Eliminate config boilerplate (Advanced)¶

Audience: Users who already understand the LightningCLI and want to customize it.

Customize the LightningCLI¶

The init parameters of the 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 LightningCLI is LightningArgumentParser which is an extension of python’s argparse, thus adding arguments can be done using the add_argument() method. In contrast to argparse it has additional methods to add arguments, for example 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 LightningCLI class has the 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 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:

class MyLightningCLI(LightningCLI):
    def add_arguments_to_parser(self, parser):
        parser.add_argument("--notification_email", default="[email protected]")

    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 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 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:

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.

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 can make it tempting to use an instance of a class as a default. For example:

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:

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:

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)})

Connect two config files¶

Another case in which it might be desired to extend 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.

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.

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.