doc/source/dev/toolchain.rst
.. _toolchain-roadmap:
The use of the SciPy library requires (or optionally depends upon) several other libraries in order to operate, the main dependencies being Python and NumPy. It requires a larger collection of libraries and tools in order to build the library or to build the documentation.
Of course, the tooling and libraries are themselves not static. This document aims to provide a guide as to how SciPy's use of these dynamic dependencies will proceed over time.
SciPy aims to be compatible with a number of releases of its dependent libraries and tools. Forcing the user base to upgrade other components for every release would greatly diminish the value of SciPy. However, maintaining backwards compatibility with very old tooling/libraries imposes limitations on which newer functionalities and capabilities can be incorporated. SciPy takes a somewhat conservative approach, maintaining compatibility with several major releases of Python and NumPy on the major platforms. (That may in and of itself impose further restrictions. See the C Compilers section for an example.)
numpy package to be installed.pytest and hypothesis Python packages.matplotlib, Sphinx and MyST-NB_ packages along with PyData theme... _MyST-NB: https://myst-nb.readthedocs.io/
The tooling used to build CPython has some implications for the tooling used in building SciPy. It also has implications for the examples used in the documentation (e.g., docstrings for functions), as these examples can only use functionality present in all supported configurations.
Python Versions ^^^^^^^^^^^^^^^
SciPy is compatible with several versions of Python. When dropping support for older Python versions, SciPy takes guidance from [NEP29]_. Generally, support for the oldest Python version is dropped 42 months after the original release. Following the acceptance of PEP 602, this mostly happens in April, and gets picked up by the mid-year release of SciPy.
.. dropdown:: Python version support over time
Python 2.7 support was dropped starting from SciPy 1.3.
================ =======================================================================
Date Pythons supported
================ =======================================================================
2026 Py3.12+
2025 Py3.11+
2024 Py3.10+
2023 Py3.9+
2022 Py3.8+
2021 Py3.7+
2020 Py3.6+
2019 Py3.5+
2018 Py2.7, Py3.4+
================ =======================================================================
NumPy ^^^^^
SciPy depends on NumPy but releases of SciPy are not tied to releases of NumPy.
SciPy attempts to be compatible with at least the 4 previous releases of NumPy.
In particular, SciPy cannot rely on features of just the latest NumPy, but
needs to be written using what is common in all of those 4 NumPy releases_.
.. _NumPy releases: https://numpy.org/doc/stable/release.html
.. dropdown:: Python and NumPy version support per SciPy version
The table shows the NumPy and Python versions suitable for each minor SciPy
version. Note that not all patch versions for a particular minor version of
SciPy support all listed versions of Python. Only the most recent patch version
within each minor version is guaranteed to support all listed Python versions.
================= ======================== =======================
SciPy version Python versions NumPy versions
================= ======================== =======================
1.16 >=3.11, <3.14 >=1.25.2, <2.6.0
1.15 >=3.10, <3.14 >=1.23.5, <2.5.0
1.14 >=3.10, <3.14 >=1.23.5, <2.3.0
1.13 >=3.9, <3.13 >=1.22.4, <2.3.0
1.12 >=3.9, <3.13 >=1.22.4, <2.0.0
1.11 >=3.9, <3.13 >=1.21.6, <1.27.0
1.10 >=3.8, <3.12 >=1.19.5, <1.26.0
1.9 >=3.8, <3.12 >=1.18.5, <1.26.0
1.8 >=3.8, <3.11 >=1.17.3, <1.24.0
1.7 >=3.7, <3.11 >=1.16.5, <1.23.0
1.6 >=3.7, <3.10 >=1.16.5, <1.21.0
1.5 >=3.6, <3.10 >=1.14.5, <1.20.0
1.4 >=3.5, <3.9 >=1.13.3, <1.18.0
1.2 2.7, >=3.4, <3.8 >=1.8.2, <1.17.0
================= ======================== =======================
In specific cases, such as a particular architecture, these requirements
could vary. Please check the release notes_ and the meta-package
oldest-supported-numpy for more info [OSN]_.
.. _release notes: https://scipy.github.io/devdocs/release.html
Compilers ^^^^^^^^^
Building SciPy requires compilers for C, C++, Fortran, as well as the python transpilers Cython and Pythran (the latter is an opt-out dependency starting from version 1.7.0).
To maintain compatibility with a large number of platforms & setups, especially where using the official wheels (or other distribution channels like Anaconda or conda-forge) is not possible, SciPy tries to keep compatibility with older compilers, on platforms that have not yet reached their official end-of-life.
