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Название: Scaling Python with Dask: From Data Science to Machine Learning (Final)
Автор: Holden Karau, Mika Kimmins
Издательство: O’Reilly Media, Inc.
Год: 2023
Страниц: 226
Язык: английский
Формат: True PDF, True EPUB (Retail Copy)
Размер: 17.5 MB
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. We wrote this book for data scientists and data engineers familiar with Python and pandas who are looking to handle larger-scale problems than their current tooling allows. Current PySpark users will find that some of this material overlaps with their existing knowledge of PySpark, but we hope they still find it helpful, and not just for getting away from the Java Virtual Machine (JVM). Dask is a framework for parallelized computing with Python that scales from multiple cores on one machine to data centers with thousands of machines. 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.
Автор: Holden Karau, Mika Kimmins
Издательство: O’Reilly Media, Inc.
Год: 2023
Страниц: 226
Язык: английский
Формат: True PDF, True EPUB (Retail Copy)
Размер: 17.5 MB
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. We wrote this book for data scientists and data engineers familiar with Python and pandas who are looking to handle larger-scale problems than their current tooling allows. Current PySpark users will find that some of this material overlaps with their existing knowledge of PySpark, but we hope they still find it helpful, and not just for getting away from the Java Virtual Machine (JVM). Dask is a framework for parallelized computing with Python that scales from multiple cores on one machine to data centers with thousands of machines. 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.