Physical AI Infrastructure Setup And Resilient Scaling
NVIDIAOfficialCreated by NVIDIA Physical AI
- Skill
- Workflow
- Cloud and DevOps
- Apache-2.0
- Medium risk
- Has scripts
AutoGineer summary
Written by AutoGineer from the skill’s own files, not by the author
This skill provides a comprehensive framework for deploying, scaling, and validating the infrastructure required for NVIDIA Physical AI synthetic data generation (SDG) workflows. It manages the lifecycle of Kubernetes clusters (MicroK8s or Azure AKS), OSMO orchestration, and inference backends (NIM Operator, NVCF, or Azure AI Foundry). The skill enforces strict preflight checks, reproducible configuration management, and systematic recovery procedures to ensure reliable workload execution.
What it does
- Provisions and configures Kubernetes clusters on MicroK8s or Azure AKS.
- Deploys and manages OSMO orchestration layers for workload submission.
- Sets up inference endpoints using NIM Operator, NVCF, or Azure AI Foundry.
- Executes mandatory preflight checks to validate infrastructure readiness.
- Provides structured recovery paths for failed workflow states or infrastructure issues.
- Ensures secure credential handling and environment-aware configuration.
When to use it
- When setting up a new Physical AI SDG environment from scratch.
- When scaling existing infrastructure to meet new workload demands.
- When validating the health and readiness of cluster, inference, or OSMO components.
- When troubleshooting and recovering from infrastructure or workflow deployment failures.
Advantages
- Standardizes infrastructure deployment across different cloud and local environments.
- Reduces configuration drift by using checked-in scripts and reproducible patterns.
- Improves reliability through mandatory preflight and verification gates.
- Provides a unified interface for complex multi-component stacks.
Usage examples
- “Set up resilient Physical AI infrastructure for VDA on Azure AKS with NIM Operator.”
- The agent will initiate the setup flow for Azure AKS, configure the necessary OSMO and NIM Operator components, run preflight checks, and prepare the environment for VDA workload submission.
- “My MicroK8s cluster for SDG is failing to schedule jobs; help me validate and recover.”
- The agent will guide you through the verification gates for the MicroK8s cluster, inspect OSMO pod status, check storage credentials, and identify the root cause of the scheduling failure.
Install
Download the skill as a ZIP:
https://autogineer.com/agent-skills/nvidia/physical-ai-infrastructure-setup-and-resilient-scaling/downloadUnzip it into the cross-client skills folder of your project:
mkdir -p .agents/skills && unzip physical-ai-infrastructure-setup-and-resilient-scaling.zip -d .agents/skills
Documentation by NVIDIA Physical AI
Physical AI Infrastructure Setup And Resilient Scaling
Canonical skill for the Physical AI infrastructure stack. Use it to compose cluster, inference, OSMO, and workload stages into a reproducible Physical AI SDG environment, then keep the environment observable and recoverable.
Operating Rules
Read only the component references needed for the selected target. Do not load every component by default.
Keep the repo as the durable artifact. Fix checked-in config or scripts, then rerun. Do not recover a failed install with untracked one-off changes.
Run mutating cluster, OSMO, Helm, Terraform, or Azure operations through checked-in scripts when a script exists. Read-only diagnostics are allowed.
Stop at the first red gate. Fix the lowest owning layer in this order: config, script, then skill guidance.
Derive values from the environment when possible. Ask only for values that cannot be inferred, such as API keys, target choice, or quota tradeoffs.
Store secrets in
${REPO_ROOT}/.env. Cluster-derived values such as storage, database, Redis, and endpoint names come from Terraform outputs or platform queries, not.env.Preflight means no deployed state: no cluster API, Terraform outputs, Helm releases, OSMO pools, or workflow state. Those belong to deploy/verify gates.
Never print, echo, or paste raw keys into commands, YAML, logs, or transcripts. Prefer credential handles, Kubernetes
secretKeyRef, and runtime-only secret injection. Scan raw transcript exports withscripts/scan_transcript_secrets.pybefore sharing.Use absolute paths. Derive repo root with
git rev-parse --show-toplevel.
