OpenPI Fine Tuning and Serving
技能 已验证 活跃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.
To enable researchers and engineers to adapt, train, and deploy OpenPI models for robot policy inference, streamlining complex ML workflows.
功能
- Fine-tune pi0, pi0-fast, pi0.5 models
- Support JAX and PyTorch backends
- Convert JAX checkpoints to PyTorch
- Serve policies via WebSocket API
- Automated environment setup and dependency management
使用场景
- Adapting pi0 models to custom datasets
- Converting JAX checkpoints to PyTorch
- Running policy inference servers for robot control
- Debugging norm stats and GPU memory issues during training
非目标
- Training or serving of models other than OpenPI variants
- General-purpose machine learning framework utilities
- Automated dataset curation or generation
工作流
- Set up environment (clone repo, sync dependencies)
- Select and configure training parameters
- Compute normalization statistics
- Launch training (JAX or PyTorch)
- Convert JAX checkpoints to PyTorch (if needed)
- Serve trained policies
- Integrate client into robot/simulation code
实践
- Model Fine-Tuning
- Policy Serving
- JAX/PyTorch Development
- Checkpoint Management
先决条件
- JAX >= 0.4.30
- PyTorch >= 2.1.0
- Transformers >= 4.53.2
- uv >= 0.4.0
安装
请先添加 Marketplace
/plugin marketplace add Orchestra-Research/AI-Research-SKILLs/plugin install AI-Research-SKILLs@ai-research-skills质量评分
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