========================= Announcing Numexpr 2.7.0 =========================
Hi everyone, This is a minor version bump for NumExpr. We would like to highlight the changes made in 2.6.9 (which in retrospect should have been a minor version bump), where the maximum number of threads spawned can be limited by setting the environment variable "NUMEXPR_MAX_THREADS". If this variable is not set, in 2.7.0 the historical limit of 8 threads will be used. The lack of a check caused some problems on very large hosts in cluster environments in 2.6.9. In addition, we are officially dropping Python 2.6 support in this release as we cannot perform continuous integration for it. Project documentation is available at: http://numexpr.readthedocs.io/ Changes from 2.6.9 to 2.7.0 ---------------------------- - The default number of 'safe' threads has been restored to the historical limit of 8, if the environment variable "NUMEXPR_MAX_THREADS" has not been set. - Thanks to @eltoder who fixed a small memory leak. - Support for Python 2.6 has been dropped, as it is no longer available via TravisCI. - A typo in the test suite that had a less than rather than greater than symbol in the NumPy version check has been corrected thanks to dhomeier. - The file `site.cfg` was being accidentally included in the sdists on PyPi. It has now been excluded. 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! -- Robert McLeod robbmcl...@gmail.com robert.mcl...@hitachi-hhtc.ca -- Python-announce-list mailing list -- python-announce-list@python.org To unsubscribe send an email to python-announce-list-le...@python.org https://mail.python.org/mailman3/lists/python-announce-list.python.org/ Support the Python Software Foundation: http://www.python.org/psf/donations/
