Faster NumPy with TensorFlow

Faster NumPy with TensorFlow

Significantly speed up your NumPy operations using Tensorflow and its new NumPy API

NumPy (Numerical Python) is the most popular Python library in the Data Science world. Countless other Python modules are based on NumPy, as it provides a powerful API for working with arrays and several other operations, covering branches of mathematics like linear algebra and Fourier transform. As an attribution, NumPy was created in 2005 by Travis Oliphant and it is one of the most active open-source projects.

In this story, we take NumPy a step further, increasing its execution speed using TensorFlow. This allows running NumPy code on GPU, accelerated by TensorFlow, while also allowing access to all of TensorFlow’s APIs.

But why should we use NumPy? In Python, we have lists that serve the purpose of arrays. You can always write a matrix multiplication algorithm using Python lists and a few for loops. Well, although you can do it doesn't mean you should.

NumPy aims to provide an array object that is up to 50x faster than traditional Python lists. In NumPy, this object is called ndarray and it’s the basic building block on the library. However, in this story, we take it a step further, increasing the speed of NumPy using TensorFlow.

As of TF 2.4, TensorFlow implements a subset of the NumPy API, available as tf.experimental.numpy. This allows running NumPy code on GPU, accelerated by TensorFlow, while also allowing access to all of TensorFlow's APIs. So, let’s go through the TF NumPy API step by step.

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