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Building from source

A general overview of building NumPy from source is given here, with detailed instructions for specific platforms given separately.

Prerequisites

Building NumPy requires the following software installed:

Basic Installation

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.

Note: for build instructions to do development work on NumPy itself, seeSetting up and using your development environmentopen in new window.

Testing

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 buildsopen in new window.

Parallel 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.

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.

Accelerated BLAS/LAPACK libraries

NumPy searches for optimized linear algebra libraries such as BLAS and LAPACK. There are specific orders for searching these libraries, as described below.

BLAS

The default order for the libraries are:

  1. MKL
  2. BLIS
  3. OpenBLAS
  4. ATLAS
  5. Accelerate (MacOS)
  6. 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=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).

LAPACK

The default order for the libraries are:

  1. MKL
  2. OpenBLAS
  3. libFLAME
  4. ATLAS
  5. Accelerate (MacOS)
  6. 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=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).

Disabling ATLAS and other accelerated libraries

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

Supplying additional compiler flags

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.

Building with ATLAS support

Ubuntu

You can install the necessary package for optimized ATLAS with this command:

sudo apt-get install libatlas-base-dev