JAX is legendary for its pace, effectivity, and suppleness when working with mathematical computations and machine studying. However certainly one of its lesser-known superpowers — one thing that may prevent from writing countless loops and boilerplate code — is computerized vectorization.
If you happen to’ve ever written code that processes arrays or batched information, you understand how tedious it may be to optimize for parallelism. However with JAX’s vmap (vectorization map) operate, you’ll be able to say goodbye to ugly loops and whats up to concise, environment friendly, and parallelized code.
On this article, we’re going to dive deep into computerized vectorization in JAX. We’ll discover how vectorization works, why it’s important for rushing up computations, and how one can leverage JAX’s vmap to keep away from writing specific loops. Alongside the way in which, we’ll take some real-world examples and stroll via code that can make you’re keen on JAX much more.
Prepared? Let’s go!
Earlier than we get into JAX specifics, let’s speak about vectorization generally. In conventional programming, you would possibly write code that processes one information level at a time in a loop. For instance, if you wish to apply a operate to each aspect of an array, you’d in all probability use a for loop…