1e130314d9
Signed-off-by: letonghan <letong.han@intel.com> Signed-off-by: root <root@a4bf019305c5.jf.intel.com> Co-authored-by: root <root@a4bf019305c5.jf.intel.com> Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>
146 lines
5.3 KiB
Markdown
146 lines
5.3 KiB
Markdown
# Build Mega Service of Translation on Xeon
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This document outlines the deployment process for a Translation 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.
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## 🚀 Apply Xeon Server on AWS
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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.
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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.
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### 1. Build LLM Image
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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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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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### 2. Build MegaService Docker Image
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To construct the Mega Service, we utilize the [GenAIComps](https://github.com/opea-project/GenAIComps.git) microservice pipeline within the `translation.py` Python script. Build MegaService Docker image via below command:
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```bash
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git clone https://github.com/opea-project/GenAIExamples
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cd GenAIExamples/Translation/
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docker build -t opea/translation:latest --build-arg https_proxy=$https_proxy --build-arg http_proxy=$http_proxy -f Dockerfile .
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```
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### 3. Build UI Docker Image
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Build frontend Docker image via below command:
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```bash
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cd GenAIExamples/Translation/ui
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docker build -t opea/translation-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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### 4. Build Nginx Docker Image
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```bash
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cd GenAIComps
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docker build -t opea/translation-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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1. `opea/llm-tgi:latest`
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2. `opea/translation:latest`
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3. `opea/translation-ui:latest`
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4. `opea/translation-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 | haoranxu/ALMA-13B |
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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=${your_nginx_port}
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```
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2. If you are in a proxy environment, also set the proxy-related environment variables:
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```bash
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export http_proxy="Your_HTTP_Proxy"
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export https_proxy="Your_HTTPs_Proxy"
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```
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3. Set up other environment variables:
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```bash
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cd ../../../
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source set_env.sh
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```
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### Start Microservice Docker Containers
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```bash
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docker compose up -d
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```
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### Validate Microservices
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1. TGI Service
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```bash
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curl http://${host_ip}:8008/generate \
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-X POST \
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-d '{"inputs":"What is Deep Learning?","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://${host_ip}:9000/v1/chat/completions \
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-X POST \
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-d '{"query":"Translate this from Chinese to English:\nChinese: 我爱机器翻译。\nEnglish:"}' \
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-H 'Content-Type: application/json'
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```
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3. MegaService
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```bash
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curl http://${host_ip}:8888/v1/translation -H "Content-Type: application/json" -d '{
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"language_from": "Chinese","language_to": "English","source_language": "我爱机器翻译。"}'
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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/translation \
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-H "Content-Type: application/json" \
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-d '{"language_from": "Chinese","language_to": "English","source_language": "我爱机器翻译。"}'
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```
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Following the validation of all aforementioned microservices, we are now prepared to construct a mega-service.
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## 🚀 Launch the UI
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Open this URL `http://{host_ip}:5173` in your browser to access the frontend.
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