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.

1. Connecting to your Mac

To connect your Mac to Edge Impulse:

1. Install Node.js.

2. Install Homebrew.

3. Open a terminal window and install the dependencies:

$ brew install sox 
$ brew install imagesnap

1. Last, install the Edge Impulse CLI:

$ npm install edge-impulse-linux -g

2. Connecting to Edge Impulse

With the software installed, open a terminal window 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.

3. Verifying that your device is connected

That's all! Your Mac 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 open a terminal 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:

Raspberry Pi 5

The Raspberry Pi 5 with 2–3× the speed of the previous generation, and featuring silicon designed in‑house for the best possible performance, we’ve redefined the Raspberry Pi experience. The Pi5 is a versatile Linux development board with a quad-core processor running at 2.4GHz 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 5 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.

Setting Up Your Raspberry Pi 5 with the New Bookworm OS

In this documentation, we will detail the steps to set up your Raspberry Pi 5 with the new Bookworm release OS for Edge Impulse. This guide includes headless setup instructions and how to connect to Edge Impulse, along with troubleshooting tips.

1. Connecting to Your Raspberry Pi

Headless Setup

You can set up your Raspberry Pi without a screen. To do so:

1. Download the Raspberry Pi OS - Bookworm Release

   • Ensure you have the latest Raspberry Pi OS which supports the new Edge Impulse Linux CLI version >= 1.3.0.

2. Flash the Raspberry Pi OS Image

   • Flash the OS image to an SD card using a tool like Balena Etcher.

3. Prepare the SD Card

   • After flashing, locate the boot mass-storage device on your computer.

   • Create a new file called wpa_supplicant.conf in the boot drive with the following content:

     Copy

     ctrl_interface=DIR=/var/run/wpa_supplicant GROUP=netdev
     update_config=1
     country=<Insert 2 letter ISO 3166-1 country code here>
     
     network={
       ssid="<Name of your wireless LAN>"
       psk="<Password for your wireless LAN>"
     }

     Replace the placeholders with your WiFi credentials.

   • Create an empty file called ssh in the boot drive to enable SSH.

4. Boot the Raspberry Pi

   • Insert the SD card into your Raspberry Pi 5 and power it on.

5. Find the IP Address

   • Locate the IP address of your Raspberry Pi using your router's DHCP logs or a network scanning tool. On macOS or Linux, use:

     Copy

     arp -na | grep -i dc:a6:32

     This will display the IP address, e.g., 192.168.1.19.

6. Connect via SSH

   • Open a terminal and connect to the Raspberry Pi:

     Copy

     ssh pi@192.168.1.19

   • Log in with the default password raspberry.

With a Screen

If you have a screen and a keyboard/mouse attached to your Raspberry Pi:

1. Flash the Raspberry Pi OS Image

   • Flash the OS image to an SD card as described above.

2. Boot the Raspberry Pi

   • Insert the SD card into your Raspberry Pi 5 and power it on.

3. Connect to WiFi

   • Use the graphical interface to connect to your WiFi network.

4. Open a Terminal

   • Click the 'Terminal' icon in the top bar of the Raspberry Pi desktop.

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
sudo npm install edge-impulse-linux -g --unsafe-perm

Then to update npm packages:

sudo npm install -g npm@10.8.1

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, then select Camera, and follow the prompt to enable the camera. Reboot the Raspberry Pi.

Install with Docker

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

sudo apt update
sudo apt install -y docker.io
sudo systemctl enable docker
sudo systemctl start docker
sudo docker run -it --rm --privileged --network=host -v /dev/:/dev/ --env UDEV=1 --device /dev:/dev --entrypoint /bin/bash ubuntu:20.04

Once in 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

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).

To connect your Raspberry Pi 5 to Edge Impulse, run the following command:

edge-impulse-linux

You can now sample raw data, build models, and deploy trained machine learning models directly from the Studio. Please let us know if you have any questions or need further assistance. forum.edgeimpulse.com

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

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

Headless

You can set up your Raspberry Pi without a screen. To do so:

1. Flash the Raspberry Pi OS image to an SD card.

1. 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:

ctrl_interface=DIR=/var/run/wpa_supplicant GROUP=netdev
update_config=1
country=<Insert 2 letter ISO 3166-1 country code here>

network={
 ssid="<Name of your wireless LAN>"
 psk="<Password for your wireless LAN>"
}

(Replace the fields marked with <> with your WiFi credentials)

1. Next, create a new file called ssh into the boot drive. You can leave this file empty.

2. Plug the SD card into your Raspberry Pi 4, and let the device boot up.

3. 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:

$ arp -na | grep -i dc:a6:32
? (192.168.1.19) at dc:a6:32:f5:b6:7e on en0 ifscope [ethernet]

Here 192.168.1.19 is your IP address.

1. Connect to the Raspberry Pi over SSH. Open a terminal window and run:

ssh pi@192.168.1.19

1. Log in with password raspberry.

With a screen

If you have a screen and a keyboard / mouse attached to your Raspberry Pi:

1. Flash the Raspberry Pi OS image to an SD card.

2. Plug the SD card into your Raspberry Pi 4, and let the device boot up.

3. Connect to your WiFi network.

4. Click the 'Terminal' icon in the top bar of the 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_16.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.

The AM62X Starter Kit from Texas Instruments is a development platform for the AM62X with quad-core Arm A53s at 1.4GHz. This general purpose microprocessor supports 1080p displays through HDMI, 5MP camera input through MIPI-CSI2, including the Raspberry Pi cam support, and multichannel audio. The Linux distribution for this device comes with Tensorflow-lite, ONNX Runtime, OpenCV, and gstreamer, all with python bindings and C++ libraries.

Using your SK-AM62 with Edge Impulse

Please visit Texas Instruments' website for instructions on how to use this board with Edge Impulse.

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.

1. Installing dependencies

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

Ubuntu/Debian:

2. Connecting to Edge Impulse

With all software set up, connect your camera or 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.

3. Verifying that your device is connected

Next steps: building a machine learning model

With everything set up you can now build your first machine learning model with these tutorials:

Deploying back to device

To run your impulse locally run on your Linux platform:

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: