> ## Documentation Index > Fetch the complete documentation index at: https://docs.adaptive-ml.com/llms.txt > Use this file to discover all available pages before exploring further. # Overview > Build custom training and evaluation workflows Write custom Python recipes for training, evaluation, and other AI workflows using `adaptive_harmony`. ## Getting started Install adaptive-harmony Connect to compute plane for interactive development Structure and entry points Define recipe parameters ## Building recipes Load and spawn models Load datasets and StringThread Custom reward functions Load graders from Adaptive ## Training & evaluation SFT, PPO, GRPO, DPO algorithms Evaluate models and create artifacts JSON and constrained generation Track recipe progress in UI ## Quick reference Upload a recipe: ```python theme={null} adaptive.recipes.upload( path="/path/to/recipe.py", # or directory recipe_key="my-recipe", name="My Training Recipe" ) ``` When uploading a directory, the entry point must be `main.py` and all imports must be relative (e.g., `from .utils import helper`). Run a recipe: ```python theme={null} job = adaptive.jobs.run( recipe_key="my-recipe", num_gpus=2, args={"model": "llama-3.1-8b", "dataset": "my-data"} ) ```