As general purpose GPU programming has risen in popularity, many Python programmers have expressed a need to use this technology in their libraries and applications. They soon realize that the GPU landscape is vast and sometimes difficult to traverse for Python users.

In this talk, I will demystify the CUDA-enabled Accelerated Python landscape, focusing on the advantages and disadvantages of popular libraries, the common performance issues encountered, and the best practices to getting the most out of your GPU. Topics include CuPy, numba, nvmath-python, cuDF, and cuML.

This talk is beginner-friendly, but even the most seasoned programmer will gain insight into the Python GPU computing landscape.