> ## Documentation Index > Fetch the complete documentation index at: https://docs.galileo.ai/llms.txt > Use this file to discover all available pages before exploring further. # Overview > End-to-end guide for generating data and training your Luna metric with the Luna Studio SDK. This section walks through the full Luna Studio SDK workflow for creating and training a custom Luna metric. ## Start with prerequisites Before running the pipeline, make sure you have: * A high-quality human-labelled test set * A reliable LLM-as-a-judge prompt for your metric * The environment and extras required for your chosen providers Start here: [Prerequisites](/luna-studio/sdk/how-to-train-your-luna-metric/prerequisites) ## The end-to-end workflow Creating a Luna metric with the SDK happens in two stages: 1. **Data generation** creates or labels the training dataset for your metric. 2. **Training** fine-tunes a LoRA adapter on the prepared dataset. Both stages are configured in the same YAML file under the `data_generation` and `training` sections. ## Choose your starting point Generate synthetic labelled training data when you only have a high-quality labelled test set. Use label-only mode when you already have raw training data but still need labels. Skip data generation and go straight to fine-tuning when your labelled dataset is already ready. ## Hardware requirements ### Data generation Data generation is CPU-friendly. The main requirement is access to the LLM provider you want to use for synthetic example generation and labeling. ### Training Training requires a GPU. We recommend an H100 for production runs. For larger jobs, use a managed training environment when needed. ## Where to go next Create or label the dataset you need before training. Fine-tune the metric and inspect the resulting artifacts.