Hi all, Over the past couple of days Nathaniel Smith, Robert McGibbon, Matthew Brett and I did a bunch of testing and bug fixing to get a working build environment to generate binary packages for cython, numpy, scipy, numpexpr, pandas, scikit-learn and statsmodels using an embedded OpenBLAS 0.2.17 on Linux (only x86_64 and i686 platforms).
Here is Matthew's call to tests of the resulting packages on the numpy mailing list: https://mail.scipy.org/pipermail/numpy-discussion/2016-April/075234.html You need pip 8.1 or later to install them (they are ignored by previous versions of pip). Please test those wheels on your machines, especially if you run exotic Linux versions or less common CPU architectures. If nobody has an objection I plan to upload the wheels for scikit-learn 0.17.1 to PyPI as soon as numpy and scipy have their own wheel packages uploaded there (probably during the coming week if no blocker is reported in the mean time). For those interested in the technical details, here are the tools that we use to generate those wheels: - manylinux is the official docker image that is used to build the original wheels from source for various versions of Python a controlled binary environment (based on Centos 5.11 and gcc/g++/gfortran 4.8). - auditwheel is a Python program to embed compiled dependencies into the wheel (e.g. libopenblas.so) https://github.com/pypa/auditwheel - Matthew's script to build wheels for the last releases of the scipy stack projects "en masse": https://github.com/matthew-brett/manylinux-builds/ The manylinux1 platform tag it-self is documented in: https://www.python.org/dev/peps/pep-0513/ Note that those wheels will not install on Alpine Linux or any other Linux distribution that does not use glibc. Alpine Linux in particular uses MUSL libc. Happy testing! -- Olivier http://twitter.com/ogrisel - http://github.com/ogrisel ------------------------------------------------------------------------------ Transform Data into Opportunity. Accelerate data analysis in your applications with Intel Data Analytics Acceleration Library. Click to learn more. http://pubads.g.doubleclick.net/gampad/clk?id=278785471&iu=/4140 _______________________________________________ Scikit-learn-general mailing list Scikit-learn-general@lists.sourceforge.net https://lists.sourceforge.net/lists/listinfo/scikit-learn-general
