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FabledCurator/backend/app/api/gpu.py
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feat(gpu): re-process trigger to apply new crop detectors to the existing library (#1202)
The siglip/ccip backfills skip images that already have current-version regions,
so adding crop detectors only affected NEW images — the back-catalogue would
never be re-cropped. Add a reprocess trigger that resets every done/error job of
a task back to pending, so the agent re-runs the FULL pipeline (figure detection
+ CCIP + concept/panel crops) over the whole library under the current detectors.

- reprocess_gpu_jobs(task='ccip') task + POST /api/gpu/reprocess.
- gpu store reprocess() + GpuAgentCard "Re-process library (re-detect + re-crop)"
  button with a confirm (it's heavy).
- Test: a done job resets to pending (attempts cleared).

Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_01Ttrj5P7upUTueSfoJcxEqa
2026-06-30 16:09:37 -04:00

262 lines
10 KiB
Python

"""GPU-job API (#114): the HTTP surface the desktop agent pulls work from.
The agent stays HTTP-only — it leases jobs, fetches image pixels via the normal
FC image URLs, and submits embeddings/regions back, all over this API. Redis and
Postgres are never exposed. The agent endpoints are gated by a bearer token
(Authorization: Bearer <token>) stored in AppSetting; the admin endpoints
(token / backfill / status) ride the browser session like the rest of FC's
homelab admin.
"""
import secrets
from quart import Blueprint, jsonify, request
from sqlalchemy import func, select
from sqlalchemy.dialects.postgresql import insert as pg_insert
from ..extensions import get_session
from ..models import AppSetting, GpuJob, ImageRecord, MLSettings
from ..services.gallery_service import image_url
from ..services.ml.gpu_jobs import GpuJobService
from ..services.ml.regions import RegionService
gpu_bp = Blueprint("gpu", __name__, url_prefix="/api/gpu")
_TOKEN_KEY = "gpu_agent_token"
def _bearer() -> str | None:
h = request.headers.get("Authorization", "")
return h[7:].strip() if h.startswith("Bearer ") else None
async def _agent_authed(session) -> bool:
supplied = _bearer()
if not supplied:
return False
stored = (
await session.execute(
select(AppSetting.value).where(AppSetting.key == _TOKEN_KEY)
)
).scalar_one_or_none()
return stored is not None and secrets.compare_digest(supplied, stored)
# --- Admin (browser): token + backfill + status -------------------------
@gpu_bp.route("/token", methods=["GET"])
async def get_token():
async with get_session() as session:
tok = (
await session.execute(
select(AppSetting.value).where(AppSetting.key == _TOKEN_KEY)
)
).scalar_one_or_none()
return jsonify({"token": tok, "configured": tok is not None})
@gpu_bp.route("/token/rotate", methods=["POST"])
async def rotate_token():
token = secrets.token_urlsafe(32)
async with get_session() as session:
await session.execute(
pg_insert(AppSetting)
.values(key=_TOKEN_KEY, value=token)
.on_conflict_do_update(index_elements=["key"], set_={"value": token})
)
await session.commit()
return jsonify({"token": token})
@gpu_bp.route("/status", methods=["GET"])
async def status():
async with get_session() as session:
rows = (
await session.execute(
select(GpuJob.status, func.count()).group_by(GpuJob.status)
)
).all()
counts = dict(rows)
return jsonify({
"pending": counts.get("pending", 0),
"leased": counts.get("leased", 0),
"done": counts.get("done", 0),
"error": counts.get("error", 0),
})
@gpu_bp.route("/backfill", methods=["POST"])
async def backfill():
"""Enqueue a job for every image that doesn't already have one for `task`."""
body = await request.get_json(silent=True) or {}
task = str(body.get("task") or "ccip")
from ..tasks.ml import enqueue_gpu_backfill
r = enqueue_gpu_backfill.delay(task)
return jsonify({"celery_task_id": r.id, "task": task}), 202
@gpu_bp.route("/reprocess", methods=["POST"])
async def reprocess():
"""Reset every done/error job of `task` back to pending so the agent re-runs
the WHOLE library under the current pipeline (e.g. after adding crop
detectors). Heavy — the back-catalogue is otherwise skipped by the backfills."""
body = await request.get_json(silent=True) or {}
task = str(body.get("task") or "ccip")
from ..tasks.ml import reprocess_gpu_jobs
r = reprocess_gpu_jobs.delay(task)
return jsonify({"celery_task_id": r.id, "task": task}), 202
# --- Agent (bearer token): lease / submit / heartbeat / fail ------------
@gpu_bp.route("/jobs/lease", methods=["POST"])
async def lease():
body = await request.get_json(silent=True) or {}
agent_id = str(body.get("agent_id") or "agent")
try:
batch = min(max(int(body.get("batch_size", 8)), 1), 64)
except (TypeError, ValueError):
batch = 8
async with get_session() as session:
if not await _agent_authed(session):
return jsonify({"error": "unauthorized"}), 401
jobs = await GpuJobService(session).lease(agent_id, batch_size=batch)
ml = (
await session.execute(select(MLSettings).where(MLSettings.id == 1))
