Opened 16 months ago
Closed 16 months ago
#18710 closed enhancement (fixed)
numpy-1.26.1 (Python module)
Reported by: | Bruce Dubbs | Owned by: | Douglas R. Reno |
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Priority: | normal | Milestone: | 12.1 |
Component: | BOOK | Version: | git |
Severity: | normal | Keywords: | |
Cc: |
Description
New point version.
Change History (3)
comment:1 by , 16 months ago
Owner: | changed from | to
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Status: | new → assigned |
comment:2 by , 16 months ago
comment:3 by , 16 months ago
Resolution: | → fixed |
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Status: | assigned → closed |
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NumPy 1.26.1 Release Notes
NumPy 1.26.1 is a maintenance release that fixes bugs and regressions discovered after the 1.26.0 release. In addition, it adds new functionality for detecting BLAS and LAPACK when building from source. Highlights are:
The 1.26.release series is the last planned minor release series before NumPy 2.0. The Python versions supported by this release are 3.9-3.12.
Build system changes
Improved BLAS/LAPACK detection and control
Auto-detection for a number of BLAS and LAPACK is now implemented for Meson. By default, the build system will try to detect MKL, Accelerate (on macOS >=13.3), OpenBLAS, FlexiBLAS, BLIS and reference BLAS/LAPACK. Support for MKL was significantly improved, and support for FlexiBLAS was added.
New command-line flags are available to further control the selection of the BLAS and LAPACK libraries to build against.
To select a specific library, use the config-settings interface via pip or pypa/build. E.g., to select libblas/liblapack, use:
$ pip install numpy -Csetup-args=-Dblas=blas -Csetup-args=-Dlapack=lapack $ # OR $ python -m build . -Csetup-args=-Dblas=blas -Csetup-args=-Dlapack=lapack
This works not only for the libraries named above, but for any library that Meson is able to detect with the given name through pkg-config or CMake.
Besides -Dblas and -Dlapack, a number of other new flags are available to control BLAS/LAPACK selection and behavior:
New features
numpy._core submodule stubs
numpy._core submodule stubs were added to provide compatibility with pickled arrays created using NumPy 2.0 when running Numpy 1.26.
Pull requests merged
A total of 20 pull requests were merged for this release.