X-LINUX-AI Starter package

1 Install from the OpenSTLinux AI package repository

Information

The STMicroelectronics packages repository service is provided for evaluation purposes only, its content may be updated at any time without notice and is therefore not approved for use in production.

All the generated X-LINUX-AI packages are available from the OpenSTLinux AI package repository service hosted at the non-browsable URL http://extra.packages.openstlinux.st.com/AI.

This repository contains AI packages that can be simply installed using apt-* utilities, which the same as those used on a Debian system:

• the main group contains the selection of AI packages whose installation is automatically tested by STMicroelectronics
• the updates group is reserved for future uses such as package revision update.

You can install them individually or by package group.

1.1 Prerequisites

• Flash the Starter Package on your SDCard

For OpenSTLinux ecosystem release v5.0.0  :

STM32MP157x-DKx Starter Package procedure

or

STM32MP157x-EV1 Starter Package procedure

or

STM32MP135x-DK Starter Package procedure

• Your board has an internet connection either through the network cable or through a WiFi connection.

Information

If your internet access depends on a proxy server, you should define the http_proxy environment variable with the following command before any apt-* commands: export http_proxy='http://<proxy url>:<proxy port>/'

1.2 Configure the AI OpenSTLinux package repository

Once the board is booted, execute the following command in the console in order to configure the AI OpenSTLinux package repository:

For ecosystem release   v5.0.0  :

• Move to the apt archives directory :

 cd /var/cache/apt/archives

• Retrieve the specific package apt-openstlinux-ai_1.0_armhf.deb:

 wget http://extra.packages.openstlinux.st.com/AI/5.0/pool/config/a/apt-openstlinux-ai/apt-openstlinux-ai_1.0_armhf.deb

• Install this package:

 apt-get install ./apt-openstlinux-ai_1.0_armhf.deb

Warning

This command may issue the following warning message (or similar): Can't drop privileges for downloading as file '/home/root/apt-openstlinux-ai_1.0_armhf.deb' couldn't be accessed by user '_apt'. - pkgAcquire::Run (13: Permission denied) You can safely ignore it.

• Then synchronize the AI OpenSTLinux package repository.

 apt-get update

1.3 Install AI packages

Warning

The software package is provided AS IS, and by downloading it, you agree to be bound to the terms of the software license agreement (SLA0048). The detailed content licenses can be found here.

1.3.1 Install all X-LINUX-AI packages

Command	Description
apt-get install packagegroup-x-linux-ai	Install all the X-LINUX-AI packages (TensorFlow™ Lite, Edge TPU™, application samples and tools)

1.3.2 Install AI framework related packages

Command	Description
apt-get install packagegroup-x-linux-ai-tflite	Install X-LINUX-AI packages related to TensorFlow™ Lite framework (including application samples)
apt-get install packagegroup-x-linux-ai-tflite-edgetpu	Install X-LINUX-AI packages related to the Coral Edge TPU™ framework (including application samples)
apt-get install packagegroup-x-linux-ai-onnxruntime	Install X-LINUX-AI packages related to ONNX Runtime™ (including application samples)

