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.
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]"
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} ...
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}
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.
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
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.