# 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. """ fleetrun is a module that spawns multiple distributed process on each training node for gpu training and cpu training. Usage: In both of single node training or multiple node training, this module launch a process on each of the given gpu card or cpu machine. GPU training: 1. for single node training with all visible gpu cards: fleetrun your_training_py (arg1 arg2 and all others) 2. for single node training with [0,4) cards fleetrun --gpus="0,1,2,3" your_training_py (arg1 arg2 and all others) 3. for multiple node training such as two node:192.168.0.16, 192.168.0.17 on 192.168.0.16: fleetrun --ips="192.168.0.16,192.168.0.17" \ your_training_py (arg1 arg2 and all others) on 192.168.0.17: fleetrun --ips="192.168.0.16,192.168.0.17" \ your_training_py (arg1 arg2 and all others) CPU training: 1. for single node training with multi servers and workers: fleetrun --server_num=2 --worker_num=2 your_training_py (arg1 arg2 and all others) 2. for multiple node training such as two node:192.168.0.16, 192.168.0.17 \ with 2 servers and 4 workers. on 192.168.0.16: fleetrun --servers="192.168.0.16:6170,192.168.0.17:6170" \ --workers="192.168.0.16,192.168.0.17,192.168.0.16,192.168.0.17" \ your_training_py (arg1 arg2 and all others) on 192.168.0.17: fleetrun --servers="192.168.0.16:6170,192.168.0.17:6171" \ --workers="192.168.0.16,192.168.0.17,192.168.0.16,192.168.0.17" \ your_training_py (arg1 arg2 and all others) 3. use gloo backend for multiple node training such as two node:192.168.0.16, 192.168.0.17 \ with 2 servers and 4 workers. (workers should set port) on 192.168.0.16: fleetrun --servers="192.168.0.16:6170,192.168.0.17:6170" \ --workers="192.168.0.16:6171,192.168.0.17:6171,192.168.0.16:6172,192.168.0.17:6172" \ your_training_py (arg1 arg2 and all others) on 192.168.0.17: fleetrun --servers="192.168.0.16:6170,192.168.0.17:6170" \ --workers="192.168.0.16:6171,192.168.0.17:6171,192.168.0.16:6172,192.168.0.17:6172" \ your_training_py (arg1 arg2 and all others) """ from __future__ import print_function import shutil import sys import tempfile from sys import version import subprocess import os import time import six import copy from argparse import ArgumentParser, REMAINDER import paddle import paddle.fluid as fluid from paddle.distributed.fleet.launch_utils import * import paddle.distributed.fleet.cloud_utils as cloud_utils 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. see: http://www.paddlepaddle.org/documentation/docs/zh/1.6/user_guides/howto/training/cluster_howto.html#permalink-8--nccl2- ''') # Optional arguments for the launch helper parser.add_argument( "--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( "--gpus", type=str, default=None, help="It's for gpu training and the training process will run on the gpus," "each process is bound to a single GPU. And if it's not set, this module will use all the gpu cards for training." ) parser.add_argument( "--servers", type=str, default="", help="User defined servers ip:port") parser.add_argument( "--workers", type=str, default="", help="User defined workers ip:port") parser.add_argument("--worker_num", type=int, help="number of workers") parser.add_argument("--server_num", type=int, help="number of servers") parser.add_argument( "--log_dir", type=str, default="log", help="The path for each process's log.If it's not set, 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 get_cluster_from_args(args, gpus): node_ips = [x.strip() for x in args.ips.split(',')] if len(node_ips) == 1: node_ip = node_ips[0] else: _, node_ip = get_host_name_ip() # node_ip = args.node_ip assert node_ip in node_ips, "Can't find your local ip {%s} in node_ips: {%s}" \ % (node_ip, node_ips) node_rank = node_ips.index(node_ip) logger.debug("parsed from args: node_ips:{} node_ip:{} node_rank:{}".format( node_ips, node_ip, node_rank)) free_ports = None if not cloud_utils.use_paddlecloud() and len( node_ips) <= 1 and os.environ.get('FLAGS_START_PORT') is None: free_ports = find_free_ports(len(gpus)) if free_ports is not None: free_ports = list(free_ports) else: start_port = 6070 if os.environ.get('FLAGS_START_PORT') is not None: start_port = int(os.environ.get('FLAGS_START_PORT')) free_ports = [x for x in range(start_port, start_port + len(gpus))] trainer_endpoints = [] for ip in node_ips: trainer_endpoints.append(["%s:%d" % (ip, port) for port in free_ports]) return get_cluster(node_ips, node_ip, trainer_endpoints, gpus) def get_gpus(gpus): if gpus is None: gpus_num = fluid.core.get_cuda_device_count() res_gpus = [str(x) for x in range(0, gpus_num)] else: cuda_visible_devices = os.getenv("CUDA_VISIBLE_DEVICES") if cuda_visible_devices is None or cuda_visible_devices == "": res_gpus = [x.strip() for x in gpus.split(',')] else: # change gpus into relative values # e.g. CUDA_VISIBLE_DEVICES=4,5,6,7; args.gpus=4,5,6,7; # therefore gpus=0,1,2,3 cuda_visible_devices_list = cuda_visible_devices.split(',') for x in gpus.split(','): assert x in cuda_visible_devices_list, "Can't find "\ "your gpus %s in CUDA_VISIBLE_DEVICES[%s]."