Keith Kraus
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bluesky:keithjkraus.bsky.social
speakerdeck:kkraus14
what they talk about
where they keep going back
no series more than once — all 4 in the filters
on a stage with
who talks about the same things
- Jacob Tomlinson10 talks
- Naty Clementi5 talks
- McCarty2 talks
- Andy Terrel11 talks
- Yaron Haviv7 talks
- Han Wang13 talks
| title | conference | date |
|---|---|---|
| GPU-Accelerated Data Analytics in Python | PyCon US 2020 | 2020-04-15 |
The RAPIDS stack demonstrates concrete speedups: GPU dataframes via cuDF read a 1.9 GB NYC taxi CSV in ~2s versus ~29s with pandas; a 200 GB end‑to‑end… | ||
| High-Performance Data Science at Scale with RAPIDS, Dask, and GPUs | PyData New York City 2019 | 2019-11-04 |
A concrete claim: RAPIDS + Dask + GPUs move entire ETL, ML and graph pipelines onto GPU memory to avoid copies and give huge real results. Tools and bridges… | ||
| GPU Data Science with Rapids | PyData Miami 2019 | 2019-01-09 |
RAPIDS shows concrete work: GPU-accelerated ETL and ML using cuDF & cuML, Arrow in GPU memory, Dask for multi‑GPU scaling and CUDA IPC/UCX to avoid copies. A… | ||
| Accelerating Data Science with RAPIDS | PyData DC 2018 | 2018-11-16 |
Claims GPU-accelerated end-to-end pipelines using cuDF and cuML with Apache Arrow memory sharing, Dask for distribution, and CUDA-backed JIT for Python UDFs… | ||
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