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FabledCurator/backend/app/tasks/import_file.py
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feat(b3): ml-worker becomes optional — embed-only role, decoupled GPU coordination, cpu-embed switch
The ml-worker's ONLY processing role is now the CPU whole-image embed fallback
(tag_and_embed renamed embed_image — Camie tagging was retired #1189 and the
name kept implying otherwise; videos were already handled agent-style: frame
sampling + mean-pool). Detection/cropping/CCIP stay GPU-agent-only, and their
completion is judged per-pipeline: ccip by gpu_job rows, siglip by concept
regions at the current model version — never by image_record.siglip_embedding.
A CPU embed therefore can NEVER close crop work for the agent (regression test
pins this; only the whole-image 'embed' job, the same artifact, is satisfied).

Making removal actually safe (operator will drop the container):
- GPU-queue coordination (enqueue_gpu_backfill, recover_orphaned_gpu_jobs,
  reprocess_gpu_jobs) moved verbatim to tasks/gpu_queue.py on the maintenance
  quick lane — it lived on the 'ml' queue only by module colocation, which made
  the ml-worker a hard dependency of the whole agent pipeline.
- New ml_settings.cpu_embed_enabled (migration 0074, default ON so agent-less
  installs keep working): OFF stops the four import hooks queueing embed work
  nothing will consume and no-ops the manual backfill; switch lives on the
  renamed 'CPU embedding backfill' card.
- NB heads training / auto-apply still run on the ml image (sklearn) — a stack
  that removes the container gives those up too.

Deploy note: in-flight messages under the old task names are dropped by the
new workers; the 60s orphan sweep + hourly backfill re-fire under the new
names immediately.

Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_01CDgx8bQS5YrGRK76v8HUnM
2026-07-02 16:53:08 -04:00

