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公开(公告)号：US11301781B2
公开(公告)日：2022-04-12
申请号：US16796489
申请日：2020-02-20
Applicant: Intel Corporation
Inventor： Patrick Hayes , Michael McCourt , Alexandra Johnson , George Ke , Scott Clark
Abstract: A system and method includes receiving a tuning work request for tuning an external machine learning model; implementing a plurality of distinct queue worker machines that perform various tuning operations based on the tuning work data of the tuning work request; implementing a plurality of distinct tuning sources that generate values for each of the one or more hyperparameters of the tuning work request; selecting, by one or more queue worker machines of the plurality of distinct queue worker machines, one or more tuning sources of the plurality of distinct tuning sources for tuning the one or more hyperparameters; and using the selected one or more tuning sources to generate one or more suggestions for the one or more hyperparameters, the one or more suggestions comprising values for the one or more hyperparameters of the tuning work request.
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公开(公告)号：US12141667B2
公开(公告)日：2024-11-12
申请号：US17561480
申请日：2021-12-23
Applicant: Intel Corporation
Inventor： Patrick Hayes , Michael McCourt , Alexandra Johnson , George Ke , Scott Clark
Abstract: A disclosed example includes implementing a first worker instance and a second worker instance to operate in parallel; running a first tuning operation via the first worker instance to tune first hyperparameters; running a second tuning operation via the second worker instance using a Bayesian-based optimization to determine a hyperparameter configuration to evaluate next; evaluating the hyperparameter configuration for an external model using a surrogate model; and selecting the hyperparameter configuration for the external model.
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公开(公告)号：US20220121993A1
公开(公告)日：2022-04-21
申请号：US17561480
申请日：2021-12-23
Applicant: Intel Corporation
Inventor： Patrick Hayes , Michael McCourt , Alexandra Johnson , George Ke , Scott Clark
Abstract: A disclosed example includes implementing a first worker instance and a second worker instance to operate in parallel running a first tuning operation via the first worker instance to tune first hyperparameters; running a second tuning operation via the second worker instance using a Bayesian-based optimization to determine a hyperparameter configuration to evaluate next; evaluating the hyperparameter configuration for an external model using a surrogate model; and selecting the hyperparameter configuration for the external model.
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