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multilabel-news-classifier / docker-compose.yml

Solareva Taisia

chore(release): initial public snapshot

198ccb0 11 months ago

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services:
  # API Service
  api:
    build:
      context: .
      dockerfile: Dockerfile.api
    container_name: news-classifier-api
    ports:
      - "8000:8000"
    volumes:
      # Mount only the required artifacts (avoid overriding code inside the image)
      - ./models/distilmbert_lora_10k_v1.pt:/app/models/distilmbert_lora_10k_v1.pt:ro
      - ./config/thresholds.json:/app/config/thresholds.json:ro
      # Persist monitoring logs across restarts (optional but nice for demos)
      - ./monitoring:/app/monitoring
    environment:
      - PYTHONUNBUFFERED=1
      - MODEL_PATH=models/distilmbert_lora_10k_v1.pt
      - THRESHOLDS_PATH=config/thresholds.json
      - TOKENIZER_NAME=distilbert-base-multilingual-cased
    restart: unless-stopped
    healthcheck:
      test: ["CMD", "curl", "-f", "http://localhost:8000/health"]
      interval: 30s
      timeout: 10s
      retries: 3
      start_period: 40s
    networks:
      - nlp-network

  # Streamlit (multipage app)
  streamlit:
    build:
      context: .
      dockerfile: Dockerfile.streamlit
    container_name: news-classifier-streamlit
    ports:
      - "8501:8501"
    volumes:
      - .:/app
    working_dir: /app
    environment:
      - API_URL=http://api:8000
    command: >
      streamlit run streamlit_app.py
      --server.port=8501
      --server.address=0.0.0.0
    depends_on:
      - api
    networks:
      - nlp-network

  # Optional: MLflow tracking server
  mlflow:
    image: python:3.10-slim
    container_name: mlflow-server
    ports:
      - "5000:5000"
    volumes:
      - ./mlruns:/mlruns
      - ./requirements.txt:/requirements.txt
    working_dir: /app
    command: >
      sh -c "pip install mlflow && 
             mlflow server --host 0.0.0.0 --port 5000 --backend-store-uri file:///mlruns"
    environment:
      - MLFLOW_BACKEND_STORE_URI=file:///mlruns
    networks:
      - nlp-network
    profiles:
      - tracking

  # Optional: Jupyter notebook for development
  jupyter:
    build:
      context: .
      dockerfile: Dockerfile.dev
    container_name: jupyter-notebook
    ports:
      - "8888:8888"
    volumes:
      - .:/app
      - jupyter-data:/home/jovyan/work
    environment:
      - JUPYTER_ENABLE_LAB=yes
    command: >
      sh -c "pip install jupyter jupyterlab && 
             jupyter lab --ip=0.0.0.0 --port=8888 --no-browser --allow-root --NotebookApp.token=''"
    networks:
      - nlp-network
    profiles:
      - dev

networks:
  nlp-network:
    driver: bridge

volumes:
  jupyter-data:



	services:
	# API Service
	api:
	build:
	context: .
	dockerfile: Dockerfile.api
	container_name: news-classifier-api
	ports:
	- "8000:8000"
	volumes:
	# Mount only the required artifacts (avoid overriding code inside the image)
	- ./models/distilmbert_lora_10k_v1.pt:/app/models/distilmbert_lora_10k_v1.pt:ro
	- ./config/thresholds.json:/app/config/thresholds.json:ro
	# Persist monitoring logs across restarts (optional but nice for demos)
	- ./monitoring:/app/monitoring
	environment:
	- PYTHONUNBUFFERED=1
	- MODEL_PATH=models/distilmbert_lora_10k_v1.pt
	- THRESHOLDS_PATH=config/thresholds.json
	- TOKENIZER_NAME=distilbert-base-multilingual-cased
	restart: unless-stopped
	healthcheck:
	test: ["CMD", "curl", "-f", "http://localhost:8000/health"]
	interval: 30s
	timeout: 10s
	retries: 3
	start_period: 40s
	networks:
	- nlp-network

	# Streamlit (multipage app)
	streamlit:
	build:
	context: .
	dockerfile: Dockerfile.streamlit
	container_name: news-classifier-streamlit
	ports:
	- "8501:8501"
	volumes:
	- .:/app
	working_dir: /app
	environment:
	- API_URL=http://api:8000
	command: >
	streamlit run streamlit_app.py
	--server.port=8501
	--server.address=0.0.0.0
	depends_on:
	- api
	networks:
	- nlp-network

	# Optional: MLflow tracking server
	mlflow:
	image: python:3.10-slim
	container_name: mlflow-server
	ports:
	- "5000:5000"
	volumes:
	- ./mlruns:/mlruns
	- ./requirements.txt:/requirements.txt
	working_dir: /app
	command: >
	sh -c "pip install mlflow &&
	mlflow server --host 0.0.0.0 --port 5000 --backend-store-uri file:///mlruns"
	environment:
	- MLFLOW_BACKEND_STORE_URI=file:///mlruns
	networks:
	- nlp-network
	profiles:
	- tracking

	# Optional: Jupyter notebook for development
	jupyter:
	build:
	context: .
	dockerfile: Dockerfile.dev
	container_name: jupyter-notebook
	ports:
	- "8888:8888"
	volumes:
	- .:/app
	- jupyter-data:/home/jovyan/work
	environment:
	- JUPYTER_ENABLE_LAB=yes
	command: >
	sh -c "pip install jupyter jupyterlab &&
	jupyter lab --ip=0.0.0.0 --port=8888 --no-browser --allow-root --NotebookApp.token=''"
	networks:
	- nlp-network
	profiles:
	- dev

	networks:
	nlp-network:
	driver: bridge

	volumes:
	jupyter-data: