# Installation

**Gunz-ML** requires Python 3.11 or higher. It is designed to work seamlessly within the Pekora ecosystem but can be used as a standalone SDK for experiment tracking.

## Standard Installation

You can install the library directly from the GitHub repository using `pip`:

```bash
pip install git+https://github.com/sXperfect/gunz-ml.git
```

## Recommended: Mamba/Conda Environment

For development and research, we recommend using a dedicated `mamba` environment to manage heavy dependencies like PyTorch and MLflow:

```bash
# Create the environment
mamba create -n gunz-ml python=3.11

# Activate it
mamba activate gunz-ml

# Install the package in editable mode
pip install -e .
```

## Optional Dependencies

The library uses optional "extras" to keep the base installation lightweight. Depending on your needs, you might want to install:

*   **`[extras]`**: Full MLflow support with SQL backends.
*   **`[all]`**: Includes all analysis and visualization tools.

```bash
pip install "gunz-ml[extras]"
```

## System Requirements

If you are using the tracking features with a remote database (Juno), ensure you have the necessary system clients for MySQL:

*   **Ubuntu/Debian:** `sudo apt-get install libmysqlclient-dev`
*   **macOS:** `brew install mysql-client`