pyarma | PyArmadillo - linear algebra library for Python - https

A precompiled version of PyArmadillo is available via the Python Package Index (PyPI). Use the following command for installation: pip3 install --user pyarma or pip3 install pyarma.
A precompiled version of PyArmadillo is available via the Python Package Index (PyPI)
Use the following command for installation: pip3 install --user pyarma or pip3 install pyarma
If pip3 cannot be found, try using the following alternatives: python3 -m pip py -m pip
To upgrade PyArmadillo via pip: pip3 install --upgrade --user pyarma or pip3 install --upgrade pyarma NOTE: It's possible that pip may erroneously not find the newest version. In that case, try the following command: pip3 install --no-cache-dir --upgrade --user pyarma or pip3 install --no-cache-dir --upgrade pyarma
More info on the pyarma package at PyPI: https://pypi.org/project/pyarma/
Preliminaries Installing PyArmadillo from source requires: at least Python 3.6; the minimum recommended version is Python 3.8 a C++ compiler that supports at least the C++11 standard at least 8 GB of RAM 64-bit CPU, preferably with 4+ cores OpenBLAS and LAPACK (or compatible implementations) Linux based operating systems (eg. Fedora, Ubuntu, CentOS, Red Hat, Debian, etc) First install OpenBLAS, LAPACK, Python 3, and pip3, along with the corresponding development/header files On CentOS 8 / RHEL 8, the CentOS PowerTools repository may need to be enabled: dnf config-manager --set-enabled powertools Recommended packages to install before installing PyArmadillo: Fedora, CentOS, RHEL: gcc-c++, libstdc++-devel, openblas-devel, lapack-devel, python3-devel, python3-pip Ubuntu and Debian: g++, libopenblas-dev, liblapack-dev, python3-dev, python3-pip pip3 needs to be updated: pip3 install --user --upgrade pip macOS First install Xcode (version 8 or later) and then type the following command in a terminal window: xcode-select --install Xcode command-line tools include the Python 3 development files, but pip3 needs to be updated: pip3 install --user --upgrade pip The "Accelerate" framework is used for accessing BLAS and LAPACK functions Windows (x64) First install Microsoft Visual Studio (2019 or later) Use the x64 Native Tools Command Prompt PyArmadillo contains pre-compiled OpenBLAS, which is used for accessing BLAS and LAPACK functions pip3 needs to be updated: py -m pip install --user --upgrade pip Alternative implementations and/or distributions of BLAS and LAPACK are available at: http://software.intel.com/en-us/intel-mkl/ http://icl.cs.utk.edu/lapack-for-windows/lapack/ http://ylzhao.blogspot.com.au/2013/10/blas-lapack-precompiled-binaries-for.html Caveat: 32-bit Windows (x86) is currently not supported
Running the Installer Open a terminal window and change into the directory containing PyArmadillo sources if the source was obtained as a package downloaded from SourceForge: tar xf pyarmadillo-0.123.4.tar.xz cd pyarmadillo-0.123.4 (change 0.123.4 to match the downloaded version) if the source was obtained by cloning the GitLab repo: git clone https://gitlab.com/jason-rumengan/pyarma/ cd pyarma Execute the following command: pip3 install --user . NOTE: the full stop character at the end is important To see the progress of installation, change the above command to pip3 install --verbose --user . If pip3 cannot be found, try using the following alternatives: python3 -m pip py -m pip Installation may take 5 to 20 minutes due to compiling C++ sources that extensively use template metaprogramming; the time taken depends on the number of CPU cores and the amount of available memory Caveat: on systems with low memory (< 8 GB), parallel compilation may fail due to template metaprogramming requiring large amounts of memory. To avoid parallel compilation, first install scikit-build using pip3 install --user scikit-build and then install PyArmadillo using python setup.py install -- -- -j1 Link-time optimisation (LTO) is off by default. LTO reduces the size of PyArmadillo at the expense of considerably longer compilation time. To enable LTO, first install scikit-build and ninja, and then enable the PYARMA_LTO option during installation: pip3 install --user scikit-build ninja python3 setup.py install -DPYARMA_LTO=ON
Support for Intel MKL and Other BLAS/LAPACK Implementations PyArmadillo can optionally use the Intel Math Kernel Library (MKL) as high-speed replacement for standard BLAS and LAPACK Intel MKL should be automatically detected during installation from source For other BLAS/LAPACK implementations, minor modifications to the built-in Armadillo sources may be required. Specifically ext/armadillo/include/armadillo_bits/config.hpp may need to be edited to ensure Armadillo uses the same integer sizes and style of function names as used by the replacement libraries. The following defines may need to be enabled or disabled: ARMA_BLAS_CAPITALS ARMA_BLAS_UNDERSCORE ARMA_BLAS_LONG ARMA_BLAS_LONG_LONG See the Armadillo site for more information: http://arma.sourceforge.net/faq.html http://arma.sourceforge.net/docs.html#config_hpp On Linux-based systems, MKL might be installed in a non-standard location such as /opt which can cause problems during linking. Before installing PyArmadillo, the system should know where the MKL libraries are located. For example, /opt/intel/mkl/lib/intel64/. This can be achieved by setting the LD_LIBRARY_PATH environment variable, or for a more permanent solution, adding the directory locations to /etc/ld.so.conf. It may also be possible to store a text file with the locations in the /etc/ld.so.conf.d directory. For example, /etc/ld.so.conf.d/mkl.conf. If /etc/ld.so.conf is modified or /etc/ld.so.conf.d/mkl.conf is created, /sbin/ldconfig must be run afterwards. Below is an example of /etc/ld.so.conf.d/mkl.conf where Intel MKL is installed in /opt/intel /opt/intel/lib/intel64 /opt/intel/mkl/lib/intel64 If MKL is installed and it is persistently giving problems during linking, support for MKL can be disabled by editing ext/armadillo/CMakeLists.txt and commenting out the line containing INCLUDE(ARMA_FindMKL), then deleting ext/armadillo/CMakeCache.txt, and finally re-running PyArmadillo installation.