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

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).

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.

This book is primarily focused on data science and related tasks because, in our opinion, that is where Dask excels the most. If you have a more general problem that Dask does not seem to be quite the right fit for, we would (with a bit of bias again) encourage you to check out Scaling Python with Ray (O’Reilly), which has less of a Data Science focus.

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. Dask is unique in the distributed computing ecosystem, because it integrates popular data science, parallel, and scientific computing libraries. Dask’s integration of different libraries allows developers to reuse much of their existing knowledge at scale. They can also frequently reuse some of their code with minimal changes.

Dask simplifies scaling analytics, ML, and other code written in Python, allowing you to handle larger and more complex data and problems. Dask aims to fill the space where your existing tools, like pandas DataFrames, or your scikit-learn machine learning pipelines start to become too slow (or do not succeed).

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