Actions for Scaling Python with Dask : from data science to machine learning

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Scaling Python with Dask : from data science to machine learning / Holden Karau & Mika Kimmins

Author
Karau, Holden
Published
Sebastopol, CA : O'Reilly Media, 2023.
Copyright Date
©2023
Edition
First edition.
Physical Description
1 online resource
Additional Creators
Kimmins, Mika
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Summary
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.
Subject(s)
ISBN
9781098119843 electronic book
1098119843 electronic book
1098119878
9781098119874
9781098119836 electronic book
1098119835 electronic book
Bibliography Note
Includes bibliographical references and index.
View MARC record | catkey: 41991006