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