wehub-resource-sync
129 lines
3.9 KiB
Python
129 lines
3.9 KiB
Python
# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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import unittest
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from argparse import REMAINDER, ArgumentParser
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from paddle.distributed.fleet.launch_utils import find_free_ports
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from paddle.distributed.utils.launch_utils import (
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_print_arguments,
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get_cluster_from_args,
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get_gpus,
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)
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from paddle.distributed.utils.process_utils import set_affinity
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def _parse_args():
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parser = ArgumentParser(
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description='''start paddle training using multi-process mode.
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NOTE: your train program ***must*** run as distributed nccl2 mode,
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see: http://www.paddlepaddle.org/documentation/docs/zh/1.6/user_guides/howto/training/cluster_howto.html#permalink-8--nccl2-
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And your train program must read environment variables below in order to let different
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process init properly:
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FLAGS_selected_gpus
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PADDLE_TRAINER_ID
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PADDLE_CURRENT_ENDPOINT
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PADDLE_TRAINERS_NUM
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PADDLE_TRAINER_ENDPOINTS
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POD_IP (current node ip address, not needed for local training)
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'''
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)
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# Optional arguments for the launch helper
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parser.add_argument(
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"--cluster_node_ips",
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type=str,
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default="127.0.0.1",
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help="Paddle cluster nodes ips, such as 192.168.0.16,192.168.0.17..",
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)
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parser.add_argument(
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"--node_ip", type=str, default="127.0.0.1", help="The current node ip. "
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)
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parser.add_argument(
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"--use_paddlecloud",
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action='store_true',
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help="whether to use paddle cloud platform to run your multi-process job. If false, no need to set this argument.",
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)
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parser.add_argument(
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"--started_port",
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type=int,
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default=None,
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help="The trainer's started port on a single node",
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)
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parser.add_argument(
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"--print_config",
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type=bool,
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default=True,
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help="Print the config or not",
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)
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parser.add_argument(
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"--selected_gpus",
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type=str,
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default=None,
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help="It's for gpu training and the training process will run on the selected_gpus,"
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"each process is bound to a single GPU. And if it's not set, this module will use all the gpu cards for training.",
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)
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parser.add_argument(
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"--log_level",
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type=int,
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default=20, # logging.INFO, details are here:https://docs.python.org/3/library/logging.html#levels
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help="Logging level, default is logging.INFO",
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)
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parser.add_argument(
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"--log_dir",
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type=str,
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help="The path for each process's log.If it's not set, the log will printed to default pipe.",
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)
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# positional
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parser.add_argument(
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"training_script",
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type=str,
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help="The full path to the single GPU training "
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"program/script to be launched in parallel, "
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"followed by all the arguments for the "
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"training script",
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)
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# rest from the training program
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parser.add_argument('training_script_args', nargs=REMAINDER)
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return parser.parse_args()
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class TestCoverage(unittest.TestCase):
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def test_gpus(self):
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args = _parse_args()
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if args.print_config:
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_print_arguments(args)
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gpus = get_gpus(None)
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args.use_paddlecloud = True
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cluster, pod = get_cluster_from_args(args, "0")
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def test_find_free_ports(self):
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find_free_ports(2)
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def test_set_affinity(self):
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set_affinity()
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if __name__ == '__main__':
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unittest.main()
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