sets default ddp mode to spawn (#2168)

* set ddp_spawn as default

* spawn message

* spawn message

* spawn message

* spawn message

* spawn message

* spawn message

* spawn message

* spawn message

pytorch_lightning/core/lightning.py

@@ -942,18 +942,18 @@ def init_ddp_connection(
             self._init_slurm_connection()
 
         if 'MASTER_ADDR' not in os.environ:
-            log.warning("MASTER_ADDR environment variable is not defined. Set as localhost")
+            rank_zero_warn("MASTER_ADDR environment variable is not defined. Set as localhost")
             os.environ['MASTER_ADDR'] = '127.0.0.1'
         log.debug(f"MASTER_ADDR: {os.environ['MASTER_ADDR']}")
 
         if 'MASTER_PORT' not in os.environ:
-            log.warning("MASTER_PORT environment variable is not defined. Set as 12910")
+            rank_zero_warn("MASTER_PORT environment variable is not defined. Set as 12910")
             os.environ['MASTER_PORT'] = '12910'
         log.debug(f"MASTER_PORT: {os.environ['MASTER_PORT']}")
 
         if 'WORLD_SIZE' in os.environ and int(os.environ['WORLD_SIZE']) != world_size:
-            log.warning(f"WORLD_SIZE environment variable ({os.environ['WORLD_SIZE']}) "
-                        f"is not equal to the computed world size ({world_size}). Ignored.")
+            rank_zero_warn(f"WORLD_SIZE environment variable ({os.environ['WORLD_SIZE']}) "
+                           f"is not equal to the computed world size ({world_size}). Ignored.")
 
         torch_backend = "nccl" if self.trainer.on_gpu else "gloo"
         log.info(f"initializing ddp: LOCAL_RANK: {proc_rank}/{world_size - 1} WORLD_SIZE:{world_size}")

pytorch_lightning/trainer/data_loading.py

@@ -96,11 +96,24 @@ def _percent_range_check(self, name: str) -> None:
     def _worker_check(self, dataloader: DataLoader, name: str) -> None:
         on_windows = platform.system() == 'Windows'
 
-        if isinstance(dataloader, DataLoader) and dataloader.num_workers <= 2 and not on_windows:
+        # ddp_spawn + num_workers > 0 don't mix! tell the user
+        is_dataloader = isinstance(dataloader, DataLoader)
+        using_spawn = self.distributed_backend == 'ddp_spawn'
+        if is_dataloader and dataloader.num_workers > 0 and not on_windows 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 '
+                           '(this is a bottleneck of Python .spawn() and PyTorch')
+
+        elif is_dataloader and dataloader.num_workers <= 2 and not on_windows and not using_spawn:
             rank_zero_warn(f'The dataloader, {name}, does not have many workers which may be a bottleneck.'
                            ' Consider increasing the value of the `num_workers` argument`'
                            ' in the `DataLoader` init to improve performance.')
 
+        elif is_dataloader and dataloader.num_workers == 0 and not on_windows 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` and set `num_workers>0`')
+
     def auto_add_sampler(self, dataloader: DataLoader, train: bool) -> DataLoader:
 
         # don't do anything if it's not a dataloader

pytorch_lightning/trainer/distrib_data_parallel.py

@@ -128,7 +128,7 @@ def train_fx(trial_hparams, cluster_manager, _):
 from pytorch_lightning.callbacks import ModelCheckpoint
 from pytorch_lightning.loggers import LightningLoggerBase
 from pytorch_lightning.utilities.exceptions import MisconfigurationException
-from pytorch_lightning.utilities.distributed import rank_zero_only, rank_zero_warn
+from pytorch_lightning.utilities.distributed import rank_zero_only, rank_zero_warn, rank_zero_info
 
 try:
     from apex import amp
@@ -220,9 +220,9 @@ def set_distributed_mode(self, distributed_backend):
             elif self.num_gpus > 1:
                 rank_zero_warn('You requested multiple GPUs but did not specify a backend, e.g.'
                                ' Trainer(distributed_backend=dp) (or ddp, ddp2).'
-                               ' Setting distributed_backend=ddp for you.')
-                self.distributed_backend = 'ddp'
-                distributed_backend = 'ddp'
+                               ' Setting distributed_backend=ddp_spawn for you.')
+                self.distributed_backend = 'ddp_spawn'
+                distributed_backend = 'ddp_spawn'
 
         if distributed_backend == "dp":
             # do nothing if num_gpus == 0
@@ -264,7 +264,7 @@ def set_distributed_mode(self, distributed_backend):
                 'To silence this warning set distributed_backend=ddp or distributed_backend=ddp2'
             )
 
-        log.info(f'GPU available: {torch.cuda.is_available()}, used: {self.on_gpu}')
+        rank_zero_info(f'GPU available: {torch.cuda.is_available()}, used: {self.on_gpu}')
 
     def configure_slurm_ddp(self, num_gpu_nodes):
         self.is_slurm_managing_tasks = False
@@ -298,7 +298,7 @@ def configure_slurm_ddp(self, num_gpu_nodes):
 
         # notify user the that slurm is managing tasks
         if self.is_slurm_managing_tasks:
-            log.info('Multi-processing is handled by Slurm.')
+            rank_zero_info('Multi-processing is handled by Slurm.')
 
     def determine_ddp_node_rank(self):
         if self.is_slurm_managing_tasks:
@@ -316,7 +316,7 @@ def determine_ddp_node_rank(self):
             log.warning(f"Multiple environment variables ({node_ids}) defined for node rank. "
                         f"Using the first one.")
         k, rank = node_ids.pop()
-        log.info(f"Using environment variable {k} for node rank ({rank}).")
+        rank_zero_info(f"Using environment variable {k} for node rank ({rank}).")
         return int(rank)
 
     def set_nvidia_flags(self, is_slurm_managing_tasks, data_parallel_device_ids):
@@ -336,7 +336,7 @@ def set_nvidia_flags(self, is_slurm_managing_tasks, data_parallel_device_ids):
                 os.environ["CUDA_VISIBLE_DEVICES"] = gpu_str
 
         # don't make this debug... this is good UX
-        log.info(f'CUDA_VISIBLE_DEVICES: [{os.environ["CUDA_VISIBLE_DEVICES"]}]')
+        rank_zero_info(f'CUDA_VISIBLE_DEVICES: [{os.environ["CUDA_VISIBLE_DEVICES"]}]')
 
     def __set_random_port(self):
         """

pytorch_lightning/trainer/trainer.py

@@ -31,7 +31,7 @@
 from pytorch_lightning.trainer.training_tricks import TrainerTrainingTricksMixin
 from pytorch_lightning.trainer.lr_finder import TrainerLRFinderMixin
 from pytorch_lightning.utilities.exceptions import MisconfigurationException
-from pytorch_lightning.utilities import rank_zero_warn, parsing
+from pytorch_lightning.utilities import rank_zero_warn, parsing, rank_zero_info
 
 try:
     from apex import amp
@@ -362,8 +362,8 @@ def __init__(
         if self.fast_dev_run:
             self.num_sanity_val_steps = 0
             self.max_epochs = 1
-            log.info('Running in fast_dev_run mode: will run a full train,'
-                     ' val and test loop using a single batch')
+            rank_zero_info('Running in fast_dev_run mode: will run a full train,'
+                           ' val and test loop using a single batch')
 
         # set default save path if user didn't provide one
         self.default_root_dir = default_root_dir
@@ -838,7 +838,7 @@ def fit(
             self.single_gpu_train(model)
 
         elif self.use_tpu:  # pragma: no-cover
-            log.info(f'training on {self.tpu_cores} TPU cores')
+            rank_zero_info(f'training on {self.tpu_cores} TPU cores')
 
             #  COLAB_GPU is an env var available by default in Colab environments.
             start_method = 'fork' if self.on_colab_kaggle else 'spawn'

pytorch_lightning/utilities/__init__.py

@@ -1,5 +1,5 @@
 """General utilities"""
 
-from pytorch_lightning.utilities.distributed import rank_zero_only, rank_zero_warn
+from pytorch_lightning.utilities.distributed import rank_zero_only, rank_zero_warn, rank_zero_info
 from pytorch_lightning.utilities.apply_func import move_data_to_device
 from pytorch_lightning.utilities.parsing import AttributeDict

pytorch_lightning/utilities/distributed.py

@@ -1,5 +1,6 @@
 from functools import wraps
 import warnings
+from pytorch_lightning import _logger as log
 
 
 def rank_zero_only(fn):
@@ -23,4 +24,9 @@ def _warn(*args, **kwargs):
     warnings.warn(*args, **kwargs)
 
 
+def _info(*args, **kwargs):
+    log.info(*args, **kwargs)
+
+
+rank_zero_info = rank_zero_only(_info)
 rank_zero_warn = rank_zero_only(_warn)