Let the loops fly away!
JAX is legendary for its pace, effectivity, and suppleness when working with mathematical computations and machine studying. However one in every of its lesser-known superpowers — one thing that may prevent from writing countless loops and boilerplate code — is computerized vectorization.
Should you’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) perform, you may say goodbye to ugly loops and hi there 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 dashing up computations, and how one can leverage JAX’s vmap to keep away from writing specific loops. Alongside the best way, we’ll take some real-world examples and stroll by means of code that can make you like JAX much more.
Prepared? Let’s go!
Earlier than we get into JAX specifics, let’s speak about vectorization normally. In conventional programming, you may write code that processes one information level at a time in a loop. For instance, if you wish to apply a perform to each aspect of an array, you’d in all probability use a for loop…