EuroScipy 2019 Bilbao - Distributed GPU Computing with Dask - Peter Entschev

(26:)

Peter Entschev - Distributed Multi-GPU Computing with Dask, CuPy and RAPIDS

(29:44)

UCX Python – Dask/RAPIDS

(48:26)

Research at NEC: Powered by Mathematics

(5:59)

A variant of the VC-dimension with applications to depth-3 circuits

(29:44)

Creating Sustainable Talent Management Ecosystems for Life Sciences 2022

(39:46)

James A Bednar: Panel: Dashboards for PyData | PyData Austin 2019

(1:26:12)

Hacking Dask: Diving into Dask's Internals | Julia Signell \u0026 James Bourbeau | Dask Summit 2021

(3:31)

Dask + Rapids | Using GPUs to Accelerate Data Science with Dask + Rapids | Jacob Schmitt

(1:41:49)

Xarray for Scalable Scientific Data Analysis | SciPy 2020 | Hamman, Abernathey, Cherian, Hoyer

(3:41:37)

Keith Kraus: High-Performance Data Science at Scale with RAPIDS, Dask, and GPUs | PyData NYC 2019

(1:21:43)

$15,000 Mac Pro vs $5,000 Threadripper - Sorry Apple..

(15:22)

RAPIDS: Open GPU Data Science | Scipy 2019 Tutorial | Scopatz, Becker, Kraus, Gama Dessavre

(2:2:53)

GPU Series: Multiple GPUs in Python with Dask

(1:3:15)

Using Multiple GPUs in Tensorflow

(48:26)

GPU-Accelerated Data Analytics in Python |SciPy 2020| Joe Eaton

(28:23)

Peter's Innovations at Amazon

(7:35)

Bringing GPU support to Datashader: A RAPIDS case study |SciPy 2020| Jon Mease

(20:59)

Navigating the PDF Ecosystem

(41:33)

t-SNE with RAPIDS (600x Speedup)

(11:25)

Swapping Multi-GPU Configuration in Fractal XL

(2:21)

Peter Norvig | The Science and Engineering of Online Learning

(40:57)

Shohei Hido - CuPy: A NumPy-compatible Library for GPU - PyCon 2018

(30:11)

napari, a fast n dimensional array viewer for scientifc Python | Juan Nunez-Iglesias | SciPy JP 2020

(26:33)

CuPy: A Numpy-compatible Library for HPC with GPU | Masayuki Takagi | SciPy Japan 2020

(19:24)