llm-twin-course Docker & Self-Hosted Deployment

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๐—Ÿ๐—ฒ๐—ฎ๐—ฟ๐—ป for ๐—ณ๐—ฟ๐—ฒ๐—ฒ how to ๐—ฏ๐˜‚๐—ถ๐—น๐—ฑ an end-to-end ๐—ฝ๐—ฟ๐—ผ๐—ฑ๐˜‚๐—ฐ๐˜๐—ถ๐—ผ๐—ป-๐—ฟ๐—ฒ๐—ฎ๐—ฑ๐˜† ๐—Ÿ๐—Ÿ๐—  & ๐—ฅ๐—”๐—š ๐˜€๐˜†๐˜€๐˜๐—ฒ๐—บ using ๐—Ÿ๐—Ÿ๐— ๐—ข๐—ฝ๐˜€ best practices: ~...

4,392 stars Python Python 10 files ยท ~15,112 tokens #aws#bytewax#comet-ml#course#docker#generative-ai#infrastructure-as-code#large-language-models
RAW Compose
1. Pull Image (DOCKERHUB) docker pull redis/redis-stack
2. Docker Run (1-Click Launch) docker run -d --name llm-twin-course -p 8080:8080 --restart always redis/redis-stack:latest
3. Podman Run podman run -d --name llm-twin-course -p 8080:8080 --restart always redis/redis-stack:latest
4. Skopeo Inspect (Zero-Pull Manifest) skopeo inspect docker://docker.io/redis/redis-stack:latest
5. ORAS Pull (OCI Artifact) oras pull redis/redis-stack:latest
6. 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: