Files
OLD-server-architecture/ml/routers/trainings.py
Giuseppe Raffa 0ce879aa44 feat: Add new API endpoints and HTML pages for ML model management
- 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
2026-04-28 09:24:38 +02:00

130 lines
4.2 KiB
Python

"""API /api/trainings — enqueue, list, get, artifacts."""
from __future__ import annotations
import json
import uuid
from typing import Optional
from fastapi import APIRouter, Depends, HTTPException, Query
from core import db, minio_client, redis_client, api_client
from core.auth import require_auth
router = APIRouter(prefix="/api/trainings", tags=["trainings"])
def _row(r) -> Optional[dict]:
if r is None:
return None
d = dict(r)
for k in ("queued_at", "started_at", "finished_at"):
if d.get(k) is not None and hasattr(d[k], "isoformat"):
d[k] = d[k].isoformat()
return d
@router.get("")
async def list_trainings(
model_id: Optional[str] = Query(None),
status: Optional[str] = Query(None),
limit: int = Query(100, le=500),
user=Depends(require_auth),
):
where = []
args: list = []
if model_id:
args.append(uuid.UUID(model_id))
where.append(f"model_id = ${len(args)}")
if status:
args.append(status)
where.append(f"status = ${len(args)}")
sql = "SELECT * FROM trainings"
if where:
sql += " WHERE " + " AND ".join(where)
args.append(limit)
sql += f" ORDER BY queued_at DESC LIMIT ${len(args)}"
rows = await db.fetch(sql, *args)
return {"count": len(rows), "trainings": [_row(r) for r in rows]}
@router.post("", status_code=202)
async def enqueue_training(body: dict, user=Depends(require_auth)):
for k in ("model_id", "version", "patch", "dataset_id"):
if not body.get(k):
raise HTTPException(400, f"missing field: {k}")
model_row = await db.fetchrow("SELECT * FROM models WHERE id = $1", uuid.UUID(body["model_id"]))
if not model_row:
raise HTTPException(404, "model not found")
ds_row = await db.fetchrow("SELECT id FROM datasets WHERE id = $1", uuid.UUID(body["dataset_id"]))
if not ds_row:
raise HTTPException(404, "dataset not found")
try:
training_row = await db.fetchrow(
"""
INSERT INTO trainings (model_id, version, patch, dataset_id, started_by, status)
VALUES ($1,$2,$3,$4,$5,'queued')
RETURNING *
""",
uuid.UUID(body["model_id"]),
body["version"],
body["patch"],
uuid.UUID(body["dataset_id"]),
user.get("username") or "unknown",
)
except Exception as e:
raise HTTPException(409, f"training already exists or invalid: {e}")
training_id = str(training_row["id"])
# crea job lato api-service (cross-service registry)
try:
await api_client.create_job(
"train",
created_by=user.get("username") or "unknown",
payload={
"training_id": training_id,
"model_id": body["model_id"],
"version": body["version"],
"patch": body["patch"],
"dataset_id": body["dataset_id"],
},
)
except Exception as e:
# non-fatale: il worker locale può comunque procedere; logghiamo e continuiamo
import logging
logging.warning("create_job failed: %s", e)
# enqueue in Redis (il worker locale lo raccoglie)
await redis_client.client().lpush("ml:queue:train", training_id)
await redis_client.client().hset(
f"ml:train:{training_id}",
mapping={"status": "queued", "progress": "0", "message": "queued"},
)
await redis_client.client().expire(f"ml:train:{training_id}", 48 * 3600)
return _row(training_row)
@router.get("/{training_id}")
async def get_training(training_id: str, user=Depends(require_auth)):
row = await db.fetchrow("SELECT * FROM trainings WHERE id = $1", uuid.UUID(training_id))
if not row:
raise HTTPException(404, "not found")
return _row(row)
@router.get("/{training_id}/artifacts")
async def list_artifacts(training_id: str, user=Depends(require_auth)):
row = await db.fetchrow(
"SELECT artifacts_prefix FROM trainings WHERE id = $1", uuid.UUID(training_id)
)
if not row or not row["artifacts_prefix"]:
raise HTTPException(404, "no artifacts")
objs = minio_client.list_prefix(row["artifacts_prefix"] + "/")
for o in objs:
o["url"] = minio_client.presigned_get(o["name"], 3600)
return objs