microsoft/physical-ai-toolchain

e2e-infrastructure

Deploy or undeploy the complete Physical AI E2E test infrastructure, including Terraform, robotics charts, Azure ML, and OSMO.

e2e-tests

Autonomously launch, monitor, retry, and report all Physical AI cloud E2E tests across independent Azure ML and OSMO GPU infrastructure.

dataviewer

Start and interact with the Dataset Analysis Tool (dataviewer) for browsing, annotating, and exporting robotic training episodes

fleet-deployment

Deploy trained robot policies to edge fleets via FluxCD GitOps, image automation, and deployment gating

fleet-intelligence

Monitor robot fleet telemetry via Azure IoT Operations, drift detection, Grafana dashboards, and Fabric analytics

infrastructure

Deploy and manage Azure infrastructure for the Physical AI Toolchain including Terraform IaC, Kubernetes setup, GPU configuration, and network topology

osmo-lerobot-training

Submit, monitor, analyze, and evaluate LeRobot imitation learning training jobs on OSMO with Azure ML MLflow integration and inference evaluation - Brought to you by microsoft/physical-ai-toolchain

synthetic-data

Generate synthetic training data using NVIDIA Cosmos world foundation models for SDG pipelines

environment-deployment

Generate, transfer, and consume environment-specific Azure, AKS, OSMO, ACR, and Azure ML deployment bundles. Use when: discovering deployed environment details; creating OSMO image manifests or platform values; preparing a HiL host; uploading or downloading…

azureml-k3s-compute-target-setup

Set up a K3s cluster on an NVIDIA GPU host, connect it to Azure Arc, and configure Azure ML to use it as a Kubernetes compute target. Includes GPU smoke-test and validation instructions for Azure ML jobs on the Arc-connected cluster - Brought to you by…