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Paddle/python/paddle/fluid/tests/unittests/test_launch_coverage.py

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# 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.
from __future__ import print_function
import sys
import subprocess
import os
import time
import six
import copy
import unittest
import paddle.fluid as fluid
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(
"--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()
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()