> ## Documentation Index
> Fetch the complete documentation index at: https://agent-observability-docs.splunk.com/llms.txt
> Use this file to discover all available pages before exploring further.

# Installation

> Install the Luna Studio SDK and optional extras.

## Prerequisites

* Python **3.12**

If you plan to fine-tune on GPU, you’ll also need a compatible CUDA environment (for example, 1x H100-class GPU).

## Install from PyPI (recommended)

Install the SDK with the extras you need:

```bash theme={null}
pip install "galileo-luna-ft[data-generation,training]"
```

### LLM provider extras (optional)

Pick the provider(s) you use for generation/labeling:

```bash theme={null}
# Example: Azure OpenAI
pip install "galileo-luna-ft[data-generation,training,llm-azure-openai]"
```

Available LLM provider extras are:

* `llm-anthropic`
* `llm-openai`
* `llm-azure-openai`
* `llm-groq`
* `llm-gemini`
* `llm-vertex-ai`

These are installation extras, not all accepted values for `data_generation.llm.provider`. The current provider registry accepts `anthropic`, `azure`, `gemini`, `groq`, `openai`, and `vegas`; see the [data-generation config reference](/luna-studio/sdk/how-to-train-your-luna-evaluator/data-generation/config/config#llm).

### Deployment extras (optional)

Pick the deployment extra for the cloud environment you use:

```bash theme={null}
# Example: Azure ML
pip install "galileo-luna-ft[data-generation,training,llm-azure-openai,deploy-azureml]"
```

Available deployment extras include `deploy-kubernetes`, `deploy-vertex-ai`, `deploy-azureml`, `deploy-sagemaker`.

## Verify install

```bash theme={null}
python -c "import galileo_luna_ft; print(galileo_luna_ft.__version__)"
```