Sinopsis
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
"Sinopsis" puede pertenecer a otra edición de este libro.
Acerca del autor
Holden Karau is a queer transgender Canadian, Apache Spark committer, Apache Software Foundation member, and an active open source contributor. As a software engineer, she's worked on a variety of distributed computing, search, and classification problems at Apple, Google, IBM, Alpine, Databricks, Foursquare, and Amazon. She graduated from the University of Waterloo with a bachelor of mathematics in computer science. Outside of software, she enjoys playing with fire, welding, riding scooters, eating poutine, and dancing. Mika Kimmins is a data engineer, distributed systems researcher, and ML consultant. She worked on a variety of NLP, language modeling, reinforcement learning, and ML pipelining at scale as a Siri Data Engineer at Apple, an academic, and in not-for-profit engineering capacities. She is currently earning an MS in Engineering Science and an MBA from Harvard, and holds a BS in Computer Science and Mathematics from the University of Toronto. As a Korean-Canadian-American trans woman, Mika is active in data-driven advocacy for queer healthcare access, advises undergraduate Computer Science students, and attempts to keep her volunteer EMT courses current. Her hobbies include figure skating, aerial arts, and sewing.
"Sobre este título" puede pertenecer a otra edición de este libro.
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Scaling Python with Dask: From Data Science to Machine Learning
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Paperback. Condición: New. 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 toolsHow to use Dask for batch data parallel processingKey distributed system concepts for working with DaskMethods for using Dask with higher-level APIs and building blocksHow to work with integrated libraries such as scikit-learn, pandas, and PyTorchHow to use Dask with GPUs. Nº de ref. del artículo: LU-9781098119874
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Paperback. Condición: new. Paperback. 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 About the Authors Holden Karau is a queer transgender Canadian, Apache Spark committer, Apache Software Foundation member, and an active open source contributor. As a software engineer, she's worked on a variety of distributed computing, search, and classification problems at Apple, Google, IBM, Alpine, Databricks, Foursquare, and Amazon. She graduated from the University of Waterloo with a bachelor of mathematics in computer science. Outside of software, she enjoys playing with fire, welding, riding scooters, eating poutine, and dancing. Mika Kimmins is a data engineer, distributed systems researcher, and ML consultant. She worked on a variety of NLP, language modeling, reinforcement learning, and ML pipelining at scale as a Siri Data Engineer at Apple, an academic, and in not-for-profit engineering capacities. She is currently earning an MS in Engineering Science and an MBA from Harvard, and holds a BS in Computer Science and Mathematics from the University of Toronto. As a Korean-Canadian-American trans woman, Mika is active in data-driven advocacy for queer healthcare access, advises undergraduate Computer Science students, and attempts to keep her volunteer EMT courses current. Her hobbies include figure skating, aerial arts, and sewing." 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. Shipping may be from multiple locations in the US or from the UK, depending on stock availability. Nº de ref. del artículo: 9781098119874
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Paperback. Condición: New. 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 toolsHow to use Dask for batch data parallel processingKey distributed system concepts for working with DaskMethods for using Dask with higher-level APIs and building blocksHow to work with integrated libraries such as scikit-learn, pandas, and PyTorchHow to use Dask with GPUs. Nº de ref. del artículo: LU-9781098119874
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