00:41.90 Tools, resources, and utilities that are part of AI Tools include optimized deep learning frameworks and Python* packages for data analysis, machine learning, and data preparation. 

Intel Distribution of Modin supports all pandas APIs and works seamlessly with the Python ecosystem. When you call Modin instead of pandas, your code looks exactly the same. 



Compare core processing of pandas and Intel Distribution of Modin: If pandas runs on one core of the CPU, Modin can use all the cores out of the box without tuning. No supercomputer needed. The performance increase happens because of the underlying oneAPI optimized libraries and other optimized APIs.

[02:37.60] The initial performance demo is run on an Intel® Xeon processor. Performance is compared by generating a syntheic array, about 1.5 GB, and is saved as a file. Reading the file with pandas takes 10 seconds; with Modin 2.5 seconds—so it's 5x faster using Modin. 

The second performance demo uses applymap to perform a Lambda function on the elements of the array. With pandas this takes 24 seconds; with Modin, this takes .04 seconds—a speedup of 60x.

The last example concantenates the same data frame and the same array four times using pandas and Modin.

This time, Modin is 50x faster.  


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Intel Distribution of Modin Documentation

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Visit Intel Distribution of Modin on GitHub*

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