• JAX on Cloud TPUs provides powerful acceleration for machine learning workflows. • When working in distributed cloud environments, you need specialized tools to debug your workflows, including accessing logs, hardware metrics, and more. • This blog post serves as a practical guide to various debugging and profiling techniques. • At the heart of the system are two main components that nearly all debugging tools depend on: The relationship between these components and the debugging tools is illustrated in the diagram below. • Here is a breakdown of the specific tools, their dependencies, and how they relate to each other. • In summary, libtpu is the central pillar that most debugging tools rely on, either for configuration (logging, HLO dumps) or for querying real-time data (monitoring, profiling).
Article Summaries:
- The blog post offers a practical guide for developers debugging JAX workloads on Google Cloud TPUs. It explains that most debugging tools rely on two core components-libtpu for configuration and real‑time data, and XProf for Python‑level inspection. The author stresses enabling verbose logging on every TPU worker, providing a gcloud command to set necessary environment variables and capture detailed logs. It also details how to collect libtpu logs from all TPU VMs via a bash script and how to access them in Colab. Sample log snippets illustrate the level of detail available, helping developers pinpoint runtime issues in distributed TPU environments.
Sources: