The published arm64 set disagreed on huggingface_hub (four bundles baked 1.22.0), scipy, and scikit-image. All six affected bundles are rebuilt under the constrained closure and published at new -r2 paths so baked manifests keep working; no rebuilt bundle carries hub and the layered compatibility check passes. verify-bundle.sh drops its stale onnxruntime expectation for upscale-enhance. Completes #669.
* fix(ai-bundles): lock the numpy-1.x ABI closure so the OCR bundle can't strand scipy
The OCR bundle installs paddleocr[doc-parser] 3.4, whose dependency closure drags
numpy 1.26.4 up to 2.5.1 and pulls scipy/scikit-learn/pandas wheels built against
the numpy 2.x ABI. build-bundle.sh re-pinned only numpy (basePackages), so those
numpy-2.x wheels stayed behind; the by-dir-name site-packages diff then shipped
them, and once merged onto the numpy==1.26.4 base they raise "numpy.dtype size
changed" on import.
Because the dispatcher pre-imports every ML library at startup and disables all AI
after 5 crashes in 60s, one stranded scipy takes down every AI tool, not just OCR
(observed on a CPU host: remove-background worked before the OCR bundle and broke
after). All-7 installs escaped it through last-writer-wins ordering; a subset
install did not, which is why it surfaced only intermittently.
Fix: add a manifest "constraints" list (numpy, scipy, scikit-learn, scikit-image,
pandas pinned to numpy-1.x-ABI versions) and apply it via PIP_CONSTRAINT to every
bundle pip install, so no bundle can pull a numpy-2.x wheel. paddleocr 3.4.1 still
resolves cleanly under the lock and the pinned stack imports without ABI error on
numpy 1.26.4 (validated on py3.12). Also import scipy/sklearn in the OCR path of
verify-bundle.sh so CI catches this class in isolation, and add a manifest
regression test.
Note: the published bundles must be rebuilt and republished (ai-bundles.yml) for
this to reach already-installed bases.
Claude-Session: https://claude.ai/code/session_01UvVCMNUBrgpghk8gye5gav
* chore(ai-bundles): sync OCR manifest sha256 to the rebuilt numpy-1.x bundles
Rebuilt the OCR bundle for both arches with the numpy-1.x-ABI constraints from
this PR and republished the tars to deepsafe/feature-bundles/v2.0.0, then updated
the baked manifest sha256 and sizes so installs verify against the fixed archives:
amd64-gpu 5.93 GB sha 2a00a3184f6a635f1fa9ae2a6517ad740a11f9e5ff58c098d2fd369a2bb1e16b
arm64-cpu 1.98 GB sha 6868c264069dcb74c6675c0b1f58dc1c9f60d9aa4459725e3dbde07a99a6a09a
Both tars ship scipy 1.12.0 / scikit-learn 1.4.2 / pandas 2.2.2 (numpy-1.x-ABI)
and zero numpy-2.x wheels, verified by listing the archive contents.
Stopgap note: these tars were built against the ghcr.io latest base (the 2.0.0
image is not published to GHCR), so they are not byte-identical to what the CI
build will produce. When ai-bundles.yml rebuilds at the 2.0.0 release, it will
mint fresh sha256 values and this manifest must be re-synced to them.
Claude-Session: https://claude.ai/code/session_01UvVCMNUBrgpghk8gye5gav
- Add || true after inference commands so set -e doesn't kill the
script before we reach the meaningful error message (exit code 3)
- Fix colorize settings: use "model" not "method" (matching colorize.py)
- Use larger font (size=40) and image (400x100) for OCR test so
PaddleOCR reliably recognizes the text
- Add || result="" fallback for stdout-capture smoke tests (OCR,
transcription) to prevent set -e on command substitution failure
- validate models field in bundle.json
- pipe JSON via stdin instead of triple-quote embedding (injection safety)
- add PNG magic byte validation for background-removal output
- add dimension assertion for upscale-enhance output
- add fixture existence guards before smoke tests
- use --no-index for offline fixup wheel install
Verifies bundle tarballs in 4 phases: SHA256 integrity, extraction
and install into the base venv, Python import checks per bundle,
and a functional inference smoke test per bundle.