As explained in more detail below, the current minimal compiler versions are:
========== =========================== =============================== ============================ Compiler Default Platform (tested) Secondary Platform (untested) Minimal Version ========== =========================== =============================== ============================ GCC Linux AIX, Alpine Linux, OSX GCC 9.x LLVM OSX Linux, FreeBSD, Windows LLVM 12.x MSVC Windows - Visual Studio 2019 (vc142) ========== =========================== =============================== ============================
Note that there is currently no dedicated CI job to test the minimum supported LLVM/Clang version. Older versions than used in SciPy CI should work, as long as they support core (non-stdlib) C++17. Please file an issue if you encounter a problem during compilation.
Official Builds
Currently, SciPy wheels are being built as follows:
========================= ============================== ==================================== =============================
Platform `CI`_ `Base`_ `Images`_ Compilers Comment
========================= ============================== ==================================== =============================
Linux x86 ``ubuntu-22.04`` GCC 10.2.1 ``cibuildwheel``
Linux arm ``docker-builder-arm64`` GCC 11.3.0 ``cibuildwheel``
OSX x86_64 (OpenBLAS) ``macos-15-intel`` Apple clang 13.1.6/gfortran 15.2.0 ``cibuildwheel``
OSX x86_64 (Accelerate) ``macos-15-intel`` Apple clang 15.0.0/gfortran 13.2.0 ``cibuildwheel``
OSX arm64 (OpenBLAS) ``macos-14`` Apple clang 15.0.0/gfortran 12.1.0 ``cibuildwheel``
OSX arm64 (Accelerate) ``macos-14`` Apple clang 15.0.0/gfortran 13.2.0 ``cibuildwheel``
Windows ``windows-2025`` GCC 15.2.0 (`rtools`_) ``cibuildwheel``
========================= ============================== ==================================== =============================
.. _CI: https://github.com/actions/runner-images
.. _Base: https://cirrus-ci.org/guide/docker-builder-vm/#under-the-hood
.. _Images: https://github.com/orgs/cirruslabs/packages?tab=packages&q=macos
.. _rtools: https://community.chocolatey.org/packages/rtools#versionhistory
Note that the OSX wheels additionally vendor the libgfortran dylib.
C Compilers
~~~~~~~~~~~
SciPy is compatible with most modern C compilers (in particular ``clang``).
Nowadays, there is reasonable support for recent C language standards across
all relevant compilers, though this is very different from how things used to
be. The following paragraphs primarily discuss the evolution of these
constraints; readers who do not care about historical context can skip ahead
to the table at the end.
.. dropdown:: Historical context around ABI vs. compiler support vs. C standards
In the past, the most restrictive compiler on relevant platforms in terms
of C support was the Microsoft Visual C++ compiler & toolset (together known
as MSVC; it has a complicated `version scheme`_) [MSVC]_.
Up until Visual Studio 2013, each MSVC version came with
an updated C Runtime (CRT) library that was incompatible with the previous
ones.
This lack of compatibility of the Application Binary Interface (ABI) meant
that all projects wanting to communicate across this interface (e.g. calling a
function from a shared library) needed to be (re)compiled with the same MSVC
version. The long support of CPython 2.7 meant that python itself was stuck
for a long time with VS 2008 (in order not to break the ABI in patch
releases), and thus SciPy was stuck on that version as well.
The use of VS 2008 (which doesn't have support for C99) to compile builds for
CPython 2.7 meant for a long time that C code in SciPy has had to conform
to the earlier C90 standard for the language and standard library. After
dropping support for CPython 2.7 in SciPy 1.3.x, that restriction was finally
lifted (though only gradually at first).
With the introduction of the "Universal C Runtime" [UCRT]_ since the
release of Visual Studio 2015, the ABI of C Runtime has been stable, which
means that the restriction of having to use the same compiler version for
SciPy as for the underlying CPython version is no longer applicable. This
stability is not indefinite though: Microsoft has been planning an
ABI-breaking release - across the compiler resp. C/C++ standard libraries -
(tentatively called "`vNext`_") for quite a while, but so far it is unclear
when this will arrive. Once that happens, SciPy will again be restricted to
at most the last ABI-compatible Visual Studio release (currently VS 2022)
until all CPython versions supported according to NEP29 have been built
upstream with vNext-compatible compilers.
More specifically, there is a distinction between the Microsoft Visual
Studio version and the version of the targeted "`toolset`_", which is defined
as "The Microsoft C++ compiler, linker, standard libraries, and related
utilities". Each version of Visual Studio comes with a default version of the
MSVC toolset (for example VS2017 with vc141, VS2019 with vc142), but it is
possible to target older toolsets even in newer versions of Visual Studio.
