85 lines
2.6 KiB
Python
85 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 tests.helpers.pipelines as tpipes
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import tests.helpers.utils as tutils
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from pytorch_lightning.callbacks import EarlyStopping
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from pytorch_lightning.trainer import Trainer
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from pytorch_lightning.utilities import memory
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from tests.helpers import BoringModel
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from tests.helpers.datamodules import ClassifDataModule
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from tests.helpers.runif import RunIf
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from tests.helpers.simple_models import ClassificationModel
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@RunIf(min_gpus=2)
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def test_multi_gpu_early_stop_ddp_spawn(tmpdir):
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tutils.set_random_master_port()
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trainer_options = dict(
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default_root_dir=tmpdir,
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callbacks=[EarlyStopping(monitor="train_acc")],
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max_epochs=50,
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limit_train_batches=10,
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limit_val_batches=10,
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gpus=[0, 1],
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accelerator="ddp_spawn",
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)
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dm = ClassifDataModule()
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model = ClassificationModel()
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tpipes.run_model_test(trainer_options, model, dm)
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@RunIf(min_gpus=2)
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def test_multi_gpu_model_ddp_spawn(tmpdir):
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tutils.set_random_master_port()
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trainer_options = dict(
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default_root_dir=tmpdir,
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max_epochs=1,
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limit_train_batches=10,
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limit_val_batches=10,
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gpus=[0, 1],
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accelerator="ddp_spawn",
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progress_bar_refresh_rate=0,
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)
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model = BoringModel()
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tpipes.run_model_test(trainer_options, model)
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# test memory helper functions
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memory.get_memory_profile("min_max")
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@RunIf(min_gpus=2)
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def test_ddp_all_dataloaders_passed_to_fit(tmpdir):
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"""Make sure DDP works with dataloaders passed to fit()"""
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tutils.set_random_master_port()
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model = BoringModel()
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fit_options = dict(train_dataloader=model.train_dataloader(), val_dataloaders=model.val_dataloader())
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trainer = Trainer(
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default_root_dir=tmpdir,
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progress_bar_refresh_rate=0,
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max_epochs=1,
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limit_train_batches=0.2,
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limit_val_batches=0.2,
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gpus=[0, 1],
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accelerator="ddp_spawn",
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)
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trainer.fit(model, **fit_options)
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assert trainer.state.finished, "DDP doesn't work with dataloaders passed to fit()."
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