update usage of deprecated distributed_backend

benchmarks/test_sharded_parity.py

@@ -105,29 +105,29 @@ def test_ddp_string_sharded_plugin_correctness_amp_multi_gpu():
 @pytest.mark.skipif(torch.cuda.device_count() < 2, reason="test requires multi-GPU machine")
 @pytest.mark.skipif(not os.getenv("PL_RUNNING_SPECIAL_TESTS", '0') == '1',
  reason="test should be run outside of pytest")
-@DDPLauncher.run("--distributed_backend ddp --gpus 2 --precision 32")
+@DDPLauncher.run("--accelerator ddp --gpus 2 --precision 32")
 def test_ddp_sharded_plugin_correctness_multi_gpu_ddp(tmpdir, args=None):
  plugin_parity_test(
  gpus=args.gpus,
  precision=args.precision,
- accelerator=args.distributed_backend,
+ accelerator=args.accelerator,
  plugin=DDPShardedPlugin(),
- model_cls=SeedTrainLoaderModel
+ model_cls=SeedTrainLoaderModel,
  )
 
 
 @pytest.mark.skipif(not FAIRSCALE_AVAILABLE, reason="Fairscale is not available")
 @pytest.mark.skipif(torch.cuda.device_count() < 2, reason="test requires multi-GPU machine")
 @pytest.mark.skipif(not os.getenv("PL_RUNNING_SPECIAL_TESTS", '0') == '1',
  reason="test should be run outside of pytest")
-@DDPLauncher.run("--distributed_backend ddp --gpus 2 --precision 16")
+@DDPLauncher.run("--accelerator ddp --gpus 2 --precision 16")
 def test_ddp_sharded_plugin_correctness_amp_multi_gpu_ddp(tmpdir, args=None):
  plugin_parity_test(
  gpus=args.gpus,
  precision=args.precision,
- accelerator=args.distributed_backend,
+ accelerator=args.accelerator,
  plugin=DDPShardedPlugin(),
- model_cls=SeedTrainLoaderModel
+ model_cls=SeedTrainLoaderModel,
  )
 
 

pl_examples/domain_templates/imagenet.py

@@ -210,7 +210,7 @@ def main(args: Namespace) -> None:
  if args.seed is not None:
  pl.seed_everything(args.seed)
 
- if args.distributed_backend == 'ddp':
+ if args.accelerator == 'ddp':
  # When using a single GPU per process and per
  # DistributedDataParallel, we need to divide the batch size
  # ourselves based on the total number of GPUs we have

pl_examples/domain_templates/reinforce_learn_Qnet.py

@@ -341,7 +341,7 @@ def main(args) -> None:
 
  trainer = pl.Trainer(
  gpus=1,
- distributed_backend='dp',
+ accelerator='dp',
  val_check_interval=100
  )
 

pl_examples/domain_templates/semantic_segmentation.py

@@ -214,7 +214,7 @@ def main(hparams: Namespace):
  logger=logger,
  max_epochs=hparams.epochs,
  accumulate_grad_batches=hparams.grad_batches,
- distributed_backend=hparams.distributed_backend,
+ accelerator=hparams.accelerator,
  precision=16 if hparams.use_amp else 32,
  )
 

pytorch_lightning/accelerators/accelerator_connector.py

@@ -87,7 +87,7 @@ def on_trainer_init(
  self.trainer.tpu_id = self.trainer.tpu_cores[0] if isinstance(self.trainer.tpu_cores, list) else None
 
  if num_processes != 1 and distributed_backend != "ddp_cpu":
- rank_zero_warn("num_processes is only used for distributed_backend=\"ddp_cpu\". Ignoring it.")
+ rank_zero_warn("num_processes is only used for `accelerator='ddp_cpu'`. Ignoring it.")
  self.trainer.num_processes = num_processes
 
