cli

Functions

instantiate_class

rtype: Any

Classes

LightningArgumentParser	Initialize argument parser that supports configuration file input.
LightningCLI	Receives as input pytorch-lightning classes (or callables which return pytorch-lightning classes), which are called / instantiated using a parsed configuration file and / or command line args.
SaveConfigCallback

Deprecated utilities for LightningCLI.

class pytorch_lightning.utilities.cli.LightningArgumentParser(*args, **kwargs)

Bases: pytorch_lightning.cli.LightningArgumentParser

Initialize argument parser that supports configuration file input.

For full details of accepted arguments see ArgumentParser.__init__.

class pytorch_lightning.utilities.cli.LightningCLI(*args, **kwargs)

Bases: pytorch_lightning.cli.LightningCLI

Receives as input pytorch-lightning classes (or callables which return pytorch-lightning classes), which are called / instantiated using a parsed configuration file and / or command line args.

Parsing of configuration from environment variables can be enabled by setting env_parse=True. A full configuration yaml would be parsed from PL_CONFIG if set. Individual settings are so parsed from variables named for example PL_TRAINER__MAX_EPOCHS.

For more info, read the CLI docs.

Warning

LightningCLI is in beta and subject to change.

Parameters:

• model_class – An optional LightningModule class to train on or a callable which returns a LightningModule instance when called. If None, you can pass a registered model with --model=MyModel.

• datamodule_class – An optional LightningDataModule class or a callable which returns a LightningDataModule instance when called. If None, you can pass a registered datamodule with --data=MyDataModule.

• save_config_callback – A callback class to save the config.

• save_config_kwargs – Parameters that will be used to instantiate the save_config_callback.

• trainer_class – An optional subclass of the Trainer class or a callable which returns a Trainer instance when called.

• trainer_defaults – Set to override Trainer defaults or add persistent callbacks. The callbacks added through this argument will not be configurable from a configuration file and will always be present for this particular CLI. Alternatively, configurable callbacks can be added as explained in the CLI docs.

• seed_everything_default – Number for the seed_everything() seed value. Set to True to automatically choose a seed value. Setting it to False will avoid calling seed_everything.

• 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 each LightningArgumentParser.

• subclass_mode_model – Whether model can be any subclass of the given class.

• subclass_mode_data –

  Whether datamodule can be any subclass of the given class.

• args (Any) – Arguments to parse. If None the arguments are taken from sys.argv. Command line style arguments can be given in a list. Alternatively, structured config options can be given in a dict or jsonargparse.Namespace.

• run – Whether subcommands should be added to run a Trainer method. If set to False, the trainer and model classes will be instantiated only.

• auto_registry – Whether to automatically fill up the registries with all defined subclasses.

class pytorch_lightning.utilities.cli.SaveConfigCallback(*args, **kwargs)

Bases: pytorch_lightning.cli.SaveConfigCallback