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Recent results in language understanding using neural networks have requiredtraining hardware of unprecedentedscale, with thousands of chips cooperating ona single training run. This paper presents techniques to scaleML models on theGoogle TPU Multipod, a mesh with 4096 TPU-v3 chips. We discuss modelparallelism toovercome scaling limitations from the fixed batch size in dataparallelism, communication/collective optimizations,distributed evaluation oftraining metrics, and host input processing scaling optimizations. Thesetechniques aredemonstrated in both the TensorFlow and JAX programmingframeworks. We also present performance resultsfrom the recent Googlesubmission to the MLPerf-v0.7 benchmark contest, achieving record trainingtimes from16 to 28 seconds in four MLPerf models on the Google TPU-v3 Multipodmachine.

Exploring the limits of Concurrency in ML Training on Google TPUs