chore: sync dev to main (0.2.11 release)

- fix: resolve onnxruntime/onnxruntime-gpu conflict clobbering GPU support
- perf: stream thumbnail download in bounded batches to cap peak RAM
- fix: log thumbnail fetch failures; restore get() for duplicate-ID safety
- ci: cancel in-progress Docker builds on superseding push
- ci: enforce GHCR package visibility public after each push
- docs: README and wiki updated with memory guidance and GPU troubleshooting
This commit is contained in:
2026-06-12 23:40:17 +00:00
7 changed files with 323 additions and 184 deletions
+18
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@@ -10,6 +10,10 @@ on:
- ".github/workflows/lint.yml" - ".github/workflows/lint.yml"
- ".github/dependabot.yml" - ".github/dependabot.yml"
concurrency:
group: docker-${{ github.ref }}
cancel-in-progress: true
env: env:
REGISTRY: ghcr.io REGISTRY: ghcr.io
IMAGE_NAME: sudolulo/winnow IMAGE_NAME: sudolulo/winnow
@@ -127,6 +131,13 @@ jobs:
run: | run: |
docker buildx imagetools inspect ${{ steps.tags.outputs.tags }} docker buildx imagetools inspect ${{ steps.tags.outputs.tags }}
- name: Ensure package is public
run: |
gh api -X PATCH /user/packages/container/winnow \
-f visibility=public || true
env:
GH_TOKEN: ${{ secrets.GITHUB_TOKEN }}
build-cpu: build-cpu:
name: Build CPU-only (amd64) name: Build CPU-only (amd64)
runs-on: ubuntu-latest runs-on: ubuntu-latest
@@ -181,3 +192,10 @@ jobs:
- name: Inspect CPU image - name: Inspect CPU image
run: | run: |
docker buildx imagetools inspect ${{ steps.cpu-tag.outputs.tag }} docker buildx imagetools inspect ${{ steps.cpu-tag.outputs.tag }}
- name: Ensure package is public
run: |
gh api -X PATCH /user/packages/container/winnow \
-f visibility=public || true
env:
GH_TOKEN: ${{ secrets.GITHUB_TOKEN }}
+7
View File
@@ -7,6 +7,13 @@ and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0
## [Unreleased] ## [Unreleased]
## [0.2.11] - 2026-06-12
### Fixed
- **GPU broken on x86_64 Linux**: `insightface` 1.0.1 (pulled in by the 0.2.10 lock update) added a hard dependency on the CPU `onnxruntime` package. Combined with an incorrect `override-dependencies` entry introduced in 0.2.10, both `onnxruntime` (CPU) and `onnxruntime-gpu` were being installed into the same venv. The CPU package landed last and overwrote the GPU one, causing `CUDAExecutionProvider` to disappear from the provider list even when a GPU was present. Fixed by declaring `onnxruntime` and `onnxruntime-gpu` as conflicting packages in uv's resolver, ensuring only the correct one is installed per platform.
- **OOM crash on large person libraries (CPU mode)**: All candidate thumbnails were downloaded into a single in-memory dict before any processing began. At ~5 MB per decoded preview image, a person with 472 candidates would accumulate ~2.4 GB of thumbnail data alone, exhausting a 4 GB container memory limit. Thumbnails are now downloaded and processed in batches of 32, with each image released immediately after embedding. Peak in-flight thumbnail memory is now bounded to ~256 MB regardless of candidate pool size. GPU users also benefit from lower host RAM pressure and faster time-to-first-result on large libraries.
## [0.2.10] - 2026-06-12 ## [0.2.10] - 2026-06-12
### Added ### Added
+1 -1
View File
@@ -142,7 +142,7 @@ services:
capabilities: [gpu] capabilities: [gpu]
``` ```
> **CPU users (`:cpu` tag):** remove the `deploy.resources` block — no NVIDIA runtime needed. > **CPU users (`:cpu` tag):** remove the `deploy.resources` block — no NVIDIA runtime needed. Add `mem_limit: 2g` to the service to prevent an OOM restart loop on large libraries.
