🤖 Azure Robotics Reference Architecture with NVIDIA OSMO

This reference architecture provides a production-ready framework for orchestrating robotics and AI workloads on Microsoft Azure using NVIDIA technologies such as Isaac Lab, Isaac Sim, and OSMO. It demonstrates end-to-end reinforcement learning workflows, scalable training pipelines, and deployment processes with Azure-native authentication, storage, and ML services.

🚀 Key Features

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 referencing microsoft/edge-ai components for reproducible deployments
• Containerized Workflows - Docker-based Isaac Lab training with NVIDIA GPU support
• CI/CD Integration - Automated deployment pipelines with GitHub Actions
• MLflow Integration - Automatic experiment tracking and model versioning
  • Automatic metric logging from SKRL agents to Azure ML
  • Comprehensive tracking of episode statistics, losses, optimization metrics, and timing data
  • Configurable logging intervals and metric filtering
  • See MLflow Integration Guide for details
• Scalable Compute - Auto-scaling GPU nodes based on workload demands
• Cost Optimization - Pay-per-use compute with automatic scaling
• Enterprise Security - Entra ID integration
• Global Deployment - Multi-region support for worldwide teams

🗼 Architecture Overview

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 - Software in the Loop (SIL) and Hardware in the Loop (HIL) training
• Azure Storage - Persistent data and checkpoint storage
• Azure Key Vault - Secure credential management
• Azure Monitor - Comprehensive logging and metrics

🌍 Real World Examples

OSMO orchestration on Azure enables production-scale robotics training across industries. Some examples include:

• Warehouse AMRs - Train navigation policies with 1000+ parallel environments on auto-scaling AKS GPU nodes, checkpoint to Azure Storage, track experiments in Azure ML
• Manufacturing Arms - Develop manipulation strategies with physics-accurate simulation, leveraging Azure's global regions for distributed teams and pay-per-use GPU compute
• Legged Robots - Optimize locomotion policies with MLflow experiment tracking for sim-to-real transfer
• Collaborative Robots - Create safe interaction policies with Azure Monitor logging and metrics, enabling compliance auditing and performance diagnostics at scale

See OSMO workflow examples for job configuration templates.

🧑🏽‍💻 Prerequisites and Requirements

Required Tools

• pyenv
• Python 3.11 (required by Isaac Sim 5.X)
• Azure CLI (v2.50+)
• Terraform (v1.5+)
• NVIDIA OSMO CLI (latest)
• Docker with NVIDIA Container Toolkit

Azure Requirements

• Azure subscription with contributor access
• Sufficient quota for GPU VMs (Standard_NC6s_v3 or higher)
• Azure Machine Learning workspace (or permissions to create one)

NVIDIA Requirements

• NVIDIA Developer account with OSMO access
• NGC API key for container registry access

🏃‍➡️ Quick Start

./setup-dev.sh

The setup script installs Python 3.11 via pyenv, creates a virtual environment at .venv/, and installs training dependencies.

VS Code Configuration

The workspace is configured with python.analysis.extraPaths pointing to src/, enabling imports like:

from training.utils import AzureMLContext, bootstrap_azure_ml

Select the .venv/bin/python interpreter in VS Code for IntelliSense support

🧱 Repository Structure

.
├── deploy/
│   ├── 000-prerequisites/              # Prerequisites validation and setup
│   ├── 001-iac/                        # Infrastructure as Code deployment
│   ├── 002-setup/                      # Post-infrastructure setup
│   ├── 003-data/                       # Data preparation and upload
│   └── 004-workflow/                   # Training workflow execution
│       ├── job-templates/              # Job configuration templates
│       └── osmo/                       # OSMO inline workflow submission (see osmo/README.md)
├── src/
│   ├── terraform/                      # Infrastructure as Code
│   │   └── modules/                    # Reusable Terraform modules
│   └── training/                       # Training code and tasks
│       ├── common/                     # Shared utilities
│       ├── scripts/                    # Framework-specific training scripts configured for Azure services
│       │   ├── rsl_rl/                 # RSL_RL training scripts
│       │   ├── skrl/                   # SKRL training scripts
│       └── tasks/                      # Placeholder for Isaac Lab training tasks

🪪 License

This project is licensed under the MIT License. See LICENSE for details.

🤝 Support

For issues and questions:

• Review microsoft/edge-ai documentation

🙏 Acknowledgments

This reference architecture builds upon:

• microsoft/edge-ai - Edge AI infrastructure components
• NVIDIA Isaac Lab - RL task framework
• NVIDIA Isaac Sim - Physics simulation
• NVIDIA OSMO - Workflow orchestration
• NVIDIA OSMO GitHub - Workflow orchestration