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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, Seeed Studio.
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.
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." Do not use the latest SD card image, but rather, download the 4.5.1 version for your respective board from this page. 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.)
Issue the following command to check:
The result should look similar to this:
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).
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:
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
.
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.
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.
To run your impulse locally, just connect to your Jetson again, and run:
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.
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:
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.
This is probably caused by a missing dependency on libjpeg. If you run:
The end of the output should show support for file import/export with libjpeg, like so:
If you don't see jpeg support as "yes", rerun the setup script and take note of any errors.
If you encounter this error, ensure that your entire home directory is owned by you (especially the .config folder):
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:
If you see an error similar to this when running Linux C++ SDK examples with GPU acceleration,
then please download and use the SD card image version 4.6.1 for your respective board from this page. The error is likely caused by an incompatible version of nVidia's GPU libraries - or the absence of these libraries. If you must use older JetPack version (4.5.1 is the earliest supported), then you need to rename libei_debug7.a located in tflite/linux-jetson-nano/ to libei_debug.a and recompile your application code.
You can use your Intel or M1-based Mac computer as a fully-supported development environment for Edge Impulse for Linux. This lets you sample raw data, build models, and deploy trained machine learning models directly from the Studio. If you have a Macbook, the webcam and microphone of your system are automatically detected and can be used to build models.
To connect your Mac to Edge Impulse:
Open a terminal window and install the dependencies:
Last, install the Edge Impulse CLI:
Problems installing the CLI?
With the software installed, open a terminal window and run::
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
.
With everything set up you can now build your first machine learning model with these tutorials:
To run your impulse locally, just open a terminal and run:
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:
You can use your Linux x86_64 device or computer as a fully-supported development environment for Edge Impulse for Linux. This lets you sample raw data, build models, and deploy trained machine learning models directly from the Studio. If you have a webcam and microphone plugged into your system, they are automatically detected and can be used to build models.
Instruction set architectures
If you are not sure about your instruction set architectures, use:
To set this device up in Edge Impulse, run the following commands:
Ubuntu/Debian:
With all software set up, connect your camera and microphone to your operating system (see 'Next steps' further on this page if you want to connect a different sensor), and run:
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
.
With everything set up you can now build your first machine learning model with these tutorials:
To run your impulse locally run on your Linux platform:
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:
Install .
Install .
See the guide.
That's all! Your Mac is now connected to Edge Impulse. To verify this, go to , and click Devices. The device will be listed here.
Looking to connect different sensors? Our lets you easily send data from any sensor and any programming language (with examples in Node.js, Python, Go and C++) into Edge Impulse.
This will automatically compile your model with full hardware acceleration, download the model to your Raspberry Pi, and then start classifying. Our has examples on how to integrate the model with your favourite programming language.
That's all! Your machine is now connected to Edge Impulse. To verify this, go to , and click Devices. The device will be listed here.
Looking to connect different sensors? Our lets you easily send data from any sensor and any programming language (with examples in Node.js, Python, Go and C++) into Edge Impulse.
This will automatically compile your model with full hardware acceleration, download the model to your local machine, and then start classifying. Our has examples on how to integrate the model with your favourite programming language.
The Raspberry Pi 4 is a versatile Linux development board with a quad-core processor running at 1.5GHz, a GPIO header to connect sensors, and the ability to easily add an 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 Raspberry Pi 4 is available from 35 USD from a wide range of distributors, including DigiKey.
In addition to the Raspberry Pi 4 we recommend that you also add a camera and / or a microphone. Most popular USB webcams and the Camera Module work fine on the development board out of the box.
You can set up your Raspberry Pi without a screen. To do so:
Raspberry Pi OS - Bullseye release
Last release of the Raspberry Pi OS requires Edge Impulse Linux CLI version >= 1.3.0.
Flash the Raspberry Pi OS image to an SD card.
After flashing the OS, find the boot
mass-storage device on your computer, and create a new file called wpa_supplicant.conf in the boot
drive. Add the following code:
(Replace the fields marked with <>
with your WiFi credentials)
Next, create a new file called ssh
into the boot
drive. You can leave this file empty.
Plug the SD card into your Raspberry Pi 4, and let the device boot up.
Find the IP address of your Raspberry Pi. You can either do this through the DHCP logs in your router, or by scanning your network. E.g. on macOS and Linux via:
Here 192.168.1.19
is your IP address.
Connect to the Raspberry Pi over SSH. Open a terminal window and run:
Log in with password raspberry
.
If you have a screen and a keyboard / mouse attached to your Raspberry Pi:
Flash the Raspberry Pi OS image to an SD card.
Plug the SD card into your Raspberry Pi 4, and let the device boot up.
Connect to your WiFi network.
Click the 'Terminal' icon in the top bar of the Raspberry Pi.
To set this device up in Edge Impulse, run the following commands:
If you have a Raspberry Pi Camera Module, you also need to activate it first. Run the following command:
Use the cursor keys to select and open Interfacing Options, and then select Camera and follow the prompt to enable the camera. Then reboot the Raspberry.
If you want to install Edge Impulse on your Raspberry Pi using Docker you can run the following commands:
Once on the Docker container, run:
and
You should now be able to run Edge Impulse CLI tools from the container running on your Raspberry.
Note that this will only work using an external USB camera
With all software set up, connect your camera and microphone to your Raspberry Pi (see 'Next steps' further on this page if you want to connect a different sensor), and run:
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
.
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.
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.
To run your impulse locally, just connect to your Raspberry Pi again, and run:
This will automatically compile your model with full hardware acceleration, download the model to your Raspberry Pi, and then start classifying. Our Linux SDK has examples on how to integrate the model with your favourite programming language.
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: