fine-tuning-serving-openpi

Original：🇺🇸 English

Translated

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.

2installs

Sourceorchestra-research/ai-research-skills

Added on2026-04-03

NPX Install

npx skill4agent add orchestra-research/ai-research-skills fine-tuning-serving-openpi

SKILL.md Content

View Translation Comparison →

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:

Compute norm stats → 2. Train → 3. Serve checkpoint → 4. Validate inference

Compute requirements

Task	GPU	VRAM	Notes
Serve pi0.5 (inference)	1x A100/H100	~24 GB	Single GPU sufficient
Fine-tune pi0.5 (JAX)	1x A100 80GB	~60 GB	Use `fsdp_devices` for multi-GPU
Fine-tune pi0 (JAX)	1x A100 80GB	~40 GB	Smaller model footprint
Fine-tune (PyTorch DDP)	1-8x A100	~40 GB/GPU	torchrun launcher
Compute norm stats	CPU or 1x GPU	~8 GB	Fast, can run on login node

Workflow 0: Blank-machine setup

Copy this checklist and track progress:

text

Setup Progress:
- [ ] Step 1: Clone the public openpi repo with submodules
- [ ] Step 2: Install uv and sync the workspace
- [ ] Step 3: Install the editable package
- [ ] Step 4: Verify core imports and serving entrypoint

Step 1: Clone repo

bash

git clone --recurse-submodules https://github.com/Physical-Intelligence/openpi.git
cd openpi

If you already cloned without submodules:

bash

git submodule update --init --recursive

Step 2: Sync dependencies

bash

GIT_LFS_SKIP_SMUDGE=1 uv sync

Step 3: Install editable package

bash

GIT_LFS_SKIP_SMUDGE=1 uv pip install -e .

Step 4: Verify installation

bash

uv run python -c "from openpi.training import config as _config; print(_config.get_config('pi05_droid').name)"
uv run scripts/serve_policy.py --help

When to use vs alternatives

Use this skill when:

Fine-tuning pi0, pi0-fast, or pi0.5 on LeRobot or RLDS datasets
Serving OpenPI policies for ALOHA, DROID, or LIBERO evaluation
Converting JAX checkpoints to PyTorch format
Debugging OpenPI training issues (norm stats, memory, config)

Use
fine-tuning-openvla-oft
instead when:

Fine-tuning OpenVLA with continuous action heads and LoRA
Reproducing OpenVLA-OFT paper results on LIBERO or ALOHA

Use
evaluating-cosmos-policy
instead when:

Evaluating NVIDIA Cosmos Policy on simulation benchmarks

Workflow 1: JAX fine-tuning on LeRobot data

Copy this checklist and track progress:

text

JAX Fine-Tuning Progress:
- [ ] Step 1: Select and copy closest training config
- [ ] Step 2: Update dataset mapping and base checkpoint
- [ ] Step 3: Compute normalization statistics
- [ ] Step 4: Launch JAX training
- [ ] Step 5: Serve checkpoint and run inference sanity check

Step 1: Select config

Copy the closest config from

src/openpi/training/config.py

Config	Use case
`pi05_libero`	pi0.5 LIBERO fine-tuning
`pi0_libero`	pi0 full fine-tuning on LIBERO
`pi0_fast_libero`	pi0-fast on LIBERO
`pi0_aloha_pen_uncap`	ALOHA custom data
`pi05_droid_finetune`	Small custom DROID dataset (LeRobot format)
`pi05_full_droid_finetune`	Full DROID RLDS large-scale training

Step 2: Update dataset and transforms

python

# In src/openpi/training/config.py, modify your config:
TrainConfig(
    name="my_custom_config",
    model_type="pi05",
    data=LeRobotDataConfig(
        repo_id="your-org/your-dataset",
        # Adjust transforms to match your data format
    ),
    weight_loader=Pi05WeightLoader(),  # Match model type
)

Set

repo_id

for your dataset and ensure

weight_loader

matches the model type (pi0 vs pi0.5).

Step 3: Compute normalization statistics

bash

uv run scripts/compute_norm_stats.py --config-name <config_name>

This must run before every training launch when config, dataset, or transforms change.

