GPU-Accelerated Data Science for PyData Users
summary
RAPIDS shows how to move real pandas and scikit‑learn workflows onto NVIDIA GPUs with cuDF and cuML, using the same PyData APIs so code changes stay minimal. The session walks through a deploy launchable GitHub repo that spins up JupyterLab on cloud GPUs, runs hands‑on notebooks and exercises, and demos end‑to‑end data processing and model training pipelines. Expect concrete commands, resource steps, and whether the cloud GPUs actually provision and speed up the runs; no CUDA or C++ required.
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chapters (6)
- 0:00Workshop setup
- 6:33GPU data science intro
- 7:38Audience and GPU basics
- 11:16GPU control and CUDA
- 12:40GPU programming levels
- 14:07Zero-code acceleration
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