Description:

In this workshop presented by NVIDIA, you'll get hands-on experience accelerating Python codes with GPUs.

We will utilize code samples in three main categories to introduce you to Python GPU accelerated computing. First, we will explore drop-in replacements for SciPy and NumPy code through the CuPy library. Next we’ll cover NVIDIA RAPIDS, which provides GPU acceleration for end-to-end data science workloads. Finally we'll cover Numba, which gives you the flexibility to write custom accelerated code without leaving the Python language. We'll finish with an end-to-end example that incorporates all the tools introduced to tackle a geospatial problem. By the end of the workshop, you will have the tools to start accelerating your own Python codes using the A100 GPUs which are available to anyone on Quest. 

Prerequisites: Experience using the Python libraries Pandas and Scikit-Learn is highly recommended to get the most from the workshop. No prior knowledge on GPU computing is required. To follow examples hands-on, sign up for a developer account on developer.nvidia.com .