Getting Started

Deploy the Physical AI Toolchain and submit your first training job. This hub guides you through setup, deployment, and verification.

The default path starts on a laptop, not in the cloud. Begin with Start Here — T0 Dev, then graduate to higher tiers only when your scale demands them.

Choose Your Tier

Adoption is modeled as six graduated tiers (T0-T5). Each tier states the minimum infrastructure needed to complete the full training lifecycle: capture demonstrations on a robot, train an imitation policy, validate it, and run that policy back on the robot. Each tier is a legitimate stopping point. Start at T0 and graduate only when a concrete trigger forces it.

Tier	When to start here	Graduate when…	Quick start
T0 — Dev ⭐	Default. One laptop, one robot; zero cloud and zero Kubernetes.	No local GPU; the task needs many training iterations as conditions vary; or a second person needs the data.	Tier 0 — Dev
T1 — Lab	One site, a few robots, a shared GPU box; first cloud storage.	Training scale or team size outgrows one GPU box; dataset governance and catalogs become necessary.	Tier 1 — Lab
T2 — Pilot ✅	Recommended production. One site at scale; cloud training default.	The robot count or update cadence makes hand-updating each robot error-prone and version skew real, while everything is still at one reachable site.	Tier 2 — Pilot
T3 — Production	Advanced. Single-site declarative deploy (local k3s + Flux, no Arc).	Robots span multiple sites, or sites become unreachable from a single operator network.	Tier 3 — Production
T4 — Scale	Advanced. Multi-site fleet delivery; Arc reachability broker.	You explicitly want production signals to drive retraining and fleet-wide health analytics. This is a deliberate decision, not an automatic consequence of scale.	Tier 4 — Scale
T5 — Operate	Roadmap. Fleet intelligence for drift detection and retraining.	Available after the roadmap implementation lands.	Tier 5 — Operate

⭐ default · ✅ recommended production

For the tier-by-tier infrastructure boundaries see the Architecture Overview. Jump to T0 — Dev, T1 — Lab, T2 — Pilot, T3 — Production, T4 — Scale, or T5 — Operate. See the canonical Tier Model for the authoritative tier table and vocabulary.

[!NOTE] Roadmap honesty. T5 (Operate / fleet intelligence) is on the roadmap and not yet available. The fleet-intelligence domain is currently specified, with implementation planned. Today's shipping capability spans T0-T4.

Start Here — T0 Dev

The default starting path is one laptop and one robot, with zero cloud and zero Kubernetes. You close the full capture -> train -> validate -> run loop entirely on local hardware.

1. Set up: clone the repo and run ./setup-dev.sh (Python 3.12 via uv, virtual environment, training dependencies). No Azure subscription required.
2. Capture: record ROS 2 bags to local disk on the robot or laptop.
3. Move data: cp or rsync from robot to laptop.
4. Curate: run the dataviewer in local mode on the laptop.
5. Train: run lerobot-train on the laptop (CPU or a local GPU).
6. Track: training outputs are written to local disk; hosted experiment tracking enters at T2.
7. Validate: run run-local-lerobot-eval.py / play.py locally.
8. Run on robot: launch the inference node as a plain process or container. No Flux, no gating, no GitOps.

Edge infra: ROS 2 and Docker only. Cloud infra: none. See the Tier 0 — Dev recipe for the step-by-step walkthrough.

[!TIP] Graduate when you have no local GPU, the task needs many training iterations as conditions vary, or a second person needs the data. At that point, step up to Tier 1 — Lab (first cloud storage) or jump straight to the recommended production path, Tier 2 — Pilot (cloud training). The Quickstart covers the cloud (T2 — Pilot) path end to end.

🚀 Guides

Guide	Description
Start Here — T0 Dev	Default local-first path: laptop + one robot, no cloud
Choose Your Tier	Pick a tier and see its graduation triggers
Quickstart	Cloud path (T2 — Pilot): clone to the first cloud training job
Architecture Overview (coming soon)	System topology, components, and data flow
Glossary (coming soon)	Term definitions for Azure, NVIDIA, and OSMO

⏱️ Time and Cost

The local default path (T0 — Dev) has no cloud cost. It runs entirely on your laptop. The estimates below apply to the cloud path (Quickstart, T2 — Pilot and up).

Item	Estimate
Total deployment time	~1.5-2 hours
Quick validation cost	~$25-50
GPU VM rate	~$3.06/hour (A100)

[!NOTE] Run terraform destroy when finished to stop incurring costs. See Cost Considerations for detailed estimates.

📋 Prerequisites Summary

The default path (T0 — Dev) needs only Python ≥3.12 plus ROS 2 and Docker, with no Azure subscription and no Kubernetes tooling. The additional tools below are required only for the cloud path (Quickstart, T2 — Pilot and up).

Tool	Version	Required for
Python	≥3.12	All tiers (incl. T0)
Terraform	≥1.9.8	Cloud path (T2+)
Azure CLI	≥2.65.0	Cloud path (T2+)
kubectl	≥1.31	Kubernetes tiers (T3+)
Helm	≥3.16	Kubernetes tiers (T3+)

For the cloud path, an Azure subscription with Contributor + User Access Administrator roles, GPU quota for Standard_NC24ads_A100_v4, and an NVIDIA NGC account are required. See Prerequisites for full details.

📚 Related Documentation

Resource	Description
Contributing Guide	Development workflow and code standards
Deployment Guide	Detailed deployment reference
Cost Considerations	Pricing breakdown and optimization