Google Inc.

Russell Power

Soham Mazumdar

Yonatan Zunger

Anna Lynn Patterson

Nadav Eiron

Pei Cao

Chi Keung Luk

Jose Americo Baiocchi Paredes

Mehmet Deveci

A method includes training, using a first computing system having a first configuration, a first machine learning model having a machine learning model architecture, and training, using a second computing system having a different second configuration, a second machine learning model having the machine learning model architecture. The method also includes determining, for a shared training operation performed by both the first computing system and the second computing system, a similarity measure that represents a similarity between: a first training output generated by the first computing system during performance of the shared training operation during training of the first machine learning model; and a second training output generated by the second computing system during performance of the shared training operation during training of the second machine learning model. The method further includes displaying, to a user, a graphical representation based on the similarity measure determined for the shared training operation.

Debugging correctness issues in training machine learning models

Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for debugging correctness issues in training machine learning models. In one aspect, a method comprises training a first machine learning model using a first computing system having a first configuration; training a second machine learning model using a second computing system having a second configuration, wherein the second configuration of the second computing system is different than the first configuration of the first computing system; and determining, for each of a plurality of shared training operations that are performed by both the first computing system and the second computing system, a respective similarity measure that measures a similarity between: a first training output generated by the first computing system by performing the shared training operation, and a second training output generated by the second computing system by performing the shared training operation.

An information retrieval system uses phrases to index, retrieve, organize and describe documents. Phrases are extracted from the document collection. Documents are the indexed according to their included phrases, using phrase posting lists. The phrase posting lists are stored in an cluster of index servers. The phrase posting lists can be tiered into groups, and sharded into partitions. Phrases in a query are identified based on possible phrasifications. A query schedule based on the phrases is created from the phrases, and then optimized to reduce query processing and communication costs. The execution of the query schedule is managed to further reduce or eliminate query processing operations at various ones of the index servers.

Index server architecture using tiered and sharded phrase posting lists

Query scheduling using hierarchical tiers of index servers

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Russell Power