Getting Started with the Jetson Nano


The approach outlined in this series will still work, but there is an interesting official alternative.

The Jetson team at NVIDIA have created an excellent self-study course, supported by a downloadable image which is similar to the one used in these articles.

To use the NVIDIA image, you'll need:

  • Jetson Nano Developer Kit
  • Computer with Internet Access and SD card port
  • microSD Memory Card (32GB UHS-I minimum)
  • USB cable (Micro-B to Type-A)

If you just want to use the course image, you can get by with those items and a 5V 2.5A power supply but to take the course you will need

  • compatible 5V 4A Power Supply with 2.1mm DC barrel connector
  • 2-pin jumper
  • compatible camera such as Logitech C270 Webcam or Raspberry Pi Camera Module v2
You will not need a monitor, mouse or keyboard.

To learn where to find the DLI course image, and how to get started with it, you should enroll on the course. It's free, takes about 8 hours, and will give you an excellent introduction to Deep Learning on the Nano.

If you prefer to follow my original guide, the details are below.

Either way, if you want to get to grips with Deep Learning at the Edge, NVIDIA's Jetson Nano is an excellent choice.

It combines a powerful, fast Quad Core Arm processor with a powerful NVIDIA GPU capable of just under 0.5 Teraflops.

Better yet, it has 4 GigaBytes of RAM. That's enough to do serious work with TensorFlow or PyTorch, both of which are supported on the platform.

And it costs around $100 in the USA, or around £120 with shipping in the UK!

Before you start

 If you're following my original approach, the first time you boot your Nano you'll want
  1. A micro-SD card. I recommend at least 32 GB; 64 GB would be better.
  2. An HDMI monitor. It must be HDMI. You cannot use a DVI monitor with a DVI-to-HDMI adaptor of the sort you might use with a Raspberry Pi or a workstation.
  3. A USB mouse.
  4. A USB keyboard.
  5. An ethernet cable.
  6. A suitable 5V power supply.

A 5V 2.5A supply with a micro USB connector will work. A 4A supply with a 5.5mm outer diameter (OD) x 2.1 mm inner diameter connector is better, for reasons I've set out below.

If you decide to go with a 4A supply you'll also need a Jumper.

Here's why I advise you to get a 4A supply.

Out of the box your Nano will use 2A of current. However, your mouse and keyboard may take the current draw over 2A. If the voltage drops your Nano will shut itself down. This happened to me on my first attempt, and it's very disconcerting.

A 2.5A supply should avoid that problem, but there's another.

The Nano has two power modes. The default mode only uses 2 amps but this restricts the CPU to single core operation. The max power configuration will give you full access to the Nano's processing power, but the Nano will then draw more than 2A of current.

If you decide to stick with a 2.5A power supply and you're based in North America you can use this one.

Alternatively you can use the official Raspberry Pi power supply for the Pi model 3B, since that also provides 2.5A.

I've recommended two 4A supplies below. One comes from North America, and the other is available from a UK supplier.
  1. In North America: Adafruit 5V 4A Switching PSU
  2. In the UK: 5V 4A 4000mA AC-DC Switching Adaptor Power Supply
The Adafruit supply was out of stock when I wrote this, possible due to the demand from Nano owners, but you can ask to be notified when stock is available.

If you're going to power the Nano with a 4A supply you'll also need a jumper to tell the Nano to power itself from its power Jack socket instead of the micro-USB socket.

The NVIDIA website has details, but I suggest you visit the excellent JetsonHacks website which has more information on power options and shows you just where to install the Jumper.

Prepare the SD card

You'll need to download the latest NVIDIA Jetson Nano image NVIDIA Jetson Nano image and write it onto the SDS card using an SD card writer and appropriate software on your computer.

If you've prepared an SD card for a Raspberry Pi the process is very similar.

The way you do it depends on the Operating System you're using. The NVIDIA website provides instructions for Windows, MAC and Linux, and they are pretty good.

In part 2 I'll go through the remainder of the setup process. After that I'll describe how to train your first Deep Learning model on the Nano.


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