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Testing ArrayBridge

This is the practical test guide. The workflow files under .github/workflows are the authority for the current CI matrix.

Local core checks

python -m pip install -e ".[dev,docs]"
ruff check src tests
black --check src tests
mypy src --ignore-missing-imports
pytest
python -m sphinx -E -W --keep-going -b html docs/source /tmp/arraybridge-docs

The default suite uses real optional frameworks when installed and otherwise exercises declared unavailable paths and focused fakes. Markers such as torch, tensorflow, jax, cupy, pyclesperanto, and gpu can select or exclude framework-specific tests.

Real GPU lattice check

On a GPU host, validate more than a single NumPy round trip:

  • import all installed frameworks in one fresh process;
  • enumerate framework-local devices through each MemoryType;
  • convert a small exact array through every available source-target pair;
  • stack and unstack one, two, and several planes in every framework;
  • verify same-framework movement across multiple local devices when present;
  • confirm absent frameworks are not imported by discovery or cleanup;
  • exercise DLPack success and explicit CPU fallback paths;
  • target OOM cleanup to the device that owned execution.

Use a fresh process and unset TensorFlow/JAX allocator variables when checking that declaration-owned import defaults work. Framework warnings about shared CUDA plugin registration should be recorded separately from value, device, or shape failures.

Interpreting CI

The main CI workflow runs the test suite across its declared Python and OS matrix. The manual GPU-named workflow installs CPU-capable Torch and JAX on a standard runner; it does not prove CUDA behavior. Real multi-framework and multi-device results therefore remain a required release check on suitable hardware.