Skip to content

P
Paddle

csdn_franckjun / Paddle
与 Fork 源项目一致

Fork自 PaddlePaddle / Paddle

通知 1
Star 1
Fork 0
P
Paddle

体验新版 GitCode，发现更多精彩内容 >>

未验证 提交 1358397e 编写于 作者: G gongweibao 提交者： GitHub
浏览文件
操作

Clean up the redundant files and unify the launch interface. (#28928)

上级 47af5c3c
隐藏空白更改
内联 并排
Showing with 745 addition and 429 deletion
python/paddle/distributed/cloud_utils.py
浏览文件 @ 1358397e
......@@ -14,7 +14,7 @@
import os
import paddle
from paddle.distributed.utils import get_cluster, logger
from paddle.distributed.utils import get_cluster, logger, get_gpus, get_cluster_from_args
def get_cloud_cluster(args_node_ips, args_node_ip, args_port, selected_gpus):
......@@ -94,5 +94,26 @@ paddlecloud environment.".format(args_node_ips, node_ips))
return cluster, cluster.pods[node_rank]
def get_trainers_num():
def _get_trainers_num():
return int(os.getenv("PADDLE_TRAINERS_NUM", "1"))
def get_cluster_and_pod(args):
# parse arguments, used for cloud-single-machine and local
selected_gpus = get_gpus(args.selected_gpus)
trainers_num = _get_trainers_num()
logger.debug("parsed from args trainerss_num:{} selected_gpus:{}".format(
trainers_num, selected_gpus))
cluster = None
pod = None
if args.use_paddlecloud and trainers_num != 1:
cluster, pod = get_cloud_cluster(args.cluster_node_ips, args.node_ip,
args.started_port, selected_gpus)
logger.info("get cluster from cloud:{}".format(cluster))
else:
cluster, pod = get_cluster_from_args(args, selected_gpus)
logger.info("get cluster from args:{}".format(cluster))
return cluster, pod
python/paddle/distributed/fleet/cloud_utils.py
浏览文件 @ 1358397e
......@@ -17,9 +17,12 @@ import paddle
from paddle.distributed.fleet.launch_utils import get_cluster, logger
def get_cloud_cluster(args_node_ips, selected_gpus, args_port=6170):
def get_cloud_cluster(args_node_ips,
device_mode,
devices_per_proc,
args_port=6170):
"""
args_node_ips:string, selected_gpus:list, args_port: int
args_node_ips:string, device_mode:DeviceMode(IntEnum), device_per_proc:list, args_port: int
"""
#you can automatically get ip info while using paddlecloud multi nodes mode.
node_ips = os.getenv("PADDLE_TRAINERS")
......@@ -55,7 +58,7 @@ paddlecloud environment.".format(args_node_ips, node_ips))
paddle_port = int(os.getenv("PADDLE_PORT", ""))
if paddle_ports_num >= len(
selected_gpus) and paddle_port != args_port:
devices_per_proc) and paddle_port != args_port:
logger.warning("Use Cloud specified port:{}.".format(
paddle_port))
started_port = paddle_port
......@@ -67,7 +70,7 @@ paddlecloud environment.".format(args_node_ips, node_ips))
if started_port is None:
started_port = 6170
ports = [
x for x in range(started_port, started_port + len(selected_gpus))
x for x in range(started_port, started_port + len(devices_per_proc))
]
trainer_endpoints = []
for ip in node_ips:
......@@ -85,7 +88,7 @@ paddlecloud environment.".format(args_node_ips, node_ips))
.format(node_ips, node_ip, node_rank, trainer_endpoints))
cluster, pod = get_cluster(node_ips, node_ip, trainer_endpoints,
selected_gpus)
device_mode, devices_per_proc)
return cluster, cluster.pods[node_rank]
......
python/paddle/distributed/fleet/launch.py
浏览文件 @ 1358397e
......@@ -68,7 +68,9 @@ import copy
from argparse import ArgumentParser, REMAINDER
import paddle
import paddle.fluid as fluid
from paddle.distributed.fleet import launch_utils
# TODO(danleifeng): Don't import * from a module
from paddle.distributed.fleet.launch_utils import *
import paddle.distributed.fleet.cloud_utils as cloud_utils
......@@ -98,12 +100,21 @@ see: http://www.paddlepaddle.org/documentation/docs/zh/1.6/user_guides/howto/tra
help="The path for each process's log.If it's not set, the log will printed to default pipe."
)
base_group.add_argument(
"--nproc_per_node",
type=int,
default=None,
help="The number of processes to launch on a node."
"In gpu training, it should be less or equal to the gpus number of you system(or you set by --gpus). And so each process can"
" bound to one or average number of gpus.")
base_group.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."
help="It's for gpu training."
"For example:"
"--gpus=\"0,1,2,3\" will launch four training processes each bound to one gpu."
)
base_group.add_argument(
......@@ -146,14 +157,13 @@ see: http://www.paddlepaddle.org/documentation/docs/zh/1.6/user_guides/howto/tra
return parser.parse_args()
def get_cluster_from_args(args, gpus):
def get_cluster_from_args(args, device_mode, devices_per_proc):
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)
......@@ -164,7 +174,7 @@ def get_cluster_from_args(args, gpus):
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))
free_ports = find_free_ports(len(devices_per_proc))
if free_ports is not None:
free_ports = list(free_ports)
else:
......@@ -172,20 +182,23 @@ def get_cluster_from_args(args, gpus):
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))]
free_ports = [
x for x in range(start_port, start_port + len(devices_per_proc))
]
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)
return get_cluster(node_ips, node_ip, trainer_endpoints, device_mode,
devices_per_proc)
def launch_collective(args):
# parse arguments, used for cloud-single-machine and local
gpus = get_gpus(args.gpus)
(device_mode, devices_per_proc) = launch_utils.get_device_proc_info(args)
trainers_num = cloud_utils.get_trainers_num()
logger.debug("parsed from args trainerss_num:{} gpus:{}".format(
trainers_num, gpus))
logger.debug("parsed from args trainerss_num:{} mode:{} devices:{}".format(
trainers_num, device_mode, devices_per_proc))
cluster = None
pod = None
......@@ -194,11 +207,13 @@ def launch_collective(args):
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)
cluster, pod = cloud_utils.get_cloud_cluster(
args.ips, device_mode, devices_per_proc, 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)
cluster, pod = get_cluster_from_args(args, device_mode,
devices_per_proc)
logger.debug("get cluster from args:{}".format(cluster))
global_envs = copy.copy(os.environ.copy())
......
python/paddle/distributed/fleet/launch_utils.py
浏览文件 @ 1358397e
......@@ -26,6 +26,8 @@ import shutil
from contextlib import closing
import socket
import warnings
import six
from enum import IntEnum
import paddle
import paddle.fluid as fluid
......@@ -33,7 +35,7 @@ logger = logging.getLogger("root")
logger.propagate = False
class DistributeMode:
class DistributeMode(IntEnum):
"""
There are various mode for fleetrun, each of them is designed for different model.
