Since NVIDIA brought GPU-accelerated computing into the mainstream with CUDA, a growing ecosystem of high-level libraries has made it straightforward to run languages such as Python, Julia, and R on GPUs.
RAPIDS cuDF extends this capability to Python DataFrames. It can be used in 3 ways: directly; through a pandas-like API; or via Polars’ lazy API.
In this course, I will compare the different ways to access RAPIDS cuDF to send Python DataFrames to the GPU. Then we will focus on using cuDF with Polars, as this approach is both the simplest and the most efficient.