  # override with environment flag
@@ -276,7 +276,7 @@ def select_accelerator(self):
  accelerator_backend = accelerators.CPUAccelerator(self.trainer, cluster_env)
  else:
  raise MisconfigurationException(
- f'Trainer(distributed_backend={self.trainer.distributed_backend} is not a supported backend'
+ f'Trainer(accelerator={self.trainer.distributed_backend} is not a supported backend'
  )
 
  return accelerator_backend
@@ -299,8 +299,8 @@ def set_distributed_mode(self):
  elif self.trainer.num_gpus > 1:
  rank_zero_warn(
  'You requested multiple GPUs but did not specify a backend, e.g.'
- ' Trainer(distributed_backend="dp"|"ddp"|"ddp2").'
- ' Setting distributed_backend="ddp_spawn" for you.'
+ ' `Trainer(accelerator="dp"|"ddp"|"ddp2")`.'
+ ' Setting `accelerator="ddp_spawn"` for you.'
  )
  self.trainer.distributed_backend = "ddp_spawn"
 
@@ -342,7 +342,7 @@ def set_distributed_mode(self):
  if self.trainer.num_nodes > 1 and not (self.trainer.use_ddp2 or self.trainer.use_ddp):
  raise MisconfigurationException(
  'DataParallel does not support num_nodes > 1. Switching to DistributedDataParallel for you. '
- 'To silence this warning set distributed_backend=ddp or distributed_backend=ddp2'
+ 'To silence this warning set `accelerator="ddp"` or `accelerator="ddp2"`'
  )
 
  rank_zero_info(f'GPU available: {torch.cuda.is_available()}, used: {self.trainer.on_gpu}')
@@ -366,7 +366,7 @@ def check_horovod(self):
  """Raises a `MisconfigurationException` if the Trainer is not configured correctly for Horovod."""
  if not HOROVOD_AVAILABLE:
  raise MisconfigurationException(
- 'Requested `distributed_backend="horovod"`, but Horovod is not installed.'
+ 'Requested `accelerator="horovod"`, but Horovod is not installed.'
  'Install with \n $HOROVOD_WITH_PYTORCH=1 pip install horovod[pytorch]'
  )
 

pytorch_lightning/accelerators/ddp_accelerator.py

@@ -174,7 +174,7 @@ def _check_can_spawn_children(self):
  if self._has_spawned_children:
  raise RuntimeError(
  "You tried to run `.fit` or `.test` multiple times in the same script."
- " This is not supported in DDP mode, switch to `distributed_backend='ddp_spawn'` instead."
+ " This is not supported in DDP mode, switch to `accelerator='ddp_spawn'` instead."
  )
 
  def set_world_ranks(self, process_idx):

pytorch_lightning/trainer/data_loading.py

@@ -69,12 +69,12 @@ def _worker_check(self, dataloader: DataLoader, name: str) -> None:
  if dataloader.num_workers > 0 and using_spawn:
  rank_zero_warn('Dataloader(num_workers>0) and ddp_spawn do not mix well!'
  ' Your performance might suffer dramatically.'
- ' Please consider setting distributed_backend=ddp to use num_workers > 0'
+ ' Please consider setting accelerator=ddp to use num_workers > 0'
  ' (this is a bottleneck of Python .spawn() and PyTorch')
 
  elif dataloader.num_workers == 0 and using_spawn:
- rank_zero_warn('You are using `distributed_backend=ddp_spawn` with num_workers=0.'
- ' For much faster performance, switch to `distributed_backend=ddp`'
+ rank_zero_warn('You are using `accelerator=ddp_spawn` with num_workers=0.'
+ ' For much faster performance, switch to `accelerator=ddp`'
  ' and set `num_workers>0`')
 
  elif dataloader.num_workers <= 2 and multiprocessing.cpu_count() > 2 and not using_spawn:

tests/backends/ddp_model.py

@@ -33,7 +33,7 @@ def main():
  parser.add_argument('--tmpdir')
  parser.add_argument('--workdir')
  parser.set_defaults(gpus=2)
- parser.set_defaults(distributed_backend="ddp")
+ parser.set_defaults(accelerator="ddp")
  args = parser.parse_args()
 
  model = EvalModelTemplate()

tests/backends/test_accelerator_connector.py

@@ -47,8 +47,8 @@ def on_fit_start(self, trainer, pl_module):
  model = BoringModel()
  trainer = Trainer(
  fast_dev_run=True,
- distributed_backend='ddp_cpu',
- callbacks=[CB()]
+ accelerator='ddp_cpu',
+ callbacks=[CB()],
  )
 
