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
docs: add mem_limit guidance for CPU users in README
2026-06-12 23:40:17 +00:00 · 2026-06-12 23:40:07 +00:00 · 2026-06-12 18:53:26 +00:00 · 2026-06-12 18:51:44 +00:00 · 2026-06-12 18:09:03 +00:00 · 2026-06-12 18:08:02 +00:00
7 changed files with 326 additions and 187 deletions
@@ -10,6 +10,10 @@ on:
      - ".github/workflows/lint.yml"
      - ".github/dependabot.yml"

+concurrency:
+  group: docker-${{ github.ref }}
+  cancel-in-progress: true
+
 env:
  REGISTRY: ghcr.io
  IMAGE_NAME: sudolulo/winnow
@@ -127,6 +131,13 @@ jobs:
        run: |
          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:
    name: Build CPU-only (amd64)
    runs-on: ubuntu-latest
@@ -181,3 +192,10 @@ jobs:
      - name: Inspect CPU image
        run: |
          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 }}
@@ -7,6 +7,13 @@ and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0

 ## [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

 ### Added
@@ -142,7 +142,7 @@ services:
              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.

@@ -239,7 +239,7 @@ uv sync
 uv run winnow
 ```

-Requires Python 3.13+ and [uv](https://astral.sh/uv/). An NVIDIA GPU is strongly recommended — CPU mode works but embedding computation is significantly slower.
+Requires Python 3.13+ and [uv](https://astral.sh/uv/). An NVIDIA GPU is strongly recommended — CPU mode works but embedding computation is slower (typically tens of seconds per person vs under a second on GPU).

 ---

@@ -1,6 +1,6 @@
 [project]
 name = "winnow"
-version = "0.2.10"
+version = "0.2.11"
 description = "Immich to Frigate training sets"
 license = "AGPL-3.0-or-later"
 requires-python = ">=3.13"
@@ -1,6 +1,6 @@
 [project]
 name = "winnow"
-version = "0.2.10"
+version = "0.2.11"
 description = "Immich to Frigate training sets"
 license = "AGPL-3.0-or-later"
 requires-python = ">=3.13"
@@ -39,7 +39,12 @@ winnow = "winnow.cli:main"
 Repository = "https://github.com/sudolulo/winnow"

 [tool.uv]
-override-dependencies = ["onnxruntime>=1.23.2"]
+conflicts = [
+    [
+        { package = "onnxruntime" },
+        { package = "onnxruntime-gpu" },
+    ],
+]
 required-environments = [
    "sys_platform == 'linux' and platform_machine == 'x86_64'",
    "sys_platform == 'linux' and platform_machine == 'aarch64'",
@@ -2,24 +2,25 @@ version = 1
 revision = 3
 requires-python = ">=3.13"
 resolution-markers = [
-    "platform_machine == 'x86_64' and sys_platform == 'linux'",
    "platform_machine == 'aarch64' and sys_platform == 'linux'",
+    "platform_machine != 'aarch64' and platform_machine != 's390x' and platform_machine != 'x86_64' and sys_platform == 'linux'",
+    "platform_machine == 's390x' and sys_platform == 'linux'",
    "platform_machine != 's390x' and sys_platform == 'win32'",
    "platform_machine == 's390x' and sys_platform == 'win32'",
    "platform_machine != 's390x' and sys_platform != 'darwin' and sys_platform != 'linux' and sys_platform != 'win32'",
    "platform_machine == 's390x' and sys_platform != 'darwin' and sys_platform != 'linux' and sys_platform != 'win32'",
    "platform_machine != 's390x' and sys_platform == 'darwin'",
    "platform_machine == 's390x' and sys_platform == 'darwin'",
-    "platform_machine != 'aarch64' and platform_machine != 's390x' and platform_machine != 'x86_64' and sys_platform == 'linux'",
-    "platform_machine == 's390x' and sys_platform == 'linux'",
+    "platform_machine == 'x86_64' and sys_platform == 'linux'",
 ]
 required-markers = [
    "platform_machine == 'x86_64' and sys_platform == 'linux'",
    "platform_machine == 'aarch64' and sys_platform == 'linux'",
 ]
-
-[manifest]
-overrides = [{ name = "onnxruntime", specifier = ">=1.23.2" }]
+conflicts = [[
+    { package = "onnxruntime" },
+    { package = "onnxruntime-gpu" },
+]]

 [[package]]
 name = "annotated-doc"
@@ -113,7 +114,7 @@ name = "click"
 version = "8.4.1"
 source = { registry = "https://pypi.org/simple" }
 dependencies = [
-    { name = "colorama", marker = "sys_platform == 'win32'" },
+    { name = "colorama", marker = "sys_platform == 'win32' or (extra == 'project-11-onnxruntime' and extra == 'project-15-onnxruntime-gpu')" },
 ]
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 ]
 dependencies = [
-    { name = "cuda-pathfinder", marker = "platform_machine == 'x86_64' and sys_platform == 'linux'" },
