How does JAX support TPUs compared to TensorFlow

JAX Support for TPUs

- Compatibility: JAX is designed to work seamlessly with TPUs, leveraging the XLA compiler for optimizations. It requires TPU VMs for versions 0.4 and newer, which are not currently available in environments like Google Colab[3][5].
- Integration: JAX integrates well with other libraries built on top of it, such as FLAX, allowing for efficient use of TPUs in neural network training and other computations[2][4].
- Performance: JAX's composable function transformations and XLA optimizations enable high-performance computations on TPUs. However, it may require more manual configuration compared to TensorFlow[6][8].

TensorFlow Support for TPUs

- Compatibility: TensorFlow supports a wide range of hardware, including TPUs, and integrates well with various libraries like Keras and TensorFlow Probability[2].
- Integration: TensorFlow provides a more established and user-friendly interface for working with TPUs, using the `TPUClusterResolver` for setup and management[2].
- Performance: TensorFlow also uses XLA for TPU optimizations, but its performance can differ from JAX due to differences in how they translate operations to XLA[8].

In summary, while both frameworks support TPUs, JAX offers a more lightweight and flexible approach with its integration with XLA, but requires specific environments like TPU VMs. TensorFlow provides a more comprehensive and user-friendly interface for TPU usage, making it suitable for a broader range of applications.