wehub-resource-sync
72 lines
2.7 KiB
Python
72 lines
2.7 KiB
Python
# Copyright (c) 2019 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 os
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import sys
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import time
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def train(prefix):
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selected_gpus = os.getenv("FLAGS_selected_gpus")
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trainer_id = int(os.getenv("PADDLE_TRAINER_ID"))
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worker_endpoints_env = os.getenv("PADDLE_TRAINER_ENDPOINTS")
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current_endpoint = os.getenv("PADDLE_CURRENT_ENDPOINT")
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worker_endpoints = worker_endpoints_env
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trainers_num = len(worker_endpoints.split(','))
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name = f"selected_gpus:{selected_gpus} worker_endpoints:{worker_endpoints} trainers_num:{trainers_num} current_endpoint:{current_endpoint} trainer_id:{trainer_id}"
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print(name)
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with open(f"multi_process_{prefix}.check_{trainer_id}.log", "w") as f:
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f.write(name)
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def train_abort(prefix):
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selected_gpus = os.getenv("FLAGS_selected_gpus")
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trainer_id = int(os.getenv("PADDLE_TRAINER_ID"))
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worker_endpoints_env = os.getenv("PADDLE_TRAINER_ENDPOINTS")
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current_endpoint = os.getenv("PADDLE_CURRENT_ENDPOINT")
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worker_endpoints = worker_endpoints_env
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trainers_num = len(worker_endpoints.split(','))
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if trainer_id == 0:
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try:
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# train abort
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sys.exit(1)
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except SystemExit:
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name = f"abort>>> selected_gpus:{selected_gpus} worker_endpoints:{worker_endpoints} trainers_num:{trainers_num} current_endpoint:{current_endpoint} trainer_id:{trainer_id}"
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print(name)
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with open(
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f"multi_process_{prefix}.check_{trainer_id}.log", "w"
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) as f:
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f.write(name)
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raise
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else:
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# sleep 30s to make sure paddle.distributed.launch will terminate this process
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time.sleep(30)
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name = f"selected_gpus:{selected_gpus} worker_endpoints:{worker_endpoints} trainers_num:{trainers_num} current_endpoint:{current_endpoint} trainer_id:{trainer_id}"
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print(name)
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with open(f"multi_process_{prefix}.check_{trainer_id}.log", "w") as f:
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f.write(name)
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if __name__ == '__main__':
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if len(sys.argv) == 3 and sys.argv[2] == "abort":
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prefix = sys.argv[1]
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train_abort(prefix)
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else:
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prefix = sys.argv[1]
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train(prefix)
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