Component References
Each component lives inside this skill so the stack has one canonical trigger. Load the component reference only when the selected target needs that slice.
| Concern | Load | Assets |
|---|---|---|
| Stage matrix and old driver notes | components/driver/reference.md | None |
| MicroK8s cluster | components/cluster-microk8s/reference.md | components/cluster-microk8s/scripts/, components/cluster-microk8s/runtimeclass-nvidia-runc.yaml |
| Azure AKS cluster | components/cluster-azure/reference.md | components/cluster-azure/scripts/, components/cluster-azure/terraform/ |
| NIM Operator inference | components/inference-nim-operator/reference.md | components/inference-nim-operator/scripts/, components/inference-nim-operator/nims/ |
| NVCF inference | components/inference-nvcf/reference.md | components/inference-nvcf/scripts/ |
| Azure AI Foundry inference | components/inference-azure/reference.md | components/inference-azure/scripts/ |
| MicroK8s OSMO | components/osmo-k8s/reference.md | components/osmo-k8s/scripts/, upstream OSMO deploy scripts |
| Azure OSMO | components/osmo-azure/reference.md | components/osmo-azure/scripts/, upstream OSMO deploy scripts plus Azure TF outputs |
| Azure access setup | components/azure-access/reference.md | None |
| OSMO CLI and workflow operations | components/osmo-cli/reference.md | components/osmo-cli/scripts/, components/osmo-cli/references/, components/osmo-cli/agents/, components/osmo-cli/tests/ |
| OpenClaw Azure device login | components/openclaw-azure-login/reference.md | None |
OSMO CLI Support Files
The OSMO CLI component has second-level support files because its command and workflow surface is large. Load these directly only for the stated case.
| File | Read when |
|---|---|
components/osmo-cli/agents/workflow-expert.md | Spawning a workflow-generation or workflow-failure subagent. |
components/osmo-cli/agents/logs-reader.md | Spawning a log summarization subagent for OSMO workflow failures. |
components/osmo-cli/references/cli-commands.md | Exact OSMO CLI flags, payloads, or command syntax are needed. |
components/osmo-cli/references/workflow-spec.md | Workflow YAML schema, credentials, outputs, or provider fields are needed. |
components/osmo-cli/references/workflow-patterns.md | Multi-task, data dependency, Jinja, serial, or parallel workflow design is needed. |
components/osmo-cli/references/advanced-patterns.md | Checkpointing, retry/exit behavior, or node exclusion is needed. |
components/osmo-cli/tests/orchestrator-runtime-failure.md | Validating or debugging the OSMO orchestration review pattern. |
Target Selection
Pick exactly one option per stage. Stage 2 follows stage 1.
Kubernetes:
MicroK8sorAzureOSMO:
MicroK8s OSMOwhen Kubernetes is MicroK8s,Azure OSMOwhen Kubernetes is AzureInference:
NIM Operator,NVCF,Azure AI Foundry, orNoneWorkload: Video Data Augmentation, Defect Image Generation, NuRec Carline Adaptation, NRE, NCore, Asset Harvester, or custom workflow YAML
Reject invalid combinations before provisioning:
| Cluster | NIM Operator | NVCF | Azure AI Foundry |
|---|---|---|---|
| MicroK8s | yes | yes | no, Foundry requires Azure identities |
| Azure | yes | yes | yes |
For OpenClaw or any chat-only environment that cannot open a browser, read
components/openclaw-azure-login/reference.md before Azure prerequisites.
For any Azure target, read components/azure-access/reference.md before Azure
component preflights.
Setup Flow
Confirm target choices and workload compute requirements.
Load the selected component references.
Resolve prerequisites up front, including API keys, Azure access, caller CIDR, GPU quota, storage class, and OSMO login requirements.
Run
scripts/preflight.shfor every selected infrastructure component plus any OSMO CLI/workload preflight before provisioning; build the implementation plan from the results and stop on red preflight.Deploy Kubernetes first. Nothing else starts until the cluster gate is green.
Deploy OSMO and inference after Kubernetes. These can proceed in parallel once the cluster exists, but workload submission waits for both selected gates.
Submit the workload only after OSMO, storage credentials, compute pool, and selected inference endpoints are verified. For VDA, this includes
preflight_credentials.sh,pre_submit_guard.pywith resolved--setvalues, non-empty model-cache prefixes, and workflow-namespace endpoint smoke checks.Monitor through completion. On failed workflow state, inspect events and logs from
components/osmo-cli/reference.md; do not resubmit blindly.
Inference Discovery
Avoid over-deploying expensive endpoints.
Scan the chosen workflow spec and default values for endpoint references:
*.osmo-nims.svc.cluster.local,api.nvcf.nvidia.com/*,*.inference.ai.azure.com, or*.cognitiveservices.azure.com.Map each reference to the selected backend:
NIM Operator: service name must match a directory under
components/inference-nim-operator/nims/.NVCF: function URL or function ID must be supplied by the environment.
