NVIDIA Jetson Nano

The Jetson Nano is an embedded Linux dev kit featuring a GPU accelerated processor (NVIDIA Tegra) targeted at edge AI applications. 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. The Jetson Nano is available from 59 USD from a wide range of distributors, including Sparkfun.
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.
Powering your Jetson
Although powering your Jetson via USB is technically supported, some users report on forums that they have issues using USB power. If you have any issues such as the board resetting or becoming unresponsive, consider powering via a 5V, 4A power supply on the DC barrel connector. Don't forget to change the jumper! Here is an example power supply for sale.
An added bonus to powering via the DC barrel plug, you can carry out your first boot w/o an external monitor or keyboard.
NVIDIA Jetson Nano
1. Setting up your Jetson Nano
Depending on your hardware, follow NVIDIA's setup instructions (NVIDIA Jetson Nano Developer Kit or NVIDIA Jetson Nano 2GB Developer Kit) for both "Write Image to SD Card" and "Setup and First Boot." When finished, you should have a bash prompt via the USB serial port, or using an external monitor and keyboard attached to the Jetson. You will also need to connect your Jetson to the internet via the Ethernet port (there is no WiFi on the Jetson). (After setting up the Jetson the first time via keyboard or the USB serial port, you can SSH in.)
2. Installing dependencies
Make sure your ethernet is connected to the Internet
Issue the following command to check:
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ping -c 3 www.google.com
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The result should look similar to this:
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3 packets transmitted, 3 received, 0% packet loss, time 2003ms
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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).
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wget -q -O - https://cdn.edgeimpulse.com/firmware/linux/jetson.sh | bash
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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:
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edge-impulse-linux
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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:
​Responding to your voice​
​Recognize sounds from audio​
​Adding sight to your sensors​
​Object detection​
​Counting objects using FOMO​
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:
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edge-impulse-linux-runner
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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:
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vips --vips-config
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The end of the output should show support for file import/export with libjpeg, like so:
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file import/export with libjpeg: yes (pkg-config)
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image pyramid export: no
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use libexif to load/save JPEG metadata: no
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[email protected]:~$ 
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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):
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sudo chown -R $(whoami) $HOME
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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:
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sudo /usr/bin/jetson_clocks
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Linux x86_64

Raspberry Pi 4

Last modified 22d ago

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Contents

1. Setting up your Jetson Nano

2. Installing 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