Raspberry Pi 4

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.

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.

1. Connecting to your Raspberry Pi

2. Installing dependencies

To set this device up in Edge Impulse, run the following commands:

sudo apt update
curl -sL https://deb.nodesource.com/setup_20.x | sudo bash -
sudo apt install -y gcc g++ make build-essential nodejs sox gstreamer1.0-tools gstreamer1.0-plugins-good gstreamer1.0-plugins-base gstreamer1.0-plugins-base-apps
npm config set user root && sudo npm install edge-impulse-linux -g --unsafe-perm

If you have a Raspberry Pi Camera Module, you also need to activate it first. Run the following command:

sudo raspi-config

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.

Install with Docker

If you want to install Edge Impulse on your Raspberry Pi using Docker you can run the following commands:

docker run -it --rm --privileged --network=host -v /dev/:/dev/ --env UDEV=1 --device /dev:/dev --entrypoint /bin/bash ubuntu:20.04

Once on the Docker container, run:

apt-get update
apt-get install wget -y
wget https://deb.nodesource.com/setup_20.x
bash setup_20.x
apt install -y gcc g++ make build-essential nodejs sox gstreamer1.0-tools gstreamer1.0-plugins-good gstreamer1.0-plugins-base gstreamer1.0-plugins-base-apps vim v4l-utils usbutils udev
apt-get install npm -y
npm config set user root
npm install edge-impulse-linux -g --unsafe-perm

and

/lib/systemd/systemd-udevd --daemon

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

3. Connecting to Edge Impulse

With all software set up, connect your camera or microphone to your Raspberry Pi (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.

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 Raspberry Pi again, and run:

edge-impulse-linux-runner

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.

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:

Troubleshooting

Wrong OS bits

If you see the following error when trying to deploy a .eim model to your Raspberry Pi:

Failed to run impulse Error: Unsupported architecture “aarch64”

It likely means you are attempting to deploy a .eim Edge Impulse model file to a 32-bit operating system running on a 64-bit CPU. To check your hardware architecture and OS in Linux, please run the following commands:

uname -m
uname -a
getconf LONG_BIT

If you see something like this:

$ uname -m
aarch64
$ uname -a
Linux raspberrypi 6.1.21-v8+ #1642 SMP PREEMPT Mon Apr  3 17:24:20 BST 2023 aarch64 GNU/Linux
$ getconf LONG_BIT
32

It means that you are running a 32-bit OS on a 64-bit CPU. To run .eim models on aarch64 CPUs, you must use a 64-bit operating system. Please download and install the 64-bit version of Raspberry Pi OS if you see aarch64 when you run uname -m.