Build Mega Service of CodeTrans on Gaudi¶
This document outlines the deployment process for a CodeTrans application utilizing the GenAIComps microservice pipeline on Intel Gaudi 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.
🚀 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-tgi:latest --no-cache --build-arg https_proxy=$https_proxy --build-arg http_proxy=$http_proxy -f comps/llms/text-generation/tgi/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/nginx/Dockerfile .
Then run the command docker images
, you will have the following Docker Images:
opea/llm-tgi: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 |
HuggingFaceH4/mistral-7b-grok |
Change the LLM_MODEL_ID
below for your needs.
Setup Environment Variables¶
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=${your_nginx_port}
If you are in a proxy environment, also set the proxy-related environment variables:
export http_proxy="Your_HTTP_Proxy" export https_proxy="Your_HTTPs_Proxy"
Set up other environment variables:
cd GenAIExamples/CodeTrans/docker_compose source ./set_env.sh
Start Microservice Docker Containers¶
cd GenAIExamples/CodeTrans/docker_compose/intel/hpu/gaudi
docker compose up -d
Validate Microservices¶
TGI Service
curl http://${host_ip}:8008/generate \ -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'
LLM Microservice
curl http://${host_ip}:9000/v1/chat/completions\ -X POST \ -d '{"text":" ### System: Please translate the following Golang codes into Python codes. ### Original codes: '\'''\'''\''Golang \npackage main\n\nimport \"fmt\"\nfunc main() {\n fmt.Println(\"Hello, World!\");\n '\'''\'''\'' ### Translated codes:"}' \ -H 'Content-Type: application/json'
MegaService
curl http://${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}"}'
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.
Here is an example for summarizing a article.