0ce879aa44
- Implemented HTML pages for datasets, models, training, testing, and results. - Created API endpoints for managing repositories, results, tests, and training sessions. - Added functionality for streaming training progress via Server-Sent Events (SSE). - Introduced a Dockerfile for the ML runner with necessary dependencies. - Developed an SDK for user code execution within the runner container. - Enhanced CSS styles for improved UI layout and navigation. - Established a layout template for consistent HTML structure across pages. - Added JavaScript for dynamic interactions on the models page. - Implemented WebSocket handling for real-time communication with kiosk devices and controllers. - Implemented model registration and management API at /api/models - Added Gitea proxy API for repository interactions at /api/repos - Created results API for listing and comparing training results at /api/results - Developed training management API for enqueueing and retrieving training jobs at /api/trainings - Introduced SSE endpoint for live training progress updates - Added HTML pages for models, datasets, and training management - Created a Dockerfile for the ML runner with necessary dependencies - Developed SDK for user code execution within the runner container - Enhanced CSS styles for improved UI/UX - Implemented WebSocket communication for real-time device and controller interactions in the kiosk system
65 lines
2.8 KiB
Python
65 lines
2.8 KiB
Python
"""Configurazione centralizzata del servizio ML, letta da env."""
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from __future__ import annotations
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import os
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from dataclasses import dataclass
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def _b(name: str, default: bool = False) -> bool:
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return os.environ.get(name, str(default)).lower() in ("1", "true", "yes", "on")
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@dataclass(frozen=True)
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class Settings:
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# Postgres (db "ml")
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pg_host: str = os.environ.get("PG_HOST", "meb-postgres")
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pg_port: int = int(os.environ.get("PG_PORT", "5432"))
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pg_user: str = os.environ.get("DB_USER", "meb")
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pg_password: str = os.environ.get("DB_PASSWORD", "meb")
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pg_db: str = os.environ.get("ML_DB", "ml")
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# Redis
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redis_host: str = os.environ.get("REDIS_HOST", "meb-redis")
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redis_port: int = int(os.environ.get("REDIS_PORT", "6379"))
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# MinIO (bucket unico)
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minio_endpoint: str = os.environ.get("MINIO_ENDPOINT", "minio")
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minio_port: int = int(os.environ.get("MINIO_PORT", "9000"))
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minio_use_ssl: bool = _b("MINIO_USE_SSL", False)
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minio_access_key: str = os.environ.get("MINIO_ACCESS_KEY", "")
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minio_secret_key: str = os.environ.get("MINIO_SECRET_KEY", "")
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minio_bucket: str = os.environ.get("MINIO_BUCKET", "ml")
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# InfluxDB — accetta sia INFLUX_* che INFLX_* per allinearsi alle var già
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# usate dagli altri servizi (realtime, api) senza dover duplicare la config.
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influx_url: str = os.environ.get("INFLUX_URL") or os.environ.get("INFLX_URL", "http://meb-influx:8086")
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influx_token: str = os.environ.get("INFLUX_TOKEN") or os.environ.get("INFLX_TOKEN", "")
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influx_org: str = os.environ.get("INFLUX_ORG") or os.environ.get("INFLX_ORG", "meb")
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# Bucket dedicato alle metriche di training/test ML, separato dai logs e
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# dai dati meteo. Sovrascrivibile via INFLUX_BUCKET o ML_INFLUX_BUCKET.
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influx_bucket: str = os.environ.get("ML_INFLUX_BUCKET") or os.environ.get("INFLUX_BUCKET", "ml_metrics")
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# Gitea (installato esternamente)
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gitea_url: str = os.environ.get("GITEA_URL", "")
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gitea_token: str = os.environ.get("GITEA_TOKEN", "")
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# API service (per jobs/queue/pageconnections)
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api_url: str = os.environ.get("API_URL", "http://api:3003")
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internal_api_key: str = os.environ.get("INTERNAL_API_KEY", "")
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# Auth (condiviso)
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jwt_secret: str = os.environ.get("JWT_SECRET", "")
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auth_login_url: str = os.environ.get("AUTH_LOGIN_URL", "https://auth.mebboat.it/login")
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# Esecuzione training
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train_concurrency: int = int(os.environ.get("ML_TRAIN_CONCURRENCY", "1"))
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runner_image: str = os.environ.get("ML_RUNNER_IMAGE", "meb-ml-runner:latest")
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runner_tmp_dir: str = os.environ.get("ML_RUNNER_TMP", "/var/ml/tmp")
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gitcache_dir: str = os.environ.get("ML_GITCACHE_DIR", "/var/ml/gitcache")
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# Limiti runtime
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max_upload_mb: int = int(os.environ.get("ML_MAX_UPLOAD_MB", "500"))
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settings = Settings()
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