Step 4: Launch JAX training

bash

XLA_PYTHON_CLIENT_MEM_FRACTION=0.9 uv run scripts/train.py <config_name> \
  --exp-name=<run_name> \
  --overwrite

For full DROID RLDS training, add the

rlds

dependency group:

bash

uv run --group rlds scripts/compute_norm_stats.py \
  --config-name pi05_full_droid_finetune \
  --max-frames 10000000

XLA_PYTHON_CLIENT_MEM_FRACTION=0.9 uv run --group rlds scripts/train.py \
  pi05_full_droid_finetune \
  --exp-name=<run_name> --overwrite

Step 5: Serve and validate

bash

uv run scripts/serve_policy.py policy:checkpoint \
  --policy.config=<config_name> \
  --policy.dir=checkpoints/<config_name>/<run_name>/<step>

Verify with a test client:

python

from openpi_client import websocket_client_policy

client = websocket_client_policy.WebsocketClientPolicy(host="localhost", port=8000)
# Build observation matching your config's expected keys
obs = {"image": img_array, "state": state_array, "prompt": "pick up the cup"}
result = client.infer(obs)
print(f"Action shape: {result['actions'].shape}")  # (chunk_size, action_dim)

Workflow 2: PyTorch training and checkpoint conversion

Copy this checklist and track progress:

text

PyTorch Setup Progress:
- [ ] Step 1: Sync dependencies and verify transformer version
- [ ] Step 2: Apply OpenPI transformer patches
- [ ] Step 3: Convert JAX checkpoint to PyTorch format
- [ ] Step 4: Launch PyTorch training or serve converted checkpoint

Step 1: Sync dependencies

bash

uv sync
uv pip show transformers

Step 2: Apply required patches

OpenPI PyTorch requires custom modifications to the installed

transformers

package:

bash

cp -r ./src/openpi/models_pytorch/transformers_replace/* \
  .venv/lib/python3.11/site-packages/transformers/

Step 3: Convert JAX checkpoint

bash

uv run examples/convert_jax_model_to_pytorch.py \
  --checkpoint_dir <jax_checkpoint_dir> \
  --config_name <config_name> \
  --output_path <pytorch_checkpoint_dir>

Step 4: Train or serve

Single GPU training:

bash

uv run scripts/train_pytorch.py <config_name> --exp_name <run_name>

Multi-GPU distributed training:

bash

uv run torchrun --standalone --nnodes=1 --nproc_per_node=<num_gpus> \
  scripts/train_pytorch.py <config_name> --exp_name <run_name>

Programmatic inference with converted checkpoint:

python

from openpi.training import config as _config
from openpi.policies import policy_config

config = _config.get_config("pi05_droid")
policy = policy_config.create_trained_policy(config, "<pytorch_checkpoint_dir>")
result = policy.infer(example)
actions = result["actions"]  # numpy array

Checkpoints follow the convention:

checkpoints/<config_name>/<exp_name>/<step>/

Workflow 3: Policy inference serving

Copy this checklist and track progress:

text

Inference Server Progress:
- [ ] Step 1: Choose target environment and checkpoint
- [ ] Step 2: Start policy server
- [ ] Step 3: Confirm server is reachable
- [ ] Step 4: Integrate client into robot or simulation code

Step 1: Choose environment

Default environment presets:

Environment	Config	Default checkpoint
`ALOHA`	`pi05_aloha`	`gs://openpi-assets/checkpoints/pi05_base`
`ALOHA_SIM`	`pi0_aloha_sim`	`gs://openpi-assets/checkpoints/pi0_aloha_sim`
`DROID`	`pi05_droid`	`gs://openpi-assets/checkpoints/pi05_droid`
`LIBERO`	`pi05_libero`	`gs://openpi-assets/checkpoints/pi05_libero`

Step 2: Start server

Default mode (uses preset checkpoint):

bash

uv run scripts/serve_policy.py --env ALOHA

Explicit checkpoint mode (custom or local model):

bash

uv run scripts/serve_policy.py policy:checkpoint \
  --policy.config=pi05_libero \
  --policy.dir=checkpoints/pi05_libero/my_run/20000

Add

--default_prompt "task description"

when runtime observations omit a prompt.

