---
title: Configure hyperparameters from the CLI (Intermediate)
description: >-
  Audience: Users who want advanced modularity via a command line interface
  (CLI).
canonical: >-
  https://lightning.ai/docs/pytorch/stable/reference/cli/lightning_cli_intermediate
---
# Configure hyperparameters from the CLI (Intermediate) {#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](/docs/pytorch/2.6.6/reference/data/datamodule.md).

## LightningCLI requirements {#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:

```bash
pip install "lightning[pytorch-extra]"
```

or if only interested in `LightningCLI`, just install jsonargparse:

```bash
pip install "jsonargparse[signatures]"
```

## Implementing a CLI {#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`:

```python
# 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:

```bash
$ python main.py --help
```

which prints out:

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

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:

```bash
python main.py [subcommand]
```

which you can use depending on your use case:

```bash
$ python main.py fit
$ python main.py validate
$ python main.py test
$ python main.py predict
```

## Train a model with the CLI {#train-a-model-with-the-cli}

To train a model, use the `fit` subcommand:

```bash
python main.py fit
```

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

```bash
$ 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:

```bash
# 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.

---

**Navigation**

- Parent: [Cli](/docs/pytorch/2.6.6/reference/cli.md)