53 lines
1.6 KiB
ReStructuredText
53 lines
1.6 KiB
ReStructuredText
:orphan:
|
|
|
|
Configure hyperparameters from the CLI
|
|
--------------------------------------
|
|
|
|
You can use any CLI tool you want with Lightning.
|
|
For beginners, we recommend using Python's built-in argument parser.
|
|
|
|
|
|
----
|
|
|
|
|
|
ArgumentParser
|
|
^^^^^^^^^^^^^^
|
|
|
|
The :class:`~argparse.ArgumentParser` is a built-in feature in Python that let's you build CLI programs.
|
|
You can use it to make hyperparameters and other training settings available from the command line:
|
|
|
|
.. code-block:: python
|
|
|
|
from argparse import ArgumentParser
|
|
|
|
parser = ArgumentParser()
|
|
|
|
# Trainer arguments
|
|
parser.add_argument("--devices", type=int, default=2)
|
|
|
|
# Hyperparameters for the model
|
|
parser.add_argument("--layer_1_dim", type=int, default=128)
|
|
|
|
# Parse the user inputs and defaults (returns a argparse.Namespace)
|
|
args = parser.parse_args()
|
|
|
|
# Use the parsed arguments in your program
|
|
trainer = Trainer(devices=args.devices)
|
|
model = MyModel(layer_1_dim=args.layer_1_dim)
|
|
|
|
This allows you to call your program like so:
|
|
|
|
.. code-block:: bash
|
|
|
|
python trainer.py --layer_1_dim 64 --devices 1
|
|
|
|
----
|
|
|
|
|
|
LightningCLI
|
|
^^^^^^^^^^^^
|
|
|
|
Python's argument parser works well for simple use cases, but it can become cumbersome to maintain for larger projects.
|
|
For example, every time you add, change, or delete an argument from your model, you will have to add, edit, or remove the corresponding ``parser.add_argument`` code.
|
|
The :doc:`Lightning CLI <../cli/lightning_cli>` provides a seamless integration with the Trainer and LightningModule for which the CLI arguments get generated automatically for you!
|