Deploying Image-to-Video Service¶
This document provides a comprehensive guide to deploying the Image-to-Video microservice pipeline on Intel platforms.
This guide covers two deployment methods:
🚀 1. Quick Start with Docker Compose: The recommended method for a fast and easy setup.
🚀 2. Manual Step-by-Step Deployment (Advanced): For users who want to build and run the container individually.
🚀 1. Quick Start with Docker Compose¶
This method uses Docker Compose to start the service with a single command. It is the fastest and easiest way to get the service running.
1.1. Access the Code¶
Clone the repository and navigate to the deployment directory:
git clone https://github.com/opea-project/GenAIComps.git
cd GenAIComps/comps/image2video/deployment/docker_compose
1.2. Deploy the Service¶
Choose the command corresponding to your target platform.
For Intel® Xeon® CPU:
docker compose -f compose.yaml up image2video -d
For Intel® Gaudi® 2 HPU:
docker compose -f compose.yaml up image2video-gaudi -d
1.3. Validate the Service¶
Once the container is running, you can validate the service. Note: Run this command from the root of the GenAIComps
repository.
export ip_address=$(hostname -I | awk '{print $1}')
curl http://${ip_address}:9369/v1/image2video -XPOST \
-d '{"images_path":[{"image_path":"https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/svd/rocket.png"}]}' \
-H 'Content-Type: application/json'
The expected output will be a JSON object containing the path to the generated video file.
1.4. Clean Up the Deployment¶
To stop and remove the containers, run the following command from the comps/image2video/deployment/docker_compose
directory:
docker compose down
🚀 2. Manual Step-by-Step Deployment (Advanced)¶
This section provides detailed instructions for building the Docker image and running the microservice container individually.
2.1. Clone the Repository¶
If you haven’t already, clone the repository and navigate to the root directory:
git clone https://github.com/opea-project/GenAIComps.git
cd GenAIComps
2.2. Build the Docker Image¶
For Intel® Xeon® CPU:
docker build -t opea/image2video:latest --build-arg https_proxy=$https_proxy --build-arg http_proxy=$http_proxy -f comps/image2video/src/Dockerfile .
For Intel® Gaudi® 2 HPU:
docker build -t opea/image2video-gaudi:latest --build-arg https_proxy=$https_proxy --build-arg http_proxy=$http_proxy -f comps/image2video/src/Dockerfile.intel_hpu .
2.3. Configure Environment Variables¶
Set the necessary environment variables for validation.
export ip_address=$(hostname -I | awk '{print $1}')
2.4. Run the Microservice Container¶
2.4.1. Run Image-to-Video Microservice on Xeon¶
docker run -d --name "image2video-service" --ipc=host -p 9369:9369 -e http_proxy=$http_proxy -e https_proxy=$https_proxy opea/image2video:latest
2.4.2. Run Image-to-Video Microservice on Gaudi¶
docker run -d --name "image2video-gaudi-service" -p 9369:9369 --runtime=habana -e HABANA_VISIBLE_DEVICES=all -e OMPI_MCA_btl_vader_single_copy_mechanism=none --cap-add=sys_nice --ipc=host -e http_proxy=$http_proxy -e https_proxy=$https_proxy opea/image2video-gaudi:latest
2.5. Validate the Service¶
After starting the container, test the service endpoint. Make sure you are in the root directory of the GenAIComps
repository.
# The ip_address variable should be set from step 2.3
curl http://${ip_address}:9369/v1/image2video -XPOST \
-d '{"images_path":[{"image_path":"https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/svd/rocket.png"}]}' \
-H 'Content-Type: application/json'
You should see a successful response with the path to the output video.
2.6. Clean Up the Deployment¶
To stop and remove the container you started manually, use the docker stop
and docker rm
commands.
For Intel® Xeon® CPU:
docker stop image2video-service docker rm image2video-service
For Intel® Gaudi® 2 HPU:
docker stop image2video-gaudi-service docker rm image2video-gaudi-service