Environments

environments

The environment-strategy umbrella for robium. Every robium build needs an answer to "how does this run, identically, on my laptop and on whatever server it ends up on" before the first line of application code is written. This skill decides uv vs venv vs Docker, and — once Docker is chosen — how to get GPU passthrough and remote/headless display right. It does not own multi-module application Dockerfiles or compose wiring across nodes; that's integration.

When to use this skill

• Starting any new robotics project and the environment strategy isn't decided

yet — this is a required early step, not an optional one.

• The trigger phrases in the description: 'uv', 'venv', 'virtualenv', 'docker

for this project', 'reproducible environment', 'GPU in container'.

• Debugging "works on my machine but not on the server" — almost always an

environment-parity bug, not an application bug.

• Cross-references — go to the sibling skill instead when the question is:
• Wiring multiple app modules together, Dockerfiles for a multi-node app, or

compose files spanning services → integration (this skill covers a single environment's shape; integration covers the app that runs in it).

• Remote visualization once headless is decided → foxglove.
• ROS 2-specific package/build questions once Docker + ROS 2 is chosen →

ros2.

• Picking a manipulation/training framework once the env is settled →

lerobot.

• Deploying a headless container to Google Cloud Run (the CPU deploy target,

alongside the GPU-cloud/RunPod one this skill owns) — the build → deploy path and the sim-on-Cloud-Run gotchas → cloud-run.

• The whole-stack decision this feeds into → architect (load that first if

you haven't; it routes here).

Key directives

• Delegation posture: embed. The decision logic (uv vs venv vs Docker) and

the concrete patterns (pyproject.toml shape, Dockerfile shape, GPU/display flags) live in this skill and its references — this is a foundational, every-build concern, not a thin pointer to someone else's docs.

• Environment before code. Decide and record the environment strategy

before writing application code. An undecided environment is an open risk, not a detail to fix later.

• Preflight the machine before deciding. Run npx robium-ai doctor --json

(the robium CLI, npm package robium-ai) at the start of an environment decision and read the report — platform/Apple Silicon, Docker daemon state, GPU, free disk, python3/uv — instead of re-deriving those facts with ad-hoc shell probes. npx robium-ai doctor is the human-readable variant. If npx is unavailable, fall back to probing manually; the decision logic below is unchanged either way.

• Never pip install into the system Python. Not on the host, not inside

a container's base image. Every install goes into a project-scoped uv environment (uv sync, uv run) or, inside Docker, a venv managed the same way. The only sanctioned exception is a deliberate, explicit --system flag (or UV_SYSTEM_PYTHON=1) inside a container build stage that is itself disposable — see references/uv-patterns.md.

• Every project states its env strategy in the architecture brief. If

you're routed here from architect, write the choice (uv / venv / Docker, and why) into docs/architecture-brief.md's env-strategy section before moving on — don't let it live only in your head or in a Dockerfile no one reads.

• Local == remote is the acceptance test. An environment strategy isn't

done until you can state, concretely, why the same commands produce the same result on a laptop and on a headless remote server (same base image digest or lockfile, same Python/CUDA versions, no host-only assumptions). If you can't state that, the strategy isn't finished — see the parity checklist in references/docker-patterns.md.

• Never write image tags or version numbers from memory. Verify current

uv usage against docs.astral.sh/uv, current ROS 2 image tags against hub.docker.com//ros, and NVIDIA Container Toolkit steps against docs.nvidia.com before committing them to a real project. Every example in this skill is marked status: unverified for exactly this reason — treat it as a starting shape to re-check, not a pinned truth.

Quick start

1. Answer one question: does this project need ROS 2 or other system-level dependencies (apt packages, native libs, a specific OS)?

• No — pure-Python (ML training/inference, data tooling, a plain script):

use uv. uv init, define dependencies in pyproject.toml, commit uv.lock, run everything through uv run. See references/uv-patterns.md and examples/pyproject-uv.toml.

• Yes — ROS 2, system packages, or a robot's exact host OS matters:

use Docker, built on an official ROS 2 image, with uv installed inside for any pure-Python pieces of the workspace. See references/docker-patterns.md and examples/Dockerfile.ros2.

• Both — ROS 2 in one place, a heavy pure-Python ML stack in another:

still Docker, but run uv inside the container for the Python side rather than fighting the container's system Python. See references/docker-patterns.md.

2. If Docker, and the project needs a GPU (training, Isaac Sim, CUDA inference): confirm nvidia-container-toolkit is installed on the host (Linux only), and run with docker run --gpus all …. See references/gpu-and-remote.md and examples/Dockerfile.gpu-ml.

3. If the project runs on a headless/remote server: don't reach for X11 forwarding as the default — route visualization to foxglove (web-based, works over SSH/remote with no display). Reserve X11/Wayland forwarding for local-Linux-only, single-user cases. See references/gpu-and-remote.md.

