Build Mega Service of VisualQnA on AMD EPYC™ Processors¶

This document outlines the deployment process for a VisualQnA application utilizing the GenAIComps microservice pipeline on AMD EPYC server. The steps include Docker image creation, container deployment via Docker Compose, and service execution to integrate microservices such as llm. We will publish the Docker images to Docker Hub soon, it will simplify the deployment process for this service.

🚀 Build Docker Images¶

First of all, you need to build Docker Images locally and install the python package of it.

1. Build LVM and NGINX Docker Images¶

git clone https://github.com/opea-project/GenAIComps.git
cd GenAIComps
docker build --no-cache -t opea/lvm:latest --build-arg https_proxy=$https_proxy --build-arg http_proxy=$http_proxy -f comps/lvms/src/Dockerfile .
docker build --no-cache -t opea/nginx:latest --build-arg https_proxy=$https_proxy --build-arg http_proxy=$http_proxy -f comps/third_parties/nginx/src/Dockerfile .

2. Build MegaService Docker Image¶

To construct the Mega Service, we utilize the GenAIComps microservice pipeline within the visualqna.py Python script. Build MegaService Docker image via below command:

git clone https://github.com/opea-project/GenAIExamples.git
cd GenAIExamples/VisualQnA
docker build --no-cache -t opea/visualqna:latest --build-arg https_proxy=$https_proxy --build-arg http_proxy=$http_proxy -f Dockerfile .

3. Build UI Docker Image¶

Build frontend Docker image via below command:

cd GenAIExamples/VisualQnA/ui
docker build --no-cache -t opea/visualqna-ui:latest --build-arg https_proxy=$https_proxy --build-arg http_proxy=$http_proxy -f docker/Dockerfile .

4. Pull vLLM/TGI epyc Image¶

# vLLM
docker pull opea/vllm:latest
# TGI (Optional)
docker pull ghcr.io/huggingface/text-generation-inference:2.4.0-intel-cpu

Then run the command docker images, you will have the following Docker Images:

opea/vllm:latest
ghcr.io/huggingface/text-generation-inference:2.4.0-intel-cpu (Optional)
opea/lvm:latest
opea/visualqna:latest
opea/visualqna-ui:latest
opea/nginx

🚀 Start Microservices¶

Setup Environment Variables¶

Since the compose.yaml will consume some environment variables, you need to setup them in advance as below.

source set_env.sh

Note: Please replace with host_ip with you external IP address, do not use localhost. Also set the HF_TOKEN.

Start all the services Docker Containers¶

Before running the docker compose command, you need to be in the folder that has the docker compose yaml file

cd GenAIExamples/VisualQnA/docker_compose/amd/cpu/epyc

docker compose -f compose.yaml up -d
# if use TGI as the LLM serving backend
docker compose -f compose_tgi.yaml up -d

Validate Microservices¶

Follow the instructions to validate MicroServices.

Note: If you see an “Internal Server Error” from the curl command, wait a few minutes for the microserver to be ready and then try again.

LLM Microservice

http_proxy="" curl http://${host_ip}:9399/v1/lvm -XPOST -d '{"image": "iVBORw0KGgoAAAANSUhEUgAAAAoAAAAKCAYAAACNMs+9AAAAFUlEQVR42mP8/5+hnoEIwDiqkL4KAcT9GO0U4BxoAAAAAElFTkSuQmCC", "prompt":"What is this?"}' -H 'Content-Type: application/json'

MegaService

curl http://${host_ip}:8888/v1/visualqna -H "Content-Type: application/json" -d '{
    "messages": [
      {
        "role": "user",
        "content": [
          {
            "type": "text",
            "text": "What'\''s in this image?"
          },
          {
            "type": "image_url",
            "image_url": {
              "url": "https://www.ilankelman.org/stopsigns/australia.jpg"
            }
          }
        ]
      }
    ],
    "max_tokens": 300
    }'

🚀 Launch the UI¶

To access the frontend, open the following URL in your browser: http://{host_ip}:5173. By default, the UI runs on port 5173 internally. If you prefer to use a different host port to access the frontend, you can modify the port mapping in the compose.yaml file as shown below:

  visualqna-gaudi-ui-server:
    image: opea/visualqna-ui:latest
    ...
    ports:
      - "80:5173"