:orphan:

.. Reason for orphan: This page is referenced by the installation
   instructions, which have moved from Sphinx to https://numpy.org/install.
   All install links in Sphinx now point there, leaving no Sphinx references
   to this page.


***************
Troubleshooting
***************

.. note::

    Since this information may be updated regularly, please ensure you are
    viewing the most `up-to-date version <https://numpy.org/devdocs/user/troubleshooting-importerror.html>`_.


ImportError
===========

In certain cases a failed installation or setup issue can cause you to
see the following error message::

    IMPORTANT: PLEASE READ THIS FOR ADVICE ON HOW TO SOLVE THIS ISSUE!

    Importing the numpy c-extensions failed. This error can happen for
    different reasons, often due to issues with your setup.

The error also has additional information to help you troubleshoot:

* Your Python version
* Your NumPy version

Please check both of these carefully to see if they are what you expect.
You may need to check your ``PATH`` or ``PYTHONPATH`` environment variables
(see `Check Environment Variables`_ below).

The following sections list commonly reported issues depending on your setup.
If you have an issue/solution that you think should appear please open a
NumPy issue so that it will be added.

There are a few commonly reported issues depending on your system/setup.
If none of the following tips help you, please be sure to note the following:

* how you installed Python
* how you installed NumPy
* your operating system
* whether or not you have multiple versions of Python installed
* if you built from source, your compiler versions and ideally a build log

when investigating further and asking for support.


Using Python from ``conda`` (Anaconda)
--------------------------------------

Please make sure that you have activated your conda environment.
See also the `conda user-guide <https://docs.conda.io/projects/conda/en/latest/user-guide/tasks/manage-environments.html#activating-an-environment>`_.
If you use an external editor/development environment it will have to be set
up correctly.  See below for solutions for some common setups.

Using PyCharm with Anaconda Python
----------------------------------

There are fairly common issues when using PyCharm together with Anaconda,
please see the `PyCharm support <https://www.jetbrains.com/help/pycharm/conda-support-creating-conda-virtual-environment.html>`_

Using VS Code with Anaconda Python (or environments)
----------------------------------------------------

A commonly reported issue is related to the environment activation within
VSCode. Please see the `VSCode support <https://code.visualstudio.com/docs/python/environments>`_
for information on how to correctly set up VSCode with virtual environments
or conda.

Using Eclipse/PyDev with Anaconda Python (or environments)
----------------------------------------------------------

Please see the
`Anaconda Documentation <https://docs.anaconda.com/anaconda/user-guide/tasks/integration/eclipse-pydev/>`_
on how to properly configure Eclipse/PyDev to use Anaconda Python with specific
conda environments.


Raspberry Pi
------------

There are sometimes issues reported on Raspberry Pi setups when installing
using ``pip3 install`` (or ``pip`` install). These will typically mention::

    libf77blas.so.3: cannot open shared object file: No such file or directory


The solution will be to either::

    sudo apt-get install libatlas-base-dev

to install the missing libraries expected by the self-compiled NumPy
(ATLAS is a possible provider of linear algebra).

*Alternatively* use the NumPy provided by Raspbian. In which case run::

    pip3 uninstall numpy  # remove previously installed version
    apt install python3-numpy


Debug build on Windows
----------------------

Rather than building your project in ``DEBUG`` mode on windows, try
building in ``RELEASE`` mode with debug symbols and no optimization.
Full ``DEBUG`` mode on windows changes the names of the DLLs python
expects to find, so if you wish to truly work in ``DEBUG`` mode you will
need to recompile the entire stack of python modules you work with
including NumPy


All setups
----------

Occasionally there may be simple issues with old or bad installations
of NumPy. In this case you may just try to uninstall and reinstall NumPy.
Make sure that NumPy is not found after uninstalling.


Development setup
-----------------

If you are using a development setup, make sure to run ``git clean -xdf``
to delete all files not under version control (be careful not to lose
any modifications you made, e.g. ``site.cfg``).
In many cases files from old builds may lead to incorrect builds.


Check environment variables
---------------------------

In general how to set and check your environment variables depends on
your system. If you can open a correct python shell, you can also run the
following in python::

    import os
    print("PYTHONPATH:", os.environ.get('PYTHONPATH'))
    print("PATH:", os.environ.get('PATH'))

This may mainly help you if you are not running the python and/or NumPy
version you are expecting to run.


