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Method, device, resource manager and storage medium for resource allocation

A resource manager and resource allocation technology, applied in the direction of resource allocation, multiprogramming devices, instruments, etc., can solve the problems of low utilization rate of hardware resource cluster resources, workers cannot be used, etc., so as to improve the utilization rate and efficiency. Effect

Active Publication Date: 2021-06-11
BEIJING DAJIA INTERNET INFORMATION TECH CO LTD
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  • Summary
  • Abstract
  • Description
  • Claims
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AI Technical Summary

Problems solved by technology

[0005] The present disclosure provides a resource allocation method, device, resource manager, and storage medium, which can overcome the problems that the remaining workers in the hardware resource cluster cannot be utilized and the resource utilization rate in the hardware resource cluster is low

Method used

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  • Method, device, resource manager and storage medium for resource allocation
  • Method, device, resource manager and storage medium for resource allocation
  • Method, device, resource manager and storage medium for resource allocation

Examples

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Embodiment Construction

[0063] Reference will now be made in detail to the exemplary embodiments, examples of which are illustrated in the accompanying drawings. When the following description refers to the accompanying drawings, the same numerals in different drawings refer to the same or similar elements unless otherwise indicated. The implementations described in the following exemplary examples do not represent all implementations consistent with the present disclosure. Rather, they are merely examples of apparatuses and methods consistent with aspects of the present disclosure as recited in the appended claims.

[0064] figure 1 is a diagram of an application scenario involved in a resource allocation method according to an exemplary embodiment, as shown in figure 1 As shown, the application scenario includes resource managers and resource clusters. The resource cluster may be a hardware resource cluster or a software resource cluster. In the embodiment of the present disclosure, description...

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Abstract

The present disclosure relates to a resource allocation method, device, resource manager, and storage medium, belonging to the field of deep learning technology. The method includes: when executing the first task in the task list, detecting idle resources in the resource cluster; When the resources are not enough to execute the first task, detect the preemptible resource in the resource cluster, the preemptible resource is the resource that can be preempted among the resources occupied by at least one second task that is running; when the idle resource and the preemptible resource are enough to execute the first task When a task is scheduled, the idle resources and preemptible resources in the resource cluster are scheduled to execute the first task, which prevents the first task from being unable to be executed due to insufficient idle resources in the resource cluster, and the idle resources in the resource cluster cannot be occupied, which improves the efficiency of the resource cluster. The utilization of medium resources enables more tasks to be executed simultaneously in the resource cluster, improving the efficiency of task execution.

Description

technical field [0001] The present disclosure relates to the technical field of deep learning, and in particular to a resource allocation method, device, resource manager and storage medium. Background technique [0002] At present, in order to improve the efficiency of model training, a distributed model training method based on PS (Parameter Server, parameter service) architecture is proposed. The PS architecture includes a parameter server (parameter server) and multiple workers (working machines). The parameter server is used to broadcast model parameters, and each worker is used to receive the model parameters broadcast by the parameter server, calculate the gradient of the model parameters, and then calculate the gradient Return to parameter server. The parameter server aggregates the model parameters returned by each worker, then updates the model parameters, and broadcasts the updated model parameters for model training. Therefore, when performing model training ba...

Claims

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): G06F9/50
CPCG06F9/5027
Inventor 程京
Owner BEIJING DAJIA INTERNET INFORMATION TECH CO LTD
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