Quickstart: Clone to First Training Job

Deploy the full Azure NVIDIA Robotics stack and submit a training job in ~1.5-2 hours. This guide uses full-public networking and Access Keys authentication for the simplest path.

[!NOTE] This guide expands on the Getting Started hub.

Prerequisites

Requirement	Details
Azure subscription	Contributor + User Access Administrator roles
GPU quota	Standard_NC24ads_A100_v4 in target region
NVIDIA NGC account	Sign up at https://ngc.nvidia.com/ for API key
Development environment	Devcontainer (recommended) or local tools

See Prerequisites for installation commands and version requirements.

Step 1: Clone and Set Up Environment

Clone the repository and initialize the development environment.

git clone https://github.com/microsoft/physical-ai-toolchain.git
cd physical-ai-toolchain

Use the devcontainer (recommended) or run local setup:

./setup-dev.sh

Step 2: Configure Azure Subscription

Authenticate with Azure and register required resource providers.

source infrastructure/terraform/prerequisites/az-sub-init.sh
bash infrastructure/terraform/prerequisites/register-azure-providers.sh

Verify your subscription:

az account show --query "{name:name, id:id}" -o table

Step 3: Configure Terraform Variables

Create a Terraform variables file for the full-public deployment path. From the repository root:

cd infrastructure/terraform
cp terraform.tfvars.example terraform.tfvars

Edit terraform.tfvars with these values:

project_name = "robotics"
environment  = "dev"
location     = "eastus"
gpu_vm_size  = "Standard_NC24ads_A100_v4"

enable_azure_ml    = true
enable_osmo        = true
enable_vpn_gateway = false
enable_private_dns = false

[!TIP] For private networking, set enable_vpn_gateway = true and enable_private_dns = true. See the Infrastructure Guide for details.

Step 4: Deploy Infrastructure

Initialize and apply the Terraform configuration. This step takes ~30-40 minutes.

terraform init
terraform plan -out=tfplan
terraform apply tfplan

Verify deployment:

terraform output

Connect to the AKS cluster:

az aks get-credentials \
  --resource-group "$(terraform output -raw resource_group_name)" \
  --name "$(terraform output -raw aks_cluster_name)"

Step 5: Configure AKS Cluster

Deploy GPU Operator, KAI Scheduler, and the AzureML extension. From the repository root:

cd infrastructure/setup
bash 01-deploy-robotics-charts.sh
bash 02-deploy-azureml-extension.sh

Verify GPU operator pods:

kubectl get pods -n gpu-operator

Step 6: Deploy OSMO Components

Deploy the OSMO control plane and backend using Access Keys authentication.

bash 03-deploy-osmo-control-plane.sh
bash 04-deploy-osmo-backend.sh --use-access-keys

Verify OSMO pods:

kubectl get pods -n osmo-control-plane

Step 7: Submit First Training Job

Navigate to the scripts directory and submit a training job. From the repository root:

cd scripts
bash submit-osmo-training.sh

Scripts auto-detect configuration from Terraform outputs. Override values with CLI arguments or environment variables as needed. See Scripts Reference for all submission options.

Step 8: Verify Results

Confirm the training job is running:

kubectl get pods -n osmo-control-plane --watch

Check OSMO training status through the OSMO web UI or query pod logs:

kubectl logs -n osmo-control-plane -l app=osmo-training --tail=50

Cleanup

Destroy all infrastructure when finished to stop incurring costs. From the repository root:

cd infrastructure/terraform
terraform destroy

See Cost Considerations for detailed pricing.

Next Steps

Resource	Description
MLflow Integration	Track experiments with MLflow
Deployment Guide	Full deployment reference and options
Contributing Guide	Development workflow and code standards