  Recent results in language understanding using neural networks have required
training hardware of unprecedentedscale, with thousands of chips cooperating on
a single training run. This paper presents techniques to scaleML models on the
Google TPU Multipod, a mesh with 4096 TPU-v3 chips. We discuss model
parallelism toovercome scaling limitations from the fixed batch size in data
parallelism, communication/collective optimizations,distributed evaluation of
training metrics, and host input processing scaling optimizations. These
techniques aredemonstrated in both the TensorFlow and JAX programming
frameworks. We also present performance resultsfrom the recent Google
submission to the MLPerf-v0.7 benchmark contest, achieving record training
times from16 to 28 seconds in four MLPerf models on the Google TPU-v3 Multipod
machine.