Due to the nature of compilers (i.e. split into frontend and backend), it
depends whether the limiting factor for supporting a given feature (e.g. in C)
is due to the version of Visual Studio or the toolset, but in general the
latter is a harder barrier and thus the effective lower bound.
This is due to the fact that while the ABI stays compatible between toolset
versions (until vNext), all linking operations must use a toolset at least
as new as the one used to build any of the involved artefacts, meaning that
toolset version bumps tend to be "infectious", as in: requiring all consuming
libraries to also bump their toolset (and probably compiler) version. This is
more of an issue for NumPy than SciPy, as the latter has only a small C API
and is compiled against by far fewer projects than NumPy. Additionally, using
a newer toolset means that users of libraries that compile C++ code (as SciPy
does) might also need a newer Microsoft Visual C++ `Redistributable`_, which
might have to be distributed to them.
Summing up, the minimal requirement for the MSVC compiler resp. toolset per
SciPy version was determined predominantly by the oldest supported CPython
version at the time. The first SciPy version to raise the minimal requirement
beyond that was SciPy 1.9, due to the inclusion of the HiGHS submodule, which
does not compile with vc141 (and the aggressive removal of VS2017 in public CI
making it infeasible to keep ensuring that everything everywhere works with
non-default toolset versions).
.. _version scheme: https://en.wikipedia.org/wiki/Microsoft_Visual_C%2B%2B#Internal_version_numbering
.. _vNext: https://github.com/microsoft/STL/issues/169
.. _toolset: https://docs.microsoft.com/en-us/cpp/build/projects-and-build-systems-cpp#the-msvc-toolset
.. _Redistributable: https://docs.microsoft.com/en-us/cpp/windows/latest-supported-vc-redist
============== ================= ================= =================
SciPy version CPython support MS Visual C++ Toolset version
============== ================= ================= =================
Until 1.2 2.7 & 3.4+ VS 2008 (9.0) vc90
1.3, 1.4 3.5+ VS 2010 (10.0) vc100
1.5 3.6+ VS 2015 (14.0) vc140
1.6, 1.7 3.7+ VS 2017 (14.1) vc141
1.8 3.8+ VS 2017 (14.1) vc141
1.9 3.8+ VS 2019 (14.20) vc142
============== ================= ================= =================
In terms of C language standards, it's relevant to note that C11 has `optional features`_
(e.g. atomics, threading), some of which (VLAs & complex types)
were mandatory in the C99 standard. C17 (occasionally called C18) can be
considered a bug fix for C11, so generally, C11 may be skipped entirely.
.. _optional features: https://en.wikipedia.org/wiki/C11_%28C_standard_revision%29#Optional_features
SciPy has been restricted in the use of more advanced language features by the
available compiler support, and Microsoft in particular has taken very long to
achieve conformance to C99/C11/C17, however starting from `Visual Studio 16.8`_,
C11/C17 is supported (though without the C11 optional features).
C99 ``<complex.h>`` `support <https://developercommunity.visualstudio.com/t/714008>`_
would be particularly interesting for SciPy.
However, it's still possible to use complex types on windows, provided that
`windows-specific types`_ are used.
.. _Visual Studio 16.8: https://docs.microsoft.com/en-us/cpp/overview/visual-cpp-language-conformance#c-standard-library-features-1
.. _windows-specific types: https://docs.microsoft.com/en-us/cpp/c-runtime-library/complex-math-support
Therefore, using C features beyond C90 was only possible insofar as there was support on
Windows; however, as of as of the end of 2021, a sufficiently recent compiler is used.
This is because GCC & LLVM support all relevant C11 features with the oldest currently
used versions, and C17 is just a bugfix for C11, as mentioned above. In short:
================ =======================================================================
Date C Standard
================ =======================================================================
<= 2018 C90
2019 C90 for old code, may consider C99 for new
2020 C99 (no ``<complex.h>``, ``<stdatomic.h>``, ``<threads.h>`` & VLAs)
2021 C17 (no ``<complex.h>``, ``<stdatomic.h>``, ``<threads.h>`` & VLAs)
? C23, ``<complex.h>``, ``<stdatomic.h>``, ...
================ =======================================================================
C++ Language Standards
C++ language standards for SciPy are generally guidelines rather than official decisions. This is particularly true of attempting to predict adoption timelines for newer standards.
