Run a TPU Notebook

Key Insight

Training and running machine learning models on a Google TPU requires compiling computations using the XLA compiler instead of relying on standard GPU runtime paradigms. By utilizing high-level frameworks like JAX or PyTorch/XLA, developers can write models in Python and let the backend optimize the execution graph for the TPU's matrix multiply units. This workflow is crucial for scaling up training across large TPU pods, where matrix operations are executed with extreme hardware efficiency.