handson-ml3 Docker & Self-Hosted Deployment

GitHub

A series of Jupyter notebooks that walk you through the fundamentals of Machine Learning and Deep Learning in Python using Scikit-Learn, Keras and TensorFlow 2.

14,368 stars Jupyter Notebook 1 file · ~3,334 tokens
RAW Compose
1. Pull Image (DOCKERHUB) docker pull ageron/handson-ml3
2. Docker Run (1-Click Launch) docker run -d --name handson-ml3 -p 8888:8888 --restart always ageron/handson-ml3:latest
3. Podman Run podman run -d --name handson-ml3 -p 8888:8888 --restart always ageron/handson-ml3:latest
4. Skopeo Inspect (Zero-Pull Manifest) skopeo inspect docker://docker.io/ageron/handson-ml3:latest
5. ORAS Pull (OCI Artifact) oras pull ageron/handson-ml3:latest
6. Compose Deployment docker compose up -d
docker/docker-compose.yml
Download .yml 
version: "3"
services:
  handson-ml3:
    build:
      context: ../
      dockerfile: ./docker/Dockerfile #Dockerfile.gpu
      args:
        - username=devel
        - userid=1000
    container_name: handson-ml3
    image: ageron/handson-ml3:latest #latest-gpu
    restart: unless-stopped
    logging:
      driver: json-file
      options:
        max-size: 50m
    ports:
      - "8888:8888"
      - "6006:6006"
    volumes:
      - ../:/home/devel/handson-ml3
    command: /opt/conda/envs/homl3/bin/jupyter lab --ip='0.0.0.0' --port=8888 --no-browser
    #deploy:
    #  resources:
    #    reservations:
    #      devices:
    #      - capabilities: [gpu]