Getting Started with GPUs in Python
GPU computing has become extremely popular for deep learning in the past 5 years, but did you know that you could do much more with a GPU? In this webinar, we will learn what use cases are ideal for GPU acceleration, and see how to get started with GPU computing in Python. We'll survey a number of useful Python libraries, such as TensorFlow, PyTorch, CuPy, RAPIDS, and Numba, and talk about some general best practices for GPU computing in Python.
Join this on-demand webinar to learn:
- When to use GPUs in your projects
- Which libraries are best for working with GPUs
- Best practices for GPU computing in Python
Meet the Speaker:
Sr. Director, Community Innovation
Stan leads the Community Innovation team at Anaconda, where his work focuses on high performance GPU computing and designing data analysis, simulation, and processing pipelines. He is a longtime advocate of the use of Python and GPU computing for research. Prior to joining Anaconda, Stan served as Chief Data Scientist at Mobi, where he worked on vehicle fleet tracking and route planning.
Stan received a PhD in experimental high energy physics from the University of Texas at Austin, and performed research at Los Alamos National Laboratory, University of Pennsylvania, and the Sudbury Neutrino Observatory.