• [Python-announce] ANN: NumExpr 2.10.0 with support for NumPy 2.0.0 is out!

    From Francesc Alted@faltet@gmail.com to comp.lang.python.announce on Tue Apr 2 11:19:46 2024
    From Newsgroup: comp.lang.python.announce

    =========================
    Announcing NumExpr 2.10.0
    =========================
    Hi everyone,
    NumExpr 2.10.0 is a release offering support for latest versions of NumPy 2.0. This is still experimental, so please report any issues you find. Thanks to Clément Robert and Thomas Caswell for the work.
    Project documentation is available at:
    http://numexpr.readthedocs.io/
    Changes from 2.9.0 to 2.10.0
    ----------------------------
    * Support for NumPy 2.0.0. This is still experimental, so please
    report any issues you find. Thanks to Clément Robert and Thomas
    Caswell for the work.
    * Avoid erroring when OMP_NUM_THREADS is empty string. Thanks to
    Patrick Hoefler.
    * Do not warn if OMP_NUM_THREAD set.
    What's Numexpr?
    ---------------
    Numexpr is a fast numerical expression evaluator for NumPy. With it, expressions that operate on arrays (like "3*a+4*b") are accelerated
    and use less memory than doing the same calculation in Python.
    It has multi-threaded capabilities, as well as support for Intel's
    MKL (Math Kernel Library), which allows an extremely fast evaluation
    of transcendental functions (sin, cos, tan, exp, log...) while
    squeezing the last drop of performance out of your multi-core
    processors. Look here for a some benchmarks of numexpr using MKL: https://github.com/pydata/numexpr/wiki/NumexprMKL
    Its only dependency is NumPy (MKL is optional), so it works well as an easy-to-deploy, easy-to-use, computational engine for projects that
    don't want to adopt other solutions requiring more heavy dependencies.
    Where I can find Numexpr?
    -------------------------
    The project is hosted at GitHub in:
    https://github.com/pydata/numexpr
    You can get the packages from PyPI as well (but not for RC releases): http://pypi.python.org/pypi/numexpr
    Documentation is hosted at:
    http://numexpr.readthedocs.io/en/latest/
    Share your experience
    ---------------------
    Let us know of any bugs, suggestions, gripes, kudos, etc. you may
    have.
    Enjoy data!
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