4. Record the decision. Write the chosen strategy (and why) into docs/architecture-brief.md's environment-strategy section.

Decision guidance

Does the project need ROS 2, system apt packages, or a specific OS?
│
├─ No → pure-Python stack
│   └─ uv
│       - `uv init`, pyproject.toml + uv.lock (commit the lock file)
│       - `uv run ` for everything — never activate-and-forget
│       - `uv venv` only if you need a venv without full project management
│       - See references/uv-patterns.md
│
├─ Yes, and it's ROS 2 / system deps only → Docker
│   └─ Base on an official ROS 2 image (hub.docker.com/_/ros); add a project
│      venv with uv inside only if there's Python glue code beyond ROS 2 nodes.
│      See references/docker-patterns.md, examples/Dockerfile.ros2.
│
└─ Yes, mixed: ROS 2/system deps AND a heavy pure-Python ML stack → Docker
    └─ Docker for the system layer, uv for the Python layer *inside* the
       container (multi-stage build: uv resolves deps in a builder stage, the
       runtime stage copies the resulting venv). Do not `pip install` into
       the container's system Python even though you're already in Docker.
       See references/docker-patterns.md, examples/Dockerfile.gpu-ml.

Local vs remote parity checklist (the acceptance test from Key directives — walk this before calling an environment strategy done):

• [ ] Base image is pinned to a specific tag (and ideally digest), not

latest — so "remote" can't silently drift from "local".

• [ ] uv.lock (or the container image itself) is the single source of

truth for dependency versions — no "just pip install X" steps documented as a workaround anywhere.

• [ ] GPU projects: the CUDA version baked into the image matches what the

remote host's driver supports (see references/gpu-and-remote.md) — don't assume the dev laptop's CUDA matches the server's.

• [ ] No hardcoded local paths, display assumptions, or "run this manual step

first" instructions that only work on one machine.

• [ ] The same docker run / uv run invocation (modulo GPU flags) is

documented for both local and remote use.

• [ ] "Clean-room" claims name what was actually cold: `docker compose down

--rmi local removes the image but NOT the buildx layer cache, so a rebuild-and-pass after it proves the committed build definition works — not that a cold host (fresh apt downloads) reproduces it. A truly cold check additionally needs docker builder prune`. Verified 2026-07-11 (nav-trial).

Platform gotchas

• macOS has no native ROS 2. There is no supported native ROS 2 install

on macOS/Apple Silicon — any ROS 2 project on a Mac dev machine goes straight to Docker, even for local development. Don't try to install ROS 2 natively on macOS as a shortcut. If plain Docker Desktop performance or networking is a problem, Lima (a lightweight Linux VM manager for macOS) is a solid alternative for running Docker/containers, and falling back to a Linux machine (local or remote) is always an option too.

• GPU containers need nvidia-container-toolkit, and it's Linux-only.

GPU passthrough into Docker (--gpus all) requires the NVIDIA Container Toolkit installed on the host, and NVIDIA's own install guide covers Linux distributions only (Ubuntu/Debian/RHEL/Fedora/SUSE) — there is no first-party Windows/macOS host path. A remote Linux GPU server is the reliable target for GPU workloads; a local macOS dev machine cannot run GPU containers at all. See references/gpu-and-remote.md.

• arm64 slim images need a compiler for source-only wheels. On

linux/arm64 a python:3.x-slim base fails at install time for any dependency with no arm64 manylinux wheel — it drops to a source build and dies with error: command 'gcc' failed: No such file or directory. Seen with pymunk (pulled by lerobot[pusht]), which has no arm64 wheel on any version. Fix: apt-get install -y build-essential before the pip/uv install (or use a non-slim base). (observed 2026-07-15, manip-trial)

• **Docker on macOS cannot see MPS — for ML/VLA on Apple Silicon this is a

latency decision, not a preference.** Docker containers on macOS run in a Linux VM with no Metal/MPS passthrough, so any policy inference inside a macOS Docker container falls back to CPU — and so does Cloud Run, which is CPU-only regardless of host. Measured on SmolVLA: 0.55s/forward pass on MPS-native (uv) vs ~9s/forward pass under CPU (Docker or Cloud Run) — roughly 17x. For an ML policy on Apple Silicon, the number that decides "can I containerize this" is CPU inference latency, not whether Docker itself works (seen 2x: manip-trial, vla-trial).

• X11/Wayland forwarding vs headless + web viz. Forwarding a display out

of a container (X11 sockets, DISPLAY env, xhost) works for local-Linux development but breaks down over SSH to a remote server and doesn't work from macOS/Windows hosts without extra tooling. For anything remote or cross-platform, default to headless containers plus web-based visualization — route that to the foxglove skill rather than fighting display forwarding.

