How Google Uses Flax

This second installment explores how Ray’s higher-level libraries—Serve, Data, and Train—abstract the complexities of running AI workloads on Google's TPU slices. Ray Serve uses a simple topology configuration to correctly gang-schedule large multi-host models, while Ray Data eliminates data-loading bottlenecks by feeding accelerators directly with native JAX batches. Finally, JaxTrainer streamlines distributed training across TPUs by automatically handling cross-slice coordination, checkpointing, and fault tolerance.

The article discusses the JAX AI Stack, a modular framework for building production AI models on Google Cloud TPUs.

Metrax is a high-performance library designed for efficient and robust model evaluation metrics in JAX, addressing the need for standardized metrics during the migration from TensorFlow.

The article introduces Tunix, a new open-source, JAX-native library designed for post-training of large language models (LLMs).

This article provides a comprehensive guide on how to train a GPT-2 model using JAX on TPU, highlighting the ease of leveraging Google TPUs for free.

The article discusses the increasing adoption of JAX in robotics, highlighting its efficiency in optimal control and simulation. It features insights from Max Muchen Sun, a Robotics Ph. D.

Stanford's Marin project introduces the first fully open foundation model developed using JAX, emphasizing transparency in the scientific process behind AI models.