1.3.3 Install individual packages

Command	Description
apt-get install libedgetpu2	Install libedgetpu for Coral Edge TPU™
apt-get install libcoral2	Install libcoral API for Coral Edge TPU™
apt-get install libtensorflow-lite-tools	Install Tensorflow™ Lite utilities
apt-get install libtensorflow-lite2	Install Tensorflow™ Lite runtime
apt-get install python3-libtensorflow-lite	Install Python TensorFlow™ Lite inference engine
apt-get install python3-pycoral	Install Python PyCoral API for Coral Edge TPU™
apt-get install tflite-cv-apps-edgetpu-image-classification-c++	Install C++ image classification example using Coral Edge TPU™ TensorFlow™ Lite API
apt-get install tflite-cv-apps-edgetpu-image-classification-python	Install Python image classification example using Coral Edge TPU™ TensorFlow™ Lite API
apt-get install tflite-cv-apps-edgetpu-object-detection-c++	Install C++ object detection example using Coral Edge TPU™ TensorFlow™ Lite API
apt-get install tflite-cv-apps-edgetpu-object-detection-python	Install Python object detection example using Coral Edge TPU™ TensorFlow™ Lite API
apt-get install tflite-cv-apps-image-classification-c++	Install C++ image classification using TensorFlow™ Lite
apt-get install tflite-cv-apps-image-classification-python	Install Python image classification example using TensorFlow™ Lite
apt-get install tflite-cv-apps-object-detection-c++	Install C++ object detection example using TensorFlow™ Lite
apt-get install tflite-cv-apps-object-detection-python	Install Python object detection example using TensorFlow™ Lite
apt-get install tflite-edgetpu-benchmark	Install benchmark application for Coral Edge TPU™ models
apt-get install tflite-models-coco-ssd-mobilenetv1	Install TensorFlow™ Lite COCO SSD Mobilenetv1 model
apt-get install tflite-models-coco-ssd-mobilenetv1-edgetpu	Install TensorFlow™ Lite COCO SSD Mobilenetv1 model for Coral Edge TPU™
apt-get install tflite-models-mobilenetv1	Install TensorFlow™ Lite Mobilenetv1 model
apt-get install tflite-models-mobilenetv1-edgetpu	Install TensorFlow™ Lite Mobilenetv1 model for Coral Edge TPU™
apt-get install onnxruntime	Install ONNX Runtime™
apt-get install onnxruntime-tools	Install ONNX Runtime™ utilities
apt-get install python3-onnxruntime	Install ONNX Runtime™ python API
apt-get install onnx-models-coco-ssd-mobilenetv1	Install ONNX Runtime™ COCO SSD Mobilenetv1 model
apt-get install onnx-models-mobilenet	Install ONNX Runtime™ Mobilenetv1 model
apt-get install onnx-cv-apps-image-classification-python	Install Python image classification example using ONNX Runtime™
apt-get install onnx-cv-apps-object-detection-c++	Install C++ object detection example using ONNX Runtime™
apt-get install onnx-cv-apps-object-detection-python	Install Python object detection example using ONNX Runtime™

Information

If you need more information about how to use apt-* utilities check the Package repository for OpenSTLinux distribution article.

2 How to use the X-LINUX-AI Expansion Package

2.1 Material needed

To use the X-LINUX-AI OpenSTLinux Expansion Package, choose one of the following materials:

• STM32MP157F-DK2 Discovery kit + an UVC USB WebCam
• STM32MP157F-EV1 Evaluation board with the built in camera module : MB1379 (OmniVision OV5640 parallel camera)
• STM32MP135F-DK Discovery kit with the built in camera module : MB1897 (GalaxyCore 2145 parallel camera)

Optional:

• Coral USB Edge TPU™ accelerator

2.2 Boot the OpenSTlinux Starter Package

At the end of the boot sequence, the demo launcher application appears on the screen.

2.3 Install the X-LINUX-AI

Warning

The software package is provided AS IS, and by downloading it, you agree to be bound to the terms of the software license agreement (SLA0048). The detailed content licenses can be found here.

After having configured the AI OpenSTLinux package you can install the X-LINUX-AI components.

 apt-get install packagegroup-x-linux-ai

And restart the demo launcher:

 systemctl restart weston-graphical-session.service

Check that X-LINUX-AI is properly installed:

 x-linux-ai -v
X-LINUX-AI version: v5.0.0

Information

With the x-linux-ai binary it is possible to check the X-LINUX-AI version and the supported frameworks/applications. Use x-linux-ai --help for more information.

2.4 Launch an AI application sample

Once the demo launcher is restarted, notice that it is slightly different because new AI application samples have been installed.
The demo launcher has the following appearance, and you can navigate into the different screens by using the NEXT or BACK buttons.

The demo launcher now contain AI application samples that are described within dedicated article available in the X-LINUX-AI application samples zoo page.

2.5 Enjoy running your own NN models

The above examples provide application samples to demonstrate how to execute models easily on the STM32MP1.

You are free to update the C/C++ application or Python scripts for your own purposes, using your own NN models.

Source code locations are provided in application sample pages.