  with pytest.raises(SystemExit):
@@ -68,9 +68,9 @@ def on_fit_start(self, trainer, pl_module):
  model = BoringModel()
  trainer = Trainer(
  fast_dev_run=True,
- distributed_backend='ddp',
+ accelerator='ddp',
  gpus=1,
- callbacks=[CB()]
+ callbacks=[CB()],
  )
 
  with pytest.raises(SystemExit):
@@ -90,9 +90,9 @@ def on_fit_start(self, trainer, pl_module):
  model = BoringModel()
  trainer = Trainer(
  fast_dev_run=True,
- distributed_backend='ddp_spawn',
+ accelerator='ddp_spawn',
  gpus=1,
- callbacks=[CB()]
+ callbacks=[CB()],
  )
 
  with pytest.raises(SystemExit):
@@ -120,9 +120,9 @@ def on_fit_start(self, trainer, pl_module):
  model = BoringModel()
  trainer = Trainer(
  fast_dev_run=True,
- distributed_backend='ddp',
+ accelerator='ddp',
  gpus=2,
- callbacks=[CB()]
+ callbacks=[CB()],
  )
 
  with pytest.raises(SystemExit):
@@ -152,9 +152,9 @@ def on_fit_start(self, trainer, pl_module):
  model = BoringModel()
  trainer = Trainer(
  fast_dev_run=True,
- distributed_backend='ddp2',
+ accelerator='ddp2',
  gpus=2,
- callbacks=[CB()]
+ callbacks=[CB()],
  )
 
  with pytest.raises(SystemExit):
@@ -181,9 +181,9 @@ def on_fit_start(self, trainer, pl_module):
  model = BoringModel()
  trainer = Trainer(
  fast_dev_run=True,
- distributed_backend='ddp',
+ accelerator='ddp',
  gpus=2,
- callbacks=[CB()]
+ callbacks=[CB()],
  )
 
  with pytest.raises(SystemExit):
@@ -210,9 +210,9 @@ def on_fit_start(self, trainer, pl_module):
  model = BoringModel()
  trainer = Trainer(
  fast_dev_run=True,
- distributed_backend='ddp2',
+ accelerator='ddp2',
  gpus=2,
- callbacks=[CB()]
+ callbacks=[CB()],
  )
 
  with pytest.raises(SystemExit):
@@ -239,9 +239,9 @@ def on_fit_start(self, trainer, pl_module):
  model = BoringModel()
  trainer = Trainer(
  fast_dev_run=True,
- distributed_backend='ddp_cpu',
+ accelerator='ddp_cpu',
  num_processes=1,
- callbacks=[CB()]
+ callbacks=[CB()],
  )
 
  with pytest.raises(SystemExit):
@@ -267,9 +267,9 @@ def on_fit_start(self, trainer, pl_module):
  model = BoringModel()
  trainer = Trainer(
  fast_dev_run=True,
- distributed_backend='ddp_cpu',
+ accelerator='ddp_cpu',
  num_processes=1,
- callbacks=[CB()]
+ callbacks=[CB()],
  )
 
  with pytest.raises(SystemExit):
@@ -304,9 +304,9 @@ def on_fit_start(self, trainer, pl_module):
  trainer = Trainer(
  plugins=[CustomCluster()],
  fast_dev_run=True,
- distributed_backend='ddp_cpu',
+ accelerator='ddp_cpu',
  num_processes=1,
- callbacks=[CB()]
+ callbacks=[CB()],
  )
 
  with pytest.raises(SystemExit):

tests/backends/test_ddp.py

@@ -22,7 +22,7 @@
 
 
 @pytest.mark.parametrize('cli_args', [
- pytest.param('--max_epochs 1 --gpus 2 --distributed_backend ddp'),
+ pytest.param('--max_epochs 1 --gpus 2 --accelerator ddp'),
 ])
 @pytest.mark.skipif(torch.cuda.device_count() < 2, reason="test requires multi-GPU machine")
 def test_multi_gpu_model_ddp_fit_only(tmpdir, cli_args):
@@ -38,7 +38,7 @@ def test_multi_gpu_model_ddp_fit_only(tmpdir, cli_args):
 
 
 @pytest.mark.parametrize('cli_args', [
- pytest.param('--max_epochs 1 --gpus 2 --distributed_backend ddp'),
+ pytest.param('--max_epochs 1 --gpus 2 --accelerator ddp'),
 ])
 @pytest.mark.skipif(torch.cuda.device_count() < 2, reason="test requires multi-GPU machine")
 def test_multi_gpu_model_ddp_test_only(tmpdir, cli_args):
@@ -54,7 +54,7 @@ def test_multi_gpu_model_ddp_test_only(tmpdir, cli_args):
 
 
 @pytest.mark.parametrize('cli_args', [
- pytest.param('--max_epochs 1 --gpus 2 --distributed_backend ddp'),
+ pytest.param('--max_epochs 1 --gpus 2 --accelerator ddp'),
 ])
 @pytest.mark.skipif(torch.cuda.device_count() < 2, reason="test requires multi-GPU machine")
 def test_multi_gpu_model_ddp_fit_test(tmpdir, cli_args):

tests/backends/test_ddp_spawn.py

@@ -34,7 +34,7 @@ def test_multi_gpu_early_stop_ddp_spawn(tmpdir):
  limit_train_batches=10,
  limit_val_batches=10,
  gpus=[0, 1],
- distributed_backend='ddp_spawn',
+ accelerator='ddp_spawn',
  )
 
  model = EvalModelTemplate()
@@ -51,8 +51,8 @@ def test_multi_gpu_model_ddp_spawn(tmpdir):
  limit_train_batches=10,
  limit_val_batches=10,
  gpus=[0, 1],
- distributed_backend='ddp_spawn',
- progress_bar_refresh_rate=0
+ accelerator='ddp_spawn',
+ progress_bar_refresh_rate=0,
  )
 
  model = EvalModelTemplate()
@@ -79,7 +79,7 @@ def test_ddp_all_dataloaders_passed_to_fit(tmpdir):
  limit_train_batches=0.2,
  limit_val_batches=0.2,
  gpus=[0, 1],
- distributed_backend='ddp_spawn'
+ accelerator='ddp_spawn',
  )
  result = trainer.fit(model, **fit_options)
  assert result == 1, "DDP doesn't work with dataloaders passed to fit()."

tests/backends/test_dp.py

@@ -37,7 +37,7 @@ def test_multi_gpu_early_stop_dp(tmpdir):
  limit_train_batches=10,
  limit_val_batches=10,
  gpus=[0, 1],
- distributed_backend='dp',
+ accelerator='dp',
  )
 
  model = EvalModelTemplate()
@@ -54,8 +54,8 @@ def test_multi_gpu_model_dp(tmpdir):
  limit_train_batches=10,
  limit_val_batches=10,
  gpus=[0, 1],
- distributed_backend='dp',
- progress_bar_refresh_rate=0
+ accelerator='dp',
+ progress_bar_refresh_rate=0,
  )
 
  model = EvalModelTemplate()
@@ -80,7 +80,7 @@ def test_dp_test(tmpdir):
  limit_train_batches=10,
  limit_val_batches=10,
  gpus=[0, 1],
- distributed_backend='dp',
+ accelerator='dp',
  )
  trainer.fit(model)
  assert 'ckpt' in trainer.checkpoint_callback.best_model_path

tests/core/test_datamodules.py

@@ -356,7 +356,7 @@ def test_full_loop_dp(tmpdir):
  default_root_dir=tmpdir,
  max_epochs=3,
  weights_summary=None,
- distributed_backend='dp',
+ accelerator='dp',
  gpus=2,
  deterministic=True,
  )

tests/loggers/test_all.py

@@ -316,7 +316,7 @@ def _test_logger_created_on_rank_zero_only(tmpdir, logger_class):
  trainer = Trainer(
  logger=logger,
  default_root_dir=tmpdir,
- distributed_backend='ddp_cpu',
+ accelerator='ddp_cpu',
  num_processes=2,
  max_steps=1,
  checkpoint_callback=True,

tests/models/data/horovod/train_default_model.py

@@ -79,7 +79,7 @@ def run_test_from_config(trainer_options):
  trainer.checkpoint_connector.hpc_load(ckpt_path, on_gpu=args.on_gpu)
 
  if args.on_gpu:
- trainer = Trainer(gpus=1, distributed_backend='horovod', max_epochs=1)
+ trainer = Trainer(gpus=1, accelerator='horovod', max_epochs=1)
  # Test the root_gpu property
  assert trainer.root_gpu == hvd.local_rank()