OpenPI Fine Tuning and Serving
Skill Verifiziert AktivFine-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.
Funktionen
- 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
Anwendungsfälle
- 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
Nicht-Ziele
- Training or serving of models other than OpenPI variants
- General-purpose machine learning framework utilities
- Automated dataset curation or generation
Workflow
- 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
Praktiken
- Model Fine-Tuning
- Policy Serving
- JAX/PyTorch Development
- Checkpoint Management
Voraussetzungen
- JAX >= 0.4.30
- PyTorch >= 2.1.0
- Transformers >= 4.53.2
- uv >= 0.4.0
Installation
Zuerst Marketplace hinzufügen
/plugin marketplace add Orchestra-Research/AI-Research-SKILLs/plugin install AI-Research-SKILLs@ai-research-skillsQualitätspunktzahl
VerifiziertVertrauenssignale
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