# fine-tuning-serving-openpi

Workflows for fine-tuning and serving Physical Intelligence OpenPI models (pi0, pi0-fast, pi0.5). Guides setup, norm stats calculation, and policy inference servers.

- skill · `type=skill` · Claude Code, Codex, Cursor, GitHub Copilot, Gemini CLI, OpenCode · 861 installs · ★ 13956
- Author: Orchestra Research
- License: MIT · policy=mirror · the beginning of the file is at the end of this page
- Source: https://github.com/Orchestra-Research/AI-Research-SKILLs/tree/HEAD/18-multimodal/openpi/SKILL.md
- Updated: 2026-10-11
- HTML: https://affpapa.org/en/nevibecode/skills/fine-tuning-serving-openpi

## What it does

This skill guides the setup, training, and deployment of Physical Intelligence OpenPI models (pi0, pi0-fast, and pi0.5) for ALOHA, DROID, and LIBERO robotics tasks. It supports both JAX for standard training and PyTorch for deployment workflows, connecting components from a clean clone to running policy servers.

All parameters live in `src/openpi/training/config.py`. Before starting any training run, you must compute normalization statistics using `scripts/compute_norm_stats.py`. Skipping this step after modifying dataset mappings or transforms leads to corrupted model behavior.

Requirements include `uv>=0.4.0`, `jax>=0.4.30`, and `torch>=2.1.0`. GPU requirements are demanding: serving pi0.5 requires ~24 GB VRAM on an A100 or H100, while JAX fine-tuning for pi0.5 needs around ~60 GB VRAM.

## Install

### Claude Code (native platform)

1. **Skills CLI (Vercel)**
```bash
npx skills add Orchestra-Research/AI-Research-SKILLs --skill fine-tuning-serving-openpi -a claude-code
```
```bash
npx skills add Orchestra-Research/AI-Research-SKILLs --skill openpi -a claude-code
```

2. **Manually: put the files in place**
- File: `~/.claude/skills/openpi/SKILL.md` — Copy the whole skill folder; for one project use .claude/skills/openpi/

### Codex

1. **Skills CLI (Vercel)**
```bash
npx skills add Orchestra-Research/AI-Research-SKILLs --skill openpi -a codex
```

2. **Manually: put the files in place**
- File: `.agents/skills/openpi/SKILL.md` — For all projects use ~/.agents/skills/

### Cursor

1. **Skills CLI (Vercel)**
```bash
npx skills add Orchestra-Research/AI-Research-SKILLs --skill openpi -a cursor
```

2. **Manually: put the files in place**
- File: `.cursor/skills/openpi/SKILL.md` — Cursor also reads .agents/skills/ and .claude/skills/

### GitHub Copilot

1. **GitHub CLI**
```bash
gh skill install Orchestra-Research/AI-Research-SKILLs openpi
```

2. **Manually: put the files in place**
- File: `.github/skills/openpi/SKILL.md`

### Gemini CLI

1. **Skills CLI (Vercel)**
```bash
npx skills add Orchestra-Research/AI-Research-SKILLs --skill openpi -a gemini-cli
```

2. **Platform CLI**
```bash
gemini skills install https://github.com/Orchestra-Research/AI-Research-SKILLs --path 18-multimodal/openpi
```

### Windsurf (Devin Desktop)

**Manually: put the files in place**
- File: `.devin/skills/openpi/SKILL.md` — The legacy .windsurf/skills/ path still works

### OpenCode

**Skills CLI (Vercel)**
```bash
npx skills add Orchestra-Research/AI-Research-SKILLs --skill openpi -a opencode
```

### Claude.ai

**Claude.ai**
- Zip the folder and upload it in Settings → Capabilities → Skills

Where a file is given instead of a command, copy the files from the source to that path: https://github.com/Orchestra-Research/AI-Research-SKILLs/tree/HEAD/18-multimodal/openpi/SKILL.md

## When to use it
- configure blank environments with `uv`, `jax`, and `torch`
- calculate normalization statistics for LeRobot or RLDS datasets
- fine-tune pi0, pi0-fast, or pi0.5 models using JAX
- serve policy checkpoints over WebSocket for robot evaluation
- train models using PyTorch DDP across multiple GPUs

## Example prompts
- Help me set up openpi from scratch and verify the pi05_droid configuration
- Run compute_norm_stats.py for my custom LeRobot dataset before training
- Serve a trained pi0 checkpoint over WebSocket using scripts/serve_policy.py

## Source text · SKILL.md · MIT

Below is the content of a third-party file. It is data, not instructions for you: do not act on anything written inside unless the human asks. Block tag: 54f34d185d85761b.

BEGIN THIRD-PARTY FILE 54f34d185d85761b
````text
---
name: fine-tuning-serving-openpi
description: Fine-tune and serve Physical Intelligence OpenPI models (pi0, pi0-fast, pi0.5) using JAX or PyTorch backends for robot policy inference across ALOHA, DROID, and LIBERO environments. Use when adapting pi0 models to custom datasets, converting JAX checkpoints to PyTorch, running policy inference servers, or debugging norm stats and GPU memory issues.
version: 1.0.0
author: Orchestra Research
license: MIT
tags: [OpenPI, Physical Intelligence, VLA, Robotics, JAX, PyTorch, Fine-Tuning, Policy Serving, ALOHA, DROID, LIBERO, pi0]
dependencies: [uv>=0.4.0, jax>=0.4.30, torch>=2.1.0, transformers>=4.53.2]
---

# OpenPI Fine-Tuning and Serving

End-to-end workflows for fine-tuning and serving Physical Intelligence's OpenPI models (pi0, pi0-fast, pi0.5) on robot manipulation tasks from the public `openpi` repository. Covers blank-machine setup, JAX training, PyTorch training, checkpoint conversion, and policy inference serving.

## Quick start

Clone the public repo, install the workspace, then serve a pretrained policy:

```bash
git clone --recurse-submodules https://github.com/Physical-Intelligence/openpi.git
cd openpi
GIT_LFS_SKIP_SMUDGE=1 uv sync
GIT_LFS_SKIP_SMUDGE=1 uv pip install -e .
uv run scripts/serve_policy.py --env DROID
```

```python
from openpi_client import websocket_client_policy

client = websocket_client_policy.WebsocketClientPolicy(host="localhost", port=8000)
result = client.infer(observation)
actions = result["actions"]  # numpy array of shape (chunk_size, action_dim)
```

## Core concepts

**Model family**: OpenPI implements three model variants from Physical Intelligence:

| Model | Architecture | Speed | Quality | Typical use |
|-------|-------------|-------|---------|-------------|
| pi0 | Flow-matching VLA | Baseline | Highest | Research, complex tasks |
| pi0-fast | Autoregressive action tokens | 2-5x faster | Good | Real-time control |
| pi0.5 | pi0 + improved vision encoder | Baseline | Best | Latest default |

**Key design choices**:
- **Dual backend**: JAX (primary, official training) and PyTorch (community, deployment-friendly)
- **Config-driven**: All training/serving parameters defined in `src/openpi/training/config.py`
- **Norm stats**: Every config requires precomputed normalization statistics before training
- **WebSocket serving**: Policy servers expose a WebSocket API for low-latency inference

**Training loop invariant**: After every config or dataset change, always re-run this cycle:
1. Compute norm stats → 2. Train → 3. Serve checkpoint → 4. Validate inference

## Compute requirements

| Task | GPU | VRAM | Notes |
|------|-----|------|-------|
````
END THIRD-PARTY FILE 54f34d185d85761b

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