Scaling Python with Dask (From Data Science to Machine Learning)

ISBN: 9781098119874
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Overview

Modern systems contain multi-core CPUs and GPUs that have the potential for parallel computing. But many scientific Python tools were not designed to leverage this parallelism. With this short but thorough resource, data scientists and Python programmers will learn how the Dask open source library for parallel computing provides APIs that make it easy to parallelize PyData libraries including NumPy, pandas, and scikit-learn.

Authors Holden Karau and Mika Kimmins show you how to use Dask computations in local systems and then scale to the cloud for heavier workloads. This practical book explains why Dask is popular among industry experts and academics and is used by organizations that include Walmart, Capital One, Harvard Medical School, and NASA.

With this book, you'll learn:

  • What Dask is, where you can use it, and how it compares with other tools
  • How to use Dask for batch data parallel processing
  • Key distributed system concepts for working with Dask
  • Methods for using Dask with higher-level APIs and building blocks
  • How to work with integrated libraries such as scikit-learn, pandas, and PyTorch
  • How to use Dask with GPUs

This book title, Scaling Python with Dask (From Data Science to Machine Learning), ISBN: 9781098119874, by Holden Karau, Mika Kimmins, published by O'Reilly Media (October 31, 2023) is available in paperback. Our minimum order quantity is 25 copies. All standard bulk book orders ship FREE in the continental USA and delivered in 4-10 business days.

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Product Details

Author:
Holden Karau, Mika Kimmins
Format:
Paperback
Pages:
223
Publisher:
O'Reilly Media (October 31, 2023)
Language:
English
ISBN-13:
9781098119874
ISBN-10:
1098119878
Dimensions:
7" x 9.19"
File:
TWO RIVERS-PERSEUS-Metadata_Only_Perseus_Distribution_Customer_Group_Metadata_20240124171555-20240124.xml
Folder:
TWO RIVERS
List Price:
$79.99
Case Pack:
17
As low as:
$45.59
Publisher Identifier:
P-PER
Discount Code:
C
Country of Origin:
United States

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