A general overview of building NumPy from source is given here, with detailed instructions for specific platforms given separately.
Building NumPy requires the following software installed:
Python 2.7.x, 3.4.x or newer
On Debian and derivatives (Ubuntu): python, python-dev (or python3-dev)
On Windows: the official python installer at www.python.org is enough
Make sure that the Python package distutils is installed before continuing. For example, in Debian GNU/Linux, installing python-dev also installs distutils.
Python must also be compiled with the zlib module enabled. This is practically always the case with pre-packaged Pythons.
Compilers
To build any extension modules for Python, you’ll need a C compiler. Various NumPy modules use FORTRAN 77 libraries, so you’ll also need a FORTRAN 77 compiler installed.
Note that NumPy is developed mainly using GNU compilers. Compilers from other vendors such as Intel, Absoft, Sun, NAG, Compaq, Vast, Portland, Lahey, HP, IBM, Microsoft are only supported in the form of community feedback, and may not work out of the box. GCC 4.x (and later) compilers are recommended.
Linear Algebra libraries
NumPy does not require any external linear algebra libraries to be installed. However, if these are available, NumPy’s setup script can detect them and use them for building. A number of different LAPACK library setups can be used, including optimized LAPACK libraries such as ATLAS, MKL or the Accelerate/vecLib framework on OS X.
Cython
To build development versions of NumPy, you’ll need a recent version of Cython. Released NumPy sources on PyPi include the C files generated from Cython code, so for released versions having Cython installed isn’t needed.
To install NumPy run:
pip install .
To perform an in-place build that can be run from the source folder run:
python setup.py build_ext --inplace
The NumPy build system uses setuptools (from numpy 1.11.0, before that it was plain distutils) and numpy.distutils. Using virtualenv should work as expected.
setuptools
distutils
numpy.distutils
virtualenv
Note: for build instructions to do development work on NumPy itself, see Setting up and using your development environment.
Make sure to test your builds. To ensure everything stays in shape, see if all tests pass:
$ python runtests.py -v -m full
For detailed info on testing, see Testing builds.
From NumPy 1.10.0 on it’s also possible to do a parallel build with:
python setup.py build -j 4 install --prefix $HOME/.local
This will compile numpy on 4 CPUs and install it into the specified prefix. to perform a parallel in-place build, run:
python setup.py build_ext --inplace -j 4
The number of build jobs can also be specified via the environment variable NPY_NUM_BUILD_JOBS.
NPY_NUM_BUILD_JOBS
The two most popular open source fortran compilers are g77 and gfortran. Unfortunately, they are not ABI compatible, which means that concretely you should avoid mixing libraries built with one with another. In particular, if your blas/lapack/atlas is built with g77, you must use g77 when building numpy and scipy; on the contrary, if your atlas is built with gfortran, you must build numpy/scipy with gfortran. This applies for most other cases where different FORTRAN compilers might have been used.
To build with gfortran:
python setup.py build --fcompiler=gnu95
For more information see:
python setup.py build --help-fcompiler
One relatively simple and reliable way to check for the compiler used to build a library is to use ldd on the library. If libg2c.so is a dependency, this means that g77 has been used. If libgfortran.so is a dependency, gfortran has been used. If both are dependencies, this means both have been used, which is almost always a very bad idea.
NumPy searches for optimized linear algebra libraries such as BLAS and LAPACK. There are specific orders for searching these libraries, as described below.
The default order for the libraries are:
MKL
BLIS
OpenBLAS
ATLAS
Accelerate (MacOS)
BLAS (NetLIB)
If you wish to build against OpenBLAS but you also have BLIS available one may predefine the order of searching via the environment variable NPY_BLAS_ORDER which is a comma-separated list of the above names which is used to determine what to search for, for instance:
NPY_BLAS_ORDER
NPY_BLAS_ORDER=ATLAS,blis,openblas,MKL python setup.py build
will prefer to use ATLAS, then BLIS, then OpenBLAS and as a last resort MKL. If neither of these exists the build will fail (names are compared lower case).
libFLAME
LAPACK (NetLIB)
If you wish to build against OpenBLAS but you also have MKL available one may predefine the order of searching via the environment variable NPY_LAPACK_ORDER which is a comma-separated list of the above names, for instance:
NPY_LAPACK_ORDER
NPY_LAPACK_ORDER=ATLAS,openblas,MKL python setup.py build
will prefer to use ATLAS, then OpenBLAS and as a last resort MKL. If neither of these exists the build will fail (names are compared lower case).
Usage of ATLAS and other accelerated libraries in NumPy can be disabled via:
NPY_BLAS_ORDER= NPY_LAPACK_ORDER= python setup.py build
or:
BLAS=None LAPACK=None ATLAS=None python setup.py build
Additional compiler flags can be supplied by setting the OPT, FOPT (for Fortran), and CC environment variables. When providing options that should improve the performance of the code ensure that you also set -DNDEBUG so that debugging code is not executed.
OPT
FOPT
CC
-DNDEBUG
You can install the necessary package for optimized ATLAS with this command:
sudo apt-get install libatlas-base-dev