Step 3: Verify connectivity

bash

uv run examples/simple_client/main.py --env DROID

Step 4: Embed remote client in robot code

Install the lightweight client in your robot environment:

bash

pip install "openpi-client @ git+https://github.com/Physical-Intelligence/openpi.git#subdirectory=packages/openpi-client"

Full integration example:

python

from openpi_client import websocket_client_policy
import numpy as np

# Connect to remote policy server
client = websocket_client_policy.WebsocketClientPolicy(
    host="gpu-server.local", port=8000
)

# Build observation (keys must match policy transforms)
observation = {
    "image": np.random.rand(224, 224, 3),  # RGB image
    "state": np.zeros(7),                   # Joint positions
    "prompt": "pick up the red block",
}

# Get actions
result = client.infer(observation)
actions = result["actions"]  # shape: (action_chunk_size, action_dim)

# Execute first action on robot
robot.step(actions[0])

Common issues

Issue: Missing norm stats error

Fix: run

scripts/compute_norm_stats.py --config-name <config_name>

before training.

Issue: Out of memory during JAX training

Fix: set

XLA_PYTHON_CLIENT_MEM_FRACTION=0.9

, lower batch size, or configure

fsdp_devices

python

# In config: use model-parallel sharding
TrainConfig(
    ...
    fsdp_devices=4,  # Shard across 4 GPUs
)

Issue: OOM while loading PyTorch checkpoints

Fix:

export PYTORCH_CUDA_ALLOC_CONF=expandable_segments:True

Issue: Config not found

Fix: ensure config name exists in

src/openpi/training/config.py

(exact match from

_CONFIGS

dict).

Issue: PyTorch training diverges after library changes

Fix: reapply the transformer patch. Run

uv cache clean transformers

to reset, then reapply.

Issue:
serve_policy.py
crashes with
ModuleNotFoundError

Fix: resync the public workspace first:

bash

GIT_LFS_SKIP_SMUDGE=1 uv sync
GIT_LFS_SKIP_SMUDGE=1 uv pip install -e .

If the missing module is simulator-related, install the extra runtime dependencies called for by that example:

bash

uv pip install pytest robosuite==1.4.0 gym bddl easydict matplotlib

Issue:
uv sync
fails with
rerun-sdk
wheel mismatch

Fix:

bash

uv sync --no-dev
# or
uv sync --no-dev --no-install-package rerun-sdk

Issue: Checkpoint download times out

Fix: install

gsutil

and prefetch manually:

bash

pip install gsutil
gsutil -m cp -r gs://openpi-assets/checkpoints/pi05_libero /local/cache/

Remove stale

.lock

files if a previous download was interrupted.

Issue: Policy server exits with code
137

Fix: OOM kill. Set JAX memory variables:

bash

export XLA_PYTHON_CLIENT_PREALLOCATE=false
export XLA_PYTHON_CLIENT_ALLOCATOR=platform

For HPC/cluster users

On Slurm-managed clusters, wrap commands with resource allocation:

bash

srun --partition=gpu --gpus-per-node=1 --mem=64G --cpus-per-task=8 --pty bash

Route caches to scratch to avoid filling

/home

bash

export HF_HOME=/scratch/$USER/.cache/huggingface
export XDG_CACHE_HOME=/scratch/$USER/.cache
export PIP_CACHE_DIR=/scratch/$USER/.cache/pip
export UV_CACHE_DIR=/scratch/$USER/.cache/uv

Avoid stacking cluster Python modules when using uv-managed environments. Typically

module load cuda

is sufficient.

Advanced topics

Config recipes and baselines: See references/config-recipes.md Training debugging guide: See references/training-debugging.md Checkpoint and environment mapping: See references/checkpoints-and-env-map.md Remote client integration: See references/remote-client-pattern.md PyTorch precision and patching gotchas: See references/pytorch-gotchas.md

Resources

OpenPI repository: https://github.com/Physical-Intelligence/openpi
OpenPI client package: https://github.com/Physical-Intelligence/openpi/tree/main/packages/openpi-client
pi0 paper: https://www.physicalintelligence.company/blog/pi0
LeRobot dataset format: https://huggingface.co/docs/lerobot

fine-tuning-serving-openpi

NPX Install

Tags

SKILL.md Content

OpenPI Fine-Tuning and Serving

Quick start

Core concepts

Compute requirements

Workflow 0: Blank-machine setup

When to use vs alternatives

Workflow 1: JAX fine-tuning on LeRobot data

Workflow 2: PyTorch training and checkpoint conversion

Workflow 3: Policy inference serving

Common issues

For HPC/cluster users

Advanced topics

Resources