65 lines
2.0 KiB
Python
65 lines
2.0 KiB
Python
# Copyright The PyTorch Lightning team.
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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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"""
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Runs either `.fit()` or `.test()` on a single node across multiple gpus.
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"""
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import os
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from argparse import ArgumentParser
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import torch
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from pytorch_lightning import seed_everything, Trainer
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from tests.helpers.datamodules import ClassifDataModule
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from tests.helpers.simple_models import ClassificationModel
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def main():
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seed_everything(4321)
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parser = ArgumentParser(add_help=False)
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parser = Trainer.add_argparse_args(parser)
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parser.add_argument('--trainer_method', default='fit')
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parser.add_argument('--tmpdir')
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parser.add_argument('--workdir')
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parser.set_defaults(gpus=2)
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parser.set_defaults(accelerator="ddp")
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args = parser.parse_args()
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dm = ClassifDataModule()
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model = ClassificationModel()
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trainer = Trainer.from_argparse_args(args)
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if args.trainer_method == 'fit':
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trainer.fit(model, datamodule=dm)
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result = None
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elif args.trainer_method == 'test':
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result = trainer.test(model, datamodule=dm)
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elif args.trainer_method == 'fit_test':
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trainer.fit(model, datamodule=dm)
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result = trainer.test(model, datamodule=dm)
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else:
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raise ValueError(f'Unsupported: {args.trainer_method}')
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result_ext = {
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'status': 'complete',
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'method': args.trainer_method,
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'result': result,
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}
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file_path = os.path.join(args.tmpdir, 'ddp.result')
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torch.save(result_ext, file_path)
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if __name__ == '__main__':
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main()
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