Configure hyperparameters from the CLI¶

Why use a CLI¶

When running deep learning experiments, there are a couple of good practices that are recommended to follow:

Separate configuration from source code
Guarantee reproducibility of experiments

Implementing a command line interface (CLI) makes it possible to execute an experiment from a shell terminal. By having a CLI, there is a clear separation between the Python source code and what hyperparameters are used for a particular experiment. If the CLI corresponds to a stable version of the code, reproducing an experiment can be achieved by installing the same version of the code plus dependencies and running with the same configuration (CLI arguments).

Basic use¶

1: Control it all from the CLI

Learn to control a LightningModule and LightningDataModule from the CLI

intermediate

2: Mix models, datasets and optimizers

Support multiple models, datasets, optimizers and learning rate schedulers

intermediate

3: Control it all via YAML

Enable composable YAMLs

advanced

Advanced use¶

YAML for production

Use the Lightning CLI with YAMLs for production environments

advanced

Customize for complex projects

Learn how to implement CLIs for complex projects

advanced

Extend the Lightning CLI

Customize the Lightning CLI

expert

Miscellaneous¶

FAQ

Frequently asked questions about working with the Lightning CLI and YAML files