"""
......@@ -42,6 +44,16 @@ class DistributeMode:
PS_HETER = 2
class DeviceMode(IntEnum):
"""
Training devices type
"""
CPU = 0
GPU = 1
KUNLUN = 2
UNKNOWN = 3
class Cluster(object):
def __init__(self, hdfs):
self.job_server = None
......@@ -243,7 +255,8 @@ def get_logger(log_level=20, name="root"):
return logger
def get_cluster(node_ips, node_ip, trainer_endpoints, selected_gpus):
def get_cluster(node_ips, node_ip, trainer_endpoints, device_mode,
devices_per_proc):
assert type(trainer_endpoints) is list, "trainer_endpoints must be list"
cluster = Cluster(hdfs=None)
trainer_rank = 0
......@@ -252,13 +265,17 @@ def get_cluster(node_ips, node_ip, trainer_endpoints, selected_gpus):
pod.rank = node_rank
pod.addr = ip
cur_node_endpoints = trainer_endpoints[node_rank]
# when use paddlecloud, endpoints may > selected_gpus(user_defined)
# when use paddlecloud, endpoints may > devices_per_proc(user_defined)
assert len(cur_node_endpoints) >= len(
selected_gpus
devices_per_proc
), "current trainer_endpoints size should be greater equal than selected_gpus size."
for i in range(len(selected_gpus)):
for i in range(len(devices_per_proc)):
trainer = Trainer()
trainer.gpus.append(selected_gpus[i])
if device_mode == DeviceMode.GPU:
if isinstance(devices_per_proc[i], (list, tuple)):
trainer.gpus.extend(devices_per_proc[i])
else:
trainer.gpus.append(devices_per_proc[i])
trainer.endpoint = "%s" % (cur_node_endpoints[i])
trainer.rank = trainer_rank
trainer_rank += 1
......@@ -432,13 +449,16 @@ def start_local_trainers(cluster,
procs = []
for idx, t in enumerate(pod.trainers):
proc_env = {
"FLAGS_selected_gpus": "%s" % ",".join([str(g) for g in t.gpus]),
"PADDLE_TRAINER_ID": "%d" % t.rank,
"PADDLE_CURRENT_ENDPOINT": "%s" % t.endpoint,
"PADDLE_TRAINERS_NUM": "%d" % cluster.trainers_nranks(),
"PADDLE_TRAINER_ENDPOINTS": ",".join(cluster.trainers_endpoints())
}
if len(t.gpus) > 0:
proc_env["FLAGS_selected_gpus"] = "%s" % ",".join(
[str(g) for g in t.gpus])
current_env.update(proc_env)
cmd = [sys.executable, "-u", training_script] + training_script_args
......@@ -565,6 +585,47 @@ def get_gpus(gpus):
return res_gpus
def get_device_mode():
#TODO(gongwb):Add XPU supported
if not fluid.core.is_compiled_with_cuda(
) or fluid.core.get_cuda_device_count() <= 0:
print("launch train in CPU mode")
return DeviceMode.CPU
print("launch train in GPU mode")
return DeviceMode.GPU
def get_device_proc_info(args):
# device_mode
device_mode = get_device_mode()
# devices
devices_per_proc = []
if device_mode == DeviceMode.GPU:
gpus = get_gpus(args.gpus)
if args.nproc_per_node is not None:
assert (len(gpus) % int(args.nproc_per_node)) ==0, \
"gpus' number:{} mod args.nproc_per_node:{} must == 0".format(len(gpus), arg.nproc_per_node)
n = int(len(gpus) / int(args.nproc_per_node))
devices_per_proc = [
gpus[i:i + n] for i in six.moves.range(0, len(gpus), n)
]
else:
devices_per_proc = gpus
elif device_mode == DeviceMode.CPU:
if args.nproc_per_node is None:
devices_per_proc = [0]
else:
devices_per_proc = [x for x in range(0, args.nproc_per_node)]
else:
assert False, "Can't support device_mode:{}, support only cpu and gpu now.".format(
device_mode)
return (device_mode, devices_per_proc)
def direct_start(args):
# run ps-cpu mode on paddlecloud, using given envs
cmd = [sys.executable, "-u", args.training_script] + \
......
python/paddle/distributed/launch.py
浏览文件 @ 1358397e
# Copyright (c) 2019 PaddlePaddle Authors. All Rights Reserved.
#
# Copyright (c) 2020 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.
r"""
paddle.distributed.launch is a module that spawns multiple distributed
process on each training node for gpu training.
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 training with all visible gpu cards:
python -m paddle.distributed.launch \
your_training_py (arg1 arg2 and all others)
2. for single node training 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 multiple 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 time
import six
import copy
from argparse import ArgumentParser, REMAINDER
from paddle.distributed.utils import *
from paddle.distributed import 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.
NOTE: your train program ***must*** run as distributed nccl2 mode,
see: http://www.paddlepaddle.org/documentation/docs/zh/1.6/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(
"--use_paddlecloud",
action='store_true',
help="wheter to use paddlecloud platform to run your multi-process job. If false, no need to set this argument."
