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
91 lines
2.3 KiB
Python
91 lines
2.3 KiB
Python
"""ml-service — FastAPI entrypoint.
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Monta:
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/ → RedirectResponse
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/datasets /models /train /test /results → pagine Jinja
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/api/datasets /api/models /api/repos /api/trainings /api/tests /api/results → JSON
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/api/trainings/{id}/events → SSE
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/health → check
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/static/* → file statici
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"""
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from __future__ import annotations
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import logging
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from contextlib import asynccontextmanager
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from pathlib import Path
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from fastapi import FastAPI
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from fastapi.staticfiles import StaticFiles
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from core import db, minio_client, redis_client, worker
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logging.basicConfig(level=logging.INFO, format="%(asctime)s %(levelname)s %(name)s: %(message)s")
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log = logging.getLogger(__name__)
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STATIC_DIR = Path(__file__).resolve().parent / "static"
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@asynccontextmanager
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async def lifespan(app: FastAPI):
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log.info("ml-service starting")
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await db.init_pool()
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try:
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minio_client.ensure_bucket()
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except Exception as e:
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log.warning("minio bucket ensure failed: %s", e)
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worker.start_workers()
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yield
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log.info("ml-service stopping")
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await worker.stop_workers()
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await db.close_pool()
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await redis_client.close()
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app = FastAPI(title="MEB ML Service", lifespan=lifespan)
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# static
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app.mount("/static", StaticFiles(directory=str(STATIC_DIR)), name="static")
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@app.get("/health")
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async def health():
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pg_ok = True
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try:
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await db.fetchrow("SELECT 1")
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except Exception:
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pg_ok = False
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redis_ok = True
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try:
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await redis_client.client().ping()
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except Exception:
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redis_ok = False
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return {
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"status": "ok" if (pg_ok and redis_ok) else "degraded",
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"service": "ml",
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"postgres": "connected" if pg_ok else "disconnected",
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"redis": "connected" if redis_ok else "disconnected",
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"minio": "connected" if minio_client.check() else "disconnected",
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"version": "2.0.0",
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}
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from routers import ( # noqa: E402
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datasets,
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models,
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pages,
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repos,
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results,
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tests,
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trainings,
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trainings_stream,
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)
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app.include_router(pages.router)
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app.include_router(datasets.router)
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app.include_router(models.router)
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app.include_router(repos.router)
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app.include_router(trainings.router)
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app.include_router(trainings_stream.router)
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app.include_router(tests.router)
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app.include_router(results.router)
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