Decentralized Model Training via Adaptive Micro-Batch Allocation
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Solution Overview
Problem
The high computational intensity and cost of training machine learning models, particularly neural networks, are exacerbated by the need for extensive cloud computing resources, which are expensive and environmentally unfriendly, and decentralized computing solutions face complex setup and vendor lock-in issues.
Innovation Solution
A decentralized training system that utilizes available computing power from diverse devices through a readymade runtime environment (RRE) like web browsers or containers, automatically translating and distributing training tasks without specific installations, using a main processor to assign mini-tasks based on performance indicators.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Power
If cloud computing services are used to provide computing power for ML model training, then large and scalable computing power is provided, but lengthy and complex setup procedures and high costs are required
Solution Approach 1:
The system enables end-user computing devices to automatically provide computing power for ML training without requiring manual setup or configuration. The runtime environment automatically translates and executes training tasks, eliminating the need for complex cloud service setup procedures while utilizing idle computing resources from diverse devices
Solution Approach 2:
The system creates a universal runtime environment that can execute ML training tasks across diverse computing devices with different hardware specifications. This universal platform eliminates vendor lock-in by supporting multiple device types and eliminating the need for device-specific setup procedures, while still providing scalable computing power
2Power
If cloud computing services are used to provide computing power for ML model training, then large and scalable computing power is provided, but high costs are incurred
Solution Approach 1:
The system allows computing devices to monetize their own idle computing resources by automatically participating in ML training tasks. This self-service approach eliminates the need to pay cloud service providers, as the computing power is provided by the devices themselves or other willing participants in the decentralized network, significantly reducing training costs
Solution Approach 2:
The system utilizes idle computing resources from everyday devices that would otherwise be underutilized. By repurposing these existing resources for ML training, the system avoids the high costs of dedicated cloud computing infrastructure while still providing sufficient computing power for training tasks
3Ease of operation
If decentralized computing resources are used for ML model training, then accessibility and cost-effectiveness are improved, but setup complexity and vendor lock-in issues arise
Solution Approach 1:
The system introduces a runtime environment as an intermediary layer between the diverse computing devices and the ML training tasks. This intermediary automatically handles task translation, resource allocation, and execution coordination, eliminating the need for users to directly manage the complexity of decentralized computing setup while maintaining broad device compatibility
4Productivity
If traditional centralized training is used, then model training can be performed, but environmental footprint and cost increase
Solution Approach 1:
The system enables computing devices to utilize their own idle resources for ML training, eliminating the need for energy-intensive centralized data centers. By distributing training tasks across many devices using their unused computing power, the system significantly reduces the carbon footprint while maintaining full model training capability
Solution Approach 2:
The system recovers and repurposes idle computing resources from devices that would otherwise be underutilized. By harvesting these wasted resources for ML training, the system eliminates the need for dedicated energy-consuming training infrastructure, thereby reducing environmental impact while maintaining productivity
Data Source
AI summary
A computer based system and method for providing decentralized training of a machine learning model, including: obtaining a mini-task of training the machine learning model wherein the mini-task comprises at least one mini-batch size; obtaining an estimation of at least one performance indicator in each trainer of a plurality of trainers that have available computing power, wherein each trainer comprises one or more processors; calculating a maximal micro-batch size for each processor of the plurality of trainers based on the at least one performance indicator of the respective trainer, and parameters of the mini-task; and assigning the mini-task to at least one designated trainer of the plurality of trainers based on the maximal micro-batch sizes of the one or more processors of the at least one designated trainer and the at least one mini-batch size.