)
parser.add_argument(
"--started_port",
type=int,
default=None,
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 training and the training process will run on the selected_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(
"--log_level",
type=int,
default=20, # logging.INFO, details are here:https://docs.python.org/3/library/logging.html#levels
help="Logging level, default is logging.INFO")
parser.add_argument(
"--log_dir",
type=str,
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, selected_gpus):
node_ips = [x.strip() for x in args.cluster_node_ips.split(',')]
node_ip = args.node_ip
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 args.use_paddlecloud and len(
node_ips) <= 1 and args.started_port is None:
free_ports = find_free_ports(len(selected_gpus))
if free_ports is not None:
free_ports = list(free_ports)
else:
started_port = 6070
if args.started_port is not None:
started_port = args.started_port
free_ports = [
x for x in range(started_port, started_port + len(selected_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, selected_gpus)
def get_gpus(selected_gpus):
if selected_gpus is None:
from paddle.fluid import core
gpus_num = core.get_cuda_device_count()
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 == "":
gpus = [x.strip() for x in selected_gpus.split(',')]
else:
# change selected_gpus into relative values
# e.g. CUDA_VISIBLE_DEVICES=4,5,6,7; args.selected_gpus=4,5,6,7;
# therefore selected_gpus=0,1,2,3
cuda_visible_devices_list = cuda_visible_devices.split(',')
for x in selected_gpus.split(','):
assert x in cuda_visible_devices_list, "Can't find "\
"your selected_gpus %s in CUDA_VISIBLE_DEVICES[%s]."\
% (x, cuda_visible_devices)
gpus = [
cuda_visible_devices_list.index(x.strip())
for x in selected_gpus.split(',')
]
logger.info("Change selected_gpus into reletive values. --ips:{} "
"will change into relative_ips:{} according to your "
"CUDA_VISIBLE_DEVICES:{}".format(
selected_gpus, gpus, cuda_visible_devices_list))
return gpus
def get_cluster_and_pod(args):
# parse arguments, used for cloud-single-machine and local
selected_gpus = get_gpus(args.selected_gpus)
trainers_num = cloud_utils.get_trainers_num()
logger.debug("parsed from args trainerss_num:{} selected_gpus:{}".format(
trainers_num, selected_gpus))
cluster = None
pod = None
if args.use_paddlecloud and trainers_num != 1:
cluster, pod = cloud_utils.get_cloud_cluster(
args.cluster_node_ips, args.node_ip, args.started_port,
selected_gpus)
logger.info("get cluster from cloud:{}".format(cluster))
else:
cluster, pod = get_cluster_from_args(args, selected_gpus)
logger.info("get cluster from args:{}".format(cluster))
return cluster, pod
def launch(args):
cluster, pod = get_cluster_and_pod(args)
procs = start_local_trainers(
cluster,
pod,
training_script=args.training_script,
training_script_args=args.training_script_args,
log_dir=args.log_dir)
while True:
alive = watch_local_trainers(procs, cluster.trainers_nranks())
if not alive:
logger.info("Local procs complete, POD info:{}".format(pod))
break
time.sleep(3)
if __name__ == "__main__":
args = _parse_args()
logger = get_logger(args.log_level)
if args.print_config:
_print_arguments(args)
launch(args)
from paddle.distributed.fleet import launch
launch.launch()
python/paddle/distributed/spawn.py
浏览文件 @ 1358397e
......@@ -21,8 +21,8 @@ import six
import sys
import warnings
from paddle.distributed.launch import get_cluster_and_pod, _print_arguments
from paddle.distributed.utils import _prepare_trainer_env
from paddle.distributed.utils import _print_arguments, _prepare_trainer_env
from paddle.distributed.cloud_utils import get_cluster_and_pod
from paddle.device import get_device
# deprecated module import
......@@ -30,10 +30,6 @@ from paddle.fluid import core
from paddle.fluid.framework import _cpu_num
# NOTE(chenweihang): The existence of this class leads to
# the maintenance of two arguments. When the launch.py arguments
# is updated, the arguments here also need to be updated,
# but I have not thought of a better way here
class ParallelEnvArgs(object):
def __init__(self):
# Paddle cluster nodes ips, such as 192.168.0.16,192.168.0.17..
......@@ -136,7 +132,6 @@ def _get_subprocess_env_list(nprocs, options):
args.use_paddlecloud = options.get('use_paddlecloud', False)
args.print_config = options.get('print_config', False)
# reuse code of launch.py
cluster, pod = get_cluster_and_pod(args)
# prepare subprocess env list
......@@ -151,7 +146,7 @@ def _get_subprocess_env_list(nprocs, options):
def _remove_risky_env():
# remove useless env vars, same as launch.py
# remove useless env vars
# no copy, each process will hold env vars itself
os.environ.pop("http_proxy", None)
os.environ.pop("https_proxy", None)
......
python/paddle/distributed/utils.py
浏览文件 @ 1358397e
......@@ -20,6 +20,7 @@ import os
import signal
import copy
import sys
import six
import subprocess
from contextlib import closing
import socket
......@@ -28,6 +29,72 @@ logger = logging.getLogger("root")
logger.propagate = False
def get_cluster_from_args(args, selected_gpus):
node_ips = [x.strip() for x in args.cluster_node_ips.split(',')]
node_ip = args.node_ip
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 args.use_paddlecloud and len(
node_ips) <= 1 and args.started_port is None:
free_ports = find_free_ports(len(selected_gpus))
if free_ports is not None:
free_ports = list(free_ports)
else:
started_port = 6070
if args.started_port is not None:
started_port = args.started_port
free_ports = [
x for x in range(started_port, started_port + len(selected_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, selected_gpus)
def get_gpus(selected_gpus):
if selected_gpus is None:
from paddle.fluid import core
gpus_num = core.get_cuda_device_count()
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 == "":
gpus = [x.strip() for x in selected_gpus.split(',')]
else:
# change selected_gpus into relative values
# e.g. CUDA_VISIBLE_DEVICES=4,5,6,7; args.selected_gpus=4,5,6,7;
# therefore selected_gpus=0,1,2,3
cuda_visible_devices_list = cuda_visible_devices.split(',')
for x in selected_gpus.split(','):
assert x in cuda_visible_devices_list, "Can't find "\
"your selected_gpus %s in CUDA_VISIBLE_DEVICES[%s]."\
% (x, cuda_visible_devices)
gpus = [
cuda_visible_devices_list.index(x.strip())
for x in selected_gpus.split(',')
]
logger.info("Change selected_gpus into reletive values. --ips:{} "
"will change into relative_ips:{} according to your "
"CUDA_VISIBLE_DEVICES:{}".format(
selected_gpus, gpus, cuda_visible_devices_list))
return gpus
def _print_arguments(args):
print("----------- Configuration Arguments -----------")
for arg, value in sorted(six.iteritems(vars(args))):
print("%s: %s" % (arg, value))
print("------------------------------------------------")
class Hdfs(object):
def __init__(self):
self.hdfs_ugi = None
......
python/paddle/fluid/dygraph/parallel.py
浏览文件 @ 1358397e
......@@ -69,7 +69,7 @@ class ParallelEnv(object):
This class is used to obtain the environment variables required for
the parallel execution of ``paddle.nn.Layer`` in dynamic mode.
