74 lines
2.6 KiB
Python
74 lines
2.6 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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import pytest
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import torch
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from tests.base import BoringModel
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import platform
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from distutils.version import LooseVersion
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from pytorch_lightning import Trainer, Callback
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from unittest import mock
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class TestModel(BoringModel):
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def on_pretrain_routine_end(self) -> None:
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with mock.patch('pytorch_lightning.loggers.base.LightningLoggerBase.agg_and_log_metrics') as m:
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self.trainer.logger_connector.log_metrics({'a': 2}, {})
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logged_times = m.call_count
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expected = int(self.trainer.is_global_zero)
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msg = f'actual logger called from non-global zero, logged_times: {logged_times}, expected: {expected}'
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assert logged_times == expected, msg
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@pytest.mark.skipif(platform.system() == "Windows",
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reason="Distributed training is not supported on Windows")
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@pytest.mark.skipif((platform.system() == "Darwin" and
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LooseVersion(torch.__version__) < LooseVersion("1.3.0")),
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reason="Distributed training is not supported on MacOS before Torch 1.3.0")
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def test_global_zero_only_logging_ddp_cpu(tmpdir):
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"""
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Makes sure logging only happens from root zero
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"""
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model = TestModel()
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model.training_epoch_end = None
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trainer = Trainer(
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distributed_backend='ddp_cpu',
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num_processes=2,
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default_root_dir=tmpdir,
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limit_train_batches=1,
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limit_val_batches=1,
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max_epochs=1,
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weights_summary=None,
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)
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trainer.fit(model)
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@pytest.mark.skipif(torch.cuda.device_count() < 2, reason="test requires multi-GPU machine")
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def test_global_zero_only_logging_ddp_spawn(tmpdir):
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"""
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Makes sure logging only happens from root zero
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"""
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model = TestModel()
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model.training_epoch_end = None
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trainer = Trainer(
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distributed_backend='ddp_spawn',
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gpus=2,
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default_root_dir=tmpdir,
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limit_train_batches=1,
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limit_val_batches=1,
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max_epochs=1,
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weights_summary=None,
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)
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trainer.fit(model)
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