![]() ![]() All of these libraries provide unique APIs for parallel processing.Īs a part of this tutorial, we'll be introducing ipyparallel and how to design programs that run in parallel using it. As are a result of this python has a bunch of libraries for running things in parallel like dask, pyspark, ipyparallel, etc. ![]() The fields of data science and machine learning many times involve quite a large dataset that might not get handled by a single core of a computer or even a single computer. Day by day python and jupyter notebook are becoming de-facto choice by many people to perform data analysis and machine learning tasks. It has even become very important to write code which can run on clusters of computer due to datasets getting gigantic. With the increase in data collection and availability of many cores on a single computer, it has become the need of an hour to write code that utilizes multiple cores of computer efficiently utilizing underlying hardware. Example 7: apply_async() with shared data. ![]() Run Functions in Parallel on IPython Engines Common Steps to Run Code in Parallel using ipyparallel.Ipyparallel - Parallel Processing in Python ¶ Table of Content ¶ ![]()
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