Default seed_everything(workers=True) in the LightningCLI (#7504)

CHANGELOG.md

@@ -40,13 +40,16 @@ The format is based on [Keep a Changelog](http://keepachangelog.com/en/1.0.0/).
 - Changed the behaviour when logging evaluation step metrics to no longer append `/epoch_*` to the metric name ([#7351](https://github.com/PyTorchLightning/pytorch-lightning/pull/7351))
 
 
-- Changed `resolve_training_type_plugins` to allow setting `num_nodes` and `sync_batchnorm` from `Trainer` setting ([7026](https://github.com/PyTorchLightning/pytorch-lightning/pull/7026))
+- Changed `resolve_training_type_plugins` to allow setting `num_nodes` and `sync_batchnorm` from `Trainer` setting ([#7026](https://github.com/PyTorchLightning/pytorch-lightning/pull/7026))
 
 
-- Changed `model.state_dict()` in `CheckpointConnector` to allow `training_type_plugin` to customize the model's `state_dict()` ([7474](https://github.com/PyTorchLightning/pytorch-lightning/pull/7474))
+- Default `seed_everything(workers=True)` in the `LightningCLI` ([#7504](https://github.com/PyTorchLightning/pytorch-lightning/pull/7504))
 
 
-- MLflowLogger now uses the env variable `MLFLOW_TRACKING_URI` as default tracking uri ([7457](https://github.com/PyTorchLightning/pytorch-lightning/pull/7457))
+- Changed `model.state_dict()` in `CheckpointConnector` to allow `training_type_plugin` to customize the model's `state_dict()` ([#7474](https://github.com/PyTorchLightning/pytorch-lightning/pull/7474))
+
+
+- MLflowLogger now uses the env variable `MLFLOW_TRACKING_URI` as default tracking uri ([#7457](https://github.com/PyTorchLightning/pytorch-lightning/pull/7457))
 
 
 ### Deprecated

docs/source/common/lightning_cli.rst

@@ -91,8 +91,8 @@ practice to create a configuration file and provide this to the tool. A way to d
 
 The instantiation of the :class:`~pytorch_lightning.utilities.cli.LightningCLI` class takes care of parsing command line
 and config file options, instantiating the classes, setting up a callback to save the config in the log directory and
-finally running :func:`trainer.fit`. The resulting object :code:`cli` can be used for instance to get the result of fit,
-i.e., :code:`cli.fit_result`.
+finally running the trainer. The resulting object :code:`cli` can be used for example to get the instance of the
+model, (:code:`cli.model`).
 
 After multiple trainings with different configurations, each run will have in its respective log directory a
 :code:`config.yaml` file. This file can be used for reference to know in detail all the settings that were used for each

pl_examples/basic_examples/autoencoder.py

@@ -116,8 +116,7 @@ def test_dataloader(self):
 
 def cli_main():
     cli = LightningCLI(LitAutoEncoder, MyDataModule, seed_everything_default=1234)
-    result = cli.trainer.test(cli.model, datamodule=cli.datamodule)
-    print(result)
+    cli.trainer.test(cli.model, datamodule=cli.datamodule)
 
 
 if __name__ == '__main__':

pl_examples/basic_examples/backbone_image_classifier.py

@@ -129,8 +129,7 @@ def test_dataloader(self):
 
 def cli_main():
     cli = LightningCLI(LitClassifier, MyDataModule, seed_everything_default=1234)
-    result = cli.trainer.test(cli.model, datamodule=cli.datamodule)
-    print(result)
+    cli.trainer.test(cli.model, datamodule=cli.datamodule)
 
 
 if __name__ == '__main__':

pl_examples/basic_examples/dali_image_classifier.py

@@ -222,8 +222,7 @@ def cli_main():
         return
 
     cli = LightningCLI(LitClassifier, MyDataModule, seed_everything_default=1234)
-    result = cli.trainer.test(cli.model, datamodule=cli.datamodule)
-    print(result)
+    cli.trainer.test(cli.model, datamodule=cli.datamodule)
 
 
 if __name__ == "__main__":

pl_examples/basic_examples/simple_image_classifier.py

@@ -77,8 +77,7 @@ def configure_optimizers(self):
 
 def cli_main():
     cli = LightningCLI(LitClassifier, MNISTDataModule, seed_everything_default=1234)
-    result = cli.trainer.test(cli.model, datamodule=cli.datamodule)
-    print(result)
+    cli.trainer.test(cli.model, datamodule=cli.datamodule)
 
 
 if __name__ == '__main__':

pytorch_lightning/utilities/cli.py

@@ -128,13 +128,14 @@ def __init__(
         .. warning:: ``LightningCLI`` is in beta and subject to change.
 
         Args:
-            model_class: The LightningModule class to train on.
-            datamodule_class: An optional LightningDataModule class.
+            model_class: :class:`~pytorch_lightning.core.lightning.LightningModule` class to train on.
+            datamodule_class: An optional :class:`~pytorch_lightning.core.datamodule.LightningDataModule` class.
             save_config_callback: A callback class to save the training config.
-            trainer_class: An optional extension of the Trainer class.
+            trainer_class: An optional subclass of the :class:`~pytorch_lightning.trainer.trainer.Trainer` class.
             trainer_defaults: Set to override Trainer defaults or add persistent callbacks.
-            seed_everything_default: Default value for seed_everything argument.
-            description: Description of the tool shown when running --help.
+            seed_everything_default: Default value for the :func:`~pytorch_lightning.utilities.seed.seed_everything`
+                seed argument.
+            description: Description of the tool shown when running ``--help``.
             env_prefix: Prefix for environment variables.
             env_parse: Whether environment variable parsing is enabled.
             parser_kwargs: Additional arguments to instantiate LightningArgumentParser.
@@ -165,7 +166,7 @@ def __init__(
         self.add_arguments_to_parser(self.parser)
         self.parse_arguments()
         if self.config['seed_everything'] is not None:
-            seed_everything(self.config['seed_everything'])
+            seed_everything(self.config['seed_everything'], workers=True)
         self.before_instantiate_classes()
         self.instantiate_classes()
         self.prepare_fit_kwargs()