A computing resource acquisition method, device, electronic device and storage medium

A computing resource and acquisition method technology, applied in the fields of devices, computing resource acquisition methods, electronic equipment and storage media, can solve problems such as large deviation of computing resources, and achieve the effect of reducing interference and reducing deviation

Active Publication Date: 2022-05-20
DATACANVAS LTD
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  • Abstract
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  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0004] The purpose of the embodiments of the present application is to provide a computing resource acquisition method, device, electronic device and storage medium, which are used to solve the problem of a large deviation between the computing resources required for algorithm model training and the computing resources configured by the user

Method used

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  • A computing resource acquisition method, device, electronic device and storage medium
  • A computing resource acquisition method, device, electronic device and storage medium
  • A computing resource acquisition method, device, electronic device and storage medium

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Embodiment Construction

[0089] The following will clearly and completely describe the technical solutions in the embodiments of the present application with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are part of the embodiments of the present application, not all of them. Based on the embodiments in this application, all other embodiments obtained by persons of ordinary skill in the art without making creative efforts belong to the scope of protection of this application.

[0090] see figure 1 , figure 1 It is a flow chart of a computing resource acquisition method provided in the embodiment of this application, such as figure 1 As shown, the above computing resource acquisition methods include:

[0091] Step 101. Determine topology information for model training according to scene information and training sample data.

[0092] Step 102, according to the topology information, obtain operator resource information of each operator in...

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Abstract

The present application provides a computing resource acquisition method, device, electronic device, and storage medium, the method including: determining topology information for model training according to scene information and training sample data; obtaining the topology information according to the topology information The information includes operator resource information of each operator; according to the operator resource information of each operator, target resource information for model training is obtained. Based on the scene information and training sample data input by the user, the topology information used for model training is determined, and the operator resource information of each operator included in the subsequent calculation topology information, as well as the target resource information used for model training, assist users Complete the configuration of computing resources corresponding to model training, reduce the interference caused by human factors, and reduce the deviation between the computing resources required for model training and the computing resources configured by users.

Description

technical field [0001] The present application relates to the technical field of machine learning, and in particular to a computing resource acquisition method, device, electronic equipment and storage medium. Background technique [0002] The algorithm model based on machine learning has the advantages of short prediction time after sufficient training and high prediction accuracy, and is widely used in various fields. [0003] During the training process of the algorithm model, the computing resources required for different algorithm model training will change accordingly. The above computing resources for model training need to be manually configured by the user. Under the interference of human factors, the required computing resources for algorithm model training The deviation between the computing resources and the computing resources configured by the user is large. If the configured computing resources are insufficient, memory overflow may easily occur, or if the conf...

Claims

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): G06N20/00G06N3/08
CPCG06N20/00G06N3/08
Inventor 李明路明奎方磊
Owner DATACANVAS LTD
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