Build Mega Service of CodeTrans on Xeon

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

The default pipeline deploys with vLLM as the LLM serving component. It also provides options of using TGI backend for LLM microservice, please refer to start-microservice-docker-containers section in this page.

🚀 Create an AWS Xeon Instance

To run the example on a AWS Xeon instance, start by creating an AWS account if you don’t have one already. Then, get started with the EC2 Console. AWS EC2 M7i, C7i, C7i-flex and M7i-flex are Intel Xeon Scalable processor instances suitable for the task. (code named Sapphire Rapids).

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.

🚀 Build Docker Images

First of all, you need to build Docker Images locally and install the python package of it. This step can be ignored after the Docker images published to Docker hub.

1. Build the LLM Docker Image

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

2. Build MegaService Docker Image

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

3. Build UI Docker Image

cd GenAIExamples/CodeTrans/ui
docker build -t opea/codetrans-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/third_parties/nginx/src/Dockerfile .

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

  • opea/llm-textgen:latest

  • opea/codetrans:latest

  • opea/codetrans-ui:latest

  • opea/nginx:latest

🚀 Start Microservices

Required Models

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

Service

Model

LLM

mistralai/Mistral-7B-Instruct-v0.3

Change the LLM_MODEL_ID below for your needs.

For users in China who are unable to download models directly from Huggingface, you can use ModelScope or a Huggingface mirror to download models. The vLLM/TGI can load the models either online or offline as described below:

  1. Online

    export HF_TOKEN=${your_hf_token}
export HF_ENDPOINT="https://hf-mirror.com"
model_name="mistralai/Mistral-7B-Instruct-v0.3"
# Start vLLM LLM Service
docker run -p 8008:80 -v ./data:/root/.cache/huggingface/hub --name vllm-service -e HF_ENDPOINT=$HF_ENDPOINT -e http_proxy=$http_proxy -e https_proxy=$https_proxy --shm-size 128g opea/vllm:latest --model $model_name --host 0.0.0.0 --port 80
# Start TGI LLM Service
docker run -p 8008:80 -v ./data:/data --name tgi-service -e HF_ENDPOINT=$HF_ENDPOINT -e http_proxy=$http_proxy -e https_proxy=$https_proxy --shm-size 1g ghcr.io/huggingface/text-generation-inference:2.4.0-intel-cpu --model-id $model_name

  2. Offline

    • Search your model name in ModelScope. For example, check this page for model mistralai/Mistral-7B-Instruct-v0.3.

    • Click on Download this model button, and choose one way to download the model to your local path /path/to/model.

    • Run the following command to start the LLM service.

      export HF_TOKEN=${your_hf_token}
export model_path="/path/to/model"
# Start vLLM LLM Service
docker run -p 8008:80 -v $model_path:/root/.cache/huggingface/hub --name vllm-service --shm-size 128g opea/vllm:latest --model /root/.cache/huggingface/hub --host 0.0.0.0 --port 80
# Start TGI LLM Service
docker run -p 8008:80 -v $model_path:/data --name tgi-service --shm-size 1g ghcr.io/huggingface/text-generation-inference:2.4.0-intel-cpu --model-id /data

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 GenAIExamples/CodeTrans/docker_compose
source ./set_env.sh

Start Microservice Docker Containers

cd GenAIExamples/CodeTrans/docker_compose/intel/cpu/xeon

If use vLLM as the LLM serving backend.

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

  1. LLM backend Service

    In the first startup, this service will take more time to download, load and warm up the model. After it’s finished, the service will be ready.

    Try the command below to check whether the LLM serving is ready.

    # vLLM service
docker logs codetrans-xeon-vllm-service 2>&1 | grep complete
# If the service is ready, you will get the response like below.
INFO:     Application startup complete.

    # TGI service
docker logs codetrans-xeon-tgi-service | grep Connected
# If the service is ready, you will get the response like below.
2024-09-03T02:47:53.402023Z  INFO text_generation_router::server: router/src/server.rs:2311: Connected

    Then try the cURL command below to validate services.

    # either vLLM or TGI service
curl http://${host_ip}:8008/v1/chat/completions \
  -X POST \
  -d '{"inputs":"    ### System: Please translate the following Golang codes into  Python codes.    ### Original codes:    '\'''\'''\''Golang    \npackage main\n\nimport \"fmt\"\nfunc main() {\n    fmt.Println(\"Hello, World!\");\n    '\'''\'''\''    ### Translated codes:","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":"    ### System: Please translate the following Golang codes into  Python codes.    ### Original codes:    '\'''\'''\''Golang    \npackage main\n\nimport \"fmt\"\nfunc main() {\n    fmt.Println(\"Hello, World!\");\n    '\'''\'''\''    ### Translated codes:"}' \
  -H 'Content-Type: application/json'

  3. MegaService

    curl http://${host_ip}:7777/v1/codetrans \
    -H "Content-Type: application/json" \
    -d '{"language_from": "Golang","language_to": "Python","source_code": "package main\n\nimport \"fmt\"\nfunc main() {\n    fmt.Println(\"Hello, World!\");\n}"}'

  4. Nginx Service

    curl http://${host_ip}:${NGINX_PORT}/v1/codetrans \
    -H "Content-Type: application/json" \
    -d '{"language_from": "Golang","language_to": "Python","source_code": "package main\n\nimport \"fmt\"\nfunc main() {\n    fmt.Println(\"Hello, World!\");\n}"}'

🚀 Launch the UI

Launch with origin port

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

Launch with Nginx

If you want to launch the UI using Nginx, open this URL: http://{host_ip}:{NGINX_PORT} in your browser to access the frontend.

image

Here is an example for summarizing a article.

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