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
2.2 KiB
Python
65 lines
2.2 KiB
Python
"""SSE endpoint per live progress del training.
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GET /api/trainings/{id}/events
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Streamma eventi dal Redis stream `ml:train:{id}:events` via Server-Sent Events.
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Termina quando lo stato del training è terminale (succeeded/failed/cancelled).
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"""
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from __future__ import annotations
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import asyncio
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import json
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import uuid
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from fastapi import APIRouter, Depends, HTTPException
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from sse_starlette.sse import EventSourceResponse
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from core import db, redis_client
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from core.auth import require_auth
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router = APIRouter(prefix="/api/trainings", tags=["trainings-sse"])
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_TERMINAL = {"succeeded", "failed", "cancelled"}
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@router.get("/{training_id}/events")
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async def training_events(training_id: str, user=Depends(require_auth)):
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# verifica esistenza
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row = await db.fetchrow("SELECT status FROM trainings WHERE id = $1", uuid.UUID(training_id))
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if not row:
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raise HTTPException(404, "not found")
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stream_key = f"ml:train:{training_id}:events"
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status_key = f"ml:train:{training_id}"
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async def gen():
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last_id = "0-0"
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r = redis_client.client()
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while True:
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try:
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# XREAD block 5s per non tenere la connessione idle troppo a lungo
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resp = await r.xread({stream_key: last_id}, count=50, block=5000)
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except Exception as e:
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yield {"event": "error", "data": json.dumps({"error": str(e)})}
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await asyncio.sleep(1)
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continue
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if resp:
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for _stream, entries in resp:
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for entry_id, fields in entries:
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last_id = entry_id
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yield {"event": "message", "id": entry_id, "data": json.dumps(fields)}
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# controlla stato terminale
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state = await r.hget(status_key, "status")
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if not state:
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# fallback su db se redis scaduto
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db_row = await db.fetchrow(
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"SELECT status FROM trainings WHERE id = $1", uuid.UUID(training_id)
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)
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state = db_row["status"] if db_row else "unknown"
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if state in _TERMINAL:
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yield {"event": "end", "data": json.dumps({"status": state})}
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return
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return EventSourceResponse(gen())
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