Parallel Python with Dask: Perform distributed computing, concurrent programming and manage large dataset

Unlock the Power of Parallel Python with Dask: A Perfect Learning Guide for Aspiring Data Scientists
Dask has revolutionized parallel computing for Python, empowering data scientists to accelerate their workflows. This comprehensive guide unravels the intricacies of Dask to help you harness its capabilities for machine learning and data analysis.

Across 10 chapters, you’ll master Dask’s fundamentals, architecture, and integration with Python’s scientific computing ecosystem.Step-by-step tutorials demonstrate parallel mapping, task scheduling, and leveraging Dask arrays for NumPy workloads.You’ll discover how Dask seamlessly scales Pandas, Scikit-Learn, PyTorch, and other libraries for large datasets.

Dedicated chapters explore scaling regression, classification, hyperparameter tuning, feature engineering, and more with clear examples. You’ll also learn to tap into the power of GPUs with Dask, RAPIDS, and Google JAX for orders of magnitude speedups.

This book places special emphasis on practical use cases related to scalability and distributed computing. You’ll learn Dask patterns for cluster computing, managing resources efficiently, and robust data pipelines. The advanced chapters on DaskML and deep learning showcase how to build scalable models with PyTorch and TensorFlow.

With this book, you’ll gain practical skills to

• Accelerate Python workloads with parallel mapping and task scheduling
• Speed up NumPy, Pandas, Scikit-Learn, PyTorch, and other libraries
• Build scalable machine learning pipelines for large datasets
• Leverage GPUs efficiently via Dask, RAPIDS and JAX
• Manage Dask clusters and workflows for distributed computing
• Streamline deep learning models with DaskML and DL frameworks
• Packed with hands-on examples and expert insights, this book provides the complete toolkit to harness Dask’s capabilities. It will empower Python programmers, data scientists, and machine learning engineers to achieve faster workflows and operationalize parallel computing.