# Copyright (c) 2019 PaddlePaddle Authors. All Rights Reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """ paddle.distributed.launch is a module that spawns multiple distributed process on each trainning node for gpu trainning. Usage: In both of single node training or multiple node training, this module launch a process on each of the given gpu card. 1. for single node trainning with all visible gpu cards: python -m paddle.distributed.launch \ your_training_py (arg1 arg2 and all others) 2. for single node trainning with [0,4) cards python -m paddle.distributed.launch --selected_gpus="0,1,2,3" \ your_training_py (arg1 arg2 and all others) 3. for mulitple node training such as two node:192.168.0.16, 192.168.0.17 on 192.168.0.16: python -m paddle.distributed.launch --cluster_node_ips="192.168.0.16,192.168.0.17" \ --node_ip=192.168.0.16 \ your_training_py (arg1 arg2 and all others) on 192.168.0.17: python -m paddle.distributed.launch --cluster_node_ips="192.168.0.16,192.168.0.17" \ --node_ip=192.168.0.17 \ your_training_py (arg1 arg2 and all others) """ from __future__ import print_function import sys from sys import version import subprocess import os import six import copy from argparse import ArgumentParser, REMAINDER import paddle.fluid as fluid def _print_arguments(args): print("----------- Configuration Arguments -----------") for arg, value in sorted(six.iteritems(vars(args))): print("%s: %s" % (arg, value)) print("------------------------------------------------") def _parse_args(): """ Helper function parsing the command line options @retval ArgumentParser """ parser = ArgumentParser( description='''start paddle training using multi-process mode. NOTE: your train program ***must*** run as distributed nccl2 mode, see: http://www.paddlepaddle.org/documentation/docs/zh/1.2/user_guides/howto/training/cluster_howto.html#permalink-8--nccl2- And your train program must read environment variables below in order to let different process init properly: FLAGS_selected_gpus PADDLE_TRAINER_ID PADDLE_CURRENT_ENDPOINT PADDLE_TRAINERS_NUM PADDLE_TRAINER_ENDPOINTS POD_IP (current node ip address, not needed for local training) ''') # Optional arguments for the launch helper parser.add_argument( "--cluster_node_ips", type=str, default="127.0.0.1", help="Paddle cluster nodes ips, such as 192.168.0.16,192.168.0.17..") parser.add_argument( "--node_ip", type=str, default="127.0.0.1", help="The current node ip. ") parser.add_argument( "--started_port", type=int, default=6170, help="The trainer's started port on a single node") parser.add_argument( "--print_config", type=bool, default=True, help="Print the config or not") parser.add_argument( "--selected_gpus", type=str, default=None, help="It's for gpu trainning and the trainning process will run on the selected_gpus," "each process is bound to a single GPU. And if it's not setted, this module will use all the gpu cards for training." ) parser.add_argument( "--log_dir", type=str, help="The path for each process's log.If it's not setted, the log will printed to default pipe." ) # 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 start_procs(args): """ """ procs = [] log_fns = [] default_env = os.environ.copy() current_node_ip = args.node_ip node_ips = [x.strip() for x in args.cluster_node_ips.split(',')] node_id = node_ips.index(current_node_ip) num_nodes = len(node_ips) if args.selected_gpus is None: gpus_num = fluid.core.get_cuda_device_count() selected_gpus = [str(x) for x in range(0, gpus_num)] else: selected_gpus = [x.strip() for x in args.selected_gpus.split(',')] selected_gpus_num = len(selected_gpus) trainers_endpoints = "" for ip in node_ips: for i in range(selected_gpus_num): if trainers_endpoints != "": trainers_endpoints += "," trainers_endpoints += "%s:617%d" % (ip, i) nranks = num_nodes * selected_gpus_num if args.print_config: print("trainers_endpoints:", trainers_endpoints, ", node_id:", node_id, ", current_node_ip:", current_node_ip, ", num_nodes:", num_nodes, ", node_ips:", node_ips, ", nranks:", nranks) current_env = copy.copy(default_env) procs = [] cmds = [] for i in range(0, selected_gpus_num): current_env.update({ "FLAGS_selected_gpus": "%s" % selected_gpus[i], "PADDLE_TRAINER_ID": "%d" % (node_id * selected_gpus_num + i), "PADDLE_CURRENT_ENDPOINT": "%s:%d" % (current_node_ip, args.started_port + i), "PADDLE_TRAINERS_NUM": "%d" % nranks, "PADDLE_TRAINER_ENDPOINTS": trainers_endpoints }) cmd = [sys.executable, "-u", args.training_script ] + args.training_script_args cmds.append(cmd) if args.log_dir is not None: fn = open("%s/workerlog.%d" % (args.log_dir, i), "w") log_fns.append(fn) proc = subprocess.Popen(cmd, env=current_env, stdout=fn, stderr=fn) else: proc = subprocess.Popen(cmd, env=current_env) procs.append(proc) for i in range(0, len(procs)): proc = procs[i] proc.wait() if len(log_fns) > 0: log_fns[i].close() if proc.returncode != 0: raise subprocess.CalledProcessError( returncode=procs[i].returncode, cmd=cmds[i]) def launch(): args = _parse_args() if args.print_config: _print_arguments(args) start_procs(args) if __name__ == "__main__": launch()