llm-twin-course

GitHub

🤖 𝗟𝗲𝗮𝗿𝗻 for 𝗳𝗿𝗲𝗲 how to 𝗯𝘂𝗶𝗹𝗱 an end-to-end 𝗽𝗿𝗼𝗱𝘂𝗰𝘁𝗶𝗼𝗻-𝗿𝗲𝗮𝗱𝘆 𝗟𝗟𝗠 & 𝗥𝗔𝗚 𝘀𝘆𝘀𝘁𝗲𝗺 using 𝗟𝗟𝗠𝗢𝗽𝘀 best practices: ~ 𝘴𝘰𝘶𝘳𝘤𝘦 𝘤𝘰𝘥𝘦 + 12 𝘩𝘢𝘯𝘥𝘴-𝘰𝘯 𝘭𝘦𝘴𝘴𝘰𝘯𝘴

4,333 stars Python #aws#bytewax#comet-ml#course#docker#generative-ai#infrastructure-as-code#large-language-models
RAW Compose
1. Pull Image docker pull redis/redis-stack
2. 1-Click Launch Command docker run -d -p 8080:8080 --name llm-twin-course redis/redis-stack:latest
3. Compose Deployment docker compose up -d
src/bonus_superlinked_rag/server/compose.yaml
Download .yml 
version: "3"

services:
  poller:
    build:
      context: runner
      dockerfile: poller/Dockerfile
    mem_limit: 256m
    cpus: '0.5'
    volumes:
      - ./cache:/code/data
      - ./config:/code/config
      - ./src:/src

  executor:
    depends_on:
      - poller
      - redis
    build:
      context: runner
      dockerfile: executor/Dockerfile
    volumes:
      - ./cache:/code/data
    environment:
      - APP_MODULE_PATH=data.src.app
    ports:
      - 8080:8080
    env_file:
      - runner/executor/.env

  redis:
    image: redis/redis-stack:latest
    ports:
      - "6379:6379"
      - "8001:8001"
    volumes:
      - redis-data:/data

volumes:
  redis-data: