JAX on Cloud TPUs provides robust acceleration for machine learning workflows in distributed cloud environments. Debugging these workflows necessitates specialized tools to access logs and hardware metrics. This guide outlines essential debugging and profiling techniques, focusing on key components: libtpu (the TPU runtime essential for communication with hardware) and JAX (the Python library for model coding). Effective debugging starts with enabling verbose logging across TPU worker nodes, which captures detailed runtime events crucial for troubleshooting. Additional tools like TPU Monitoring Library allow real-time insights into TPU performance metrics, while tpu-info provides a GPU-like view of TPU memory usage. Enabling these components and understanding their interrelations can optimize data modeling and debugging efforts. Proper installation and command execution across TPU environments ensure comprehensive monitoring. This post lays the groundwork for further exploration on profiling JAX programs with tools such as HLO dumps and XProf.
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