Deep learning job scheduling method, system and related equipment

Abstract

The application discloses a deep learning job scheduling method, system and related equipment. The method includes: obtaining a job request for a deep learning job, the job request carrying a deep learning library type and a job type; determining a target job description file template from a plurality of pre-stored job description file templates according to the deep learning library type and job type ; According to the deep learning library type and the job type, determine the identity of the target job base image from the identifiers of multiple pre-stored job base images; generate the target job description file according to the target job description file template and the identity of the target job base image; The target job description file is sent to the container scheduler; the container scheduler selects the target job base image from pre-stored job base images according to the target job description file, and creates at least one container for executing the job request. The above scheme can improve the compatibility rate of deep learning job scheduling.

Description

technical field [0001] The present application relates to the field of artificial intelligence, and in particular, to a deep learning job scheduling method, system and related equipment. Background technique [0002] In recent years, deep learning technology has been widely used in all walks of life. Major public cloud service providers at home and abroad have launched deep learning cloud services. This type of cloud service has become an inevitable choice for enterprises to lower the threshold of technology use and reduce the cost of software and hardware deployment. When cloud service providers provide deep learning services, they often need to consider many indicators such as cost, performance, resource utilization, reliability, scalability, maintainability, etc. The advantages and disadvantages of the scheduling system largely determine the above indicators . This is because the "on-demand" and "elastic" usage characteristics of cloud services need to be achieved thro...

AI Technical Summary

Problems solved by technology

These complex factors make it difficult for the batch job scheduler to schedule deep learning jobs in a simple way, and users have to write some adaptation scripts that are not highly reusable

[0009] (2) Although deep learning commissioning jobs and online reasoning jobs are similar to traditional services, as an application service submitted by users, their life cycle is often relatively short, and their scheduling requirements are also different from those of web servers and databases. Typical System Services

For the service scheduler designed for system service scenarios with relatively stable quantity and life cycle, and lacking batch processing abstraction and mechanism, these special scheduling requirements are either completely impossible to achieve, or need to be assisted by complex external mechanisms

[0010] Neither of the two types of traditional schedulers can fully meet the complex and diverse scheduling requirements of multiple deep learning libraries and multiple types of deep learning jobs, which is an important obstacle to providing deep learning services in the public cloud

Simply using the original batch job scheduler or service scheduler not only cannot realize the scheduling strategy proprietary to deep learning, thereby reducing user experience and increasing the complexity of operation and maintenance; but also potentially affects the utilization of hardware resources and improves Operating costs of public cloud

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