JAX: High-Performance Array Computing#
JAX is Autograd and XLA, brought together for high-performance numerical computing.
JAX provides a familiar NumPy-style API for ease of adoption by researchers and engineers.
JAX includes composable function transformations for compilation, batching, automatic differentiation, and parallelization.
The same code executes on multiple backends, including CPU, GPU, & TPU
pip install "jax[cpu]"
pip install "jax[cuda]" -f https://storage.googleapis.com/jax-releases/jax_cuda_releases.html
pip install "jax[tpu]" -f https://storage.googleapis.com/jax-releases/libtpu_releases.html
For more information about supported accelerators and platforms, and for other installation options, see the Install Guide in the project README.