Downstream ImportError, AttributeError or C-API/ABI incompatibility
===================================================================

If you see a message such as::

    A module that was compiled using NumPy 1.x cannot be run in
    NumPy 2.0.0 as it may crash. To support both 1.x and 2.x
    versions of NumPy, modules must be compiled with NumPy 2.0.
    Some module may need to rebuild instead e.g. with 'pybind11>=2.12'.

either as an ``ImportError`` or with::

    AttributeError: _ARRAY_API not found

or other errors such as::

    RuntimeError: module compiled against API version v1 but this version of numpy is v2

or when a package implemented with Cython::

    ValueError: numpy.dtype size changed, may indicate binary incompatibility. Expected 96 from C header, got 88 from PyObject

This means that a package depending on NumPy was build in a way that is not
compatible with the NumPy version found.
If this error is due to a recent upgrade to NumPy 2, the easiest solution may
be to simply downgrade NumPy to ``'numpy<2'``.

To understand the cause, search the traceback (from the back) to find the first
line that isn't inside NumPy to see which package has the incompatibility.
Note your NumPy version and the version of the incompatible package to
help you find the best solution.

There can be various reason for the incompatibility:

* You have recently upgraded NumPy, most likely to NumPy 2, and the other
  module now also needs to be upgraded.  (NumPy 2 was released in June 2024.)

* You have version constraints and ``pip`` may
  have installed a combination of incompatible packages.

* You have compiled locally or have copied a compiled extension from
  elsewhere (which is, in general, a bad idea).

The best solution will usually be to upgrade the failing package:

* If you installed it for example through ``pip``, try upgrading it with
  ``pip install package_name --upgrade``.

* If it is your own package or it is build locally, you need recompiled
  for the new NumPy version (for details see :ref:`depending_on_numpy`).
  It may be that a reinstall of the package is sufficient to fix it.

When these steps fail, you should inform the package maintainers since they
probably need to make a new, compatible, release.

However, upgrading may not always be possible because a compatible version does
not yet exist or cannot be installed for other reasons.  In that case:

* Install a compatible NumPy version:

  * Try downgrading NumPy with ``pip install 'numpy<2'``
    (NumPy 2 was released in June 2024).
  * If your NumPy version is old, you can try upgrading it for
    example with ``pip install numpy --upgrade``.

* Add additional version pins to the failing package to help ``pip``
  resolve compatible versions of NumPy and the package.



Segfaults or crashes
====================

NumPy tries to use advanced CPU features (SIMD) to speed up operations. If you
are getting an "illegal instruction" error or a segfault, one cause could be
that the environment claims it can support one or more of these features but
actually cannot. This can happen inside a docker image or a VM (qemu, VMWare,
...)

You can use the output of ``np.show_runtime()`` to show which SIMD features are
detected. For instance::

    >>> np.show_runtime()
    WARNING: `threadpoolctl` not found in system! Install it by `pip install \
    threadpoolctl`. Once installed, try `np.show_runtime` again for more detailed
    build information
    [{'simd_extensions': {'baseline': ['SSE', 'SSE2', 'SSE3'],
                          'found': ['SSSE3',
                                    'SSE41',
                                    'POPCNT',
                                    'SSE42',
                                    'AVX',
                                    'F16C',
                                    'FMA3',
                                    'AVX2'],
                          'not_found': ['AVX512F',
                                        'AVX512CD',
                                        'AVX512_KNL',
                                        'AVX512_KNM',
                                        'AVX512_SKX',
                                        'AVX512_CLX',
                                        'AVX512_CNL',
                                        'AVX512_ICL']}}]

In this case, it shows AVX2 and FMA3 under the ``found`` section, so you can
try disabling them by setting ``NPY_DISABLE_CPU_FEATURES="AVX2,FMA3"`` in your
environment before running python (for cmd.exe on windows)::

    >SET NPY_DISABLE_CPU_FEATURES="AVX2,FMA3"
    >python <myprogram.py>

By installing threadpoolctl ``np.show_runtime()`` will show additional information::

    ...
    {'architecture': 'Zen',
      'filepath': '/tmp/venv3/lib/python3.9/site-packages/numpy.libs/libopenblas64_p-r0-15028c96.3.21.so',
      'internal_api': 'openblas',
      'num_threads': 24,
      'prefix': 'libopenblas',
      'threading_layer': 'pthreads',
      'user_api': 'blas',
      'version': '0.3.21'}]

If you use the wheel from PyPI, it contains code from the OpenBLAS project to
speed up matrix operations. This code too can try to use SIMD instructions. It
has a different mechanism for choosing which to use, based on a CPU
architecture, You can override this architecture by setting
``OPENBLAS_CORETYPE``: a minimal value for ``x86_64`` is
``OPENBLAS_CORETYPE=Haswell``.  This too needs to be set before running your
python (this time for posix)::

    $ OPENBLAS_CORETYPE=Haswell python <myprogram.py>
