公开(公告)号：US20240296062A1

公开(公告)日：2024-09-05

申请号：US18662892

申请日：2024-05-13

Applicant: Amazon Technologies, Inc.

Inventor： Thomas Albert Faulhaber ,  Jonathan Esterhazy ,  Vladimir Zhukov ,  Stefano Stefani

IPC: G06F9/455 ,  G06F9/54

CPC classification number: G06F9/45558 ,  G06F9/54 ,  G06F2009/45562 ,  G06F2009/45591

Abstract: Methods and apparatus for providing persistent execution environments for computation systems including but not limited to interactive computation systems. A service is provided that extends the notion of static containers to dynamically changing execution environments into which users can install code, add files, etc. The execution environments are monitored, and changes to an execution environment are automatically persisted to environment versions(s) so that code run in the execution environment can be run later or elsewhere simply by referring to the environment. There is no explicit build step for the user. Instead, incremental changes are added to environment versions which are stored and are ready to be used to instantiate respective execution environments on other compute instances.

公开(公告)号：US11423283B1

公开(公告)日：2022-08-23

申请号：US15933114

申请日：2018-03-22

Applicant: Amazon Technologies, Inc.

Inventor： Hagay Lupesko ,  Dominic Rajeev Divakaruni ,  Jonathan Esterhazy ,  Sandeep Krishnamurthy ,  Vikram Madan ,  Roshani Nagmote ,  Naveen Mysore Nagendra Swamy ,  Yao Wang

IPC: G06N3/04 ,  G06N3/08 ,  G06N3/10

Abstract: Techniques for model adaptation are described. For example, a method of receiving a call to provide either a model variant or a model variant profile of a deep learning model, the call including desired performance of the deep learning model, a deep learning model identifier, and current edge device characteristics; comparing the received current edge device characteristics to available model variants and profiles based on the desired performance of the deep learning model to generate or select a model variant or profile, the available model variants and profiles determined by the model identifier; and sending the generated or selected model variant or profile to the edge device to use in inference is detailed.

公开(公告)号：US10949252B1

公开(公告)日：2021-03-16

申请号：US15895747

申请日：2018-02-13

Applicant: Amazon Technologies, Inc.

Inventor： Sandeep Krishnamurthy ,  Jiajie Chen ,  Jonathan Esterhazy ,  Naveen Mysore Nagendra Swamy ,  Ruofei Yu ,  Yao Wang ,  Roshani Nagmote ,  Hagay Lupesko ,  Vikram Madan

IPC: G06F9/48 ,  G06N20/00

Abstract: Techniques for benchmarking a machine learning model/algorithm are described. For example, in some instances a method includes generating an execution plan for benchmarking of at least one task corresponding to a machine learning model based on an identified machine learning model, identified training data, and at least one objective for the benchmarking job; receiving execution statistics about the execution of the task as a part of the benchmarking job according to the execution plan; and updating the execution plan based at least in part on the received execution statistics of the task.

公开(公告)号：US12008390B1

公开(公告)日：2024-06-11

申请号：US16699488

申请日：2019-11-29

Applicant: Amazon Technologies, Inc.

Inventor： Thomas Albert Faulhaber ,  Jonathan Esterhazy ,  Vladimir Zhukov ,  Stefano Stefani

IPC: G06F9/455 ,  G06F9/54

CPC classification number: G06F9/45558 ,  G06F9/54 ,  G06F2009/45562 ,  G06F2009/45591

Abstract: Methods and apparatus for providing persistent execution environments for computation systems including but not limited to interactive computation systems. A service is provided that extends the notion of static containers to dynamically changing execution environments into which users can install code, add files, etc. The execution environments are monitored, and changes to an execution environment are automatically persisted to environment versions(s) so that code run in the execution environment can be run later or elsewhere simply by referring to the environment. There is no explicit build step for the user. Instead, incremental changes are added to environment versions which are stored and are ready to be used to instantiate respective execution environments on other compute instances.