Скачать или смотреть How Can Jupyter Notebooks Use Distributed Computing? - The Friendly Statistician

How Can Jupyter Notebooks Use Distributed Computing? Have you ever wondered how large datasets and complex computations are handled efficiently within a Jupyter Notebook? In this video, we'll explore how Jupyter Notebooks can be connected to distributed computing tools and systems to speed up data analysis, simulations, and machine learning tasks. We'll start by explaining how the core execution model of Jupyter can be expanded to run code across multiple machines, enabling parallel processing and faster results. You'll learn how resource managers like Slurm and Kubernetes help manage job scheduling and resource allocation, making it easier to handle large-scale projects directly from your notebook interface. We'll also cover popular libraries such as Apache Spark, Dask, and Accelerate, which are designed to process big data and distribute tasks over many computers or GPUs. Additionally, we'll discuss JupyterHub, a platform that allows multiple users to run their own notebooks on shared infrastructure, perfect for team collaborations on large projects. Finally, you'll see how designing workflows with parallel cells can turn a linear notebook into a powerful pipeline for scientific simulations, data analysis, or training machine learning models across multiple servers. Whether you're working with massive datasets or complex models, this video will show you how to take advantage of distributed computing within Jupyter Notebooks for faster, more efficient work.

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