Configure hyperparameters from the CLI (Intermediate)

Audience: Users who want advanced modularity via a command line interface (CLI).

Pre-reqs: You must already understand how to use the command line and LightningDataModule.

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LightningCLI requirements

The LightningCLI class is designed to significantly ease the implementation of CLIs. To use this class, an additional Python requirement is necessary than the minimal installation of Lightning provides. To enable, either install all extras:

pip install "pytorch-lightning[extra]"

or if only interested in LightningCLI, just install jsonargparse:

pip install "jsonargparse[signatures]"

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Implementing a CLI

Implementing a CLI is as simple as instantiating a LightningCLI object giving as arguments classes for a LightningModule and optionally a LightningDataModule:

# main.py
from lightning.pytorch.cli import LightningCLI

# simple demo classes for your convenience
from lightning.pytorch.demos.boring_classes import DemoModel, BoringDataModule


def cli_main():
    cli = LightningCLI(DemoModel, BoringDataModule)
    # note: don't call fit!!


if __name__ == "__main__":
    cli_main()
    # note: it is good practice to implement the CLI in a function and call it in the main if block

Now your model can be managed via the CLI. To see the available commands type:

$ python main.py --help

which prints out:

usage: main.py [-h] [-c CONFIG] [--print_config [={comments,skip_null,skip_default}+]]
        {fit,validate,test,predict,tune} ...

pytorch-lightning trainer command line tool

optional arguments:
-h, --help            Show this help message and exit.
-c CONFIG, --config CONFIG
                        Path to a configuration file in json or yaml format.
--print_config [={comments,skip_null,skip_default}+]
                        Print configuration and exit.

subcommands:
For more details of each subcommand add it as argument followed by --help.

{fit,validate,test,predict,tune}
    fit                 Runs the full optimization routine.
    validate            Perform one evaluation epoch over the validation set.
    test                Perform one evaluation epoch over the test set.
    predict             Run inference on your data.
    tune                Runs routines to tune hyperparameters before training.

The message tells us that we have a few available subcommands:

python main.py [subcommand]

which you can use depending on your use case:

$ python main.py fit
$ python main.py validate
$ python main.py test
$ python main.py predict
$ python main.py tune

---

Train a model with the CLI

To train a model, use the fit subcommand:

python main.py fit

View all available options with the --help argument given after the subcommand:

$ python main.py fit --help

usage: main.py [options] fit [-h] [-c CONFIG]
                            [--seed_everything SEED_EVERYTHING] [--trainer CONFIG]
                            ...
                            [--ckpt_path CKPT_PATH]
    --trainer.logger LOGGER

optional arguments:
<class '__main__.DemoModel'>:
    --model.out_dim OUT_DIM
                            (type: int, default: 10)
    --model.learning_rate LEARNING_RATE
                            (type: float, default: 0.02)
<class 'lightning.pytorch.demos.boring_classes.BoringDataModule'>:
--data CONFIG         Path to a configuration file.
--data.data_dir DATA_DIR
                        (type: str, default: ./)

With the Lightning CLI enabled, you can now change the parameters without touching your code:

# change the learning_rate
python main.py fit --model.learning_rate 0.1

# change the output dimensions also
python main.py fit --model.out_dim 10 --model.learning_rate 0.1

# change trainer and data arguments too
python main.py fit --model.out_dim 2 --model.learning_rate 0.1 --data.data_dir '~/' --trainer.logger False

Tip

The options that become available in the CLI are the __init__ parameters of the LightningModule and LightningDataModule classes. Thus, to make hyperparameters configurable, just add them to your class’s __init__. It is highly recommended that these parameters are described in the docstring so that the CLI shows them in the help. Also, the parameters should have accurate type hints so that the CLI can fail early and give understandable error messages when incorrect values are given.