Refactor folder to support different vendors (#743)
Signed-off-by: Xinyao Wang <xinyao.wang@intel.com> Signed-off-by: chensuyue <suyue.chen@intel.com>
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XinyaoWa
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CodeTrans/docker_compose/intel/cpu/xeon/README.md
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CodeTrans/docker_compose/intel/cpu/xeon/README.md
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# Build Mega Service of CodeTrans on Xeon
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This document outlines the deployment process for a CodeTrans 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 using microservices `llm`. We will publish the Docker images to Docker Hub soon, it will simplify the deployment process for this service.
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## 🚀 Create an AWS Xeon Instance
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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](https://console.aws.amazon.com/ec2/v2/home). AWS EC2 M7i, C7i, C7i-flex and M7i-flex instances are next-generation compute optimized instances powered by custom 4th Generation Intel Xeon Scalable processors (code named Sapphire Rapids). These instances are optimized for high-performance computing and demanding workloads.
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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.
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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.
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## 🚀 Build Docker Images
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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.
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### 1. Install GenAIComps from Source Code
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```bash
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git clone https://github.com/opea-project/GenAIComps.git
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cd GenAIComps
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```
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### 2. Build the LLM Docker Image
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```bash
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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 .
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```
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### 3. Build MegaService Docker Image
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```bash
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git clone https://github.com/opea-project/GenAIExamples.git
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cd GenAIExamples/CodeTrans
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docker build -t opea/codetrans:latest --build-arg https_proxy=$https_proxy --build-arg http_proxy=$http_proxy -f Dockerfile .
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```
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### 4. Build UI Docker Image
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```bash
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cd GenAIExamples/CodeTrans/ui
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docker build -t opea/codetrans-ui:latest --build-arg https_proxy=$https_proxy --build-arg http_proxy=$http_proxy -f ./docker/Dockerfile .
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```
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### 5. Build Nginx Docker Image
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```bash
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cd GenAIComps
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docker build -t opea/nginx:latest --build-arg https_proxy=$https_proxy --build-arg http_proxy=$http_proxy -f comps/nginx/Dockerfile .
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```
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Then run the command `docker images`, you will have the following Docker Images:
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- `opea/llm-tgi:latest`
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- `opea/codetrans:latest`
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- `opea/codetrans-ui:latest`
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- `opea/nginx:latest`
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## 🚀 Start Microservices
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### Required Models
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By default, the LLM model is set to a default value as listed below:
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| Service | Model |
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| ------- | ----------------------------- |
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| LLM | HuggingFaceH4/mistral-7b-grok |
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Change the `LLM_MODEL_ID` below for your needs.
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### Setup Environment Variables
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1. Set the required environment variables:
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```bash
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# Example: host_ip="192.168.1.1"
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export host_ip="External_Public_IP"
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# Example: no_proxy="localhost, 127.0.0.1, 192.168.1.1"
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export no_proxy="Your_No_Proxy"
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export HUGGINGFACEHUB_API_TOKEN="Your_Huggingface_API_Token"
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# Example: NGINX_PORT=80
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export NGINX_PORT=${your_nginx_port}
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```
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2. If you are in a proxy environment, also set the proxy-related environment variables:
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```bash
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export http_proxy="Your_HTTP_Proxy"
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export https_proxy="Your_HTTPs_Proxy"
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```
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3. Set up other environment variables:
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```bash
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source ../set_env.sh
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```
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### Start Microservice Docker Containers
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```bash
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cd GenAIExamples/CodeTrans/docker_compose/intel/cpu/xeon
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docker compose up -d
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```
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### Validate Microservices
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1. TGI Service
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```bash
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curl http://${host_ip}:8008/generate \
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-X POST \
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-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}}' \
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-H 'Content-Type: application/json'
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```
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2. LLM Microservice
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```bash
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curl http://${host_ip}:9000/v1/chat/completions\
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-X POST \
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-d '{"query":" ### System: Please translate the following Golang codes into Python codes. ### Original codes: '\'''\'''\''Golang \npackage main\n\nimport \"fmt\"\nfunc main() {\n fmt.Println(\"Hello, World!\");\n '\'''\'''\'' ### Translated codes:"}' \
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-H 'Content-Type: application/json'
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```
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3. MegaService
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```bash
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curl http://${host_ip}:7777/v1/codetrans \
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-H "Content-Type: application/json" \
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-d '{"language_from": "Golang","language_to": "Python","source_code": "package main\n\nimport \"fmt\"\nfunc main() {\n fmt.Println(\"Hello, World!\");\n}"}'
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```
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4. Nginx Service
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```bash
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curl http://${host_ip}:${NGINX_PORT}/v1/codetrans \
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-H "Content-Type: application/json" \
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-d '{"language_from": "Golang","language_to": "Python","source_code": "package main\n\nimport \"fmt\"\nfunc main() {\n fmt.Println(\"Hello, World!\");\n}"}'
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```
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## 🚀 Launch the UI
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### Launch with origin port
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Open this URL `http://{host_ip}:5173` in your browser to access the frontend.
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### Launch with Nginx
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If you want to launch the UI using Nginx, open this URL: `http://{host_ip}:{NGINX_PORT}` in your browser to access the frontend.
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Here is an example for summarizing a article.
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