The aim of this post is to explore a method of calling C++ library
functions from Numba compiled
functions
--- Python functions that are decorated with
numba.jit(nopython=True).
While there exist ways to wrap C++ codes to Python (see Appendix below), calling these wrappers from Numba compiled functions is often not as straightforward and efficient as one would hope.
In this blog post I talk about the work that I was able to accomplish during my internship at Quansight Labs and the efforts being made towards making array libraries more interoperable.
Going ahead, I'll assume basic understanding of array and tensor libraries with their usage in the Python Scientific and Data Science software stack.
Master NumPy leading the young Tensor Turtles
In this blog post I talk about the projects and my work during my internship at Quansight Labs. My efforts were geared towards re-engineering CI/CD pipelines for SciPy to make them more efficient to use with GitHub Actions. I also talk about the milestones that I achieved, along with the associated learnings and improvements that I made.
This blog post would assume a basic understanding of CI/CD and GitHub Actions. I will also assume a basic understanding of Python and the SciPy ecosystem.
Re-Engineering CI/CD pipelines for SciPy
Over the summer,
I've been interning at Quansight Labs
to develop testing tools
for the developers and users
of the upcoming Array API standard.
Specifically,
I contributed "strategies"
to the testing library Hypothesis,
which I'm excited to announce
are now available in hypothesis.extra.array_api.
Check out the primary pull request I made
for more background.
This blog post is for anyone developing array-consuming methods (think SciPy and scikit-learn) and is new to property-based testing. I demonstrate a typical workflow of testing with Hypothesis whilst writing an array-consuming function that works for all libraries adopting the Array API, catching bugs before your users do.