28 auto evaluate skills after saving

[BenediktStuhrmann]

What does this PR do?

• extracted logic from predict and evaluate endpoints into evaluation-handler class. The evaluation-handler defines a method that allows to trigger prediction and evaluation at once, as well as separately
• Triggered evaluations are now saved in DB with reference to the user that started it
• predictions/metrics will only be computed if not already done before
• whenever a skill is created or updated via the skill-manager the evaluation will be triggerd on the default metric of the selected datasets

How to run locally: you need both the skill-manager and the evaluator to be running locally!

• in root folder: create env and activate it
• go into skill-manager and do make install and make install-dev
• specify EVALUATOR_API_URL in skill-managers .local.env file (http://square-core-evaluator-1:8081/api)
• go into evaluator and do make install and make install-dev
• specify SKILL_MANAGER_API_URL in evaluators .local.env file (http://square-core-skill-manager-1:8000/api)
• create docker-compose file in root folder

• in root folder: docker compose --env-file evaluator/.local.env up -d

  
  services:
  
  evaluator:
  image: ukpsquare/evaluator:latest
  build:
    context: evaluator
    target: build
  command: uvicorn evaluator.app.main:app --reload --host 0.0.0.0 --port 8081 --log-config logging.conf
  expose:
  - 8081
  ports:
    - 8081:8081
  env_file:
  - ./evaluator/.local.env
  environment:
    API_PREFIX: ""
    WEB_CONCURRENCY: "1"
    KEYCLOAK_BASE_URL: ""
    MODEL_API_URL: ""
    MONGO_HOST: mongodb
    MONGO_PORT: 27017
    VERIFY_ISSUER: "0"
    SQUARE_PRIVATE_KEY_FILE: /app/private_key.pem
    DATASET_DIR: /app/datasets/
    REDIS_HOST: redis
    REDIS_PORT: 6379
  depends_on:
  - mongodb
  - redis
  - rabbit
  volumes:
    - ./evaluator/evaluator:/app/evaluator
    - ./evaluator/datasets:/app/datasets/
    - ./evaluator/private_key.pem:/app/private_key.pem
  
  evaluation_worker:
  image: ukpsquare/evaluator:latest
  build:
    context: evaluator
    target: build
  command: celery -A evaluator.tasks worker --loglevel=info
  env_file:
  - ./evaluator/.local.env
  environment:
    API_PREFIX: ""
    WEB_CONCURRENCY: "1"
    KEYCLOAK_BASE_URL: ""
    MODEL_API_URL: ""
    MONGO_HOST: mongodb
    MONGO_PORT: 27017
    VERIFY_ISSUER: "0"
    SQUARE_PRIVATE_KEY_FILE: /app/private_key.pem
    DATASET_DIR: /app/datasets/
    REDIS_HOST: redis
    REDIS_PORT: 6379
  depends_on:
  - mongodb
  - redis
  - rabbit
  volumes:
    - ./evaluator/evaluator:/app/evaluator
    - ./evaluator//datasets:/app/datasets/
    - ./evaluator/private_key.pem:/app/private_key.pem
  
  mongodb:
  image: mongo:5.0.4
  restart: always
  volumes:
  - mongo-data:/data/db
  expose:
  - 27017
  ports:
  - 27017:27017
  env_file:
  - ./evaluator/.local.env
  
  redis:
  image: redis:latest
  ports:
  - 6379:6379
  env_file:
  - ./evaluator/.local.env
  command:
  - redis-server
  - --requirepass ${REDIS_PASSWORD}
  
  rabbit:
  hostname: rabbit
  image: rabbitmq:3-management
  ports:
    - 5672:5672
    - 15672:15672
  env_file:
    - ./evaluator/.local.env
  
  skill-manager:
  image: ukpsquare/skill-manager:latest
  build:
    context: skill-manager
    target: build
  command: uvicorn skill_manager.main:app --reload --host 0.0.0.0 --port 8000 --log-config logging.conf --workers 2
  ports:
  - 8000:8000
  expose:
  - 8000
  env_file:
  - ./skill-manager/.local.env
  environment:
    API_PREFIX: ""
    WEB_CONCURRENCY: "1"
    KEYCLOAK_BASE_URL: ""
    MODEL_API_URL: ""
    MONGO_HOST: mongodb
    MONGO_PORT: 27017
    VERIFY_ISSUER: "0"
    REDIS_HOST: redis
    REDIS_PORT: 6379
    SQUARE_PRIVATE_KEY_FILE: /app/private_key.pem
  depends_on:
  - mongodb
  - redis
  volumes:
    - ./skill-manager/skill_manager:/app/skill_manager
    - ./evaluator/private_key.pem:/app/private_key.pem

volumes: mongo-data: