Add conda-forge only build and test workflow - #3035
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View rendered docs @ https://intelpython.github.io/dpnp/index.html |
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Array API standard conformance tests for dpnp=0.21.0dev6=py314ha0e2e8e_18 ran successfully. |
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coverage: 78.477%. remained the same — add-conda-forge-package-workflow into master |
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Add a 'Conda package (conda-forge)' workflow with a dedicated conda-recipe-cf recipe that builds and tests dpnp using compiler, oneMKL, dpctl and NumPy dependencies resolved solely from conda-forge.
The host dependency on dpctl was missing, so find_package(Dpctl) had nothing to locate in the build environment.
A free-threaded (cp314t) dpctl is not yet available on conda-forge, so the free-threaded build/test entries fail to solve. Remove them from the build, test_linux and test_windows matrices with a TODO to restore them once conda-forge ships a free-threaded dpctl.
…pe-cf conda-forge folds the Intel compiler runtime libraries (libircmt.lib on Windows, libirc.a on Linux) into the build-only dpcpp_impl package, so they land in the build prefix and are off the linker's default search path. Add that directory via LIB (Windows) / LIBRARY_PATH (Linux) so icx can link.
dpctl.get_devices() returns a list in dpctl < 0.23 and a tuple from 0.23 onwards, so gate the tuple-equality assertion with with_requires.
Everything in this lane comes from conda-forge, so the Intel-channel set-intel-ocl-icd-registry.ps1 helper and cl.cfg are never present. Drop the dead branches and keep only the manual ICD-loader registration of intelocl64.dll.
Intel internal validation scans every recipe meta.yaml in the source tree and requires the LEGAL NOTICE / EULA block in about/description, so keep it in sync with conda-recipe/meta.yaml.
dpctl 0.23 changed get_devices() and SyclDevice.create_sub_devices() to return tuples instead of lists. Gate the affected tensor tests with with_requires so they skip on older dpctl (e.g. conda-forge 0.22.1).
Unify the two test jobs into a single matrix job (ubuntu-latest, windows-2022) using bash for both OSes, keeping only Set Swap Space (Linux) and the OpenCL CPU driver registration (Windows) OS-specific. Also drop the unused cmd-only steps carried over from the legacy layout.
Move the build and test jobs into a reusable workflow_call workflow (conda-build-test.yml) parameterized by runner, conda subdir, channels and recipe dir. Reduce conda-package-cf.yml to thin per-OS callers with a concurrency group replacing the cancel-workflow action.
github.workspace and $CONDA are backslash paths on Windows that bash mangles. Use workspace-relative paths for the channel/version/repo dirs and convert $CONDA via cygpath before sourcing conda in the retry steps.
Under Git Bash on Windows the mamba wrapper mangles the -c ./channel
argument ('hannel' is not recognized), while conda parses it correctly.
Switch install/list to conda; keep use-mamba for the libmamba solver.
- conda-recipe-cf: remove unused py_build_deps load; bump cmake to >=3.31.6 and python-build to >=1.2.2 to match pyproject.toml; drop the run_test-duplicated 'python -m dpctl -f' from test.commands. - conda-build-test.yml: drop the no-op MAMBA_NO_LOW_SPEED_LIMIT and the dead non-retry test branches (rerun-tests-on-failure toggle).
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LGTM, thank you @antonwolfy
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This PR adds a `Conda package (conda-forge)` workflow that builds and tests dpnp using its build/runtime dependencies (compiler, oneMKL, dpctl and NumPy) resolved purely from conda-forge with `--override-channels`. It validates that dpnp works when built and installed solely from conda-forge, independently of the Intel channel. The workflow relies on a dedicated `conda-recipe-cf` recipe: - pins the compiler and oneMKL to conda-forge builds (`gxx 15` / `sysroot 2.28` / NumPy 2); - lists `cmake` and `ninja` under `requirements/build` and keeps the remaining host dependencies in sync with `pyproject.toml`; - always relies on the conda-forge OpenCL ICD loaders (`ocl-icd-system` on Linux, `khronos-opencl-icd-loader` on Windows). Build and test stages follow the same naming and structure as the existing `Conda package` workflow: a build matrix over Python 3.10-3.14 (including the free-threaded builds), retry-on-failure test runs, separate tensor tests, and OpenCL CPU driver registration on Windows. 817eafb
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This PR adds a
Conda package (conda-forge)workflow that builds and tests dpnp using its build/runtime dependencies (compiler, oneMKL, dpctl and NumPy) resolved purely from conda-forge with--override-channels. It validates that dpnp works when built and installed solely from conda-forge, independently of the Intel channel.The workflow relies on a dedicated
conda-recipe-cfrecipe:gxx 15/sysroot 2.28/ NumPy 2);cmakeandninjaunderrequirements/buildand keeps the remaining host dependencies in sync withpyproject.toml;ocl-icd-systemon Linux,khronos-opencl-icd-loaderon Windows).Build and test stages follow the same naming and structure as the existing
Conda packageworkflow: a build matrix over Python 3.10-3.14 (including the free-threaded builds), retry-on-failure test runs, separate tensor tests, and OpenCL CPU driver registration on Windows.