+    { name = "cuda-pathfinder", 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')" },
 ]
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@@ -222,15 +231,18 @@ resolution-markers = [
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 ]
 dependencies = [
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 [[package]]
@@ -254,37 +266,37 @@ wheels = [

 [package.optional-dependencies]
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 ]
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 ]
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    { url = "https://download-r2.pytorch.org/whl/cu126/torchvision-0.27.0%2Bcu126-cp314-cp314t-manylinux_2_28_x86_64.whl", hash = "sha256:60e070ef008c2e74724d2bf3949c7ec2a0fc25e34bafe1cee1d83b4e2782874c", upload-time = "2026-05-12T16:20:42Z" },
+    { url = "https://download-r2.pytorch.org/whl/cu126/torchvision-0.27.0%2Bcu126-cp314-cp314t-win_amd64.whl", hash = "sha256:9db7a154702b4404f1af0b48ac0098aa9ddcea3c80c8e9b8bf8f3fdd87e20946", upload-time = "2026-05-13T02:00:42Z" },
 ]

 [[package]]
@@ -2072,7 +2174,7 @@ name = "tqdm"
 version = "4.68.2"
 source = { registry = "https://pypi.org/simple" }
 dependencies = [
-    { name = "colorama", marker = "sys_platform == 'win32'" },
+    { name = "colorama", marker = "sys_platform == 'win32' or (extra == 'project-11-onnxruntime' and extra == 'project-15-onnxruntime-gpu')" },
 ]
 sdist = { url = "https://files.pythonhosted.org/packages/85/05/0d5260f1f1ca784f4a4a0def9cbe6affe587f5b4025328d446c3d67765f4/tqdm-4.68.2.tar.gz", hash = "sha256:89c230e8dbc67c7615c142487111222f878c77427ea09549960f62389e258add", size = 171923, upload-time = "2026-06-09T13:26:42.539Z" }
 wheels = [
@@ -2153,14 +2255,14 @@ dependencies = [
    { name = "pyyaml" },
    { name = "requests" },
    { name = "scipy" },
-    { name = "torch", version = "2.12.0", source = { registry = "https://download.pytorch.org/whl/cpu" }, marker = "sys_platform == 'darwin'" },
-    { 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+cpu", source = { registry = "https://download.pytorch.org/whl/cpu" }, marker = "(platform_machine == 'aarch64' and sys_platform == 'linux') or (sys_platform != 'darwin' 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'" },
-    { name = "torchvision", version = "0.27.0", source = { registry = "https://download.pytorch.org/whl/cpu" }, marker = "sys_platform == 'darwin'" },
-    { 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+cpu", source = { registry = "https://download.pytorch.org/whl/cpu" }, marker = "(platform_machine == 'aarch64' and sys_platform == 'linux') or (sys_platform != 'darwin' 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'" },
+    { 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') 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') 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') 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') 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') 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') 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') 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 = "ultralytics-thop" },
 ]
 sdist = { url = "https://files.pythonhosted.org/packages/5b/d6/7d95601a9078b614e87d14f93bf597a6bd75aa2bfe19cbe57e0016005c35/ultralytics-8.4.66.tar.gz", hash = "sha256:bb6e70e6b6de5399f9fc29a7c57495d6272529cc4896faf548372ddfa1c3365f", size = 1104120, upload-time = "2026-06-11T16:48:03.3Z" }
@@ -2174,10 +2276,10 @@ version = "2.0.20"
 source = { registry = "https://pypi.org/simple" }
 dependencies = [
    { name = "numpy" },
-    { name = "torch", version = "2.12.0", source = { registry = "https://download.pytorch.org/whl/cpu" }, marker = "sys_platform == 'darwin'" },
-    { 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+cpu", source = { registry = "https://download.pytorch.org/whl/cpu" }, marker = "(platform_machine == 'aarch64' and sys_platform == 'linux') or (sys_platform != 'darwin' 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'" },
+    { 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') 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') 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') 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')" },
 ]
 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 = [
@@ -2195,29 +2297,29 @@ wheels = [

 [[package]]
 name = "winnow"
-version = "0.2.9"
+version = "0.2.11"
 source = { editable = "." }
 dependencies = [
    { name = "croniter" },
    { name = "insightface" },
    { 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 = "9.23.1.3", source = { registry = "https://pypi.org/simple" }, marker = "platform_machine != 'x86_64' or sys_platform != 'linux'" },
-    { name = "onnxruntime" },
-    { name = "onnxruntime-gpu", 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' or (extra == 'project-11-onnxruntime' and extra == 'project-15-onnxruntime-gpu')" },
+    { 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') 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 = "pillow" },
    { name = "python-dotenv" },
    { name = "requests" },
    { name = "rich" },
-    { name = "torch", version = "2.12.0", source = { registry = "https://download.pytorch.org/whl/cpu" }, marker = "sys_platform == 'darwin'" },
-    { 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+cpu", source = { registry = "https://download.pytorch.org/whl/cpu" }, marker = "(platform_machine == 'aarch64' and sys_platform == 'linux') or (sys_platform != 'darwin' 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'" },
-    { name = "torchvision", version = "0.27.0", source = { registry = "https://download.pytorch.org/whl/cpu" }, marker = "sys_platform == 'darwin'" },
-    { 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+cpu", source = { registry = "https://download.pytorch.org/whl/cpu" }, marker = "(platform_machine == 'aarch64' and sys_platform == 'linux') or (sys_platform != 'darwin' 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'" },
+    { 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') 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') 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') 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') 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') 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') 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') 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 = "ultralytics" },
 ]
@@ -203,35 +203,46 @@ def _select_by_embedding(
    else:
        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

-    thumbnail_map: dict[str, Image.Image] = {}
-    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))
+    _BATCH = 32
+    embeddings, valid_candidates, confidence_scores = [], [], []
+    quality_filtered = 0
+    processed = 0
+
+    for batch_start in range(0, len(candidates), _BATCH):
+        batch = candidates[batch_start : batch_start + _BATCH]
+
+        # 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:
-                    thumbnail_map[asset["id"]] = img
-            except Exception:
+                        batch_images[asset["id"]] = img
+                except Exception as e:
+                    logger.debug(f"Failed to fetch thumbnail for {asset['id']}: {e}")
                    continue

-    # --- Phase 2-4: Quality filter → Crop → Embed ---
-    embeddings, valid_candidates, confidence_scores = [], [], []
-    quality_filtered = 0
-
-    for asset in candidates:
-        img = thumbnail_map.get(asset["id"])
+        # Process each image; batch_images goes out of scope after this loop,
+        # bounding peak thumbnail memory to _BATCH images per iteration.
+        for asset in batch:
+            img = batch_images.get(asset["id"])
+            processed += 1
+            if progress_callback:
+                progress_callback(processed, len(candidates))
            if img is None:
                continue

            confidence = _get_face_confidence(asset, person_id=person_id)

-        # Quality gate: filter before expensive embedding computation
            if entity_type == "face":
                face_bbox = _get_face_bbox(asset, person_id=person_id)
                quality = assess_quality(
@@ -247,7 +258,6 @@ def _select_by_embedding(
                    logger.debug(f"Quality filtered {asset['id']}: {quality.reason}")
                    continue

-            # Crop the target person's face before embedding
                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:
@@ -259,9 +269,6 @@ def _select_by_embedding(
                valid_candidates.append(asset)
                confidence_scores.append(confidence)

-    if progress_callback:
-        progress_callback(len(candidates), len(candidates))
-
    if quality_filtered > 0:
        logger.info(f"Quality filtering removed {quality_filtered} images.")