The parallel execution in dynamic mode needs to be started using ``paddle.distributed.launch``
The parallel execution in dynamic mode needs to be started using ``paddle.distributed.launch``
or ``paddle.distributed.spawn`` .
Examples:
......@@ -104,7 +104,11 @@ class ParallelEnv(object):
def __init__(self):
self._rank = int(os.getenv("PADDLE_TRAINER_ID", "0"))
self._world_size = int(os.getenv("PADDLE_TRAINERS_NUM", "1"))
self._device_id = int(os.getenv("FLAGS_selected_gpus", "0"))
# imperative only support one gpu
selected_gpus = os.getenv("FLAGS_selected_gpus", "0").split(",")
self._device_id = int(selected_gpus[0])
self._trainer_endpoints = os.getenv("PADDLE_TRAINER_ENDPOINTS",
"").split(",")
self._current_endpoint = os.getenv("PADDLE_CURRENT_ENDPOINT", "")
......@@ -347,7 +351,7 @@ class DataParallel(layers.Layer):
2. start by ``paddle.distributed.launch`` module, for example:
``python -m paddle.distributed.launch --selected_gpus=0,1 demo.py`` .
``python -m paddle.distributed.launch --gpus=0,1 demo.py`` .
And the content of `demo.py` is the code of examples.
......
python/paddle/fluid/tests/unittests/CMakeLists.txt
浏览文件 @ 1358397e
......@@ -26,14 +26,18 @@ list(APPEND MIXED_DIST_TEST_OPS test_dgc_optimizer)
list(APPEND MIXED_DIST_TEST_OPS test_simple_dist_transpiler)
list(APPEND MIXED_DIST_TEST_OPS test_recv_save_op)
list(APPEND MIXED_DIST_TEST_OPS test_transpiler_ops)
list(APPEND MIXED_DIST_TEST_OPS test_launch)
list(APPEND MIXED_DIST_TEST_OPS test_c_comm_init_op)
list(APPEND MIXED_DIST_TEST_OPS test_launch_ps)
list(APPEND MIXED_DIST_TEST_OPS test_communicator_async)
list(APPEND MIXED_DIST_TEST_OPS test_communicator_geo)
list(APPEND MIXED_DIST_TEST_OPS test_communicator_half_async)
list(APPEND MIXED_DIST_TEST_OPS test_communicator_sync)
list(APPEND MIXED_DIST_TEST_OPS test_fleet_launch)
list(APPEND MIXED_DIST_TEST_OPS test_fleet_launch_ps)
list(APPEND MIXED_DIST_TEST_OPS test_launch_coverage)
list(APPEND MIXED_DIST_TEST_OPS test_fleetrun)
list(APPEND MIXED_DIST_TEST_OPS test_fleet_run_random_port)
list(APPEND MIXED_DIST_TEST_OPS test_fleet_launch_async)
list(APPEND MIXED_DIST_TEST_OPS test_fleet_launch_cloud)
list(APPEND MIXED_DIST_TEST_OPS test_fleet_launch_nproc)
list(APPEND MIXED_DIST_TEST_OPS test_fleet_api_input)
list(APPEND MIXED_DIST_TEST_OPS test_fleet_checkpoint)
list(APPEND MIXED_DIST_TEST_OPS test_collective_optimizer)
......@@ -494,14 +498,17 @@ if(WITH_DISTRIBUTE)
endif()
if(NOT APPLE)
if(WITH_GPU)
# NOTE. test_launch only work in gpu collective mode
bash_test_modules(test_launch START_BASH test_launch.sh ENVS PADDLE_BINARY_DIR=${PADDLE_BINARY_DIR})
bash_test_modules(test_c_comm_init_op START_BASH test_c_comm_init_op.sh ENVS PADDLE_BINARY_DIR=${PADDLE_BINARY_DIR})
py_test_modules(test_fleet_checkpoint MODULES test_fleet_checkpoint)
py_test_modules(test_launch_coverage MODULES test_launch_coverage)
endif()
bash_test_modules(test_launch_ps START_BASH test_launch_ps.sh ENVS PADDLE_BINARY_DIR=${PADDLE_BINARY_DIR})
bash_test_modules(test_fleet_launch START_BASH test_fleet_launch.sh ENVS PADDLE_BINARY_DIR=${PADDLE_BINARY_DIR})
bash_test_modules(test_fleet_launch_ps START_BASH test_fleet_launch_ps.sh ENVS PADDLE_BINARY_DIR=${PADDLE_BINARY_DIR})
bash_test_modules(test_fleetrun START_BASH test_fleetrun.sh ENVS PADDLE_BINARY_DIR=${PADDLE_BINARY_DIR})
bash_test_modules(test_fleet_run_random_port START_BASH test_fleet_run_random_port.sh ENVS PADDLE_BINARY_DIR=${PADDLE_BINARY_DIR})
bash_test_modules(test_fleet_launch_async START_BASH test_fleet_launch_async.sh ENVS PADDLE_BINARY_DIR=${PADDLE_BINARY_DIR})
bash_test_modules(test_fleet_launch_cloud START_BASH test_fleet_launch_cloud.sh ENVS PADDLE_BINARY_DIR=${PADDLE_BINARY_DIR})
bash_test_modules(test_fleet_launch_nproc START_BASH test_fleet_launch_nproc.sh ENVS PADDLE_BINARY_DIR=${PADDLE_BINARY_DIR})
# port range (20000, 23000) is reserved for dist-ops
set(dist_ut_port 20001)
......@@ -624,9 +631,7 @@ if (WITH_DISTRIBUTE AND NOT APPLE)
if(WITH_GPU)
set_tests_properties(test_c_comm_init_op PROPERTIES TIMEOUT 120)
set_tests_properties(test_fleet_checkpoint PROPERTIES TIMEOUT 120)
set_tests_properties(test_launch PROPERTIES TIMEOUT 120)
endif()
set_tests_properties(test_fleet_launch PROPERTIES TIMEOUT 120)
endif()
# setting timeout value as 15S
......
