View source for 20240045733. METHOD AND APPARATUS FOR DEPLOYING DEEP LEARNING SYSTEM simplified abstract (INSPUR SUZHOU INTELLIGENT TECHNOLOGY CO., LTD.)

=METHOD AND APPARATUS FOR DEPLOYING DEEP LEARNING SYSTEM=


==Organization Name==

[[:Category:INSPUR SUZHOU INTELLIGENT TECHNOLOGY CO., LTD.|INSPUR SUZHOU INTELLIGENT TECHNOLOGY CO., LTD.]]


[[Category:INSPUR SUZHOU INTELLIGENT TECHNOLOGY CO., LTD.]]

==Inventor(s)==

[[:Category:Faling Rui of Jinan, Shandong (CN)|Faling Rui of Jinan, Shandong (CN)]][[Category:Faling Rui of Jinan, Shandong (CN)]]

==METHOD AND APPARATUS FOR DEPLOYING DEEP LEARNING SYSTEM - A simplified explanation of the abstract==


This abstract first appeared for US patent application 20240045733 titled 'METHOD AND APPARATUS FOR DEPLOYING DEEP LEARNING SYSTEM

==Simplified Explanation==

The abstract describes a method for deploying a deep learning system by defining node group templates and cluster templates, validating the configuration, creating virtual machines, and configuring communication benchmarks and importing a deep learning mirror image.

* The method involves defining templates for node groups and device groups, indicating the components installed by each group.
* The cluster template is created based on the node group template and specifies the number of nodes in each group.
* The cluster template is validated to ensure rational configuration.
* Virtual machines are created based on the cluster template, with each machine having an artificial intelligence framework.
* A communication benchmark is configured for the virtual machines.
* A deep learning mirror image is imported into the artificial intelligence frameworks of the virtual machines.

Potential applications of this technology:

* Deployment of deep learning systems in various industries such as healthcare, finance, and manufacturing.
* Training and inference of deep learning models for tasks like image recognition, natural language processing, and anomaly detection.

Problems solved by this technology:

* Streamlining the deployment process of deep learning systems by automating the creation of virtual machines and configuration of communication benchmarks.
* Ensuring rational configuration of the cluster template to optimize the performance and resource allocation of the deep learning system.

Benefits of this technology:

* Improved efficiency and scalability in deploying deep learning systems.
* Reduction in manual configuration efforts and potential human errors.
* Standardization of the deployment process, enabling easier replication and maintenance of deep learning systems.


==Original Abstract Submitted==

a method for deploying a deep learning system, including: defining a node group template for a first node group and a second node group, the node group template including indications of components installed by the first node group and components installed by the second node group; defining a cluster template for a device group based on the node group template, the cluster template including indications of the number of first nodes and the number of second nodes; validating whether the cluster template is rationally configured, and creating, based on the cluster template, virtual machines, that correspond to first nodes and second nodes and each has an artificial intelligence framework, in response to the cluster template being rationally configured; and configuring a communication benchmark for virtual machines, and importing a deep learning mirror image into the artificial intelligence frameworks of the virtual machines respectively.



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