).scalar_one()
# image rows for url/mime in one shot
ids = [j.image_record_id for j in jobs]
imgs = {
i.id: i for i in (
await session.execute(
select(ImageRecord).where(ImageRecord.id.in_(ids))
)
).scalars()
} if ids else {}
await session.commit()
out = []
for j in jobs:
img = imgs.get(j.image_record_id)
if img is None:
continue
out.append({
"job_id": j.id,
"image_id": j.image_record_id,
"task": j.task,
"mime": img.mime,
"image_url": image_url(img.path),
# For video/animated: the agent samples at this cadence.
"frame_interval_seconds": ml.video_frame_interval_seconds,
"max_frames": ml.video_max_frames,
# The embedding model the agent must use for concept crops + the
# whole-image 'embed' task, so its vectors land in the SAME space
# the heads trained in. Server-announced FROM THE SETTING → the
# agent stays model-agnostic; an operator swap is a setting + a
# re-embed, never an agent change.
"embed_model_name": ml.embedder_model_name,
"embed_version": ml.embedder_model_version,
})
return jsonify({"jobs": out})
@gpu_bp.route("/jobs/heartbeat", methods=["POST"])
async def heartbeat():
body = await request.get_json(silent=True) or {}
agent_id = str(body.get("agent_id") or "agent")
job_ids = [int(x) for x in (body.get("job_ids") or [])]
async with get_session() as session:
if not await _agent_authed(session):
return jsonify({"error": "unauthorized"}), 401
n = await GpuJobService(session).heartbeat(agent_id, job_ids)
await session.commit()
return jsonify({"extended": n})
@gpu_bp.route("/jobs/submit", methods=["POST"])
async def submit():
"""Store a job's regions + close it. regions: [{kind, bbox:[x,y,w,h],
frame_time?, score?, *_version?, ccip_embedding?, siglip_embedding?}].
replace_kinds defaults to the kinds present in the submitted regions."""
body = await request.get_json(silent=True) or {}
agent_id = str(body.get("agent_id") or "agent")
job_id = body.get("job_id")
regions = body.get("regions") or []
if job_id is None:
return jsonify({"error": "job_id required"}), 400
kinds = body.get("replace_kinds") or sorted({r["kind"] for r in regions})
async with get_session() as session:
if not await _agent_authed(session):
return jsonify({"error": "unauthorized"}), 401
job = await session.get(GpuJob, int(job_id))
if job is None or job.status != "leased" or job.lease_token != agent_id:
return jsonify({"error": "lease_invalid"}), 409
if kinds:
await RegionService(session).replace_regions(
job.image_record_id, kinds, regions
)
await GpuJobService(session).complete(agent_id, int(job_id))
await session.commit()
return jsonify({"ok": True, "stored": len(regions)})
@gpu_bp.route("/jobs/submit_embedding", methods=["POST"])
async def submit_embedding():
"""Store a whole-image SigLIP embedding (the 'embed' task) on image_record +
close the job. Body: {agent_id, job_id, embedding:[...], embedding_version}.
This is how the GPU agent re-embeds the library under a new model (#1190) —
much faster than the CPU ml-worker at higher resolutions."""
body = await request.get_json(silent=True) or {}
agent_id = str(body.get("agent_id") or "agent")
job_id = body.get("job_id")
embedding = body.get("embedding")
version = body.get("embedding_version")
if job_id is None or not embedding or not version:
return jsonify({"error": "job_id, embedding, embedding_version required"}), 400
async with get_session() as session:
if not await _agent_authed(session):
return jsonify({"error": "unauthorized"}), 401
job = await session.get(GpuJob, int(job_id))
if job is None or job.status != "leased" or job.lease_token != agent_id:
return jsonify({"error": "lease_invalid"}), 409
img = await session.get(ImageRecord, job.image_record_id)
if img is not None:
img.siglip_embedding = embedding
img.siglip_model_version = version
await GpuJobService(session).complete(agent_id, int(job_id))
await session.commit()
return jsonify({"ok": True})
@gpu_bp.route("/jobs/fail", methods=["POST"])
async def fail():
body = await request.get_json(silent=True) or {}
agent_id = str(body.get("agent_id") or "agent")
job_id = body.get("job_id")
if job_id is None:
return jsonify({"error": "job_id required"}), 400
async with get_session() as session:
if not await _agent_authed(session):
return jsonify({"error": "unauthorized"}), 401
ok = await GpuJobService(session).fail(
agent_id, int(job_id), str(body.get("error") or "")
)
await session.commit()
return jsonify({"ok": ok})
@gpu_bp.route("/jobs/release", methods=["POST"])
async def release():
"""Graceful stop: the agent hands its still-leased jobs back to pending so
they're picked up immediately instead of waiting out the lease."""
body = await request.get_json(silent=True) or {}
agent_id = str(body.get("agent_id") or "agent")
job_ids = [int(x) for x in (body.get("job_ids") or [])]
async with get_session() as session:
if not await _agent_authed(session):
return jsonify({"error": "unauthorized"}), 401
n = await GpuJobService(session).release(agent_id, job_ids)
await session.commit()
return jsonify({"released": n})