Название: Scaling Python with Dask: From Data Science to Machine Learning (Sixth Early Release)Автор: Holden Karau, Mika Kimmins
Издательство: O’Reilly Media, Inc.
Год: 2023-03-27
Страниц: 150
Язык: английский
Формат: pdf, epub (true), mobi
Размер: 10.2 MB
Dask is a free and open source library for parallel computing in Python that helps you scale your data science and machine learning workflows. With this quick but thorough resource, data scientists and Python programmers will learn how Dask provides APIs that make it easy to parallelize PyData libraries like NumPy, Pandas, and Scikit-learn.
Dask is a framework for parallelized computing with Python that scales from multiple cores on one machine to data centers with thousands of machines. It has both low-level task APIs and higher-level data-focused APIs. The low-level task APIs power Dask’s integration with a wide variety of Python libraries. Having public APIs has allowed an ecosystem of tools to grow around Dask for various use cases.
Continuum Analytics, now known as Anaconda Inc, started the open-source DARPA funded BLAZE project, which has evolved into Dask. Continuum has participated in developing many essential libraries and even conferences in the Python data analytics space. Dask remains an open-source project, with much of its development now being supported by Coiled.
Author Holden Karau shows you how you can 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 used by organizations that include Walmart, Capital One, Harvard Medical School, and NASA.
With this book, you'll learn аbout:
What is Dask is, where you can use it, and how it compares to other tools
Batch data parallel processing
Key distributed system concepts for Dask users
Higher-level APIs and building blocks
Integrated libraries, such as Scikit-learn, Pandas, and PyTorch
How to use Dask with GPUs
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