================ ======================================================================= Date C++ Standard ================ ======================================================================= <= 2019 C++03 2020 C++11 2021 C++14 2022 C++17 (core language + universally available stdlib features) ? C++17 (with full stdlib), C++20, C++23, C++26 ================ =======================================================================
.. dropdown:: Historical context for compiler constraints due to manylinux
Since dropping support for Python 2.7, C++11 can be used
universally, and since dropping Python 3.6, the Visual Studio version
(that had previously been stuck with 14.0 due to ABI compatibility with
CPython) has been recent enough to support even C++17.
Since the official builds (see above) use a pretty recent version of LLVM,
the bottleneck for C++ support is therefore the oldest supported GCC version,
where SciPy has been constrained mainly by the version in the oldest supported
manylinux versions & images [MANY]_.
At the end of 2021 (with the final removal of ``manylinux1`` wheels), the
minimal requirement of GCC moved to 6.3, which has full C++14 support [CPP]_.
This corresponded to the lowest-present GCC version in relevant manylinux
versions, though this was still considering the Debian-based "outlier"
``manylinux_2_24``, which - in contrast to previous manylinux images based on
RHEL-derivative CentOS that could benefit from the ABI-compatible GCC backports
in the "RHEL Dev Toolset" - was stuck with GCC 6.3. That image failed to take
off not least due to those `outdated compilers`_ and reached its EOL in
mid-2022. For different reasons, ``manylinux2010`` also reached its EOL
around the `same time <https://github.com/pypa/manylinux/issues/1281>`_.
The remaining images ``manylinux2014`` and ``manylinux_2_28`` currently support
GCC 10 and 12, respectively. The latter will continue to receive updates as new
GCC versions become available as backports, but the former will likely not
change since the CentOS project is not responsive anymore about publishing
aarch64 `backports <https://github.com/pypa/manylinux/issues/1266>`_ of GCC 11.
This leaves all the main platforms and their compilers with comparatively recent versions. However, SciPy has historically also endeavored to support less common platforms as well - if not with binary artefacts (i.e. wheels), then at least by remaining compilable from source - which includes for example AIX, Alpine Linux and FreeBSD.
.. dropdown:: Platform support and other constraints on compiler
For AIX 7.2 & 7.3 the default compiler is GCC 10 (AIX 7.1 had its EOL in 2023),
but GCC 11/12 is installable `side-by-side`_, and similarly, there is the
LLVM 17-based `Open XL`_ for AIX.
The oldest currently-supported `Alpine Linux`_ release is 3.16, and already
`comes with <https://distrowatch.com/table.php?distribution=alpine>`_ GCC 11.
For `FreeBSD`_, the oldest currently-supported `13.x release`_ comes with
LLVM 14 (and GCC 13 is available as a `freebsd-port`_).
Finally there is the question of which machines are widely used by people
needing to compile SciPy from source for other reasons (e.g. SciPy developers,
or people wanting to compile for themselves for performance reasons).
The oldest relevant distributions (without RHEL-style backports) are Ubuntu
20.04 LTS (which has GCC 9 but also has a backport of GCC 10; Ubuntu 22.04 LTS
has GCC 11) and Debian Bullseye (with GCC 10; Bookworm has GCC 12).
This is the weakest restriction for determining the lower bounds of compiler
support (power users and developers can be expected to keep their systems at
least somewhat up-to-date, or use backports where available), and gradually
becomes less important as usage numbers of old distributions dwindle.
.. _outdated compilers: https://github.com/pypa/manylinux/issues/1012 .. _side-by-side: https://www.ibm.com/support/pages/aix-toolbox-open-source-software-downloads-alpha#G .. _Open XL: https://www.ibm.com/docs/en/openxl-c-and-cpp-aix/17.1.2?topic=new-enhanced-llvm-clang-support .. _Alpine Linux: https://alpinelinux.org/releases/ .. _FreeBSD: https://www.freebsd.org/releases/ .. _13.x release: https://www.freebsd.org/releases/13.2R/relnotes/ .. _freebsd-port: https://ports.freebsd.org/cgi/ports.cgi?query=gcc
All the currently lowest-supported compiler versions (GCC 9, LLVM 14, VS2019 with vc142) have full support for the C++17 core language, which can therefore be used unconditionally. However, as of mid-2024, support for the entirety of the C++17 standard library has not yet been completed across all compilers [CPP]_, particularly LLVM. It is therefore necessary to check if a given stdlib-feature is supported by all compilers before it can be used in SciPy.
