Building from source¶
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.
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.
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:
python setup.py 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
virtualenv should work as expected.
Note: for build instructions to do development work on NumPy itself, see Setting up and using your development environment.
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
FORTRAN ABI mismatch¶
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.
Choosing the fortran compiler¶
To build with gfortran:
python setup.py build --fcompiler=gnu95
For more information see:
python setup.py build --help-fcompiler
How to check the ABI of blas/lapack/atlas¶
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.
Disabling ATLAS and other accelerated libraries¶
Usage of ATLAS and other accelerated libraries in NumPy can be disabled via:
BLAS=None LAPACK=None ATLAS=None python setup.py build
Supplying additional compiler flags¶
Additional compiler flags can be supplied by setting the
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.