Ready, Set, Go with Numba

About this talk

The prominent reasons for the wide adoption of Python is the ease of learning, usability and readability coupled with the powerful ecosystem of Python packages. This often makes Python an attractive language for researchers & scholars to undertake computational projects and thesis. The ease of prototyping and tinkering allows for higher number of iterations and customization, leading to an increase in research output. But, a major pain point of using Python is its speed when compared to languages like C++ or FORTRAN which are still widely used in research.

Developers, when hit by the performance bottleneck of python code, often come across some methods to increase their code performance like using PyMPI, Numpy or CPython. But, the learning curve is steep and APIs get less familiar.

If learning Python is so easy, why should increasing the performance of Python code be so difficult?

This talk will address this question and get us up and running with Numba, an open source JIT compiler that translates Python and NumPy code into fast machine code. We will delve into real world exercises which will demonstrate the core concepts, ease and effectiveness of using Numba and how it can be useful in lowering the barrier to achieve code performance in Python.