python/paddle/fluid/tests/unittests/detected_gpu.py 0 → 100644
浏览文件 @ 1358397e
# Copyright (c) 2020 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.
import paddle
import sys
import paddle.fluid as fluid
print("compile with cuda:", fluid.core.is_compiled_with_cuda())
print("get_cuda_device_count:", fluid.core.get_cuda_device_count())
if fluid.core.is_compiled_with_cuda() and fluid.core.get_cuda_device_count(
) > 0:
sys.exit(0)
else:
sys.exit(1)
python/paddle/fluid/tests/unittests/nproc_process.py 0 → 100644
浏览文件 @ 1358397e
# 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.
import os
import sys
import time
def train(prefix):
selected_gpus = os.getenv("FLAGS_selected_gpus")
trainer_id = int(os.getenv("PADDLE_TRAINER_ID"))
worker_endpoints_env = os.getenv("PADDLE_TRAINER_ENDPOINTS")
current_endpoint = os.getenv("PADDLE_CURRENT_ENDPOINT")
worker_endpoints = worker_endpoints_env
trainers_num = len(worker_endpoints.split(','))
name = "selected_gpus:{} worker_endpoints:{} trainers_num:{} current_endpoint:{} trainer_id:{}"\
.format(selected_gpus, worker_endpoints, trainers_num, current_endpoint,trainer_id)
print(name)
with open("{}.check_{}.log".format(prefix, trainer_id), "w") as f:
f.write(name)
if __name__ == '__main__':
prefix = sys.argv[1]
train(prefix)
python/paddle/fluid/tests/unittests/test_fleet_launch.sh 已删除 100644 → 0
浏览文件 @ 47af5c3c
#!/bin/bash
set -e
function test_launch_ps(){
fleetrun --server_num=2 --worker_num=2 fleet_ps_training.py 2> ut.elog
if grep -q "server are killed" ut.elog; then
echo "test pserver launch succeed"
else
echo "test pserver launch failed"
exit -1
fi
fleetrun --servers="127.0.0.1:6780,127.0.0.1:6781" --workers="127.0.0.1:6782,127.0.0.1:6783" fleet_ps_training.py 2> ut.elog
if grep -q "server are killed" ut.elog; then
echo "test pserver launch succeed"
else
echo "test pserver launch failed"
exit -1
fi
fleetrun --servers="127.0.0.1:6780,127.0.0.1:6781" --workers="127.0.0.1,127.0.0.1" fleet_ps_training.py 2> ut.elog
if grep -q "server are killed" ut.elog; then
echo "test pserver launch succeed"
else
echo "test pserver launch failed"
exit -1
fi
}
function test_launch_ps_heter(){
fleetrun --server_num=2 --worker_num=2 --heter_worker_num=2 fleet_ps_training.py 2> ut.elog
if grep -q "server are killed" ut.elog; then
echo "test heter pserver launch succeed"
else
echo "test pserver launch failed"
exit -1
fi
}
if [[ ${WITH_GPU} == "OFF" ]]; then
echo "in cpu test mode"
test_launch_ps
exit 0
fi
echo "No.1 unittest"
test_launch_ps
test_launch_ps_heter
# use default values
echo "No.2 unittest"
fleetrun multi_process.py fleetrun
# use paddlecloud
echo "begin test use paddlecloud"
cluster_node_ips="127.0.0.1,127.0.0.2"
export PADDLE_TRAINERS_NUM=2
export POD_IP=127.0.0.1
export PADDLE_TRAINERS=127.0.0.1,127.0.0.2
export PADDLE_TRAINER_ID=0
export PADDLE_PORT=35789
export TRAINER_PORTS_NUM=2
echo "No.3 unittest"
distributed_args="--ips=${cluster_node_ips} --gpus=0,1 --log_dir=testlog"
CUDA_VISIBLE_DEVICES=0,1 fleetrun ${distributed_args} multi_process.py fleetrun
str1="selected_gpus:0 worker_endpoints:127.0.0.1:35789,127.0.0.1:35790,127.0.0.2:35789,127.0.0.2:35790 trainers_num:4 current_endpoint:127.0.0.1:35789 trainer_id:0"
str2="selected_gpus:1 worker_endpoints:127.0.0.1:35789,127.0.0.1:35790,127.0.0.2:35789,127.0.0.2:35790 trainers_num:4 current_endpoint:127.0.0.1:35790 trainer_id:1"
file_0="multi_process_fleetrun.check_0.log"
file_1="multi_process_fleetrun.check_1.log"
echo "paddlecloud params test"
if grep -q "$str1" "$file_0"; then
echo "find trainer 0"
else
echo "not find trainer 0"
exit -1
fi
if grep -q "$str2" "$file_1"; then
echo "find trainer 1"
else
echo "not find trainer 1"
exit -1
fi
# test async poll process
if [ -f $file_0 ]; then
rm $file_0
fi
if [ -f $file_1 ]; then
rm $file_1
fi
# test use DISTRIBUTED_TRAINER_ENDPOINTS env in paddlecloud
unset PADDLE_PORT
export DISTRIBUTED_TRAINER_ENDPOINTS=127.0.0.1:6170,127.0.0.1:6171,127.0.0.2:6170,127.0.0.2:6171
echo "No.4 unittest"
echo "paddle.distributed.launch async poll process test"
if ! CUDA_VISIBLE_DEVICES=0,1 fleetrun ${distributed_args} multi_process.py fleetrun abort; then
echo "train abort as planned"
fi
abort_str1="abort>>> selected_gpus:0 worker_endpoints:127.0.0.1:6170,127.0.0.1:6171,127.0.0.2:6170,127.0.0.2:6171 trainers_num:4 current_endpoint:127.0.0.1:6170 trainer_id:0"
if grep -q "$abort_str1" "$file_0"; then
echo "trainer 0 abort as planned"
else
echo "trainer 0 not abort as planned"
exit -1
fi
if [ ! -f $file_1 ]; then
echo "trainer 1 terminate as planned"
else
echo "trainer 1 not terminate as planned"
exit -1
fi
#test for random ports
file_0_0="test_launch_filelock_0_0.log"
file_1_0="test_launch_filelock_1_0.log"
rm -rf $file_0_0 $file_0_1
distributed_args="--gpus=0,1 --log_dir=testlog"
export PADDLE_LAUNCH_LOG="test_launch_filelock_0"
echo "No.5 unittest"
CUDA_VISIBLE_DEVICES=0,1 fleetrun ${distributed_args} find_ports.py
str_0="worker_endpoints:127.0.0.1:6070,127.0.0.1:6071"
python/paddle/fluid/tests/unittests/test_launch.sh → python/paddle/fluid/tests/unittests/test_fleet_launch_async.sh
