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.

$ uname -m
x86_64

1. Installing dependencies

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

Ubuntu/Debian:

sudo apt install -y curl
curl -sL https://deb.nodesource.com/setup_12.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

3. 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:

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 machine 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 run on your Linux platform:

edge-impulse-linux-runner

This will automatically compile your model with full hardware acceleration, download the model to your local machine, 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:

• macOS

• Linux x86_64

• NVIDIA Jetson Nano

• Raspberry Pi 4

• Renesas RZ/V2L

• Texas Instruments SK-TDA4VM

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.

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." 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.)

2. Installing 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 or 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.

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:

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:

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
alex@jetson1:~$

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

Program fails to find shared library

If you see an error similar to this when running Linux C++ SDK examples with GPU acceleration,

jetson@localhost:~/example-standalone-inferencing-linux$ ./build/custom
./build/custom: error while loading shared libraries: libnvinfer.so.8: cannot open shared object file: No such file or directory

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.

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.

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.

2. 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
sudo apt upgrade
curl -sL https://deb.nodesource.com/setup_12.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
wgethttps://deb.nodesource.com/setup_12.x
bash setup_12.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:

The Renesas RZ/V2L is the latest state-of-the-art general-purpose 64-bit Linux MPU with a dual-core ARM Cortex-A55 processor running at 1.2GHz and ARM Mali-G31 3D graphic engine.

The RZ/V2L EVK consists of a SMARC SOM module and an I/O carrier board that provides a USB serial interface, 2 channel Ethernet interfaces, a camera and an HDMI display interface, in addition to many other interfaces (PMOD, microphone, audio output, etc.). The RZ/V2L EVK can be acquired directly through the Renesas website. Since the RZ/V2L is intended for vision AI, the EVK already contains the Google Coral Camera Module.

The Renesas RZ/V2L board realizes hardware acceleration through the DRP-AI IP that consists of a Dynamically Configurable Processor (DRP), and Multiply and Accumulate unit (AI-MAC). The DRP-AI IP is designed to process the entire neural network plus the required pre- and post-processing steps. Additional optimization techniques reduce power consumption and increase processing performance. This leads to high power efficiency and allows using the MPU without a heat sink.

Note that, the DRP-AI is designed for feed-forward neural networks that are usually in vision-based architectures. For more information about the DRP-AI, please refer to the white paper published by the Renesas team.

The Renesas tool “DRP-AI translator” is used to translate machine learning models and optimize the processing for DRP-AI. The tool is fully supported by Edge Impulse. This means that machine learning models downloaded from the studio can be directly deployed to the RZ/V2L board.

For more technical information about RZ/V2L, please refer to the Renesas RZ/V2L documentation.

1. Linux image preparation

Renesas provides Yocto build system to build all the necessary packages and create the Linux image. In this section, we will build the Linux image with the Edge Impulse CLI tools. However, Renesas recommends using the Ubuntu 20.04 Linux distribution to build the Linux image. Therefore, we recommend building the image inside a Docker container if you are not using Ubuntu 20.04.

Docker

Install Docker Desktop for macOS and Windows. For Linux, please refer to the Ubuntu installation instructions.

Yocto

This guide assumes that the user does not have any experience in Yocto. The objective is to provide the user with the necessary configurations to build the Linux image and Edge Impulse CLI. For further details about Yocto please refer to this page.

In order to build the Yocto Image, please download the latest version of RZ/V2L Verified Linux Package (v3.0.0) from the Renesas download section (can be found here). Please create an account on Renesas' website to be able to download the package. Once the package is downloaded, please copy the package to the docker container using this command

docker cp /host/path/target <containerId>:/file/path/within/container

In addition to the Verified Linux Package (VLP) v3.0.0, you need to download the DRP-AI package from Renesas' website as well. Please consult this link.

Decompress the package using the unzip command. Inside the package, you will find a directory that contains several PDF files. Please refer to the file that ends with rz-v2l-linux.pdf to have a look at the build instruction. You need also to do a similar thing for the DRP-AI package. The idea is to extract the Yocto layers for the Linux image and the DRP-AI. Please follow the Renesas documentation to see how to compile these two layers together.

Note that, it is recommended to add the Mali GPU support layer and the codec layer to get the advantage of the GPU hardware acceleration. Installation instructions for GPU and codec layers can be found on Renesas' website.

Note: The Renesas documentation might refer to adding additional layers such as the ISP layer. Please do not add this layer for now (current version VLP 3.0.0), as the SW setup isn’t compatible yet. This is going to change with the next release b/o 2023.

Please build the Weston image instead of building the minimal image when going through the instructions.

If you are a root user inside a docker container, you will need to disable the security check in order to allow for bitbake to start the build process. This can be done by commenting out the sanity check in poky/meta/conf/sanity.conf as follows:

# INHERIT +=  "sanity"

In addition, it is required to modify the BSP layer as follows in order to be able to start the build:

mv -n ../meta-renesas/include/core-image-bsp.inc ../meta-renesas/include/core-image-bsp.inc_org

grep -v "lttng" ../meta-renesas/include/core-image-bsp.inc_org >> ../meta-renesas/include/core-image-bsp.inc

Yocto configurations without Firefox

Once you finish the build instructions, we need to add Edge Impulse CLI packages to the Yocto build. Edge Impulse CLI requires to have nodejs and npm packages installed in addition to upgrading the glibc version from 2.28 to 2.31. To do this, we need to add the following configurations to Yocto build configurations page at the end of local.conf file that is located inside the build directory build/conf/local.conf.

# Select CIP Core packages 
CIP_CORE = "0"

IMAGE_INSTALL_append = " \
    nodejs \
    nodejs-npm \
    "

BBMASK = "meta-renesas/recipes-common/recipes-debian"

Yocto configurations with Firefox

This step is optional, but it allows adding support for the Firefox browser. First, you need to follow the above instructions on installing nodejs and upgrading glibc. Second, you need to follow the instructions on Adding the HTML5 website from Renesas' website.

Flashing the SD card with the Yocto image

Once the image has been built you will see the images subdirectory inside the build/tmp/deploy directory. To flash the image to an SD card, Renesas has published a guide on how to do this on their renesas.info website. Please go to this page and go to section 4 on that page.

If you are inside a docker container, you will need to copy the build directory from the container to the host. Use this command to do so, you need to specify the path on the container and the path on the host:

docker cp <containerId>:/file/path/within/container /host/path/target

If you are not using the docker container then it should be straightforward as described above.

Accessing the board using screen

The easiest way is to connect through serial to the RZ/V2L board using the USB mini b port.

1. After connecting the board with a USB-C cable, please power the board with the red power button.

2. Please install screen to the host machine and then execute the following command from Linux to access the board:

   Copy

   screen /dev/ttyUSB0 115200

3. You will see the boot process, then you will be asked to log in:

   • Log in with username root.

   • There is no password

Note that, it should be possible to use an Ethernet cable and log in via SSH if the daemon is installed on the image. However, for simplicity purposes, we do not refer to this one here.

2. Installing Edge Impulse Linux CLI

Once you have logged in to the board, please run the following command to install Edge Impulse Linux CLI

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 google coral camera to your Renesas board (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

Currently, all Edge Impulse models can run on the RZ/V2L CPU which is a dedicated Cortex A55. In addition, you can bring your own model to Edge Impulse and use it on the device. However, if you would like to benefit from the DRP-AI hardware acceleration support including higher performance and power efficiency, please use one of the following models:

For object detection:

• Yolov5 (v5)

• FOMO (Faster objects More Objects)

For Image classification:

• MobileNet v1, v2

It supports as well models built within the studio using the available layers on the training page.

Note that, on the training page you have to select the target before starting the training in order to tell the studio that you are training the model for the RZ/V2L. This can be done on the top right in the training page.

If you would like to do object detection with Yolov5 (v5) you need to fix the image resolution in the impulse design to 320x320, otherwise, you might risk that the training fails.

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

• Adding sight to your sensors.

• Detect objects using FOMO.

EON tuner

If you are interested in using the EON tuner in order to improve the accuracy of the model this is possible only for image classification for now. EON tuner supports for object detection is arriving soon.

If you use the EON tuner with image classification, you need to filter the int8 models since they are not supported by the DRP-AI. Also, you need to filter the grayscale models as well. Note that if you leave the EON tuner page, the filter will reset to the default settings, which means you need to re-filter the above models.

Deploying back to the device

To run your impulse locally, just connect to your Renesas RZ/V2L and run:

edge-impulse-linux-runner

This will automatically compile your model with full hardware acceleration and download the model to your Renesas board, and then start classifying.

Or you can select the RZ/V2L board from the deployment page, this will download an eim model that you can use with the above runner as follows:

Go to the deployment page and select:

Then run the following on the RZ/V2L:

edge-impulse-linux-runner --model-file downloaded-model.eim

You will see the model inferencing results in the terminal also we stream the results to the local network. This allows you to see the output of the model in real-time in your web browser. Open the URL shown when you start the runner and you will see both the camera feed and the classification results.

DRP-AI library

Since the RZ/V2L benefits from hardware acceleration using the DRP-AI, we provide you with the drp-ai library that uses our C++ Edge Impulse SDK and models headers that run on the hardware accelerator. If you would like to integrate the model source code into your applications and benefit from the drp-ai then you need to select the drp-ai library.

We have an example showing how to use the drp-ai library that can be found in Deploy your model as a DRP-AI library.

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:

3. Open a terminal window and install the dependencies:

1. Last, install the Edge Impulse CLI:

2. Connecting to Edge Impulse

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.

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, just open a terminal and run:

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:

The is a Linux enabled development kit from Texas Instruments with a focus on smart cameras, robots, and ADAS that need multiple connectivity options and ML acceleration. The has 8 TOPS of hardware-accelerated AI combined with low power capabilities to make this device capable of many applications.

In order to take full advantage of the TDA4VM's AI hardware acceleration Edge Impulse has integrated and TDA4VM optimized for low-to-no-code training and deployments from Edge Impulse Studio.

1. Installing dependencies

To set this device up in Edge Impulse, run the following commands on the SK-TDA4VM:

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

4. Next steps: building a machine learning model

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

5. 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:

Optimized Models for the TDA4VM