Azure AI Foundry: endpoint name must be deployed through
components/inference-azure/scripts/install.sh.
If the workflow needs a capability the selected backend lacks, stop and report the mismatch. Do not silently substitute another model.
Verification Gates
Each stage has its own Verify section in the component reference. These gates are mandatory:
| Stage | Gate |
|---|---|
| Kubernetes | Cluster API reachable, nodes Ready, GPU capacity advertised for GPU paths, and CPU+NVCF paths have runtimeclass/nvidia mapped to runc. |
| Inference | Every endpoint referenced by the workload is reachable. NIM readiness uses /v1/health/ready; NVCF and Foundry still need task-specific authenticated checks. |
| OSMO | OSMO pods Ready, pool ONLINE, port-forward watchdogs alive, storage credentials configured, and verify-hello workflow COMPLETED. |
| Workload | Selected workload pre-submit guards pass before submit. osmo workflow query <id> reports COMPLETED and every task is green. Failed terminal states require events and logs before retry. |
Resilient Scaling
Size the cluster from workload needs before provisioning. For Azure, check CPU and GPU quota for the selected VM families before
terraform apply.For NIM Operator, deploy only the NIMServices referenced by the workload. Each service pins GPU and model-cache storage for the lifetime of the cluster.
Keep OSMO storage URL schemes aligned with the active backend. Local MicroK8s uses MinIO, Azure uses Blob-backed configuration.
Treat Pending, Unknown, ImagePullBackOff, unbound PVCs, or 0 Ready replicas as layer failures. Investigate scheduling, storage, image credentials, and adjacent platform state before retrying the same command.
For long deploys or workflow watches, provide heartbeat updates with current state, elapsed time, last useful observation, and next check.
Workload Routing
Video Data Augmentation: use
skills/physical-ai-video-data-augmentation/SKILL.md.Defect Image Generation: use
skills/physical-ai-defect-image-generation/SKILL.md.NuRec carline adaptation: use
skills/carline-adaptation/SKILL.md.NRE, NCore, and Asset Harvester live in the canonical NuRec catalog listed in
skills/INDEX.md.Custom workload: submit the provided workflow YAML through OSMO after checking resource requests, image credentials, data credentials, and inference URLs.
Evaluation Prompts And Results
Positive trigger: "Set up resilient Physical AI infrastructure for VDA on Azure AKS with NIM Operator." Expected: use this skill.
Negative trigger: "Summarize recent OSMO workflow logs for this workflow ID." Expected: do not use this infrastructure setup skill unless the request also involves setup, scaling, validation, or recovery of the infrastructure stack.
Latest static review: 2026-05-26, description keywords match the expected routes above.
Files
- ATTRIBUTION.mdAdded by AutoGineer1.0 KiB
- BENCHMARK.md6.3 KiB
- LICENSELicence file added by AutoGineer11.2 KiB
- SKILL.md10.9 KiB
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- components/cluster-microk8s/reference.md1.8 KiB
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Requirements and compatibility
Works with: Claude Code, Claude apps, OpenAI Codex, GitHub Copilot, Cursor, Gemini CLI, Kiro, OpenCode, Goose, Amp, Roo Code, JetBrains Junie, Factory Droid, Devin (ex-Windsurf), Mistral Vibe, OpenHands, Letta
“Works with” means the tool reads the open Agent Skills format; a skill written for one tool may use features others lack.
Needs: Needs an API key, Needs Node.js
The author says: Requires the selected component prerequisites, usually kubectl plus either MicroK8s or Azure CLI/Terraform, and OSMO or inference credentials for the chosen target.
Security
What our scanner noticed· Medium risk
- Large base64-encoded blobs
- Instructions that use the network
- Scripts that your agent may run
This skill includes scripts that your agent may run. Read them before you use it.
A static scan is not a guarantee. Skills are third-party content: review a skill before you let an agent use it.
Attribution and licence
- Created by
- NVIDIA Physical AI
- Publisher
- NVIDIA (Official)
- Source
- NVIDIA/skills · skills/physical-ai-infrastructure-setup-and-resilient-scaling · commit fce3cca
- Licence
- Apache License 2.0 (redistribution allowed)
- LICENSE (added by AutoGineer from LICENSE-APACHE)
- ATTRIBUTION.md (written by AutoGineer)
- Synced
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