Summary
JAX is a Python library for high-performance numerical computing and large-scale machine learning, providing a familiar NumPy-style API and composable function transformations for various purposes.
Abstract
JAX is a powerful Python library designed for high-performance numerical computing and large-scale machine learning. It offers a familiar NumPy-style API, making it easy to adopt for researchers and engineers. JAX provides various composable function transformations, which enable compilation, batching, automatic differentiation, and parallelization. Moreover, the same code can run on multiple backends, including CPU, GPU, and TPU, making JAX a versatile tool for various computational tasks. If you are looking to train neural networks, JAX's associated tools, such as Flax, Optax, and Orbax, can be helpful.
Bullet Points
- •JAX is a Python library for high-performance numerical computing and large-scale machine learning.
- •It provides a familiar NumPy-style API for ease of adoption.
- •JAX includes composable function transformations for compilation, batching, automatic differentiation, and parallelization.
- •The same code can run on multiple backends, including CPU, GPU, and TPU.
- •JAX has associated tools for training neural networks, such as Flax, Optax, and Orbax.
- •MaxText is an end-to-end transformer library built on JAX.