Google Inc.

Microsoft Technology Licensing, LLC

Srinidhi Viswanatha

Vanja Josifovski

Jie Yang

Mike Bendersky

Luis Garcia Pueyo

Amitabh Saikia

Marc-Allen Cartright

Dharma Kiritkumar Shukla

Ramachandran Ramjee

Pranav Tiwari

Muthian Sivathanu

Pankaj Sharma

Rimma V Nehme

Nipun Kwatra

Bhalakumaaran Erode Ranganathan

Vaibhav Sharma

Rajesh S R

Vijay Sainath Thadkal

Avi Dullu

The disclosure herein describes platform-level checkpointing for deep learning (DL) jobs. The checkpointing is performed through capturing two kinds of state data: (i) GPU state (device state), and (ii) CPU state (host state). The GPU state includes GPU data (e.g., model parameters, optimizer state, etc.) that is located in the GPU and GPU context (e.g., the default stream in GPU, various handles created by the libraries such as DNN, Blas, etc.). Only a fraction of the GPU memory is copied because the checkpointing is done in a domain-aware manner. The 'active' memory contains useful data like model parameters. To be able to capture the useful data, memory management is controlled to identify which parts of the memory are active. Also, to restore the destination GPU to the same context/state, a mechanism is used to capture such state-changing events on an original GPU and replayed on a destination GPU.

Transparent pre-emption and migration for planet-scale computer

Methods, apparatus, and computer-readable media are provided for generating and applying data extraction templates. In various implementations, a corpus of structured communications such as emails may be grouped into clusters based on one or more similarities between the structured communications. A set of structural paths may be identified from structured communications of a particular cluster. One or more structural paths of the set may be classified as transient wherein a count of occurrences of one or more associated segments of text across the particular cluster satisfies a criterion. One or more transient paths may be assigned a semantic data type and/or a confidentiality designation based on various signals. A data extraction template may be generated to extract, from subsequent structured communications, segments of text associated with transient (and in some cases, non-confidential) structural paths.

Generating and applying data extraction templates

Techniques for detecting poor machine translations are disclosed herein. The techniques can include various diagnostics that can be performed to determine whether to adopt a potential translated key term as a translated key term. The diagnostics include a geographic location diagnostic, a reverse translation diagnostic, a local term diagnostic, and a narrow-to-broad diagnostic.

Techniques for detecting poor machine translations of key terms

Growing community of inventors

Bengaluru, India

Srinidhi Viswanatha