262 lines
10 KiB
Python

"""import_media_file task: imports one file via the Importer service and
updates the ImportTask state machine + ImportBatch counters atomically.
Resilience contract (2026-05-24, operator-mandated): once a row has been
flipped to 'processing' inside this task, EVERY exit path MUST flip it
to a terminal state (complete / skipped / failed) or rely on Celery's
autoretry to attempt the work again. No exit path is allowed to leave
the row stuck in 'processing'. The `recover_interrupted_tasks`
maintenance sweep is the safety net (5 min threshold) for the case
where even the failure-marking commit can't be written.
"""
from datetime import UTC, datetime
from pathlib import Path
from celery.exceptions import SoftTimeLimitExceeded
from sqlalchemy import select, update
from sqlalchemy.exc import DBAPIError, OperationalError
from ..celery_app import celery
from ..models import ImportBatch, ImportSettings, ImportTask
from ..services.importer import Importer
from ..services.thumbnailer import Thumbnailer
from ._sync_engine import sync_session_factory as _sync_session_factory
IMAGES_ROOT = Path("/images")
def _map_result_to_status(result):
"""(ImportTask.status, should_requeue_ml_and_thumb) for an ImportResult.
'superseded' = the kept row's file/ML changed → complete + re-derive.
'attached' = a non-art file preserved → complete, no ML/thumb.
'refreshed' = deep scan refreshed sidecar/phash on an existing row →
complete, no ML/thumb re-derive (file/pixels unchanged)."""
if result.status in ("imported", "superseded"):
return ("complete", True)
if result.status in ("attached", "refreshed"):
return ("complete", False)
if result.status == "skipped":
return ("skipped", False)
return ("failed", False)
def _mark_failed(session, task, error_msg: str) -> None:
"""Best-effort flip of a 'processing' row to 'failed' + batch counter
increment. Wrapped in its own try because if the DB is what just
broke, this commit will also fail — that's why the maintenance sweep
exists as a backstop."""
try:
task.status = "failed"
task.error = error_msg
task.finished_at = datetime.now(UTC)
session.add(task)
session.execute(
update(ImportBatch)
.where(ImportBatch.id == task.batch_id)
.values(failed=ImportBatch.failed + 1)
)
session.commit()
except Exception: # noqa: BLE001 — best-effort, sweep catches the rest
try:
session.rollback()
except Exception: # noqa: BLE001
pass
def _run_import_task(import_task_id: int) -> dict:
"""Shared body for import_media_file + import_archive_file. The two
tasks differ ONLY in their Celery time limits (a single media file
is sub-second; an archive runs the full per-member pipeline inline
for every member and can take many minutes). Both flip the row to
'processing', dispatch to `_do_import`, and honor the
flip-to-terminal resilience contract.
"""
SessionLocal = _sync_session_factory()
with SessionLocal() as session:
task = session.get(ImportTask, import_task_id)
if task is None:
return {"status": "missing", "task_id": import_task_id}
task.status = "processing"
task.started_at = datetime.now(UTC)
session.add(task)
session.commit()
try:
return _do_import(session, task, import_task_id)
except SoftTimeLimitExceeded:
_mark_failed(session, task, "soft_time_limit exceeded")
raise
except (OperationalError, DBAPIError, OSError):
# Retryable per the decorator; do NOT mark failed (let
# autoretry have a clean go at it). If autoretry exhausts,
# the row stays 'processing' and the maintenance sweep
# flips it.
raise
except Exception as exc: # noqa: BLE001 — pipeline crash, mark + re-raise
_mark_failed(session, task, f"{type(exc).__name__}: {exc}")
raise
@celery.task(
name="backend.app.tasks.import_file.import_media_file",
bind=True,
autoretry_for=(OperationalError, DBAPIError, OSError),
retry_backoff=5,
retry_backoff_max=60,
retry_jitter=True,
max_retries=3,
soft_time_limit=300,
time_limit=360,
)
def import_media_file(self, import_task_id: int) -> dict:
"""Import ONE media file (or non-media → PostAttachment). Sub-second
for the common case; the tight 5-min soft limit keeps a genuinely
stuck single-file import detectable fast.
Decorator notes:
- autoretry_for: transient DB / filesystem errors retry with
exponential backoff (5s base, jitter, max 3 attempts). On final
give-up the task raises and acks_late=True (set globally on the
Celery app) does NOT redeliver — the recovery sweep catches the
row instead.
- soft_time_limit (300s) raises SoftTimeLimitExceeded in-process
so the task can mark its row failed before being killed.
- time_limit (360s) is the hard SIGKILL cap.
"""
return _run_import_task(import_task_id)
@celery.task(
name="backend.app.tasks.import_file.import_archive_file",
bind=True,
autoretry_for=(OperationalError, DBAPIError, OSError),
retry_backoff=5,
retry_backoff_max=60,
retry_jitter=True,
max_retries=3,
# Archives run the full per-member pipeline (sha256 + pHash + dedup
# query + copy + provenance) for EVERY media member inline, under a
# single task budget. A multi-hundred-member archive blows the
# 5-min media limit. soft=30min / hard=35min sizes for a large
# archive. Operator-flagged 2026-05-28 (target 1645019 hit the old
# shared 300s soft limit). The recovery sweep gives this task its
# own 40-min threshold via maintenance.TASK_STUCK_THRESHOLD_MINUTES
# so it isn't preempted while legitimately grinding through members.
soft_time_limit=1800,
time_limit=2100,
)
def import_archive_file(self, import_task_id: int) -> dict:
"""Import an archive: extract + run the per-member media pipeline for
every member inline, then preserve the archive as a PostAttachment.
Same body as import_media_file (dispatch is by file kind inside
Importer.import_one); split out purely for the larger time budget."""
return _run_import_task(import_task_id)
def enqueue_import(task_id: int, task_type: str) -> None:
"""Route an ImportTask to the right Celery task by its task_type.
Single source of truth for the media-vs-archive dispatch so the
scan, retry, and recovery-requeue paths stay in sync."""
if task_type == "archive":
import_archive_file.delay(task_id)
else:
import_media_file.delay(task_id)
def _do_import(session, task, import_task_id: int) -> dict:
"""Actual work, called from inside the resilience wrapper."""
settings = ImportSettings.load_sync(session)
import_root = Path(settings.import_scan_path)
batch = session.get(ImportBatch, task.batch_id)
deep = bool(batch and batch.scan_mode == "deep")
importer = Importer(
session=session,
images_root=IMAGES_ROOT,
import_root=import_root,
thumbnailer=Thumbnailer(images_root=IMAGES_ROOT),
settings=settings,
deep=deep,
)
result = importer.import_one(Path(task.source_path))
if result.status in ("imported", "superseded"):
task.status = "complete"
task.result_image_id = result.image_id
counter_col_name = "imported"
counter_col = ImportBatch.imported
elif result.status == "refreshed":
# Deep-scan rederive: existing row got phash/artist/sidecar
# refreshed. Task is complete (no further work), but counted in
# `refreshed` not `imported` so the UI can surface the actual
# work done. operator-flagged 2026-05-25.
task.status = "complete"
task.result_image_id = result.image_id
counter_col_name = "refreshed"
counter_col = ImportBatch.refreshed
elif result.status == "attached":
task.status = "complete"
counter_col_name = "attachments"
counter_col = ImportBatch.attachments
elif result.status == "skipped":
task.status = "skipped"
task.error = (
f"{result.skip_reason.value}: {result.error}"
if result.skip_reason
else result.error
)
task.result_image_id = result.image_id
counter_col_name = "skipped"
counter_col = ImportBatch.skipped
else:
task.status = "failed"
task.error = result.error
counter_col_name = "failed"
counter_col = ImportBatch.failed
task.finished_at = datetime.now(UTC)
session.execute(
update(ImportBatch)
.where(ImportBatch.id == task.batch_id)
.values({counter_col_name: counter_col + 1})
)
session.add(task)
session.commit()
# Enqueue thumbnail + ML for newly imported AND superseded images
# (a superseded row has cleared ML + no thumbnail).
if result.status in ("imported", "superseded"):
from .ml import cpu_embed_enabled, embed_image
from .thumbnail import generate_thumbnail
do_embed = cpu_embed_enabled()
ids = list(result.member_image_ids)
if result.image_id is not None and result.image_id not in ids:
ids.append(result.image_id)
for img_id in ids:
generate_thumbnail.delay(img_id)
if do_embed:
embed_image.delay(img_id)
# If this was the last task in the batch, mark the batch complete.
remaining = session.execute(
select(ImportTask.id)
.where(ImportTask.batch_id == task.batch_id)
.where(ImportTask.status.in_(["pending", "queued", "processing"]))
.limit(1)
).scalar_one_or_none()
if remaining is None:
session.execute(
update(ImportBatch)
.where(ImportBatch.id == task.batch_id)
.values(
status="complete",
finished_at=datetime.now(UTC),
)
)
session.commit()
return {"task_id": import_task_id, "status": task.status}