Over the past few years, I've participated in and led projects with DataKind, a data-science non-profit. DataKind brings together data scientists and engineers with leading social change organizations to collaborate on projects to maximize social impact.
Please feel free to visit my personal website (https://beckernick.github.io/) for more information or send me an email at email@example.com if you want to chat.
About the talk
The RAPIDS suite of open source software libraries gives you the freedom to execute end-to-end data science pipelines entirely on GPUs. RAPIDS is incubated by NVIDIA® based on years of accelerated data science experience. RAPIDS relies on NVIDIA CUDA® primitives for low-level compute optimization, GPU parallelism, and high-bandwidth memory speed through user-friendly Python interfaces. This tutorial will teach you how to use the RAPIDS software stack from Python, including cuDF (a DataFrame library interoperable with Pandas), dask-cudf (for distributing DataFrame work over many GPUs), and cuML (a machine learning library that provides GPU-accelerated versions of the algorithms in scikit-learn).
See tutorial materials here: https://www.scipy2019.scipy.org/tutorial-participant-instructions
See the full SciPy 2019 playlist at https://www.youtube.com/playlist?list=PLYx7XA2nY5GcDQblpQ_M1V3PQPoLWiDAC
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