• **A Mac wired to a no-internet robot LAN loses internet — reorder network

services.** macOS ranks a wired (USB-Ethernet) service above Wi-Fi, so when you cable a Mac into a robot's private router (common for a real-robot bring -up), the default route goes down that dead-end LAN and the browser goes offline — including app.foxglove.dev, which needs internet to load even though the robot WebSocket rides the LAN. Fix: System Settings → Network → ⋯ → Set Service Order → drag Wi-Fi above the USB LAN. The directly-connected robot subnet still routes out the cable regardless of default-route order. Verified 2026-07-24 (tb4-teleop).

• **Real-robot bring-up networking is its own layer — before any container

or lockfile matters.** IPv6 link-local first contact, netplan Wi-Fi reconfig, why the ROS↔browser bridge must run on the robot (DDS multicast crosses neither NAT nor Docker Desktop's VM), two-DHCP-servers-per-SSID, Wi-Fi MAC randomization, and validating a camera panel on a Mac with no V4L2 — all in references/robot-networking.md (tb4-teleop, 2026-07-25).

• **Some GPU apps have no local mirror on a Mac at all — the one sanctioned

exception to local==remote.** Isaac Sim / Isaac Lab (Omniverse Kit needs NVIDIA RTX+CUDA on Linux/Windows), GR00T, and heavy-CUDA training have no macOS path and Docker can't pass through a nonexistent Mac GPU: the Mac is a thin SSH/browser client and the work runs on a cloud GPU. Provider and the sharp RunPod networking edges (silently-dead HTTP proxy, no SSH -L, ports fixed at pod creation, GCP's global-quota auto-deny) are in references/gpu-cloud.md (go2-locomotion, 2026-07-28).

Customization

• Different Python version: pin it explicitly — `uv python pin

for uv projects, or the base image tag for Docker (e.g. the Python tag on the official ROS 2 / python` images) — rather than relying on whatever the environment happens to have.

• Different ROS 2 distro: swap the base image tag in

examples/Dockerfile.ros2 (e.g. jazzy ↔ lyrical); re-verify the tag exists on hub.docker.com/_/ros first — see architect's Platform gotchas for the current distro recommendation (Lyrical Luth generally; Jazzy Jalisco for the Nav2 vertical).

• Different GPU / CUDA version: swap the nvidia/cuda base tag in

examples/Dockerfile.gpu-ml to match the target host's driver-supported CUDA version — check with nvidia-smi on that host, don't assume.

• Adding system packages to a uv-only project: that's the signal to

graduate from uv to Docker, not to reach for pip install --system or host-level apt install as a workaround — see the decision tree above. Exception (verified 2026-07-12, manip-trial): a single trivial host package (e.g. ffmpeg for dataset video decode) on a macOS ML project, where Docker would forfeit the MPS accelerator — a ~17x inference-latency hit, per Platform gotchas — document the one brew install step in the project README and stay on uv.

References

• references/uv-patterns.md — pyproject.toml shape, uv sync/uv run,

lockfiles, dependency groups, and when to graduate to Docker.

• references/docker-patterns.md — multi-stage Docker builds with uv inside,

official ROS 2 image tags and variants, local/remote parity mechanics.

• references/gpu-and-remote.md — NVIDIA Container Toolkit setup, `--gpus

all, headless/remote display strategy and the handoff to foxglove`.

• references/robot-networking.md — real-robot bring-up host/LAN plumbing:

IPv6 link-local first contact, IPv4-alias fallback, netplan Wi-Fi reconfig, DDS-vs-NAT, bridge-on-robot, DHCP/MAC gotchas, Mac camera-panel validation.

• references/gpu-cloud.md — cloud-GPU environments (the local==remote

exception): RunPod-first provider choice, RunPod networking edges, GCP GPU quota gotchas. Robium's owner for the cross-cutting GPU-cloud/RunPod facts.

• examples/pyproject-uv.toml — minimal pure-Python uv project (status:

unverified).

• examples/Dockerfile.ros2 — ROS 2 workspace container with uv for the

Python-glue layer (status: unverified).

• examples/Dockerfile.gpu-ml — GPU-enabled multi-stage uv build for an ML

training/inference container (status: unverified).

• Upstream: uv docs, [uv + Docker

guide](https://docs.astral.sh/uv/guides/integration/docker/), official ROS 2 images, NVIDIA Container Toolkit docs. Sibling skills: architect (routes here early), integration (multi-module app Dockerfiles/compose — not duplicated here), foxglove (remote/headless visualization), cloud-run (the CPU Cloud Run deploy target

…

Source & license

This open-source skill is cataloged on AgentStack and links to its original source — we do not rehost the code.

• Author: robium-ai
• Source: robium-ai/robium
• License: MIT
• Homepage: https://robium.ai

Install and usage instructions live in the source repository linked above.