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Build Mega Service of VisualQnA on Xeon

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.

🚀 Apply Xeon Server on AWS

To apply a Xeon server on AWS, start by creating an AWS account if you don't have one already. Then, head to the EC2 Console to begin the process. Within the EC2 service, select the Amazon EC2 M7i or M7i-flex instance type to leverage 4th Generation Intel Xeon Scalable processors. These instances are optimized for high-performance computing and demanding workloads.

For detailed information about these instance types, you can refer to this link. Once you've chosen the appropriate instance type, proceed with configuring your instance settings, including network configurations, security groups, and storage options.

After launching your instance, you can connect to it using SSH (for Linux instances) or Remote Desktop Protocol (RDP) (for Windows instances). From there, you'll have full access to your Xeon server, allowing you to install, configure, and manage your applications as needed.

Certain ports in the EC2 instance need to opened up in the security group, for the microservices to work with the curl commands

See one example below. Please open up these ports in the EC2 instance based on the IP addresses you want to allow

llava-tgi-service
===========
Port 8399 - Open to 0.0.0.0/0

llm
===
Port 9399 - Open to 0.0.0.0/0

visualqna-xeon-backend-server
==========================
Port 8888 - Open to 0.0.0.0/0

visualqna-xeon-ui-server
=====================
Port 5173 - Open to 0.0.0.0/0

🚀 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 Xeon Image

docker pull ghcr.io/huggingface/text-generation-inference:2.4.0-intel-cpu

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

  1. ghcr.io/huggingface/text-generation-inference:2.4.0-intel-cpu
  2. opea/lvm-tgi:latest
  3. opea/visualqna:latest
  4. opea/visualqna-ui:latest
  5. 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 Xeon 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 no_proxy=${your_no_proxy}
export http_proxy=${your_http_proxy}
export https_proxy=${your_http_proxy}
export LVM_MODEL_ID="llava-hf/llava-v1.6-mistral-7b-hf"
export LVM_ENDPOINT="http://${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}:8888/v1/visualqna"

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

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/intel/cpu/xeon
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.

  1. 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'

  2. 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"