seeed-projects/seeed-jetson-developtool/jetson-docker-setup

Getting Started with Docker on Jetson

Set up and use Docker on Seeed reComputer Jetson devices. Docker comes pre-installed on reComputer. This skill covers verification, Docker Compose installation, CUDA testing, and GPU-enabled container workflows.

Hardware: reComputer J1020 (Jetson Nano) or compatible Jetson device Software: JetPack 4.6+ (Docker pre-installed)

Execution model

Run one phase at a time. After each phase:

• If output contains [STOP] → stop immediately, consult the failure decision tree
• If output ends with [OK] → tell the user "Phase N complete" and proceed

Phase 1 — verify Docker installation (~30 s)

sudo docker version

Expected: Docker Engine 20.10+ with OS/Arch: linux/arm64.

docker info | grep -i runtime

Expected: nvidia should appear in the runtimes list.

[OK] when Docker is running and nvidia runtime is available. [STOP] if Docker is not installed or nvidia runtime missing.

Phase 2 — verify CUDA (~1 min)

cd /usr/local/cuda/samples/1_Utilities/deviceQuery
sudo make
./deviceQuery

Expected: Result = PASS with device info (e.g. "NVIDIA Tegra X1").

[OK] when CUDA deviceQuery passes.

Phase 3 — install Docker Compose (~2 min)

export DOCKER_COMPOSE_VERSION=2.6.0
sudo apt-get install -y libhdf5-dev libssl-dev
sudo pip3 install docker-compose=="${DOCKER_COMPOSE_VERSION}"

If pip3 is not available:

sudo apt-get install -y python3 python3-pip
pip install docker-compose

[OK] when docker-compose --version returns successfully.

Phase 4 — install CUDA toolkit (optional, ~5 min)

Only needed if upgrading CUDA beyond the pre-installed version:

wget https://developer.download.nvidia.com/compute/cuda/repos/ubuntu1804/sbsa/cuda-ubuntu1804.pin
sudo mv cuda-ubuntu1804.pin /etc/apt/preferences.d/cuda-repository-pin-600
wget https://developer.download.nvidia.com/compute/cuda/11.3.1/local_installers/cuda-repo-ubuntu1804-11-3-local_11.3.1-465.19.01-1_arm64.deb
sudo dpkg -i cuda-repo-ubuntu1804-11-3-local_11.3.1-465.19.01-1_arm64.deb
sudo apt-key add /var/cuda-repo-ubuntu1804-11-3-local/7fa2af80.pub
sudo apt-get update
sudo apt-get -y install cuda

[OK] when CUDA installs without errors.

Phase 5 — run a test container (~1 min)

sudo docker run arm64v8/python:slim python3 -c "print('Hello from Jetson Docker!')"

[OK] when the container runs and prints output.

Phase 6 — set up JTOP monitoring container (optional, ~3 min)

Create a Dockerfile:

FROM python:3-alpine
RUN apk update \
    && apk --no-cache add bash \
    && pip install jetson-stats \
    && rm -rf /var/cache/apk/*

Build and run:

docker build -t jetson-stats-nano .
docker run --rm -it --gpus all -v /run/jtop.sock:/run/jtop.sock jetson-stats-nano jtop

[OK] when JTOP displays CPU/GPU/memory stats.

Failure decision tree

Reference files

• references/source.body.md — Original blog post with hardware details, screenshots, and application examples