See [compose.yml](compose.yml) for the full annotated example. See [compose.yml](compose.yml) for the full annotated example.
+1 -1
View File
@@ -1,6 +1,6 @@
[project] [project]
name = "winnow" name = "winnow"
version = "0.2.10" version = "0.2.11"
description = "Immich to Frigate training sets" description = "Immich to Frigate training sets"
license = "AGPL-3.0-or-later" license = "AGPL-3.0-or-later"
requires-python = ">=3.13" requires-python = ">=3.13"
+7 -2
View File
@@ -1,6 +1,6 @@
[project] [project]
name = "winnow" name = "winnow"
version = "0.2.10" version = "0.2.11"
description = "Immich to Frigate training sets" description = "Immich to Frigate training sets"
license = "AGPL-3.0-or-later" license = "AGPL-3.0-or-later"
requires-python = ">=3.13" requires-python = ">=3.13"
@@ -39,7 +39,12 @@ winnow = "winnow.cli:main"
Repository = "https://github.com/sudolulo/winnow" Repository = "https://github.com/sudolulo/winnow"
[tool.uv] [tool.uv]
override-dependencies = ["onnxruntime>=1.23.2"] conflicts = [
[
{ package = "onnxruntime" },
{ package = "onnxruntime-gpu" },
],
]
required-environments = [ required-environments = [
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"sys_platform == 'linux' and platform_machine == 'aarch64'", "sys_platform == 'linux' and platform_machine == 'aarch64'",
Generated
+235 -133
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revision = 3 revision = 3
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[[package]] [[package]]
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sdist = { url = "https://files.pythonhosted.org/packages/98/c6/d25cb53e141242f74950744179c21b8798bb09b9e7161465ecda4f577ddf/ultralytics_thop-2.0.20.tar.gz", hash = "sha256:f3595e0d8c6fd0b9f62fc2cd9be921755e2649a05c34f1fabaea0bff7295d641", size = 34682, upload-time = "2026-06-06T11:42:42.184Z" } sdist = { url = "https://files.pythonhosted.org/packages/98/c6/d25cb53e141242f74950744179c21b8798bb09b9e7161465ecda4f577ddf/ultralytics_thop-2.0.20.tar.gz", hash = "sha256:f3595e0d8c6fd0b9f62fc2cd9be921755e2649a05c34f1fabaea0bff7295d641", size = 34682, upload-time = "2026-06-06T11:42:42.184Z" }
wheels = [ wheels = [
@@ -2195,29 +2297,29 @@ wheels = [
[[package]] [[package]]
name = "winnow" name = "winnow"
version = "0.2.10" version = "0.2.11"
source = { editable = "." } source = { editable = "." }
dependencies = [ dependencies = [
{ name = "croniter" }, { name = "croniter" },
{ name = "insightface" }, { name = "insightface" },
{ name = "numpy" }, { name = "numpy" },
{ name = "nvidia-cudnn-cu12", version = "9.10.2.21", source = { registry = "https://pypi.org/simple" }, marker = "platform_machine == 'x86_64' and sys_platform == 'linux'" }, { name = "nvidia-cudnn-cu12", version = "9.10.2.21", source = { registry = "https://pypi.org/simple" }, marker = "(platform_machine == 'x86_64' and sys_platform == 'linux') or (platform_machine != 'x86_64' and extra == 'project-11-onnxruntime' and extra == 'project-15-onnxruntime-gpu') or (sys_platform != 'linux' and extra == 'project-11-onnxruntime' and extra == 'project-15-onnxruntime-gpu')" },
{ name = "nvidia-cudnn-cu12", version = "9.23.1.3", source = { registry = "https://pypi.org/simple" }, marker = "platform_machine != 'x86_64' or sys_platform != 'linux'" }, { name = "nvidia-cudnn-cu12", version = "9.23.1.3", source = { registry = "https://pypi.org/simple" }, marker = "platform_machine != 'x86_64' or sys_platform != 'linux' or (extra == 'project-11-onnxruntime' and extra == 'project-15-onnxruntime-gpu')" },
