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RAPIDS: Dask and cuDF NYCTaxi Screencast
Deploying RAPIDS at Scale with Dask and Coiled | Matt Rocklin
Data Science on GPU clusters with Dask and RAPIDS
Accelerated DataFrame with Dask-cuDF on multiple GPUs
RAPIDS: Open GPU Data Science | Scipy 2019 Tutorial | Scopatz, Becker, Kraus, Gama Dessavre
Scaling Up and Out Programming GPU Clusters with Numba and Dask | SciPy 2016 | Siu Kwan Lam
Accelerating ETL/SQL Workloads with RAPIDS-Dask + Dask-SQL - Charles Blackmon-Luca | SciPy 2022
GPU-Accelerated Streaming at Scale Using Dask | Chinmay Chandak | Dask Summit 2021
Multi-GPU Rapids Academy
An Introduction to GPU Accelerated DataFrames in Python
EuroSciPy 2023 - Deploying multi-GPU workloads on Kubernetes in Python
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Last Updated: September 20, 2026
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I took a neural network I had trained on a single CPU to generate pet names and tried retraining it with tons of connected Speakers - Benjamin Zaitlen, Nick Becker Data volumes and computational complexity of analysis techniques have increased, but ... Learn more at bit.ly/3vLTz9t Recording for the GTC 2020 talk "Deploying RAPIDS at Scale with Data scientists spend their time combing through data sources, generating insights, and experimenting with models. While The RAPIDS suite of open source software libraries gives you the freedom to execute end-to-end data science pipelines entirely ... Stream processing is experiencing exponential growth with businesses and services relying heavily on real-time analytics, ... Proposal link: pretalx.com/euroscipy-2023/talk/HXD9EH/ By using
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