Deep Learning Cluster Templates for Automated Node Deployment
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Solution Overview
Problem
The deployment of AIStation clusters is cumbersome and labor-intensive, particularly with many nodes, due to the need for manual installation of operating systems and configuration files, which can fail easily.
Innovation Solution
A method and apparatus for deploying a deep learning system that involves defining node group and cluster templates, validating their configuration, creating virtual machines with AI frameworks, and configuring communication benchmarks to automate the deployment process, including the use of plug-ins for component management and validation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual installation of operating systems and configuration files is performed for each node, then the deployment process can be completed, but the deployment becomes cumbersome and labor-intensive
Solution Approach 1:
The patent implements automated self-service deployment through deployment scripts that automatically install operating systems, configure files, and set up deep learning frameworks across multiple nodes without manual intervention. The system performs self-configuration by reading cluster templates and automatically provisioning resources.
Solution Approach 2:
The patent applies preliminary action by pre-defining cluster templates with node group configurations, component specifications, and deployment parameters before actual deployment. These templates contain all necessary configuration information that will be automatically applied during the deployment process.
2Reliability
If manual configuration file modification and installation script execution is performed, then the system can be deployed, but errors may occur due to carelessness
Solution Approach 1:
The deployment system performs self-validation by automatically checking configuration files and deployment parameters against predefined rules and templates. The system validates node configurations, component specifications, and resource allocations to ensure correctness before execution.
Solution Approach 2:
The patent implements feedback mechanisms where the deployment system continuously monitors the deployment process, validates intermediate results, and provides error feedback. If configuration errors or deployment issues are detected, the system generates error messages and can rollback to previous states.
3Quantity of substance
If operating systems are installed on many nodes, then the cluster capacity increases, but the installation becomes very time-consuming
Solution Approach 1:
The patent applies preliminary action by pre-configuring cluster templates with all necessary node specifications, component lists, and deployment parameters before actual deployment. This allows the deployment system to automatically provision multiple nodes in parallel using the pre-defined templates.
Solution Approach 2:
The patent merges the deployment processes of multiple nodes into a single automated workflow. Instead of installing operating systems and configuring each node separately, the system combines all deployment tasks into one unified process that manages multiple nodes simultaneously through template-based provisioning.
4Productivity
If automated deployment scripts are used, then deployment efficiency improves, but the initial setup complexity increases
Solution Approach 1:
The patent segments the deployment automation framework into distinct modular components: cluster template definition, node group configuration, component specification, validation rules, and deployment execution. Each module handles a specific aspect of the deployment process, making the overall system more manageable and maintainable.
Data Source
AI summary
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.