noteId

Intel-Edge-AI-Foundation-Course

Use the image in another container You can use this Docker image as a base image and use it in multiple Dockerfiles. An example of how to do this has been provided:

Move to sample-app directory and build the image

docker build -t intel-edge .

Run the the container with X enabled (Linux) Additionally, for running a intel-edge application that displays an image, you need to share the host display to be accessed from guest Docker container.

The X server on the host should be enabled for remote connections:

xhost +

The following flags needs to be added to the docker run command:

--net=host
--env="DISPLAY"
--volume="$HOME/.Xauthority:/root/.Xauthority:rw"

To run the intel-edge image with the display enabled:

docker run --net=host --env="DISPLAY" --volume="$HOME/.Xauthority:/root/.Xauthority:rw" -ti d9c72c1ee970 /bin/bash

Finally disable the remote connections to the X server

xhost -

Use the image in another container

You can use this Docker image as a base image and use it in multiple Dockerfiles. An example of how to do this has been provided:

Move to root directory and build the image

cd root directory
docker build -t my-intel-edge .

Run a container

You can directly run a container based on this image or use this image across other images.

To run a container based on this image:

docker run -ti d9c72c1ee970 /bin/bash

Choosing Models

I chose the following models for the three tasks:

Human Pose Estimation: human-pose-estimation-0001 Text Detection: text-detection-0004 Determining Car Type & Color: vehicle-attributes-recognition-barrier-0039 Downloading Models To navigate to the directory containing the Model Downloader:

cd /opt/intel/openvino/deployment_tools/open_model_zoo/tools/downloader

Within there, you'll notice a downloader.py file, and can use the -h argument with it to see available arguments. For this exercise, --name for model name, and --precisions, used when only certain precisions are desired, are the important arguments. Note that running downloader.py without these will download all available pre-trained models, which will be multiple gigabytes. You can do this on your local machine, if desired, but the workspace will not allow you to store that much information.

Note: In the classroom workspace, you will not be able to write to the /opt/intel directory, so you should also use the -o argument to specify your output directory as /home/workspace (which will download into a created intel folder therein).

Downloading Human Pose Model

sudo ./downloader.py --name human-pose-estimation-0001 -o /home/workspace

sudo ./downloader.py --name text-detection-0004 --precisions FP16 -o /home/workspace

sudo ./downloader.py --name vehicle-attributes-recognition-barrier-0039 --precisions INT8 -o /home/workspace

Copying files from host to Docker container

From a container to the host

docker cp CONTAINER_ID:./bar/foo.txt .

From the host to a container

docker exec -i CONTAINER_ID sh -c 'cat > ./bar/foo.txt' < ./foo.txt

Commit a container with new configurations

sudo docker commit --change "ENV DEBUG true" CONTAINER_ID  my_name/my_image:version3

sudo docker commit --change "ENV DEBUG true" fb63594bf93b  guillainbisimwa/openvino:version4

To access the camera inside docker ( ls -ltrh /dev/video* )

docker run --net=host --env="DISPLAY" --volume="$HOME/.Xauthority:/root/.Xauthority:rw" -v /dev/video1:/dev/video1 -ti b213c7e52101 /bin/bash

docker run --net=host --env="DISPLAY" --volume="$HOME/.Xauthority:/root/.Xauthority:rw" --device=/dev/video0:/dev/video0 -ti 05d6111af32e /bin/bash

guillainbisimwa/Intel-Edge-AI-Foundation-Course