👍 JAX in Google AI Blog

[8bitmp3]

In case some of you missed it, this is from Google Research: Looking Back at 2020, and Forward to 2021:

In addition, we redoubled our investment in JAX, an open-source, research-focused ML system that has been actively developed over the past two years. Researchers at Google and beyond are now using JAX in a wide range of fields, including differential privacy, neural rendering, physics-informed networks, fast attention, molecular dynamics, tensor networks, neural tangent kernels, and neural ODEs. JAX accelerates research at DeepMind, powering a growing ecosystem of libraries and work on GANs, meta-gradients, reinforcement learning, and more. We also used JAX and the Flax neural network library to build record-setting MLPerf benchmark submissions, which we demonstrated live at NeurIPS on a large TPU Pod slice with a next-generation Cloud TPU user experience (slides, video, sign-up form). Finally, we’re ensuring that JAX works seamlessly with TF ecosystem tooling, from TF.data for data preprocessing and TensorBoard for experiment visualization to the TF Profiler for performance debugging, with more to come in 2021.