C++20 support is stabilizing very slowly, even aside from modules, coroutines
and several not-yet-universally-supported stdlib features. Given how big of a
release the C++20 standard was, it is expected that it will take a while yet_
before we can start considering moving our baseline.
Compiler support for C++23 and C++26 is still under heavy development [CPP]_.
.. _while yet: https://discourse.llvm.org/t/rfc-clang-17-0-6-would-be-minimum-version-to-build-llvm-in-c-20/75345/8
Fortran Compilers
Generally, any well-maintained compiler is likely suitable and can be
used to build SciPy. That said, we do not test with old ``gfortran`` versions,
which is why we are matching the lower bound with the one for GCC above.
============= =====================================
Tool Version
============= =====================================
gfortran >= 9.x
ifort/ifx A recent version (not tested in CI)
flang (LLVM) >= 17.x
============= =====================================
Cython & Pythran
~~~~~~~~~~~~~~~~
SciPy always requires a recent Cython compiler. Since 1.7, Pythran
is a build dependency (currently with the possibility to opt out).
OpenMP support
^^^^^^^^^^^^^^
For `various reasons`_, SciPy cannot be distributed with built-in OpenMP support.
When using the optional Pythran support, OpenMP-enabled parallel code can be
generated when building from source.
.. _various reasons: https://github.com/scipy/scipy/issues/10239
Other Libraries
^^^^^^^^^^^^^^^
Any library conforming to the BLAS/LAPACK interface may be used.
OpenBLAS, ATLAS, MKL, BLIS, and reference Netlib libraries are known to work.
=============== =====================================================
Library Minimum version
=============== =====================================================
LAPACK 3.7.1
BLAS A recent version of OpenBLAS, MKL or ATLAS.
The Accelerate BLAS library is no longer supported.
=============== =====================================================
There are some additional optional dependencies.
=============== ======== ==========================================
Library Version URL
=============== ======== ==========================================
mpmath Recent http://mpmath.org/
scikit-umfpack Recent https://pypi.org/project/scikit-umfpack/
pooch Recent https://pypi.org/project/pooch/
=============== ======== ==========================================
Moreover, SciPy supports interaction with other libraries. The test suite
has additional compatibility tests that are run when these are installed:
========================= ======== ====================================
Tool Version URL
========================= ======== ====================================
pydata/sparse Recent https://github.com/pydata/sparse/
========================= ======== ====================================
Testing and Benchmarking
--------------------------
Testing and benchmarking require recent versions of:
========================= ======== ====================================
Tool Version URL
========================= ======== ====================================
pytest Recent https://docs.pytest.org/en/latest/
Hypothesis Recent https://hypothesis.readthedocs.io/
asv (airspeed velocity) Recent https://asv.readthedocs.io/
========================= ======== ====================================
Building the Documentation
--------------------------
============================ =================================================
Tool Version
============================ =================================================
Sphinx Whatever recent versions work. >= 5.0.
PyData Sphinx theme Whatever recent versions work. >= 0.15.2.
Sphinx-Design Whatever recent versions work. >= 0.4.0.
numpydoc Whatever recent versions work. >= 1.5.0.
matplotlib Generally suggest >= 3.5.
MyST-NB Whatever recent versions work. >= 0.17.1
jupyterlite-sphinx Whatever recent versions work. >= 0.17.1
jupyterlite-pyodide-kernel Whatever recent versions work. >= 0.1.0
============================ =================================================
.. note::
Developer Note: The versions of ``numpy`` and ``matplotlib`` required have
implications for the examples in Python docstrings.
Examples must be able to be executed both in the environment used to
build the documentation,
as well as with any supported versions of ``numpy/matplotlib`` that
a user may use with this release of SciPy.
Packaging
---------
A Recent version of:
============= ======== =============================================
Tool Version URL
============= ======== =============================================
setuptools Recent https://pypi.org/project/setuptools/
wheel Recent https://pythonwheels.com
multibuild Recent https://github.com/matthew-brett/multibuild
============= ======== =============================================
:ref:`making-a-release` and :ref:`distributing-a-release` contain information on
making and distributing a SciPy release.
References
----------
.. [CPP] https://en.cppreference.com/w/cpp/compiler_support
.. [MANY] https://github.com/mayeut/pep600_compliance
.. [MSVC] https://docs.microsoft.com/en-us/cpp/overview/visual-cpp-in-visual-studio
.. [NEP29] https://numpy.org/neps/nep-0029-deprecation_policy.html
.. [OSN] https://github.com/scipy/oldest-supported-numpy
.. [UCRT] https://docs.microsoft.com/en-gb/cpp/windows/universal-crt-deployment