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.
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.
Create CUDA kernels from Python using Numba and CuPy.
We'll explain how to do GPU-Accelerated numerical computing from Python using the Numba Python compiler in combination with the CuPy GPU array library.