{ name = "onnxruntime" }, { name = "onnxruntime", marker = "platform_machine != 'x86_64' or sys_platform != 'linux' or (extra == 'project-11-onnxruntime' and extra == 'project-15-onnxruntime-gpu')" },
{ name = "onnxruntime-gpu", marker = "platform_machine == 'x86_64' and sys_platform == 'linux'" }, { name = "onnxruntime-gpu", marker = "(platform_machine == 'x86_64' and sys_platform == 'linux') or (platform_machine != 'x86_64' and extra == 'project-11-onnxruntime' and extra == 'project-15-onnxruntime-gpu') or (sys_platform != 'linux' and extra == 'project-11-onnxruntime' and extra == 'project-15-onnxruntime-gpu')" },
{ name = "opencv-python-headless" }, { name = "opencv-python-headless" },
{ name = "pillow" }, { name = "pillow" },
{ name = "python-dotenv" }, { name = "python-dotenv" },
{ name = "requests" }, { name = "requests" },
{ name = "rich" }, { name = "rich" },
{ name = "torch", version = "2.12.0", source = { registry = "https://download.pytorch.org/whl/cpu" }, marker = "platform_machine != 's390x' and sys_platform == 'darwin'" }, { name = "torch", version = "2.12.0", source = { registry = "https://download.pytorch.org/whl/cpu" }, marker = "(platform_machine != 's390x' and sys_platform == 'darwin') or (platform_machine == 's390x' and extra == 'project-11-onnxruntime' and extra == 'project-15-onnxruntime-gpu') or (sys_platform != 'darwin' and extra == 'project-11-onnxruntime' and extra == 'project-15-onnxruntime-gpu')" },
{ name = "torch", version = "2.12.0", source = { registry = "https://pypi.org/simple" }, marker = "platform_machine != 'aarch64' and platform_machine != 'x86_64' and sys_platform == 'linux'" }, { name = "torch", version = "2.12.0", source = { registry = "https://pypi.org/simple" }, marker = "(platform_machine != 'aarch64' and platform_machine != 'x86_64' and sys_platform == 'linux') or (platform_machine == 'aarch64' and extra == 'project-11-onnxruntime' and extra == 'project-15-onnxruntime-gpu') or (platform_machine == 'x86_64' and extra == 'project-11-onnxruntime' and extra == 'project-15-onnxruntime-gpu') or (sys_platform != 'linux' and extra == 'project-11-onnxruntime' and extra == 'project-15-onnxruntime-gpu')" },
{ name = "torch", version = "2.12.0+cpu", source = { registry = "https://download.pytorch.org/whl/cpu" }, marker = "(platform_machine == 's390x' and sys_platform == 'darwin') or (platform_machine == 'aarch64' and sys_platform == 'linux') or (sys_platform != 'darwin' and sys_platform != 'linux')" }, { name = "torch", version = "2.12.0+cpu", source = { registry = "https://download.pytorch.org/whl/cpu" }, marker = "(platform_machine == 's390x' and sys_platform == 'darwin') or (platform_machine == 'aarch64' and sys_platform == 'linux') or (sys_platform != 'darwin' and sys_platform != 'linux') or (sys_platform == 'darwin' and extra == 'project-11-onnxruntime' and extra == 'project-15-onnxruntime-gpu') or (sys_platform == 'linux' and extra == 'project-11-onnxruntime' and extra == 'project-15-onnxruntime-gpu')" },
{ name = "torch", version = "2.12.0+cu126", source = { registry = "https://download.pytorch.org/whl/cu126" }, marker = "platform_machine == 'x86_64' and sys_platform == 'linux'" }, { name = "torch", version = "2.12.0+cu126", source = { registry = "https://download.pytorch.org/whl/cu126" }, marker = "(platform_machine == 'x86_64' and sys_platform == 'linux') or (platform_machine != 'x86_64' and extra == 'project-11-onnxruntime' and extra == 'project-15-onnxruntime-gpu') or (sys_platform != 'linux' and extra == 'project-11-onnxruntime' and extra == 'project-15-onnxruntime-gpu')" },
{ name = "torchvision", version = "0.27.0", source = { registry = "https://download.pytorch.org/whl/cpu" }, marker = "platform_machine != 's390x' and sys_platform == 'darwin'" }, { name = "torchvision", version = "0.27.0", source = { registry = "https://download.pytorch.org/whl/cpu" }, marker = "(platform_machine != 's390x' and sys_platform == 'darwin') or (platform_machine == 's390x' and extra == 'project-11-onnxruntime' and extra == 'project-15-onnxruntime-gpu') or (sys_platform != 'darwin' and extra == 'project-11-onnxruntime' and extra == 'project-15-onnxruntime-gpu')" },
{ name = "torchvision", version = "0.27.0", source = { registry = "https://pypi.org/simple" }, marker = "platform_machine != 'aarch64' and platform_machine != 'x86_64' and sys_platform == 'linux'" }, { name = "torchvision", version = "0.27.0", source = { registry = "https://pypi.org/simple" }, marker = "(platform_machine != 'aarch64' and platform_machine != 'x86_64' and sys_platform == 'linux') or (platform_machine == 'aarch64' and extra == 'project-11-onnxruntime' and extra == 'project-15-onnxruntime-gpu') or (platform_machine == 'x86_64' and extra == 'project-11-onnxruntime' and extra == 'project-15-onnxruntime-gpu') or (sys_platform != 'linux' and extra == 'project-11-onnxruntime' and extra == 'project-15-onnxruntime-gpu')" },
{ name = "torchvision", version = "0.27.0+cpu", source = { registry = "https://download.pytorch.org/whl/cpu" }, marker = "(platform_machine == 's390x' and sys_platform == 'darwin') or (platform_machine == 'aarch64' and sys_platform == 'linux') or (sys_platform != 'darwin' and sys_platform != 'linux')" }, { name = "torchvision", version = "0.27.0+cpu", source = { registry = "https://download.pytorch.org/whl/cpu" }, marker = "(platform_machine == 's390x' and sys_platform == 'darwin') or (platform_machine == 'aarch64' and sys_platform == 'linux') or (sys_platform != 'darwin' and sys_platform != 'linux') or (sys_platform == 'darwin' and extra == 'project-11-onnxruntime' and extra == 'project-15-onnxruntime-gpu') or (sys_platform == 'linux' and extra == 'project-11-onnxruntime' and extra == 'project-15-onnxruntime-gpu')" },
{ name = "torchvision", version = "0.27.0+cu126", source = { registry = "https://download.pytorch.org/whl/cu126" }, marker = "platform_machine == 'x86_64' and sys_platform == 'linux'" }, { name = "torchvision", version = "0.27.0+cu126", source = { registry = "https://download.pytorch.org/whl/cu126" }, marker = "(platform_machine == 'x86_64' and sys_platform == 'linux') or (platform_machine != 'x86_64' and extra == 'project-11-onnxruntime' and extra == 'project-15-onnxruntime-gpu') or (sys_platform != 'linux' and extra == 'project-11-onnxruntime' and extra == 'project-15-onnxruntime-gpu')" },
{ name = "transformers" }, { name = "transformers" },
{ name = "ultralytics" }, { name = "ultralytics" },
] ]
+54 -47
View File
@@ -203,64 +203,71 @@ def _select_by_embedding(
else: else:
