Seeed reComputer Jetson
reComputer for Jetson series are compact edge computers built with NVIDIA advanced AI embedded systems: Jetson-10 (Nano) and Jetson-20 (Xavier NX). With rich extension modules, industrial peripherals, thermal management combined with decades of Seeed’s hardware expertise, reComputer for Jetson is ready to help you accelerate and scale the next-gen AI product emerging in diverse AI scenarios.
Seeed reComputer for Jetson Series
You can easily add a USB external microphone or camera - and it's fully supported by Edge Impulse. You'll be able to sample raw data, build models, and deploy trained machine learning models directly from the Studio. Currently, four versions have been launched. See reComputer Series Getting Started web page.
This guide has only been tested with the reComputer J1020.
SKU
110061362
110061361
110061363
110061401
Side View
Equipped Module
Jetson Nano 4GB
Jetson Nano 4GB
Jetson Xavier NX 8GB
Jetson Xavier NX 16GB
Operating carrier Board
J1010 Carrier Board
Jetson A206
Jetson A206
Jetson A206
Power Interface
Type-C connector
DC power adapter
DC power adapter
DC power adapter
In addition to the Jetson Nano we recommend that you also add a camera and / or a microphone. Most popular USB webcams work fine on the development board out of the box.

1. Setting up your reComputer for Jetson

You will also need the following equipment to complete your first boot.
  • A monitor with HDMI interface. (For the A206 carrier board, a DP interface monitor can also be used.)
  • A set of mouse and keyboard.
  • An ethernet cable or an external WiFi adapter (there is no WiFi on the Jetson)
The reComputer is shipped with the an operating system burned in. Before we use it, it is required to complete some necessary configuration steps: Follow reComputer Series Getting Started web page. When completed, open a new Terminal by pressing CTRL + Alt + T. It will look as shown:
reComputer Terminal

2. Installing Edge Impulse dependencies

Make sure your ethernet is connected to the Internet

Issue the following command to check:
ping -c 3 www.google.com
The result should look similar to this:
3 packets transmitted, 3 received, 0% packet loss, time 2003ms

Running the setup script

To set this device up in Edge Impulse, run the following commands (from any folder). When prompted, enter the password you created for the user on your Jetson in step 1. The entire script takes a few minutes to run (using a fast microSD card).
wget -q -O - https://cdn.edgeimpulse.com/firmware/linux/jetson.sh | bash

3. Connecting to Edge Impulse

With all software set up, connect your camera and microphone to your Jetson (see 'Next steps' further on this page if you want to connect a different sensor), and run:
edge-impulse-linux
This will start a wizard which will ask you to log in, and choose an Edge Impulse project. If you want to switch projects run the command with --clean.

4. Verifying that your device is connected

That's all! Your device is now connected to Edge Impulse. To verify this, go to your Edge Impulse project, and click Devices. The device will be listed here.
Device connected to Edge Impulse.

Next steps: building a machine learning model

With everything set up you can now build your first machine learning model with these tutorials:
Looking to connect different sensors? Our Linux SDK lets you easily send data from any sensor and any programming language (with examples in Node.js, Python, Go and C++) into Edge Impulse.

Deploying back to device

To run your impulse locally, just connect to your Jetson again, and run:
edge-impulse-linux-runner
This will automatically compile your model with full hardware acceleration, download the model to your Jetson, and then start classifying. Our Linux SDK has examples on how to integrate the model with your favourite programming language.

Image model?

If you have an image model then you can get a peek of what your device sees by being on the same network as your device, and finding the 'Want to see a feed of the camera and live classification in your browser' message in the console. Open the URL in a browser and both the camera feed and the classification are shown:
Live feed with classification results

Running models on the GPU

Due to some incompatibilities we don't run models on the GPU by default. You can enable this by following the TensorRT instructions in the C++ SDK.

Troubleshooting

edge-impulse-linux reports "[Error: Input buffer contains unsupported image format]"

This is probably caused by a missing dependency on libjpeg. If you run:
vips --vips-config
The end of the output should show support for file import/export with libjpeg, like so:
file import/export with libjpeg: yes (pkg-config)
image pyramid export: no
use libexif to load/save JPEG metadata: no
If you don't see jpeg support as "yes", rerun the setup script and take note of any errors.

edge-impulse-linux reports "Failed to start device monitor!"

If you encounter this error, ensure that your entire home directory is owned by you (especially the .config folder):
sudo chown -R $(whoami) $HOME

Long warm-up time and under-performance

By default, the Jetson Nano enables a number of aggressive power saving features to disable and slow down hardware that is detected to be not in use. Experience indicates that sometimes the GPU cannot power up fast enough, nor stay on long enough, to enjoy best performance. You can run a script to enable maximum performance on your Jetson Nano.
ONLY DO THIS IF YOU ARE POWERING YOUR JETSON NANO FROM A DEDICATED POWER SUPPLY. DO NOT RUN THIS SCRIPT WHILE POWERING YOUR JETSON NANO THROUGH USB.
To enable maximum performance, run:
sudo /usr/bin/jetson_clocks

External Resources

Hackster.io tutorial: Train an embedded Machine Learning model based on Edge Impulse to detect hard hat and deploy it to the reComputer J1010 for Jetson Nano.
Export as PDF
Copy link
Outline
1. Setting up your reComputer for Jetson
2. Installing Edge Impulse dependencies
3. Connecting to Edge Impulse
4. Verifying that your device is connected
Next steps: building a machine learning model
Deploying back to device
Troubleshooting
External Resources