#, #, #, # ] print( grad( lambda x: jnp. JAX has roughly the same API as Autograd. Here are four transformations of primary interest: grad, jit, vmap, and You can also take a look at the mini-libraries inĪnd optimizers for first-order stochasticĪt its core, JAX is an extensible system for transforming numerical functions. The Autodiff Cookbook, Part 1: easy and powerful automatic differentiation in JAX.To try out the preview, see the Cloud TPU Training a Simple Neural Network, with TensorFlow Dataset Data Loading.The basics: NumPy on accelerators, grad for differentiation, jit for compilation, and vmap for vectorization.Jump right in using a notebook in your browser, connected to a Google Cloud GPU. Grad_loss = jit( grad( loss)) # compiled gradient evaluation function perex_grads = jit( vmap( grad_loss, in_axes =( None, 0, 0))) # fast per-example grads Contents tanh( outputs) # inputs to the next layer return outputs # no activation on last layer def loss( params, inputs, targets): numpy as jnp from jax import grad, jit, vmap def predict( params, inputs): This is a research project, not an official Google product.

Parallel programming of multiple accelerators, with more to come. Pmap for single-program multiple-data (SPMD) BothĪre instances of such transformations. You can even program multiple GPUsĭig a little deeper, and you'll see that JAX is really an extensible system forĬomposable function transformations. Maximal performance without leaving Python. Compilation and automatic differentiation can beĬomposed arbitrarily, so you can express sophisticated algorithms and get Into XLA-optimized kernels using a one-function API, But JAX also lets you just-in-time compile your own Python functions Under the hood by default, with library calls getting just-in-time compiled andĮxecuted. To compile and run your NumPy programs on GPUs and TPUs. Via grad as well as forward-mode differentiation,Īnd the two can be composed arbitrarily to any order. It supports reverse-mode differentiation (a.k.a. Recursion, and closures, and it can take derivatives of derivatives ofĭerivatives. It can differentiate through loops, branches, JAX can automatically differentiate native Brought together for high-performance machine learning research.

Jax sheets