2019-09-05 –, Track 2 (Baroja)
In this talk I will take you on a whirlwind tour of Numba and you will be quipped with a mental model of how Numba works and what it is good at. At the end, you will be able to decide if Numba could be useful for you.
In this talk I will take you on a whirlwind tour of Numba, the just-in-time,
type-specializing, function compiler for accelerating numerically-focused
Python. Numba can compile the computationally intensive functions of your
numerical programs and libraries from Python/NumPy to highly optimized binary
code. It does this by inferring the data types used inside these functions and
uses that information to generate code that is specific to those data types
and specialised for your target hardware. On top of that, it does all of this
on-the-fly---or just-in-time---as your program runs. This significantly reduces
the potential complexity that traditionally comes with pre-compiling and
shipping numerical code for a variety of operating systems, Python versions and
hardware architectures. All you need in principle, is to conda install numba
and decorate your compute intensive functions with @nuba.jit!
This talk will equip you with a mental model of how Numba is implemented and
how it works at the algorithmic level. You will gain a deeper understanding of
the types of use-cases where Numba excels and why. Also, you will understand
the limitations and caveats that exist within Numba, including any potential
ideas and strategies that might alleviate these. At the end of the talk you
will be in a good position to decide if Numba is for you and you will have
learnt about the concrete steps you need to take to include it as a dependency
in your program or library.
A whirlwind tour of Numba: the just-in-time, type-specializing, function compiler for accelerating numerically-focused Python.
Python Skill Level –expert
Domain Expertise –none
Domains –Open Source, Parallel computing / HPC, Vector and array manipulation
Valentin is a long-time "Python for Data" user and developer who still
remembers hearing Travis Oliphant's keynote at the EuroScipy 2007. This was
during a time where he first became aware of the nascent scientific Python
stack. He started using Python for simple modeling of spiking neurons and
evaluation of data from perception experiments during his Masters degree in
computational neuroscience. Since then he has been active as a contributor
across more than 75 open source projects. For example, within the Blosc
ecosystem where he still maintains and contributes to Python-Blosc and
Bloscpack. Furthermore, he has acquired significant experience as a Git
trainer and consultant and had published the first German language book about
the topic in 2011. In 2014 and 2015 he helped kickstart the PyData Berlin
community alongside a few other volunteers and co-organized the first two
editions of the PyData Berlin Conference. He now works for Anaconda as a
software engineer / open source developer on the Numba project.