Build Mega Service of Translation on Xeon

This document outlines the deployment process for a Translation 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.

🚀 Prepare Docker Images

For Docker Images, you have two options to prepare them.

  1. Pull the docker images from docker hub.

    • More stable to use.

    • Will be automatically downloaded when using docker compose command.

  2. Build the docker images from source.

    • Contain the latest new features.

    • Need to be manually build.

If you choose to pull docker images form docker hub, skip this section and go to Start Microservices part directly.

Follow the instructions below to build the docker images from source.

1. Build LLM Image

git clone https://github.com/opea-project/GenAIComps.git
cd GenAIComps
docker build -t opea/llm-tgi:latest --build-arg https_proxy=$https_proxy --build-arg http_proxy=$http_proxy -f comps/llms/text-generation/tgi/Dockerfile .

2. Build MegaService Docker Image

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

git clone https://github.com/opea-project/GenAIExamples
cd GenAIExamples/Translation/
docker build -t opea/translation: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/Translation/ui
docker build -t opea/translation-ui:latest --build-arg https_proxy=$https_proxy --build-arg http_proxy=$http_proxy -f docker/Dockerfile .

4. Build Nginx Docker Image

cd GenAIComps
docker build -t opea/nginx:latest --build-arg https_proxy=$https_proxy --build-arg http_proxy=$http_proxy -f comps/nginx/Dockerfile .

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

  1. opea/llm-tgi:latest

  2. opea/translation:latest

  3. opea/translation-ui:latest

  4. opea/nginx:latest

🚀 Start Microservices

Required Models

By default, the LLM model is set to a default value as listed below:

Service

Model

LLM

haoranxu/ALMA-13B

Change the LLM_MODEL_ID below for your needs.

Setup Environment Variables

  1. Set the required environment variables:

    # Example: host_ip="192.168.1.1"
export host_ip="External_Public_IP"
# Example: no_proxy="localhost, 127.0.0.1, 192.168.1.1"
export no_proxy="Your_No_Proxy"
export HUGGINGFACEHUB_API_TOKEN="Your_Huggingface_API_Token"
# Example: NGINX_PORT=80
export NGINX_PORT=${your_nginx_port}

  2. If you are in a proxy environment, also set the proxy-related environment variables:

    export http_proxy="Your_HTTP_Proxy"
export https_proxy="Your_HTTPs_Proxy"

  3. Set up other environment variables:

    cd ../../../
source set_env.sh

Start Microservice Docker Containers

docker compose up -d

Note: The docker images will be automatically downloaded from docker hub:

docker pull opea/llm-tgi:latest
docker pull opea/translation:latest
docker pull opea/translation-ui:latest
docker pull opea/nginx:latest

Validate Microservices

  1. TGI Service

    curl http://${host_ip}:8008/generate \
  -X POST \
  -d '{"inputs":"What is Deep Learning?","parameters":{"max_new_tokens":17, "do_sample": true}}' \
  -H 'Content-Type: application/json'

  2. LLM Microservice

    curl http://${host_ip}:9000/v1/chat/completions \
  -X POST \
  -d '{"query":"Translate this from Chinese to English:\nChinese: 我爱机器翻译。\nEnglish:"}' \
  -H 'Content-Type: application/json'

  3. MegaService

    curl http://${host_ip}:8888/v1/translation -H "Content-Type: application/json" -d '{
     "language_from": "Chinese","language_to": "English","source_language": "我爱机器翻译。"}'

  4. Nginx Service

    curl http://${host_ip}:${NGINX_PORT}/v1/translation \
    -H "Content-Type: application/json" \
    -d '{"language_from": "Chinese","language_to": "English","source_language": "我爱机器翻译。"}'

Following the validation of all aforementioned microservices, we are now prepared to construct a mega-service.

🚀 Launch the UI

Open this URL http://{host_ip}:5173 in your browser to access the frontend. project-screenshot project-screenshot