Through a series of 4 blog posts, we’ll discuss and provide working examples of how one can use the open-source library Ray to (a) scale computing locally (single machine), (b) distribute scaling remotely (multiple-machines), and (c) serve deep learning models across a cluster (basic/advanced). Please note that the blog posts in this series increasingly raise in difficulty!

I am personally very excited by the opportunities afforded by Ray, its been a long time desire to have such an easy-to-use library!

Okay, lets start off by talking about scaling local computation with Ray!