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
90 lines
2.8 KiB
Python
90 lines
2.8 KiB
Python
"""API /api/results — lista trainings/tests + compare multi-training."""
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from __future__ import annotations
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import uuid
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from typing import Optional
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from fastapi import APIRouter, Depends, HTTPException, Query
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from core import db, influx_client
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from core.auth import require_auth
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from core.config import settings
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router = APIRouter(prefix="/api/results", tags=["results"])
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def _row(r):
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if r is None:
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return None
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d = dict(r)
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for k in ("queued_at", "started_at", "finished_at", "started_at", "ended_at"):
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if d.get(k) is not None and hasattr(d[k], "isoformat"):
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d[k] = d[k].isoformat()
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return d
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@router.get("")
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async def list_results(
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model_id: Optional[str] = Query(None),
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user=Depends(require_auth),
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):
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where = []
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args: list = []
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if model_id:
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args.append(uuid.UUID(model_id))
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where.append(f"model_id = ${len(args)}")
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sql = "SELECT * FROM trainings"
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if where:
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sql += " WHERE " + " AND ".join(where)
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sql += " ORDER BY finished_at DESC NULLS LAST, queued_at DESC LIMIT 200"
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rows = await db.fetch(sql, *args)
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return {"count": len(rows), "trainings": [_row(r) for r in rows]}
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@router.get("/{training_id}")
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async def get_result(training_id: str, user=Depends(require_auth)):
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row = await db.fetchrow("SELECT * 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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# timeseries via Influx: loss per iter + cpu/mem
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flux = (
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f'from(bucket:"{settings.influx_bucket}") '
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f'|> range(start:-90d) '
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f'|> filter(fn: (r) => r._measurement == "ml_training" and r.training_id == "{training_id}")'
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)
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try:
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ts = await influx_client.query_flux(flux)
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except Exception:
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ts = []
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return {"training": _row(row), "timeseries": ts}
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@router.get("/compare")
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async def compare(
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trainings: str = Query(..., description="comma-separated training IDs"),
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user=Depends(require_auth),
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):
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ids = [s.strip() for s in trainings.split(",") if s.strip()]
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if len(ids) < 2:
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raise HTTPException(400, "at least 2 training IDs required")
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out = []
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for tid in ids:
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try:
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tid_uuid = uuid.UUID(tid)
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except ValueError:
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continue
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row = await db.fetchrow("SELECT * FROM trainings WHERE id = $1", tid_uuid)
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if not row:
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continue
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flux = (
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f'from(bucket:"{settings.influx_bucket}") '
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f'|> range(start:-90d) '
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f'|> filter(fn: (r) => r._measurement == "ml_training" and r.training_id == "{tid}")'
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)
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try:
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ts = await influx_client.query_flux(flux)
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except Exception:
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ts = []
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out.append({"training": _row(row), "timeseries": ts})
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return {"results": out}
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