浏览文件 @ 1358397e
#!/bin/bash
# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
set -e
# use default values
# FIXME: random fails on Unknown command lines -c (or -m).
launch_py=${PADDLE_BINARY_DIR}/python/paddle/distributed/launch.py
python ${launch_py} multi_process.py launch
# use paddlecloud
echo "begin test use paddlecloud"
cluster_node_ips="10.0.0.1"
node_ip="10.0.0.1"
# test use DISTRIBUTED_TRAINER_ENDPOINTS env in paddlecloud
unset PADDLE_PORT
export DISTRIBUTED_TRAINER_ENDPOINTS=127.0.0.1:6170,127.0.0.1:6171,127.0.0.2:6170,127.0.0.2:6171
export cluster_node_ips="127.0.0.1,127.0.0.2"
export PADDLE_TRAINERS_NUM=2
export POD_IP=127.0.0.1
export PADDLE_TRAINERS=127.0.0.1,127.0.0.2
export PADDLE_TRAINER_ID=0
export PADDLE_PORT=35019
export TRAINER_PORTS_NUM=2
distributed_args="--use_paddlecloud --cluster_node_ips=${cluster_node_ips} --node_ip=${node_ip} --selected_gpus=0,1 --log_dir=testlog"
CUDA_VISIBLE_DEVICES=0,1 python ${launch_py} ${distributed_args} multi_process.py launch
str1="selected_gpus:0 worker_endpoints:127.0.0.1:35019,127.0.0.1:35020,127.0.0.2:35019,127.0.0.2:35020 trainers_num:4 current_endpoint:127.0.0.1:35019 trainer_id:0"
str2="selected_gpus:1 worker_endpoints:127.0.0.1:35019,127.0.0.1:35020,127.0.0.2:35019,127.0.0.2:35020 trainers_num:4 current_endpoint:127.0.0.1:35020 trainer_id:1"
file_0="multi_process_launch.check_0.log"
file_1="multi_process_launch.check_1.log"
echo "paddlecloud params test"
if grep -q "$str1" "$file_0"; then
echo "find trainer 0"
else
echo "not find trainer 0"
exit -1
fi
if grep -q "$str2" "$file_1"; then
echo "find trainer 1"
else
echo "not find trainer 1"
exit -1
fi
file_0="multi_process_fleetrun.check_0.log"
file_1="multi_process_fleetrun.check_1.log"
# test async poll process
if [ -f $file_0 ]; then
rm $file_0
fi
if [ -f $file_1 ]; then
rm $file_1
fi
distributed_args="--ips=${cluster_node_ips} --gpus=0,1 --log_dir=testlog"
# test use DISTRIBUTED_TRAINER_ENDPOINTS env in paddlecloud
unset PADDLE_PORT
export DISTRIBUTED_TRAINER_ENDPOINTS=127.0.0.1:6170,127.0.0.1:6171,127.0.0.2:6170,127.0.0.2:6171
echo ""
echo "paddle.distributed.launch async poll process test"
if ! CUDA_VISIBLE_DEVICES=0,1 python ${launch_py} ${distributed_args} multi_process.py launch abort; then
echo "paddle.distributed.fleet.launch async poll process test"
if ! CUDA_VISIBLE_DEVICES=0,1 python -m paddle.distributed.fleet.launch ${distributed_args} multi_process.py fleetrun abort; then
echo "train abort as planned"
fi
......@@ -73,13 +52,3 @@ else
echo "trainer 1 not terminate as planned"
exit -1
fi
#test for random ports
file_0_0="test_launch_filelock_0_0.log"
file_1_0="test_launch_filelock_1_0.log"
rm -rf $file_0_0 $file_0_1
distributed_args="--selected_gpus=0,1 --log_dir=testlog"
export PADDLE_LAUNCH_LOG="test_launch_filelock_0"
CUDA_VISIBLE_DEVICES=0,1 python ${launch_py} ${distributed_args} find_ports.py
str_0="worker_endpoints:127.0.0.1:6070,127.0.0.1:6071"
python/paddle/fluid/tests/unittests/test_fleet_launch_cloud.sh 0 → 100644
浏览文件 @ 1358397e
#!/bin/bash
# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
set -e
# use paddlecloud
echo "begin test use paddlecloud"
cluster_node_ips="127.0.0.1,127.0.0.2"
export PADDLE_TRAINERS_NUM=2
export POD_IP=127.0.0.1
export PADDLE_TRAINERS=127.0.0.1,127.0.0.2
export PADDLE_TRAINER_ID=0
export PADDLE_PORT=35789
export TRAINER_PORTS_NUM=2
distributed_args="--ips=${cluster_node_ips} --gpus=0,1 --log_dir=testlog"
CUDA_VISIBLE_DEVICES=0,1 python -m paddle.distributed.fleet.launch ${distributed_args} multi_process.py fleetrun
str1="selected_gpus:0 worker_endpoints:127.0.0.1:35789,127.0.0.1:35790,127.0.0.2:35789,127.0.0.2:35790 trainers_num:4 current_endpoint:127.0.0.1:35789 trainer_id:0"
str2="selected_gpus:1 worker_endpoints:127.0.0.1:35789,127.0.0.1:35790,127.0.0.2:35789,127.0.0.2:35790 trainers_num:4 current_endpoint:127.0.0.1:35790 trainer_id:1"
file_0="multi_process_fleetrun.check_0.log"
file_1="multi_process_fleetrun.check_1.log"
echo "paddlecloud params test"
if grep -q "$str1" "$file_0"; then
echo "find trainer 0"
else
echo "not find trainer 0"
exit -1
fi
if grep -q "$str2" "$file_1"; then
echo "find trainer 1"
else
echo "not find trainer 1"
exit -1
fi
# test async poll process
if [ -f $file_0 ]; then
rm $file_0
fi
if [ -f $file_1 ]; then
rm $file_1
fi
python/paddle/fluid/tests/unittests/test_fleet_launch_nproc.sh 0 → 100644
浏览文件 @ 1358397e
#!/bin/bash
# Copyright (c) 2020 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.
set -e
export FLAGS_START_PORT=35789
#local_ip=`ip route get 1 | awk '{print $NF;exit}'`
file_0="fleet_nproc_0.check_0.log"
function test_nproc_0(){
gpus=$1
rm -f ${file_0}
distributed_args="--log_dir=testlog --nproc_per_node=1"
# nproc_per_node=1, each with 2 gpus
python -m paddle.distributed.launch ${distributed_args} nproc_process.py fleet_nproc_0
str0="selected_gpus:${gpus} worker_endpoints:127.0.0.1:35789 trainers_num:1 current_endpoint:127.0.0.1:35789 trainer_id:0"
if grep -q "$str0" "$file_0"; then
echo "find trainer 0"
else
echo "not find trainer 0"
exit -1
fi
}
# unittest1:gpu
if python detected_gpu.py ; then
echo "begin ut 1:"
export CUDA_VISIBLE_DEVICES=0,1
test_nproc_0 "0,1"
fi
# unittest2:cpu
if ! python detected_gpu.py ; then
echo "begin ut 2:"
export CUDA_VISIBLE_DEVICES=""
test_nproc_0 ""
fi
function test_nproc_1_gpu(){
file_0="fleet_nproc_1.check_0.log"
file_1="fleet_nproc_1.check_1.log"
rm -f ${file_0} ${file_1}
distributed_args="--log_dir=testlog --nproc_per_node=2"
python -m paddle.distributed.launch ${distributed_args} nproc_process.py fleet_nproc_1
str0="selected_gpus:0 worker_endpoints:127.0.0.1:35789,127.0.0.1:35790 trainers_num:2 current_endpoint:127.0.0.1:35789 trainer_id:0"
if grep -q "$str0" "$file_0"; then
echo "find trainer 0"
else
echo "not find trainer 0"
exit -1
fi
str1="selected_gpus:1 worker_endpoints:127.0.0.1:35789,127.0.0.1:35790 trainers_num:2 current_endpoint:127.0.0.1:35790 trainer_id:1"
if grep -q "$str1" "$file_1"; then
echo "find trainer 1"
else
echo "not find trainer 1"
exit -1
fi
}
# unittest3: nproc_per_node=2, each with 1 gpus
if python detected_gpu.py ; then
echo "begin ut 3:"
export CUDA_VISIBLE_DEVICES=0,1
test_nproc_1_gpu
fi
function test_nproc_1_cpu(){
file_0="fleet_nproc_1.check_0.log"
file_1="fleet_nproc_1.check_1.log"
rm -f ${file_0} ${file_1}
distributed_args="--log_dir=testlog --nproc_per_node=2"
python -m paddle.distributed.launch ${distributed_args} nproc_process.py fleet_nproc_1
str0="selected_gpus: worker_endpoints:127.0.0.1:35789,127.0.0.1:35790 trainers_num:2 current_endpoint:127.0.0.1:35789 trainer_id:0"
if grep -q "$str0" "$file_0"; then
echo "find trainer 0"
else
echo "not find trainer 0"
exit -1
fi
str1="selected_gpus: worker_endpoints:127.0.0.1:35789,127.0.0.1:35790 trainers_num:2 current_endpoint:127.0.0.1:35790 trainer_id:1"
if grep -q "$str1" "$file_1"; then
echo "find trainer 1"
else
echo "not find trainer 1"
exit -1
fi
}
# unittest4: nproc_per_node=2, cpu
if ! python detected_gpu.py ; then
echo "begin ut 4:"
export CUDA_VISIBLE_DEVICES=""
test_nproc_1_cpu
fi
python/paddle/fluid/tests/unittests/test_fleet_launch_ps.sh 0 → 100644
浏览文件 @ 1358397e
#!/bin/bash
# Copyright (c) 2020 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.
set -e
function test_launch_ps(){
python -m paddle.distributed.fleet.launch --server_num=2 --worker_num=2 fleet_ps_training.py 2> ut.elog
if grep -q "server are killed" ut.elog; then
echo "test pserver launch succeed"
else
echo "test pserver launch failed"
exit -1
fi
python -m paddle.distributed.fleet.launch --servers="127.0.0.1:6780,127.0.0.1:6781" --workers="127.0.0.1:6782,127.0.0.1:6783" fleet_ps_training.py 2> ut.elog
if grep -q "server are killed" ut.elog; then
echo "test pserver launch succeed"
else
echo "test pserver launch failed"
exit -1
fi
python -m paddle.distributed.fleet.launch --servers="127.0.0.1:6780,127.0.0.1:6781" --workers="127.0.0.1,127.0.0.1" fleet_ps_training.py 2> ut.elog
if grep -q "server are killed" ut.elog; then
echo "test pserver launch succeed"
else
echo "test pserver launch failed"
exit -1
fi
}
function test_launch_ps_heter(){
python -m paddle.distributed.fleet.launch --server_num=2 --worker_num=2 --heter_worker_num=2 fleet_ps_training.py 2> ut.elog
if grep -q "server are killed" ut.elog; then
echo "test heter pserver launch succeed"
else
echo "test pserver launch failed"
exit -1
fi
}
if [[ ${WITH_GPU} == "OFF" ]]; then
echo "in cpu test mode"
test_launch_ps
exit 0
fi
test_launch_ps
test_launch_ps_heter
python/paddle/fluid/tests/unittests/test_fleet_run_random_port.sh 0 → 100644
浏览文件 @ 1358397e
#!/bin/bash
# Copyright (c) 2020 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.
set -e
#test for random ports
file_0_0="test_launch_filelock_0_0.log"
file_1_0="test_launch_filelock_1_0.log"
rm -rf $file_0_0 $file_0_1
distributed_args="--gpus=0,1 --log_dir=testlog"
export PADDLE_LAUNCH_LOG="test_launch_filelock_0"
CUDA_VISIBLE_DEVICES=0,1 python -m paddle.distributed.fleet.launch ${distributed_args} find_ports.py
str_0="worker_endpoints:127.0.0.1:6070,127.0.0.1:6071"
python/paddle/fluid/tests/unittests/test_fleetrun.sh 0 → 100644
浏览文件 @ 1358397e
#!/bin/bash
# Copyright (c) 2020 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.
set -e
# use default values
fleetrun multi_process.py fleetrun
python/paddle/distributed/launch_ps.py → python/paddle/fluid/tests/unittests/test_launch_coverage.py
浏览文件 @ 1358397e
# Copyright (c) 2019 PaddlePaddle Authors. All Rights Reserved.
#
# Copyright (c) 2020 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.
......@@ -13,34 +13,55 @@
# limitations under the License.
from __future__ import print_function
from __future__ import unicode_literals
import subprocess
import sys
import subprocess
import os
import time
import six
import copy
from argparse import ArgumentParser, REMAINDER
import unittest
import paddle.fluid as fluid
def parse_args():
# Optional arguments for the launch helper
parser = ArgumentParser(description="Distributed training")
from argparse import ArgumentParser, REMAINDER
from paddle.distributed.utils import _print_arguments, get_gpus, get_cluster_from_args
def _parse_args():
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/1.6/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(
"--start_port",
"--use_paddlecloud",
action='store_true',
help="wheter to use paddlecloud platform to run your multi-process job. If false, no need to set this argument."
)
parser.add_argument(
"--started_port",
type=int,
default=6170,
help="The trainer's start port on a single node")
default=None,
help="The trainer's started port on a single node")
parser.add_argument(
"--print_config",
......@@ -49,22 +70,26 @@ def parse_args():
help="Print the config or not")
parser.add_argument(
"--endpoints", type=str, default="", help="User defined endpoints")
parser.add_argument(
"--worker_num", type=int, default=2, help="number of workers")
"--selected_gpus",
type=str,
default=None,
help="It's for gpu training and the training process will run on the selected_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(
"--server_num", type=int, default=2, help="number of servers")
"--log_level",
type=int,
default=20, # logging.INFO, details are here:https://docs.python.org/3/library/logging.html#levels
help="Logging level, default is logging.INFO")
parser.add_argument(
"--log_dir",
default="logs",
type=str,
help="The path for each process's log.If it's not set, the log will printed to default pipe."
)
# positional
#positional
parser.add_argument(
"training_script",
type=str,
......@@ -73,93 +98,23 @@ def parse_args():
"followed by all the arguments for the "
"training script")
# rest from the training program
#rest from the training program
parser.add_argument('training_script_args', nargs=REMAINDER)
return parser.parse_args()
def start_procs(args):
worker_num = args.worker_num
server_num = args.server_num
start_port = args.start_port
default_env = os.environ.copy()
current_env = copy.copy(default_env)
current_env.pop("http_proxy", None)
current_env.pop("https_proxy", None)
procs = []
cmds = []
log_fns = []
ports = range(start_port, start_port + server_num, 1)
default_endpoints = ",".join(["127.0.0.1:" + str(x) for x in ports])
user_endpoints = ""
if args.endpoints == "":
user_endpoints = default_endpoints
else:
user_endpoints = args.endpoints
user_endpoints_ips = [x.split(":")[0] for x in user_endpoints.split(",")]
user_endpoints_port = [x.split(":")[1] for x in user_endpoints.split(",")]
for i in range(server_num):
current_env.update({
"PADDLE_PSERVERS_IP_PORT_LIST": user_endpoints,
"PADDLE_PORT": user_endpoints_port[i],
"TRAINING_ROLE": "PSERVER",
"PADDLE_TRAINERS_NUM": str(worker_num),
"POD_IP": user_endpoints_ips[i]
})
cmd = [sys.executable, "-u", args.training_script
] + args.training_script_args
cmds.append(cmd)
if args.log_dir is not None:
os.system("mkdir -p {}".format(args.log_dir))
fn = open("%s/serverlog.%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(worker_num):
current_env.update({
"PADDLE_PSERVERS_IP_PORT_LIST": user_endpoints,
"PADDLE_TRAINERS_NUM": str(worker_num),
"TRAINING_ROLE": "TRAINER",
"PADDLE_TRAINER_ID": str(i)
})
cmd = [sys.executable, "-u", args.training_script
] + args.training_script_args
cmds.append(cmd)
if args.log_dir is not None:
os.system("mkdir -p {}".format(args.log_dir))
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)
# only wait worker to finish here
for i, proc in enumerate(procs):
if i < server_num:
continue
procs[i].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(server_num):
if len(log_fns) > 0:
log_fns[i].close()
procs[i].terminate()
print("all parameter server are killed", file=sys.stderr)
def launch():
args = parse_args()
if args.print_config:
start_procs(args)
# server num, worker num
if __name__ == "__main__":
launch()
class TestCoverage(unittest.TestCase):
def test_gpus(self):
args = _parse_args()
if args.print_config:
_print_arguments(args)
gpus = get_gpus(None)
args.use_paddlecloud = True
cluster, pod = get_cluster_from_args(args, "0")
if __name__ == '__main__':
unittest.main()
python/paddle/fluid/tests/unittests/test_launch_ps.sh 已删除 100644 → 0
浏览文件 @ 47af5c3c
#!/bin/bash
set -e
# use default values
launch_py=${PADDLE_BINARY_DIR}/python/paddle/distributed/launch_ps.py
python ${launch_py} fleet_ps_training.py 2> ut.elog
if grep -q "server are killed" ut.elog; then
echo "succeed"
else
echo "failed"
exit -1
fi
Markdown is supported
0% 或 .
You are about to add 0 people to the discussion. Proceed with caution.
先完成此消息的编辑！
想要评论请 注册 或