (rank 0)'s address, should be either "
                             "the IP address or the hostname of node 0, for "
                             "single node multi-proc training, the "
                             "--master_addr can simply be 127.0.0.1")
    parser.add_argument("--master_port", default=2901, type=int,
                        help="Master node (rank 0)'s free port that needs to "
                             "be used for communication during distributed "
                             "training")
    parser.add_argument("-m", "--module", default=False, action="store_true",
                        help="Changes each process to interpret the launch script "
                             "as a python module, executing with the same behavior as"
                             "'python -m'.")

    # positional
    parser.add_argument("training_script", type=str,
                        help="The full path to the single GPU training "
                             "program/script to be launched in parallel, "
                             "followed by all the arguments for the "
                             "training script")

    # rest from the training program
    parser.add_argument('training_script_args', nargs=REMAINDER)
    return parser.parse_args()

def main():
    args = parse_args()

    # world size in terms of number of processes
    dist_world_size = args.nproc_per_node * args.nnodes

    # set PyTorch distributed related environmental variables
    current_env = os.environ.copy()
    current_env["MASTER_ADDR"] = args.master_addr
    current_env["MASTER_PORT"] = str(args.master_port)
    current_env["WORLD_SIZE"] = str(dist_world_size)

    processes = []

    if 'OMP_NUM_THREADS' not in os.environ and args.nproc_per_node > 1:
        current_env["OMP_NUM_THREADS"] = str(1)
        print("*****************************************\n"
              "Setting OMP_NUM_THREADS environment variable for each process "
              "to be {} in default, to avoid your system being overloaded, "
              "please further tune the variable for optimal performance in "
              "your application as needed. \n"
              "*****************************************".format(current_env["OMP_NUM_THREADS"]))

    for local_rank in range(0, args.nproc_per_node):
        # each process's rank
        dist_rank = args.nproc_per_node * args.node_rank + local_rank
        current_env["RANK"] = str(dist_rank)
        current_env["LOCAL_RANK"] = str(local_rank)

        # spawn the processes
        cmd = [sys.executable, "-u"]
        if args.module:
            cmd.append("-m")

        cmd.append(args.training_script)

        cmd.append("dist.local_rank={}".format(local_rank))
        cmd.append("dist.nproc_per_node={}".format(args.nproc_per_node))
        cmd.append("dist.world_rank={}".format(dist_rank))
        cmd.append("dist.world_size={}".format(dist_world_size))

        cmd.extend(args.training_script_args)

        process = subprocess.Popen(cmd, env=current_env)
        processes.append(process)

    for process in processes:
        process.wait()
        if process.returncode != 0:
            raise subprocess.CalledProcessError(returncode=process.returncode,
                                                cmd=cmd)


if __name__ == "__main__":
    main()