JAX: Accelerated Machine Learning Research | SciPy 2020 | VanderPlas

JAX is a system for high-performance machine learning research and numerical computing. It offers the familiarity of Python+NumPy together with hardware acceleration, and it enables the definition and composition of user-wielded function transformations. These transformations include automatic differentiation, automatic vectorized batching, end-to-end compilation (via XLA), parallelizing over multiple accelerators, and more. JAX had its initial open-source release in December 2018 (https://github.com/google/jax). This talk will introduce JAX and its core function transformations with a live demo. You’ll learn about JAX’s core design, how it’s powering new research, and how you can start using it too! ***************** https://twitter.com/enthought https://www.facebook.com/Enthought/ https://www.linkedin.com/company/enthought