- Dockerfile: ARG VERSION + OCI labels (title, description, source, licenses, version)
- release.yml: pass VERSION build-arg to all four image builds
- docker-publish.yml: extend paths-ignore to cover community files
- .github/ISSUE_TEMPLATE/config.yml: disable blank issues, link to Discussions and wiki
- README.md: add Getting Help section
- pyproject.toml: expand description; add System Administrators audience classifier
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
New image variants:
- :rocm — InsightFace via ROCmExecutionProvider, SigLIP via PyTorch ROCm 6.3
- :intel — InsightFace via OpenVINOExecutionProvider (onnxruntime-openvino);
Intel GPU compute runtime auto-installed from Intel graphics repo;
OPENVINO_DEVICE=GPU opts into Arc/iGPU inference (default: CPU)
Also adds:
- pyproject-rocm.toml + uv-rocm.lock, pyproject-intel.toml + uv-intel.lock
- compose.yml device passthrough snippets for AMD and Intel
- CI: build-rocm and build-intel jobs in docker-publish.yml; all four
variants built and tagged in release.yml
- README reworked: cleaner structure, GPU variant quick-start examples,
OPENVINO_DEVICE env var documented
- CHANGELOG entry and version bump to 0.2.12
Fix: IntPrompt in dict literal was eagerly evaluated in the no-embedding
fallback path of _get_strategy_choice, prompting users for a custom count
regardless of which strategy they picked.
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
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>
onnxruntime-gpu has no arm64 wheels (manylinux_2_27_x86_64 /
manylinux_2_28_x86_64 only). uv sync --frozen failed on the arm64
image with exit code 2. Gated onnxruntime-gpu behind the x86_64
marker; arm64 and non-Linux use the CPU onnxruntime package. Added
required-environments so the lockfile is solved for both platforms.
Removed onnxruntime-gpu from override-dependencies (it had no marker
support and blocked arm64 resolution).
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
curl|gpg --dearmor was silently dropping the deadsnakes key (gpg exits 0
on bad input), leaving apt unable to find python3.13. Reverted to
add-apt-repository with GNUPGHOME=$(mktemp -d) to isolate gpg from any
pre-existing agent socket. Removed --platform=\$BUILDPLATFORM from the
arm64 base so the image contains real arm64 binaries. License updated
from MIT to AGPL-3.0-or-later.
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
Ubuntu 26.04 ships Python 3.14, not 3.13. Reverted arm64 to ubuntu:24.04.
Both architectures now add the deadsnakes PPA by fetching the GPG key via
curl and piping through gpg --dearmor — no gpg-agent, safe under QEMU.
Removes the per-arch conditional and ARG TARGETARCH dependency in RUN commands.
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
Docker automatic platform ARGs are only in scope for FROM instructions.
$TARGETARCH in RUN commands was always empty, so the deadsnakes PPA
conditional never ran and python3.13 could not be found on the Ubuntu
22.04 CUDA base.
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
All platforms now use Python 3.13. amd64 installs via deadsnakes PPA on
the Ubuntu 22.04 CUDA base; arm64 gets Python 3.13 natively from Ubuntu
26.04. Verified cp313 wheels exist for onnxruntime-gpu 1.26.0 and
torch 2.12.0+cu126. uv.lock regenerated under CPython 3.13.5.
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
- Store Immich face confidence scores per asset in frigate_uploaded_ids.json
- Add frigate_api.py: query GET /api/faces to count trained images per person
- Record Frigate training count as frigate_count in tracker for offline fallback
- MAX_AUTO_IMAGES cap now uses live Frigate count → cached frigate_count → local uploaded count
- Startup summary shows last known Frigate training count per person
- Migrate by_person entries from flat list to {asset_ids, scores, frigate_count} dict
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
- entrypoint.sh: replace `uv run` with `/app/.venv/bin/python` to skip
uv's sync check which was re-downloading ruff and rebuilding the package
on every container startup
- compose.yml, .env.example: fix INSIGHTFACE_HOME /models → /models/.insightface;
InsightFace appends models/ to root, so /models produced /models/models/buffalo_l
- README.md: add Immich and Frigate badges from upstream
- CHANGELOG.md, pyproject.toml, uv.lock: bump to 0.2.1
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
- pyproject.toml: version 0.1.0 → 0.2.0
- CHANGELOG.md: full [0.2.0] entry covering all changes since the fork —
headless operation, Docker/scheduling, object mode, people filtering,
quality controls, CI/CD, tests, docs, and all bug fixes
- uv.lock: regenerated after version bump
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
- LICENSE: add copyright line for Holden Salomon (retain original)
- pyproject.toml: update authors to Holden Salomon <holden@arch.fyi>
- README: lead with what the software does rather than fork attribution;
merge split env var tables into one unified reference; add lint/test
badges; move attribution to footer
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
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