Fine Tuning Serving Openpi
Fine-tune and serve Physical Intelligence OpenPI models (pi0, pi0-fast, pi0.5) using...
Install
npx skills add Orchestra-Research/AI-Research-SKILLs --skill fine-tuning-serving-openpi -g -a claude-code -y- Repository
- Orchestra-Research/AI-Research-SKILLs14.2k
- Category
- Testing
- Updated
- 25d ago
- License
- MIT
- Context cost
- Mediumabout 3,000 tokens
- Runs scripts
- No, instructions only
What it does
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.
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:
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
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:
- Compute norm stats → 2. Train → 3. Serve checkpoint → 4. Validate inference
