TensorFlow: Speed Up NumPy by over 10,000x with GPUs

Author(s): Louis Chan

Originally published on Towards AI.

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Photo by Marc-Olivier Jodoin on Unsplash

If you have used Python for any data processing, you have most likely used NumPy (short for Numerical Python). It provides a rich arsenal of complex data types and efficient matrix manipulation functions. Its C-accelerated implementation of vectorisable functions has earned it its reputation for processing n-dimensional array at lightning speed. But can we go faster than that?

NumPy’s C-accelerated implementation of vectorisable functions enables us to efficiently process large multi-dimensional arrays

In comes, TensorFlow’s take on NumPy API.

Thanks to TensorFlow’s GPU acceleration, we can now run NumPy situationally even faster than we can already — faster… Read the full blog for free on Medium.

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Published via Towards AI