orphan: |
---|
.. testsetup:: * :skipif: not _JSONARGPARSE_AVAILABLE import torch from unittest import mock from typing import List import pytorch_lightning as pl 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()
Audience: Users who already understand the LightningCLI and want to customize it.
The init parameters of the :class:`~pytorch_lightning.cli.LightningCLI` class can be used to customize some things, namely: 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 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` adds all arguments from the init of a class, though requiring parameters to have type hints. For more details about this please refer to the respective documentation.
The :class:`~pytorch_lightning.cli.LightningCLI` class has the
:meth:`~pytorch_lightning.cli.LightningCLI.add_arguments_to_parser` method which 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 for these would be to send an email before and after the execution.
The code for the fit
subcommand would be something like:
.. 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 dictionary whose keys are global options or groups of options. It
has the same structure as the yaml format described previously. This means for instance 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.
As explained previously, any Lightning callback can be added by passing it through 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 configuration of the EarlyStopping
in the config it would be:
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 of defaults in the source code is not intended to be used to
store the best hyperparameters for a task after experimentation. To ease reproducibility the source code should be
stable. It is better practice to store the best hyperparameters for a task in a configuration file independent from
the source code.
The support for classes as type hints allows to try many possibilities with the same CLI. This is a useful feature, but it can make it 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 it is 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 which very likely is not what the developer intended.
Having an instance as default also makes it impossible to generate the complete config file since for arbitrary classes
it is not known which arguments were used to instantiate it.
A good solution to these problems is to not have a default or set the default to a special value (e.g. a string) which would be checked in the init and instantiated accordingly. 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)})
Another case in which it might be desired to extend :class:`~pytorch_lightning.cli.LightningCLI` is that the
model and data module depend on a common parameter. For example in some cases both classes require to know the
batch_size
. It is a burden and error prone giving 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 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:
$ python trainer.py fit --help
...
--data.batch_size BATCH_SIZE
Number of samples in a batch (type: int, default: 8)
Linked arguments:
model.batch_size <-- data.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.
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.
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 for providing more parameters. A related concept is variable interpolation which in contrast keeps things being configurable.