DeepLearningExamples/PyTorch/Classification/ConvNets/multiproc.py

# From PyTorch:
#
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# Copyright (c) 2016-     Facebook, Inc            (Adam Paszke)
# Copyright (c) 2014-     Facebook, Inc            (Soumith Chintala)
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import sys
import subprocess
import os
import socket
import time
from argparse import ArgumentParser, REMAINDER

import torch

def parse_args():
    """
    Helper function parsing the command line options
    @retval ArgumentParser
    """
    parser = ArgumentParser(description="PyTorch distributed training launch "
                                        "helper utilty 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=29500, type=int,
                        help="Master node (rank 0)'s free port that needs to "
                             "be used for communciation during distributed "
                             "training")

    # 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 = []

    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)

        # spawn the processes
        cmd = [sys.executable,
               "-u",
               args.training_script,
               "--local_rank={}".format(local_rank)] + args.training_script_args

        print(cmd)

        stdout = None if local_rank == 0 else open("GPU_"+str(local_rank)+".log", "w")

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

    try:
        up = True
        error = False
        while up and not error:
            up = False
            for p in processes:
                ret = p.poll()
                if ret is None:
                    up = True
                elif ret != 0:
                    error = True
            time.sleep(1)
    
        if error:
            for p in processes:
                if p.poll() is None:
                    p.terminate()
            exit(1)
    
    except KeyboardInterrupt:
        for p in processes:
            p.terminate()
        raise
    except SystemExit:
        for p in processes:
            p.terminate()
        raise
    except:
        for p in processes:
            p.terminate()
        raise


if __name__ == "__main__":
    main()