docker-compose.yml

version: "3.8"

services:

  mlflow:
    build:
      context: .
      dockerfile: train/Dockerfile
      args:
        - MLFLOW_HOST=${MLFLOW_HOST}
        - ARTIFACT_STORE=${ARTIFACT_STORE}
        - AWS_SECRET_ACCESS_KEY=${AWS_SECRET_ACCESS_KEY}
        - AWS_ACCESS_KEY_ID=${AWS_ACCESS_KEY_ID}
    restart: unless-stopped
    container_name: mlflow
    image: language_identification_mlflow
    ports:
      - "5000:5000"
    volumes:
      - ./mlruns:/opt/mlflow/mlruns
      - ./input:/opt/mlflow/input

  serve:
    build:
      context: .
      dockerfile: serve/Dockerfile
      args:
          - ARTIFACT_STORE=${ARTIFACT_STORE}
          - AWS_SECRET_ACCESS_KEY=${AWS_SECRET_ACCESS_KEY}
          - AWS_ACCESS_KEY_ID=${AWS_ACCESS_KEY_ID}
          - LOCAL_MODEL_STORAGE=${LOCAL_MODEL_STORAGE}
    restart: unless-stopped
    container_name: serve
    image: language_identification_serve
    ports:
      - "8080:8080"

  prometheus:
    image: prom/prometheus:latest
    restart: unless-stopped
    container_name: prometheus
    ports:
      - "9090:9090"
    volumes:
      - ./monitoring/prometheus.yml:/etc/prometheus/prometheus.yml
    command:
      - '--config.file=/etc/prometheus/prometheus.yml'

  grafana:
    image: grafana/grafana:latest
    restart: unless-stopped
    user: "472"
    container_name: grafana
    depends_on:
      - prometheus
    ports:
      - "3000:3000"
    volumes:
      - ./monitoring/datasource.yml:/etc/grafana/provisioning/datasource.yml
    env_file:
      - ./monitoring/config.monitoring