lightning/docs/source-pytorch/cli/lightning_cli_expert.rst

:orphan:

.. testsetup:: *
    :skipif: not _JSONARGPARSE_AVAILABLE

    import torch
    from unittest import mock
    from typing import List
    import pytorch_lightning.cli as pl_cli
    from pytorch_lightning import LightningModule, LightningDataModule, Trainer, Callback


    class NoFitTrainer(Trainer):
        def fit(self, *_, **__):
            pass


    class LightningCLI(pl_cli.LightningCLI):
        def __init__(self, *args, trainer_class=NoFitTrainer, run=False, **kwargs):
            super().__init__(*args, trainer_class=trainer_class, run=run, **kwargs)


    class MyModel(LightningModule):
        def __init__(
            self,
            encoder_layers: int = 12,
            decoder_layers: List[int] = [2, 4],
            batch_size: int = 8,
        ):
            pass


    class MyClassModel(LightningModule):
        def __init__(self, num_classes: int):
            pass


    class MyDataModule(LightningDataModule):
        def __init__(self, batch_size: int = 8):
            self.num_classes = 5


    def send_email(address, message):
        pass


    mock_argv = mock.patch("sys.argv", ["any.py"])
    mock_argv.start()

.. testcleanup:: *

    mock_argv.stop()

###############################################
Configure hyperparameters from the CLI (Expert)
###############################################
**Audience:** Users who already understand the LightningCLI and want to customize it.

----

**************************
Customize the LightningCLI
**************************

The init parameters of the :class:`~pytorch_lightning.cli.LightningCLI` class can be used to customize some things,
e.g., the description of the tool, enabling parsing of environment variables, and additional arguments to instantiate
the trainer and configuration parser.

Nevertheless, the init arguments are not enough for many use cases. For this reason, the class is designed so that it
can be extended to customize different parts of the command line tool. The argument parser class used by
:class:`~pytorch_lightning.cli.LightningCLI` is :class:`~pytorch_lightning.cli.LightningArgumentParser`, which is an
extension of python's argparse, thus adding arguments can be done using the :func:`add_argument` method. In contrast to
argparse, it has additional methods to add arguments. For example :func:`add_class_arguments` add all arguments from the
init of a class. For more details, see the `respective documentation
<https://jsonargparse.readthedocs.io/en/stable/#classes-methods-and-functions>`_.

The :class:`~pytorch_lightning.cli.LightningCLI` class has the
:meth:`~pytorch_lightning.cli.LightningCLI.add_arguments_to_parser` method can be implemented to include more arguments.
After parsing, the configuration is stored in the ``config`` attribute of the class instance. The
:class:`~pytorch_lightning.cli.LightningCLI` class also has two methods that can be used to run code before and after
the trainer runs: ``before_<subcommand>`` and ``after_<subcommand>``. A realistic example of this would be to send an
email before and after the execution. The code for the ``fit`` subcommand would be something like this:

.. testcode::

    class MyLightningCLI(LightningCLI):
        def add_arguments_to_parser(self, parser):
            parser.add_argument("--notification_email", default="will@email.com")

        def before_fit(self):
            send_email(address=self.config["notification_email"], message="trainer.fit starting")

        def after_fit(self):
            send_email(address=self.config["notification_email"], message="trainer.fit finished")


    cli = MyLightningCLI(MyModel)

Note that the config object ``self.config`` is a namespace whose keys are global options or groups of options. It has
the same structure as the YAML format described previously. This means that the parameters used for instantiating the
trainer class can be found in ``self.config['fit']['trainer']``.

.. tip::

    Have a look at the :class:`~pytorch_lightning.cli.LightningCLI` class API reference to learn about other methods
    that can be extended to customize a CLI.

----

**************************
Configure forced callbacks
**************************
As explained previously, any Lightning callback can be added by passing it through the command line or including it in
the config via ``class_path`` and ``init_args`` entries.

However, certain callbacks **must** be coupled with a model so they are always present and configurable. This can be
implemented as follows:

.. testcode::

    from pytorch_lightning.callbacks import EarlyStopping


    class MyLightningCLI(LightningCLI):
        def add_arguments_to_parser(self, parser):
            parser.add_lightning_class_args(EarlyStopping, "my_early_stopping")
            parser.set_defaults({"my_early_stopping.monitor": "val_loss", "my_early_stopping.patience": 5})


    cli = MyLightningCLI(MyModel)

To change the parameters for ``EarlyStopping`` in the config it would be:

.. code-block:: yaml

    model:
      ...
    trainer:
      ...
    my_early_stopping:
      patience: 5

.. note::

    The example above overrides a default in ``add_arguments_to_parser``. This is included to show that defaults can be
    changed if needed. However, note that overriding defaults in the source code is not intended to be used to store the
    best hyperparameters for a task after experimentation. To guarantee reproducibility, the source code should be
    stable. It is better to practice storing the best hyperparameters for a task in a configuration file independent
    from the source code.

----

*******************
Class type defaults
*******************

The support for classes as type hints allows to try many possibilities with the same CLI. This is a useful feature, but
it is tempting to use an instance of a class as a default. For example:

.. testcode::

    class MyMainModel(LightningModule):
        def __init__(
            self,
            backbone: torch.nn.Module = MyModel(encoder_layers=24),  # BAD PRACTICE!
        ):
            super().__init__()
            self.backbone = backbone

Normally classes are mutable, as in this case. The instance of ``MyModel`` would be created the moment that the module
that defines ``MyMainModel`` is first imported. This means that the default of ``backbone`` will be initialized before
the CLI class runs ``seed_everything``, making it non-reproducible. Furthermore, if ``MyMainModel`` is used more than
once in the same Python process and the ``backbone`` parameter is not overridden, the same instance would be used in
multiple places. Most likely, this is not what the developer intended. Having an instance as default also makes it
impossible to generate the complete config file since it is not known which arguments were used to instantiate it for
arbitrary classes.

An excellent solution to these problems is not to have a default or set the default to a unique value (e.g., a string).
Then check this value and instantiate it in the ``__init__`` body. If a class parameter has no default and the CLI is
subclassed, then a default can be set as follows:

.. testcode::

    default_backbone = {
        "class_path": "import.path.of.MyModel",
        "init_args": {
            "encoder_layers": 24,
        },
    }


    class MyLightningCLI(LightningCLI):
        def add_arguments_to_parser(self, parser):
            parser.set_defaults({"model.backbone": default_backbone})

A more compact version that avoids writing a dictionary would be:

.. testcode::

    from jsonargparse import lazy_instance


    class MyLightningCLI(LightningCLI):
        def add_arguments_to_parser(self, parser):
            parser.set_defaults({"model.backbone": lazy_instance(MyModel, encoder_layers=24)})

----

.. _cli_link_arguments:

****************
Argument linking
****************
Another case in which it might be desired to extend :class:`~pytorch_lightning.cli.LightningCLI` is that the model and
data module depends on a common parameter. For example, in some cases, both classes require to know the ``batch_size``.
It is a burden and error-prone to give the same value twice in a config file. To avoid this, the parser can be
configured so that a value is only given once and then propagated accordingly. With a tool implemented like the one
shown below, the ``batch_size`` only has to be provided in the ``data`` section of the config.

.. testcode::

    class MyLightningCLI(LightningCLI):
        def add_arguments_to_parser(self, parser):
            parser.link_arguments("data.batch_size", "model.batch_size")


    cli = MyLightningCLI(MyModel, MyDataModule)

The linking of arguments is observed in the help of the tool, which for this example would look like:

.. code-block:: bash

    $ python trainer.py fit --help
      ...
        --data.batch_size BATCH_SIZE
                              Number of samples in a batch (type: int, default: 8)

      Linked arguments:
        data.batch_size --> model.batch_size
                              Number of samples in a batch (type: int)

Sometimes a parameter value is only available after class instantiation. An example could be that your model requires
the number of classes to instantiate its fully connected layer (for a classification task). But the value is not
available until the data module has been instantiated. The code below illustrates how to address this.

.. testcode::

    class MyLightningCLI(LightningCLI):
        def add_arguments_to_parser(self, parser):
            parser.link_arguments("data.num_classes", "model.num_classes", apply_on="instantiate")


    cli = MyLightningCLI(MyClassModel, MyDataModule)

Instantiation links are used to automatically determine the order of instantiation, in this case data first.

.. note::

    The linking of arguments is intended for things that are meant to be non-configurable. This improves the CLI user
    experience since it avoids the need to provide more parameters. A related concept is a variable interpolation that
    keeps things configurable.

.. tip::

    The linking of arguments can be used for more complex cases. For example to derive a value via a function that takes
    multiple settings as input. For more details have a look at the API of `link_arguments
    <https://jsonargparse.readthedocs.io/en/stable/#jsonargparse.ArgumentParser.link_arguments>`_.