Install Guide RHEL
Install Guide RHEL#
For Red Hat Enterprise Linux 8, Intel® Deep Learning Streamer (Intel® DL Streamer) Pipeline Framework can be built from the source code provided in this repository as Docker image.
Compile Intel® DL Streamer Pipeline Framework as Docker image#
To be able to create Intel® DL Streamer Pipeline Framework Docker image, activated RHEL Docker image is required.
Step 1: Install Docker CE (if not installed)#
Refer to Docker installation documentation
Step 2: Clone the repository#
Clone this repository into folder
mkdir -p ~/intel git clone https://github.com/dlstreamer/dlstreamer.git ~/intel/dlstreamer cd ~/intel/dlstreamer
Step 3: Build Docker image#
Run the following command to build Docker image:
cd ~/intel/dlstreamer/docker/source/rhel8 # Provide a name and a tag of activated RHEL Docker image as a first argument. # If you want to build an image with a specific tag then provide it # as a second argument (by default latest). ./build_docker_image.sh <image name> [tag]
The build process may take significant time and should finally create
dlstreamer:<tag> (by default
latest). Obtained Docker image
can be validated with command:
docker images | grep dlstreamer
This command will return a line with image
description. If the image is absent in the output, please repeat all
the steps above.
Step 4: Run Docker image#
Some Pipeline Framework samples use display to render results. In order to allow connection from Docker container to host X server run the following commands.
xhost local:root setfacl -m user:1000:r ~/.Xauthority
Then, run container:
docker run -it --privileged --net=host \ -v ~/.Xauthority:/home/dlstreamer/.Xauthority \ -v /tmp/.X11-unix:/tmp/.X11-unix \ -e DISPLAY=$DISPLAY \ -e HTTP_PROXY=$HTTP_PROXY \ -e HTTPS_PROXY=$HTTPS_PROXY \ -e http_proxy=$http_proxy \ -e https_proxy=$https_proxy \ \ -v ~/intel/dlstreamer/models:/home/intel/dlstreamer/models \ -v ~/intel/dlstreamer/video:/home/video-examples:ro \ -e VIDEO_EXAMPLES_DIR=/home/video-examples \ \ dlstreamer:<tag>
--group-add=$(stat -c "%g" /dev/dri/render*)argument for
docker runin order to setup access to GPU from container.
Here is the additional information and the meaning of some options in the Docker run command:
--privilegedis required for Docker container to access the host system’s GPU.
--net=hostprovides host network access to container. It is needed for correct interaction with X server.
/tmp/.X11-unixmapped to the container are needed to ensure smooth authentication with X server.
-vinstances are needed to map host system directories inside Docker container.
-einstances set Docker container environment variables. The samples need some of them set in order to operate correctly. Proxy variables are needed if the host is behind a firewall.
Volume provided for
modelsfolder will be used to download and store the models. Environment variable MODELS_PATH in the Docker container is responsible for it.
Entrypoint of the Docker is by default
Inside Docker image you can find Pipeline Framework samples at the entrypoint.
Before using the samples, run the script
samples folder) to download the models required for samples.
If you want to use video from web camera as an input in
face_detection_and_classification.sh you should mount the
device with this command (add this command when running the container):
Now you can run the sample with video from web camera:
You can run Docker image using utility script located in
cd ~/intel/dlstreamer/docker/source/rhel8 # essential export DATA_PATH=~/intel/dlstreamer # essential sudo ./run_docker_container.sh --video-examples-path=$DATA_PATH/video --models-path=$DATA_PATH/models --image-name=dlstreamer:<tag>
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