Build and Run the AD Insertion Sample on public cloud or local machine
The Open Visual Cloud is highly optimized for the latest Intel® Xeon® Scalable Processors. Developers can use their own x86 development system to build, customize, and evaluate Open Visual Cloud reference pipelines, or use a public cloud to assess system scalability and performance.
This tutorial builds and runs the AD Insertion Sample on the Amazon Web Services (AWS), Azure Virtual Machine and Google Cloud Platform Virtual Machine (GCP) or your local machine. The following figures show the running compute instances of the 3 cloud services.
AWS EC2 Instance, Amazon Linux 2
Azure VM Instance, Ubuntu 16.04 LTS
Google Cloud Platform VM instance, Ubuntu 16.04 LTS
Unless specifically specified, all the commands in the tutorial apply to all the 3 cloud services.
$ sudo yum update –y $ sudo amazon-linux-extras install –y docker $ sudo service docker start $ sudo yum install –y cmake git
$ sudo apt-get install –y cmake git
$ sudo docker swarm init Swarm initialized: current node (04k9ydxpdtx40nym8ag2j499r) is now a manager.
docker swarm join --token SWMTKN-1-2jthey7uay7ym5ko1db4etp9lt5sjmi8u4qhkv4owe1ysip943-5novs363q4nby8kgaek8ury6i 172.31.18.164:2377 $
2. Clone the Sample Source Code
$ git clone https://github.com/OpenVisualCloud/AD-Insertion-Sample
3. Build the Sample
$ cd AD-Insertion-Sample $ mkdir build $ cd build $ cmake .. This script will build third party components licensed under various open source licenses into your container images. The terms under which those components may be used and distributed can be found with the license document that is provided with those components. Please familiarize yourself with those terms to ensure your distribution of those components complies with the terms of those licenses. -- Configuring done -- Generating done -- Build files have been written to: /home/ec2-user/ad-insertion-aws/ad-insertion/build $ make Scanning dependencies of target build_docker_compose Sending build context to Docker daemon 30.21kB ... Successfully built b82eefa9409f Successfully tagged ssai_cdn_service:latest Built target build_ssai_cdn_service
4. Start the Sample Service
$ make start_docker_swarm Total reclaimed space: 0B Total reclaimed space: 0B bash into new container...ssai_self_certificate ... Creating network adinsert_default Creating secret adinsert_self_key Creating secret adinsert_self_crt Creating secret adinsert_dhparam_pem Creating service adinsert_zookeeper Creating service adinsert_video-analytic-gstreamer Creating service adinsert_database Creating service adinsert_ad-decision Creating service adinsert_content-provider Creating service adinsert_video-analytic-ffmpeg Creating service adinsert_kafka-init Creating service adinsert_kafka Creating service adinsert_ad-insertion-frontend Creating service adinsert_account-service Creating service adinsert_ad-content Creating service adinsert_content-provider-transcode Creating service adinsert_analytic-db Creating service adinsert_ad-transcode Creating service adinsert_cdn Built target start_docker_swarm $
5. Test the Sample
$ ssh -L 8443:localhost:443 -Nf -i "keypair.pem" email@example.com
The SSH tunnel maps localhost port 443 to the AWS EC2 instance port 443, and “keypair.pem” is the ssh access key pair generated for accessing the AWS EC2 instance.
For Azure or GCP
For Azure or GCP, the command is almost the same.
$ ssh -L 8443:localhost:443 -Nf -i ssh_private_key user_name@instance_public_ip
ssh_private_key is ssh access private key for accessing Azure or GCP VM instances.
Open your browser to https://localhost:8443 and accept the self-signed certificate to proceed to the sample UI.
Optionally, click the user name to change the sample settings, such as turning on the analytics console to peek into service-side activities.
The analytics console will show the analytic data as an overlay on top of the video playback window, and the server workload will show the CPU utilization at the bottom right corner.