Quickstart — JAX documentation
This document provides a quick overview of essential JAX features, so you can get started with JAX quickly: JAX provides a unified NumPy-like interface to computations that run on CPU, GPU, or TPU, in local or distributed settings.
GPU memory allocation — JAX documentation - Read the Docs
If your JAX process fails with OOM, the following environment variables can be used to override the default behavior: XLA_PYTHON_CLIENT_PREALLOCATE=false. This disables the preallocation behavior. JAX will instead allocate GPU memory as needed, potentially decreasing the overall memory usage.
jax.random module — JAX documentation - Read the Docs
Both forms of key array can still be created and used with the jax.random module. New-style typed key arrays are made with jax.random.key(). Legacy uint32 key arrays are made with jax.random.PRNGKey(). To convert between the two, use jax.random.key_data() and jax.random.wrap_key_data(). The legacy key format may be needed when interfacing with ...
jax.vmap — JAX documentation - Read the Docs
jax. vmap (fun, in_axes = 0, out_axes = 0, axis_name = None, axis_size = None, spmd_axis_name = None) [source] # Vectorizing map. Creates a function which maps fun over argument axes.
jax.numpy.ndarray.at — JAX documentation - Read the Docs
jax.numpy.ndarray.at# abstract property ndarray. at [source] # Helper property for index update functionality. The at property provides a functionally pure equivalent of in-place array modifications. In particular:
jax.numpy.where — JAX documentation - Read the Docs
jax.numpy. where (condition, x = None, y = None, /, *, size = None, fill_value = None) [source] # Select elements from two arrays based on a condition. JAX implementation of numpy.where() .
jax.grad — JAX documentation - Read the Docs
jax.grad# jax. grad (fun, argnums = 0, has_aux = False, holomorphic = False, allow_int = False, reduce_axes = ()) [source] # Creates a function that evaluates the gradient of fun. Parameters: fun (Callable) – Function to be differentiated. Its arguments at positions specified by argnums should be arrays
Installation — JAX documentation - Read the Docs
Using JAX requires installing two packages: jax, which is pure Python and cross-platform, and jaxlib which contains compiled binaries, and requires different builds for different operating systems and accelerators.
jax.lax module — JAX documentation - Read the Docs
jax.lax is a library of primitives operations that underpins libraries such as jax.numpy. Transformation rules, such as JVP and batching rules, are typically defined as transformations on jax.lax primitives.
JAX: High performance array computing — JAX documentation
JAX is a Python library for accelerator-oriented array computation and program transformation, designed for high-performance numerical computing and large-scale machine learning.