\ % (x, cuda_visible_devices) res_gpus = [ cuda_visible_devices_list.index(x.strip()) for x in gpus.split(',') ] logger.info("Change selected_gpus into reletive values. --ips:{} " "will change into relative_ips:{} according to your " "CUDA_VISIBLE_DEVICES:{}".format( gpus, res_gpus, cuda_visible_devices_list)) return res_gpus def launch_collective(args): # parse arguments, used for cloud-single-machine and local gpus = get_gpus(args.gpus) trainers_num = cloud_utils.get_trainers_num() logger.debug("parsed from args trainerss_num:{} gpus:{}".format( trainers_num, gpus)) cluster = None pod = None start_port = 6170 if os.environ.get('FLAGS_START_PORT') is not None: start_port = os.environ.get('FLAGS_START_PORT') if cloud_utils.use_paddlecloud() and trainers_num != 1: cluster, pod = cloud_utils.get_cloud_cluster(args.ips, gpus, start_port) logger.debug("get cluster from cloud:{}".format(cluster)) else: # trainers_num = 1 or not use paddlecloud ips="a,b" cluster, pod = get_cluster_from_args(args, gpus) logger.debug("get cluster from args:{}".format(cluster)) global_envs = copy.copy(os.environ.copy()) gloo_rendezvous_dir = tempfile.mkdtemp() # add gloo env global_envs["PADDLE_WITH_GLOO"] = "1" global_envs["PADDLE_GLOO_RENDEZVOUS"] = "3" global_envs["PADDLE_GLOO_FS_PATH"] = gloo_rendezvous_dir procs = start_local_trainers( cluster, pod, training_script=args.training_script, training_script_args=args.training_script_args, log_dir=args.log_dir, envs=global_envs) while True: alive = watch_local_trainers(procs, cluster.trainers_nranks()) if not alive: logger.info("Local processes completed.") logger.debug("POD info:{}".format(pod)) break time.sleep(3) if os.path.exists(gloo_rendezvous_dir): shutil.rmtree(gloo_rendezvous_dir) def launch_ps(args): ports = None start_port = 6170 if args.server_num: server_num = args.server_num ports = get_ports(server_num, 0) server_endpoints = ",".join(["127.0.0.1:" + str(x) for x in ports]) else: assert args.servers != "", "The setting of CPU mode must be either server_num or servers." server_endpoints = args.servers server_endpoints_ips = [ x.strip().split(":")[0] for x in server_endpoints.split(",") ] server_endpoints_port = [ x.strip().split(":")[1] for x in server_endpoints.split(",") ] server_num = len(server_endpoints_ips) if args.worker_num: worker_num = args.worker_num ports = get_ports(worker_num, server_num) worker_endpoints = ",".join(["127.0.0.1:" + str(x) for x in ports]) else: assert args.workers != "", "The setting of CPU mode must be either worker_num or workers." worker_endpoints = args.workers worker_endpoints_ips = [ x.strip().split(":")[0] for x in worker_endpoints.split(",") ] worker_num = len(worker_endpoints_ips) node_ips = list(set(server_endpoints_ips + worker_endpoints_ips)) worker_endpoints_len = [ len(x.strip().split(":")) for x in worker_endpoints.split(",") ] if 1 in worker_endpoints_len: # if no port value in worker_endpoints, will set default port values. worker_endpoints_port = range(start_port + server_num, start_port + server_num + worker_num, 1) else: worker_endpoints_port = [ x.strip().split(":")[1] for x in worker_endpoints.split(",") ] # local train if len(set(node_ips)) == 1: current_node_ip = node_ips[0] else: _, current_node_ip = get_host_name_ip() assert current_node_ip in node_ips, "Can't find your local ip {%s} in args.servers and args.workers ips: {%s}" \ % (current_node_ip, node_ips) node_rank = node_ips.index(current_node_ip) logger.debug( "parsed from args: node_ips:{} current_node_ip:{} node_rank:{}, server_ports:{}". format(node_ips, current_node_ip, node_rank, server_endpoints_port)) cluster = Cluster(hdfs=None) server_rank = 0 worker_rank = 0 for node_rank, ip in enumerate(node_ips): pod = Pod() pod.rank = node_rank pod.addr = ip for i in range(len(server_endpoints_ips)): if ip == server_endpoints_ips[i]: server = Trainer() server.endpoint = "%s:%s" % (ip, server_endpoints_port[i]) server.rank = server_rank server_rank += 1 pod.servers.append(server) for j in range(len(worker_endpoints_ips)): if ip == worker_endpoints_ips[j]: worker = Trainer() worker.endpoint = "%s:%s" % (ip, worker_endpoints_port[i]) worker.rank = worker_rank worker_rank += 1 pod.workers.append(worker) cluster.pods.append(pod) pod_rank = node_ips.index(current_node_ip) pod = cluster.pods[pod_rank] default_env = os.environ.copy() current_env = copy.copy(default_env) gloo_rendezvous_dir = tempfile.mkdtemp() # add gloo env current_env["PADDLE_WITH_GLOO"] = "1" current_env["PADDLE_GLOO_RENDEZVOUS"] = "3" current_env["PADDLE_GLOO_FS_PATH"] = gloo_rendezvous_dir current_env.pop("http_proxy", None) current_env.pop("https_proxy", None) procs = [] cmds = [] log_fns = [] for idx, cur_server in enumerate(pod.servers): proc_env = { "PADDLE_PSERVERS_IP_PORT_LIST": server_endpoints, "PADDLE_TRAINER_ENDPOINTS": worker_endpoints, "PADDLE_PORT": cur_server.endpoint.split(":")[1], "TRAINING_ROLE": "PSERVER", "PADDLE_TRAINERS_NUM": str(worker_num), "POD_IP": cur_server.endpoint.split(":")[0] } current_env.update(proc_env) cmd = [sys.executable, "-u", args.training_script ] + args.training_script_args cmds.append(cmd) if idx == 0: logger.info( "Local server start {} processes. First process distributed " "environment info (Only For Debug): {}".format( len(pod.servers), pretty_print_envs(proc_env, ("Distributed Envs", "Value")))) if args.log_dir is not None: os.system("mkdir -p {}".format(args.log_dir)) fn = open("%s/serverlog.%d" % (args.log_dir, idx), "w") log_fns.append(fn) proc = subprocess.Popen(cmd, env=current_env, stdout=fn, stderr=fn) else: proc = subprocess.Popen(cmd, env=current_env) tp = TrainerProc() tp.proc = proc tp.rank = cur_server.rank tp.local_rank = idx tp.log_fn = fn tp.log_offset = fn.tell() if fn else None tp.cmd = cmd procs.append(tp) for idx, cur_worker in enumerate(pod.workers): proc_env = { "PADDLE_PSERVERS_IP_PORT_LIST": server_endpoints, "PADDLE_TRAINER_ENDPOINTS": worker_endpoints, "PADDLE_TRAINERS_NUM": str(worker_num), "TRAINING_ROLE": "TRAINER", "PADDLE_TRAINER_ID": str(cur_worker.rank) } current_env.update(proc_env) cmd = [sys.executable, "-u", args.training_script ] + args.training_script_args cmds.append(cmd) if idx == 0: logger.info( "Local worker start {} processes. First process distributed " "environment info (Only For Debug): {}".format( len(pod.workers), pretty_print_envs(proc_env, ("Distributed Envs", "Value")))) if args.log_dir is not None: os.system("mkdir -p {}".format(args.log_dir)) fn = open("%s/workerlog.%d" % (args.log_dir, idx), "w") log_fns.append(fn) proc = subprocess.Popen(cmd, env=current_env, stdout=fn, stderr=fn) else: proc = subprocess.Popen(cmd, env=current_env) tp = TrainerProc() tp.proc = proc tp.rank = cur_worker.rank tp.local_rank = idx tp.log_fn = fn tp.log_offset = fn.tell() if fn else None tp.cmd = cmd procs.append(tp) logger.info( "Please check servers and workers logs in {}/workerlog.* and {}/serverlog.*". format(args.log_dir, args.log_dir)) # only wait worker to finish here for i, proc in enumerate(procs): if i < len(pod.servers): continue procs[i].proc.wait() if len(log_fns) > 0: log_fns[i].close() print("all workers exit, going to finish parameter server", file=sys.stderr) for i in range(len(pod.servers)): if len(log_fns) > 0: log_fns[i].close() procs[i].proc.terminate() print("all parameter server are killed", file=sys.stderr) if os.path.exists(gloo_rendezvous_dir): shutil.rmtree(gloo_rendezvous_dir) def launch(): args = _parse_args() logger = get_logger() _print_arguments(args) ps_args = ['--worker_num', '--server_num', '--servers', '--workers'] collective_args = ['--ips', '--gpus'] has_ps_args = [ ps_arg for ps_arg in ps_args if ps_arg in " ".join(sys.argv[1:-1]) ] has_collective_args = [ co_arg for co_arg in collective_args if co_arg in " ".join(sys.argv[1:-1]) ] if fluid.core.is_compiled_with_cuda(): cuda_device_num = fluid.core.get_cuda_device_count() else: cuda_device_num = 0 if len(has_ps_args) > 0 or cuda_device_num == 0: logger.info("Run parameter-sever cpu mode. pserver arguments:{}".format( has_ps_args)) launch_ps(args) elif len(has_collective_args) > 0: logger.info("Run collective gpu mode. gpu arguments:{}, cuda count:{}". format(has_collective_args, cuda_device_num)) launch_collective(args) else: logger.warning( "Not found distinct arguments. Default use gpu collective mode") launch_collective(args) if __name__ == "__main__": launch()