Deploying Avatar Animation Service

This document provides a comprehensive guide to deploying the Avatar Animation microservice pipeline on Intel platforms.

This guide covers two deployment methods:

🚀 1. Quick Start with Docker Compose

This method uses Docker Compose to start all necessary services 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/animation/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 animation -d

  • For Intel® Gaudi® 2 HPU:

    docker compose -f compose.yaml up animation-gaudi -d

1.3. Validate the Service

Once the containers are running, you can validate the service. Note: Run these commands from the root of the GenAIComps repository.

# Navigate back to the root directory if you are in the docker_compose folder
cd ../../..

# Validate the Animation service endpoint
export ip_address=$(hostname -I | awk '{print $1}')
curl http://${ip_address}:9066/v1/animation -X POST \
-H "Content-Type: application/json" \
-d @comps/animation/src/assets/audio/sample_question.json

The expected output will be a JSON object containing the path to the generated video file:

{ "wav2lip_result": ".../GenAIComps/comps/animation/src/assets/outputs/result.mp4" }

The generated video result.mp4 will be available in the comps/animation/src/assets/outputs/ directory.

1.4. Clean Up the Deployment

To stop and remove the containers, run the following command from the comps/animation/deployment/docker_compose directory:

docker compose down

🚀 2. Manual Step-by-Step Deployment (Advanced)

This section provides detailed instructions for building the Docker images and running each 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 Images

2.2.1. Build Wav2Lip Server Image

  • For Intel® Xeon® CPU:

    docker build -t opea/wav2lip:latest -f comps/third_parties/wav2lip/src/Dockerfile .

  • For Intel® Gaudi® 2 HPU:

    docker build -t opea/wav2lip-gaudi:latest -f comps/third_parties/wav2lip/src/Dockerfile.intel_hpu .

2.2.2. Build Animation Server Image

docker build -t opea/animation:latest --build-arg https_proxy=$https_proxy --build-arg http_proxy=$http_proxy -f comps/animation/src/Dockerfile .

2.3. Configure Environment Variables

Set the necessary environment variables for the containers.

  • For Intel® Xeon® CPU:

    export ip_address=$(hostname -I | awk '{print $1}')
export DEVICE="cpu"
export WAV2LIP_PORT=7860
export CHECKPOINT_PATH='/usr/local/lib/python3.11/site-packages/Wav2Lip/checkpoints/wav2lip_gan.pth'
export PYTHON_PATH='/usr/bin/python3.11'

  • For Intel® Gaudi® 2 HPU:

    export ip_address=$(hostname -I | awk '{print $1}')
export DEVICE="hpu"
export WAV2LIP_PORT=7860
export CHECKPOINT_PATH='/usr/local/lib/python3.10/dist-packages/Wav2Lip/checkpoints/wav2lip_gan.pth'
export PYTHON_PATH='/usr/bin/python3.10'

2.4. Run the Microservice Containers

2.4.1. Run Wav2Lip Microservice

  • For Intel® Xeon® CPU:

    docker run --privileged -d --name "wav2lip-service" -p $WAV2LIP_PORT:$WAV2LIP_PORT --ipc=host \
-w /home/user/comps/animation/src \
-v $(pwd)/comps/animation/src/assets:/home/user/comps/animation/src/assets \
-e PYTHON=$PYTHON_PATH \
-e DEVICE=$DEVICE \
-e CHECKPOINT_PATH=$CHECKPOINT_PATH \
-e WAV2LIP_PORT=$WAV2LIP_PORT \
opea/wav2lip:latest

  • For Intel® Gaudi® 2 HPU:

    docker run --privileged -d --name "wav2lip-gaudi-service" -p $WAV2LIP_PORT:$WAV2LIP_PORT --runtime=habana --cap-add=sys_nice --ipc=host \
-w /home/user/comps/animation/src \
-v $(pwd)/comps/animation/src/assets:/home/user/comps/animation/src/assets \
-e HABANA_VISIBLE_DEVICES=all -e OMPI_MCA_btl_vader_single_copy_mechanism=none \
-e PYTHON=$PYTHON_PATH \
-e DEVICE=$DEVICE \
-e CHECKPOINT_PATH=$CHECKPOINT_PATH \
-e WAV2LIP_PORT=$WAV2LIP_PORT \
opea/wav2lip-gaudi:latest

2.4.2. Run Animation Microservice

docker run -d --name "animation-service" -p 9066:9066 --ipc=host \
  -e http_proxy=$http_proxy \
  -e https_proxy=$https_proxy \
  -e WAV2LIP_ENDPOINT=http://$ip_address:$WAV2LIP_PORT \
  opea/animation:latest

2.5. Validate the Service

After starting both containers, test the animation 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}:9066/v1/animation -X POST \
-H "Content-Type: application/json" \
-d @comps/animation/src/assets/audio/sample_question.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 containers you started manually, use the docker stop and docker rm commands.

  • For Intel® Xeon® CPU:

    docker stop wav2lip-service animation-service
docker rm wav2lip-service animation-service

  • For Intel® Gaudi® 2 HPU:

    docker stop wav2lip-gaudi-service animation-service
docker rm wav2lip-gaudi-service animation-service