candidates = assets candidates = assets
# --- Phase 1: Concurrent thumbnail download --- # --- Phases 1-4: Batched download → quality filter → crop → embed ---
# Process in bounded batches so at most _BATCH decoded images live in RAM
# at once. With 472 candidates each thumbnail is ~3-8 MB decoded; loading
# all at once easily exhausts a 4 GB container limit on CPU.
from concurrent.futures import ThreadPoolExecutor, as_completed from concurrent.futures import ThreadPoolExecutor, as_completed
thumbnail_map: dict[str, Image.Image] = {} _BATCH = 32
with ThreadPoolExecutor(max_workers=8) as pool:
futures = {pool.submit(_fetch_thumbnail, a["id"]): a for a in candidates}
for i, future in enumerate(as_completed(futures)):
if progress_callback:
progress_callback(i, len(candidates))
asset = futures[future]
try:
img = future.result()
if img is not None:
thumbnail_map[asset["id"]] = img
except Exception:
continue
# --- Phase 2-4: Quality filter → Crop → Embed ---
embeddings, valid_candidates, confidence_scores = [], [], [] embeddings, valid_candidates, confidence_scores = [], [], []
quality_filtered = 0 quality_filtered = 0
processed = 0
for asset in candidates: for batch_start in range(0, len(candidates), _BATCH):
img = thumbnail_map.get(asset["id"]) batch = candidates[batch_start : batch_start + _BATCH]
if img is None:
continue
confidence = _get_face_confidence(asset, person_id=person_id) # Download this batch concurrently
batch_images: dict[str, Image.Image] = {}
with ThreadPoolExecutor(max_workers=min(8, len(batch))) as pool:
futures = {pool.submit(_fetch_thumbnail, a["id"]): a for a in batch}
for future in as_completed(futures):
asset = futures[future]
try:
img = future.result()
if img is not None:
batch_images[asset["id"]] = img
except Exception as e:
logger.debug(f"Failed to fetch thumbnail for {asset['id']}: {e}")
continue
# Quality gate: filter before expensive embedding computation # Process each image; batch_images goes out of scope after this loop,
if entity_type == "face": # bounding peak thumbnail memory to _BATCH images per iteration.
face_bbox = _get_face_bbox(asset, person_id=person_id) for asset in batch:
quality = assess_quality( img = batch_images.get(asset["id"])
img, processed += 1
face_bbox=face_bbox, if progress_callback:
confidence=confidence, progress_callback(processed, len(candidates))
blur_threshold=Config.BLUR_THRESHOLD, if img is None:
min_face_px=Config.MIN_FACE_WIDTH,
min_confidence=Config.MIN_CONFIDENCE,
)
if not quality.passed:
quality_filtered += 1
logger.debug(f"Quality filtered {asset['id']}: {quality.reason}")
continue continue
# Crop the target person's face before embedding confidence = _get_face_confidence(asset, person_id=person_id)
face_crop = _crop_face_from_thumbnail(img, asset, person_id=person_id)
embed_img = face_crop if face_crop is not None else img
else:
embed_img = img
emb = get_embedding(embed_img, entity_type, asset_id=asset["id"]) if entity_type == "face":
if emb is not None: face_bbox = _get_face_bbox(asset, person_id=person_id)
embeddings.append(emb) quality = assess_quality(
valid_candidates.append(asset) img,
confidence_scores.append(confidence) face_bbox=face_bbox,
confidence=confidence,
blur_threshold=Config.BLUR_THRESHOLD,
min_face_px=Config.MIN_FACE_WIDTH,
min_confidence=Config.MIN_CONFIDENCE,
)
if not quality.passed:
quality_filtered += 1
logger.debug(f"Quality filtered {asset['id']}: {quality.reason}")
continue
if progress_callback: face_crop = _crop_face_from_thumbnail(img, asset, person_id=person_id)
progress_callback(len(candidates), len(candidates)) embed_img = face_crop if face_crop is not None else img
else:
embed_img = img
emb = get_embedding(embed_img, entity_type, asset_id=asset["id"])
if emb is not None:
embeddings.append(emb)
valid_candidates.append(asset)
confidence_scores.append(confidence)
if quality_filtered > 0: if quality_filtered > 0:
logger.info(f"Quality filtering removed {quality_filtered} images.") logger.info(f"Quality filtering removed {quality_filtered} images.")