# Copyright The PyTorch Lightning team. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import pytest import torch import tests.base.develop_pipelines as tpipes import tests.base.develop_utils as tutils from pytorch_lightning.callbacks import EarlyStopping from tests.base import EvalModelTemplate from pytorch_lightning.core import memory from pytorch_lightning.trainer import Trainer @pytest.mark.skipif(torch.cuda.device_count() < 2, reason="test requires multi-GPU machine") def test_multi_gpu_early_stop_ddp_spawn(tmpdir): """Make sure DDP works. with early stopping""" tutils.set_random_master_port() trainer_options = dict( default_root_dir=tmpdir, callbacks=[EarlyStopping()], max_epochs=50, limit_train_batches=10, limit_val_batches=10, gpus=[0, 1], distributed_backend='ddp_spawn', ) model = EvalModelTemplate() tpipes.run_model_test(trainer_options, model) @pytest.mark.skipif(torch.cuda.device_count() < 2, reason="test requires multi-GPU machine") def test_multi_gpu_model_ddp_spawn(tmpdir): tutils.set_random_master_port() trainer_options = dict( default_root_dir=tmpdir, max_epochs=1, limit_train_batches=10, limit_val_batches=10, gpus=[0, 1], distributed_backend='ddp_spawn', progress_bar_refresh_rate=0 ) model = EvalModelTemplate() tpipes.run_model_test(trainer_options, model) # test memory helper functions memory.get_memory_profile('min_max') @pytest.mark.skipif(torch.cuda.device_count() < 2, reason="test requires multi-GPU machine") def test_ddp_all_dataloaders_passed_to_fit(tmpdir): """Make sure DDP works with dataloaders passed to fit()""" tutils.set_random_master_port() model = EvalModelTemplate() fit_options = dict(train_dataloader=model.train_dataloader(), val_dataloaders=model.val_dataloader()) trainer = Trainer( default_root_dir=tmpdir, progress_bar_refresh_rate=0, max_epochs=1, limit_train_batches=0.2, limit_val_batches=0.2, gpus=[0, 1], distributed_backend='ddp_spawn' ) result = trainer.fit(model, **fit_options) assert result == 1, "DDP doesn't work with dataloaders passed to fit()."