Author	SHA1	Message	Date
flan	8103801ec8	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	2026-06-12 23:40:17 +00:00
flanandClaude Sonnet 4.6	a281b4b896	docs: add mem_limit guidance for CPU users in README Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>	2026-06-12 23:40:07 +00:00
flanandClaude Sonnet 4.6	5345798dc1	ci: enforce GHCR package visibility public after each push GHCR packages default private on first creation. Add a best-effort gh api PATCH call at the end of both the multi-arch merge job and the cpu build job so any new package version is immediately public without requiring a manual UI step. Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>	2026-06-12 18:53:26 +00:00
flanandClaude Sonnet 4.6	046004a5d0	fix: log thumbnail fetch failures and restore get() for duplicate-ID safety Two small correctness fixes from post-commit code review: - except Exception: continue swallowed network/auth errors silently; add logger.debug so systematic failures are diagnosable in winnow.log - batch_images.pop() regressed duplicate-asset-ID handling: if Immich returns the same asset ID twice within the same 32-item batch window (pagination edge case), the second occurrence got None and its embedding was silently dropped. Switching back to .get() matches the old thumbnail_map.get() behaviour. Peak memory is still bounded to _BATCH images because batch_images goes out of scope between batches. Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>	2026-06-12 18:51:44 +00:00
flanandClaude Sonnet 4.6	86ee9a5ba2	ci: cancel in-progress Docker builds when a newer push supersedes them Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>	2026-06-12 18:09:03 +00:00
flanandClaude Sonnet 4.6	248b7a6270	docs: add batched thumbnail memory fix to 0.2.11 changelog Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>	2026-06-12 18:08:02 +00:00
flanandClaude Sonnet 4.6	5b25b0df06	perf: stream thumbnail download in bounded batches to cap peak RAM Previously all candidate thumbnails (up to 3000) were loaded into a single dict before any processing started. At ~5 MB per decoded preview image, 472 candidates = ~2.4 GB of thumbnail data alone, easily exhausting a 4 GB container memory limit on CPU. Now thumbnails are downloaded and processed in batches of 32. Each image is pop()'d from the batch dict immediately after embedding so the decoder memory is released before the next batch starts. Peak in-flight thumbnail memory is now bounded to ~32 × 8 MB = ~256 MB regardless of candidate pool size. GPU users benefit too — faster first results and lower host RAM pressure during large runs. Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>	2026-06-12 18:07:40 +00:00
github-actions[bot]	af9949c47f	chore: update uv.lock	2026-06-12 17:45:50 +00:00
flanandClaude Sonnet 4.6	82d057b235	fix: resolve onnxruntime/onnxruntime-gpu conflict clobbering GPU support insightface 1.0.1 added a hard dep on the CPU onnxruntime package. Combined with an incorrect override-dependencies entry in 0.2.10 that forced onnxruntime (no platform marker) unconditionally, both packages were installed into the venv on x86_64 Linux — the CPU package landed last and overwrote onnxruntime-gpu, removing CUDAExecutionProvider from the provider list. Fix: declare the two packages as conflicting in uv's resolver so only the correct one is installed per environment. Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>	2026-06-12 17:45:05 +00:00
flanandClaude Sonnet 4.6	fa6dc01366	chore: sync dev to main (0.2.10 release) Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>	2026-06-12 17:01:01 +00:00
flanandClaude Sonnet 4.6	567e568c47	docs: correct CPU embedding speed estimate Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>	2026-06-12 17:00:37 +00:00
github-actions[bot]	1ed8d7e25e	chore: update uv.lock	2026-06-12 16:56:13 +00:00
github-actions[bot]	f1df1c6bb3	chore: update uv.lock	2026-06-12 16:49:21 +00:00