Production-ready framework for orchestrating robotics and AI workloads on Microsoft Azure using NVIDIA Isaac Lab, Isaac Sim, and OSMO.
Tip
Get started in under 2 hours — follow the Quickstart Guide.
This reference architecture demonstrates end-to-end reinforcement learning workflows, scalable training pipelines, and deployment processes with Azure-native authentication, storage, and ML services. OSMO handles workflow orchestration and job scheduling while Azure provides elastic GPU compute, persistent checkpointing, MLflow experiment tracking, and enterprise-grade security.
- Infrastructure as Code — Terraform modules for reproducible Azure deployments
- Containerized Workflows — Docker-based Isaac Lab training with NVIDIA GPU support
- MLflow Integration — Automatic experiment tracking and model versioning
- Scalable Compute — Auto-scaling GPU nodes with pay-per-use cost optimization
- Enterprise Security — Entra ID integration with managed identities
- CI/CD Integration — Automated deployment pipelines with GitHub Actions
./setup-dev.shThe setup script installs Python 3.11 via uv, creates a virtual environment, and installs training dependencies. Follow the Quickstart Guide for the full deployment walkthrough.
Full documentation is available in the docs/ directory.
| Guide | Description |
|---|---|
| Getting Started | Prerequisites, quickstart, and first training job |
| Deployment | Infrastructure provisioning and setup |
| Training | RL training workflows, MLflow, and checkpointing |
| Security | Threat model, security guide, deployment responsibilities |
| Contributing | Architecture, style guides, contribution workflow |
This reference architecture integrates:
- NVIDIA OSMO — Workflow orchestration and job scheduling
- Azure Machine Learning — Experiment tracking and model management
- Azure Kubernetes Service — Software in the Loop (SIL) training
- Azure Arc for Kubernetes — Hardware in the Loop (HIL) training
- Azure Storage — Persistent data and checkpoint storage
See Architecture Overview for the full design.
Contributions are welcome. Whether fixing documentation or adding new training tasks:
- Read the Contributing Guide
- Review open issues
- See the prerequisites for required tools
See the project roadmap for priorities, timelines, and success metrics.
This reference architecture builds upon:
- NVIDIA Isaac Lab — RL task framework
- NVIDIA Isaac Sim — Physics simulation
- NVIDIA OSMO — Workflow orchestration
Microsoft encourages customers to review its Responsible AI Standard when developing AI-enabled systems to ensure ethical, safe, and inclusive AI practices. Learn more at Microsoft's Responsible AI.
This project is licensed under the MIT License.
See SECURITY.md for the security policy and vulnerability reporting.
See GOVERNANCE.md for the project governance model.
See SUPPORT.md for support options and issue reporting.
This project may contain trademarks or logos for projects, products, or services. Authorized use of Microsoft trademarks or logos is subject to and must follow Microsoft's Trademark & Brand Guidelines. Use of Microsoft trademarks or logos in modified versions of this project must not cause confusion or imply Microsoft sponsorship. Any use of third-party trademarks or logos are subject to those third-party's policies.
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