Build Mega Service of VisualQnA on AMD ROCm¶

This document outlines the deployment process for a VisualQnA application utilizing the GenAIComps microservice pipeline on Intel Xeon 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-tgi:latest --build-arg https_proxy=$https_proxy --build-arg http_proxy=$http_proxy -f comps/lvms/tgi-llava/Dockerfile .
docker build --no-cache -t opea/nginx:latest --build-arg https_proxy=$https_proxy --build-arg http_proxy=$http_proxy -f comps/nginx/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 TGI AMD ROCm Image¶

docker pull ghcr.io/huggingface/text-generation-inference:2.4.1-rocm

Then run the command docker images, you will have the following 5 Docker Images:

ghcr.io/huggingface/text-generation-inference:2.4.1-rocm
opea/lvm-tgi: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.

Export the value of the public IP address of your ROCM server to the host_ip environment variable

Change the External_Public_IP below with the actual IPV4 value

export host_ip="External_Public_IP"

Append the value of the public IP address to the no_proxy list

export your_no_proxy="${your_no_proxy},${host_ip}"

export HOST_IP=${your_host_ip}
export VISUALQNA_TGI_SERVICE_PORT="8399"
export VISUALQNA_HUGGINGFACEHUB_API_TOKEN={your_hugginface_api_token}
export VISUALQNA_CARD_ID="card1"
export VISUALQNA_RENDER_ID="renderD136"
export LVM_MODEL_ID="Xkev/Llama-3.2V-11B-cot"
export MODEL="llava-hf/llava-v1.6-mistral-7b-hf"
export LVM_ENDPOINT="http://${HOST_IP}:8399"
export LVM_SERVICE_PORT=9399
export MEGA_SERVICE_HOST_IP=${HOST_IP}
export LVM_SERVICE_HOST_IP=${HOST_IP}
export BACKEND_SERVICE_ENDPOINT="http://${HOST_IP}:18003/v1/visualqna"
export FRONTEND_SERVICE_IP=${HOST_IP}
export FRONTEND_SERVICE_PORT=18001
export BACKEND_SERVICE_NAME=visualqna
export BACKEND_SERVICE_IP=${HOST_IP}
export BACKEND_SERVICE_PORT=18002
export NGINX_PORT=18003

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

Note: You can use set_env.sh file with bash command (. setset_env.sh) to set up needed variables.

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/gpu/rocm

docker compose -f compose.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"