Using GPUs with Python

Research Computing Center

Skill Level:

Intermediate

 

Course Abstract:

In this workshop, you&#x27;ll get hands-on experience accelerating Python codes with NVIDIA 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&#x27;ll cover Numba, which gives you the flexibility to write custom accelerated code without leaving the Python language. By the end of the workshop, you&#x27;ll have the skills to start accelerating your own Python codes with NVIDIA GPUs!

 

Learning Objectives :

By participating in this workshop, you’ll learn how to:

• Learn to accelerate existing Python applications on GPU

• Introduce CuPy and RAPIDS for drop-in GPU-accelerated replacements for Numpy and Scikit learn

• Introduce Numba for writing custom GPU accelerated functions

 

Prerequisites:

• Basic familiarity with Python

• Familiarity with Numpy, and/or Scikit-learn is beneficial

 

Tools, Libraries, and Frameworks Used :

• Numba

• CuPy

• RAPIDS

 

Course Length:

2 hours

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Using GPUs with Python

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Using GPUs with Python

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