This document contains tips to develop Cython code in scikit-learn.
Tips for developing with Cython in scikit-learn# Tips to ease development#Time spent reading Cython’s documentation is not time lost.
If you intend to use OpenMP: On MacOS, system’s distribution of clang does not implement OpenMP. You can install the compilers package available on conda-forge which comes with an implementation of OpenMP.
Activating checks might help. E.g. for activating boundscheck use:
export SKLEARN_ENABLE_DEBUG_CYTHON_DIRECTIVES=1
Start from scratch in a notebook to understand how to use Cython and to get feedback on your work quickly. If you plan to use OpenMP for your implementations in your Jupyter Notebook, do add extra compiler and linkers arguments in the Cython magic.
# For GCC and for clang %%cython --compile-args=-fopenmp --link-args=-fopenmp # For Microsoft's compilers %%cython --compile-args=/openmp --link-args=/openmp
To debug C code (e.g. a segfault), do use gdb with:
gdb --ex r --args python ./entrypoint_to_bug_reproducer.py
To have access to some value in place to debug in cdef (nogil) context, use:
with gil:
print(state_to_print)
Note that Cython cannot parse f-strings with {var=} expressions, e.g.
scikit-learn codebase has a lot of non-unified (fused) types (re)definitions. There currently is ongoing work to simplify and unify that across the codebase. For now, make sure you understand which concrete types are used ultimately.
You might find this alias to compile individual Cython extension handy:
# You might want to add this alias to your shell script config. alias cythonX="cython -X language_level=3 -X boundscheck=False -X wraparound=False -X initializedcheck=False -X nonecheck=False -X cdivision=True" # This generates `source.c` as if you had recompiled scikit-learn entirely. cythonX --annotate source.pyx
Using the --annotate option with this flag allows generating a HTML report of code annotation. This report indicates interactions with the CPython interpreter on a line-by-line basis. Interactions with the CPython interpreter must be avoided as much as possible in the computationally intensive sections of the algorithms. For more information, please refer to this section of Cython’s tutorial
# This generates a HTML report (`source.html`) for `source.c`. cythonX --annotate source.pyx
Understand the GIL in context for CPython (which problems it solves, what are its limitations) and get a good understanding of when Cython will be mapped to C code free of interactions with CPython, when it will not, and when it cannot (e.g. presence of interactions with Python objects, which include functions). In this regard, PEP073 provides a good overview and context and pathways for removal.
Make sure you have deactivated checks.
Always prefer memoryviews instead of cnp.ndarray when possible: memoryviews are lightweight.
Avoid memoryview slicing: memoryview slicing might be costly or misleading in some cases and we better not use it, even if handling fewer dimensions in some context would be preferable.
Decorate final classes or methods with @final (this allows removing virtual tables when needed)
Inline methods and functions when it makes sense
In doubt, read the generated C or C++ code if you can: “The fewer C instructions and indirections for a line of Cython code, the better” is a good rule of thumb.
nogil declarations are just hints: when declaring the cdef functions as nogil, it means that they can be called without holding the GIL, but it does not release the GIL when entering them. You have to do that yourself either by passing nogil=True to cython.parallel.prange explicitly, or by using an explicit context manager:
cdef inline void my_func(self) nogil:
# Some logic interacting with CPython, e.g. allocating arrays via NumPy.
with nogil:
# The code here is run as if it were written in C.
return 0
This item is based on this comment from Stéfan’s Benhel
Direct calls to BLAS routines are possible via interfaces defined in sklearn.utils._cython_blas.
Since scikit-learn can be built without OpenMP, it’s necessary to protect each direct call to OpenMP.
The _openmp_helpers module, available in sklearn/utils/_openmp_helpers.pyx provides protected versions of the OpenMP routines. To use OpenMP routines, they must be cimported from this module and not from the OpenMP library directly:
from sklearn.utils._openmp_helpers cimport omp_get_max_threads max_threads = omp_get_max_threads()
The parallel loop, prange, is already protected by cython and can be used directly from cython.parallel.
Cython code requires to use explicit types. This is one of the reasons you get a performance boost. In order to avoid code duplication, we have a central place for the most used types in sklearn/utils/_typedefs.pyd. Ideally you start by having a look there and cimport types you need, for example
from sklearn.utils._typedefs cimport float32, float64
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