# Build Mega Service of VisualQnA on Xeon

This document outlines the deployment process for a VisualQnA application utilizing the [GenAIComps](https://github.com/opea-project/GenAIComps.git) 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](https://console.aws.amazon.com/ec2/v2/home) 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](https://aws.amazon.com/ec2/instance-types/m7i/). 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.

## 🚀 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

```bash
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](https://github.com/opea-project/GenAIComps.git) microservice pipeline within the `visualqna.py` Python script. Build MegaService Docker image via below command:

```bash
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:

```bash
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 Xeon Image

```bash
# 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:

1. `opea/vllm:latest`
2. `ghcr.io/huggingface/text-generation-inference:2.4.0-intel-cpu` (Optional)
3. `opea/lvm:latest`
4. `opea/visualqna:latest`
5. `opea/visualqna-ui:latest`
6. `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.

```bash
source set_env.sh
```

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

```bash
cd GenAIExamples/VisualQnA/docker_compose/intel/cpu/xeon
```

```bash
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.

1. LLM Microservice

   ```bash
   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

```bash
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:

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