公开(公告)号：US11157812B2

公开(公告)日：2021-10-26

申请号：US16849422

申请日：2020-04-15

Applicant: Intel Corporation

Inventor： Michael McCourt ,  Taylor Jackie Springs ,  Ben Hsu ,  Simon Howey ,  Halley Nicki Vance ,  James Blomo ,  Patrick Hayes ,  Scott Clark

IPC: G06N3/08

Abstract: A system and method for tuning hyperparameters and training a model includes implementing a hyperparameter tuning service that tunes hyperparameters of a model that includes receiving, via an API, a tuning request that includes: (i) a first part comprising tuning parameters for generating tuned hyperparameter values for hyperparameters of the model; and (ii) a second part comprising model training control parameters for monitoring and controlling a training of the model, wherein the model training control parameters include criteria for generating instructions for curtailing a training run of the model; monitoring the training run for training the model based on the second part of the tuning request, wherein the monitoring of the training run includes periodically collecting training run data; and computing an advanced training curtailment instruction based on the training run data that automatically curtails the training run prior to a predefined maximum training schedule of the training run.

公开(公告)号：US20220114450A1

公开(公告)日：2022-04-14

申请号：US17508665

申请日：2021-10-22

Applicant: Intel Corporation

Inventor： Michael McCourt ,  Taylor Jackie Springs ,  Ben Hsu ,  Simon Howey ,  Halley Nicki Vance ,  James Blomo ,  Patrick Hayes ,  Scott Clark

IPC: G06N3/08

Abstract: A system and method for tuning hyperparameters and training a model includes implementing a hyperparameter tuning service that tunes hyperparameters of a model that includes receiving, via an API, a tuning request that includes: (i) a first part comprising tuning parameters for generating tuned hyperparameter values for hyperparameters of the model; and (ii) a second part comprising model training control parameters for monitoring and controlling a training of the model, wherein the model training control parameters include criteria for generating instructions for curtailing a training run of the model; monitoring the training run for training the model based on the second part of the tuning request, wherein the monitoring of the training run includes periodically collecting training run data; and computing an advanced training curtailment instruction based on the training run data that automatically curtails the training run prior to a predefined maximum training schedule of the training run.