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The NVIDIA Jetson Nano 2GB Developer Kit is the ideal platform for teaching, learning, and developing AI and robotics applications. It uses the same proven NVIDIA JetPack Software Development Kit (SDK) used in breakthrough AI-based products. The new developer kit is unique in its ability to utilise the entire NVIDIA CUDA-X™ accelerated computing software stack including TensorRT for fast and efficient AI inference — all in a small form factor and at a significantly lower price. The Jetson Nano 2GB Developer Kit is priced at $59 and will be available for purchase starting end-October. 

Don’t miss: Object Detection with Yolo Made Simple using Docker on NVIDIA Jetson Nano

Under this blog post, I will show you how to get started with NVIDIA Jetson Nano from the scratch

Hardware

  • Jetson Nano
  • A Camera Module
  • A 5V 4Ampere Charger
  • 64GB SD card

Software

Preparing Your Jetson Nano

1. Preparing Your Raspberry Pi Flashing Jetson SD Card Image

  • Unzip the SD card image
  • Insert SD card into your system.
  • Bring up Etcher tool and select the target SD card to which you want to flash the image.
My Image

2. Verifying if it is shipped with Docker Binaries

ajeetraina@ajeetraina-desktop:~$ sudo docker version

3. Checking Docker runtime

Starting with JetPack 4.2, NVIDIA has introduced a container runtime with Docker integration. This custom runtime enables Docker containers to access the underlying GPUs available in the Jetson family.

pico@pico1:/tmp/docker-build$ sudo nvidia-docker version
NVIDIA Docker: 2.0.3
Client:
 Version:           19.03.6
 API version:       1.40
 Go version:        go1.12.17
 Git commit:        369ce74a3c
 Built:             Fri Feb 28 23:47:53 2020
 OS/Arch:           linux/arm64
 Experimental:      false

Server:
 Engine:
  Version:          19.03.6
  API version:      1.40 (minimum version 1.12)
  Go version:       go1.12.17
  Git commit:       369ce74a3c
  Built:            Wed Feb 19 01:06:16 2020
  OS/Arch:          linux/arm64
  Experimental:     false
 containerd:
  Version:          1.3.3-0ubuntu1~18.04.2
  GitCommit:        
 runc:
  Version:          spec: 1.0.1-dev
  GitCommit:        
 docker-init:
  Version:          0.18.0
  GitCommit:

Installing Docker Compose on NVIDIA Jetson Nano

Jetson Nano doesn’t come with Docker Compose installed by default. You will need to install it first:

export DOCKER_COMPOSE_VERSION=1.27.4
sudo apt-get install libhdf5-dev
sudo apt-get install libssl-dev
sudo pip3 install docker-compose=="${DOCKER_COMPOSE_VERSION}"
apt install python3
apt install python3-pip
pip install docker-compose
docker-compose version
docker-compose version 1.26.2, build unknown
docker-py version: 4.3.1
CPython version: 3.6.9
OpenSSL version: OpenSSL 1.1.1  11 Sep 2018

Next, add default runtime for NVIDIA:

Edit /etc/docker/daemon.json

{
    "runtimes": {
        "nvidia": {
            "path": "/usr/bin/nvidia-container-runtime",
            "runtimeArgs": []
        }
    },

    "default-runtime": "nvidia",
    "node-generic-resources": [ "NVIDIA-GPU=0" ]
}

Restart the Docker Daemon

systemctl restart docker

Clap

Categories: Docker

Ajeet Raina

My name is Ajeet Singh Raina and I am an author of this blogging site. I am a Docker Captain, ARM Innovator & Docker Bangalore Community Leader. I bagged 2 special awards last year(2019) : Firstly, “The Tip of Captain’s Hat Award” at Dockercon 2019, San Francisco and secondly, “2019 Docker Community Award“. I run Collabnix Community Slack with over 4700+ audience as well as Redis Community Slack with close to 500+ members. I have built popular GITHUB repositories like DockerLabs, KubeLabs, Kubetools, RedisPlanet Terraform etc. with the support of Collabnix Community. Currently working as Developer Relations Manager at Redis Labs where I help customers and community members adopt Redis. With over 11,000+ followers over LinkedIn & close to 4800+ twitter followers, I like sharing Docker and Kubernetes related content . You can follow me on Twitter(@ajeetsraina) & GitHub(@ajeetraina)

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