Distributed AI Model Partition Control for Low-Cost Training
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Solution Overview
Problem
Existing technologies fail to efficiently utilize and manage large AI models due to high computational costs, making them inaccessible to smaller companies and individuals.
Innovation Solution
The AI model is split into multiple partitions deployed in a distributed system, with each entity instantiating one or more partitions, and utilizing parallelization techniques and fine-grained training control to optimize training across entities with varying computing resources.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If an AI model is split into partitions and deployed in a distributed system, then the computational cost is reduced and accessibility is improved, but the device complexity and system coordination overhead increase
Solution Approach 1:
The AI model is divided into multiple partitions that can be independently deployed across different computing entities in the distributed system. Each partition processes a portion of the training data, enabling parallel computation and reducing the computational burden on individual nodes while maintaining the overall model's functionality.
2Productivity
If parallelization techniques are applied to accelerate training, then the training speed is improved, but the coordination overhead and communication requirements increase
Solution Approach 1:
Multiple computing entities are merged into a coordinated distributed system where partitions work together in parallel. The system combines the computational power of multiple nodes while managing their coordination through a unified training framework that handles data distribution, synchronization, and result aggregation.
3Productivity
If fine-grained training control is implemented on partition granularity, then the training efficiency is improved, but the control mechanism complexity increases
Solution Approach 1:
The training control mechanism dynamically adjusts the training process at the partition level based on real-time system state, computing capabilities of individual entities, and data distribution characteristics. This dynamic control enables efficient resource utilization and adaptive training scheduling without requiring complex static configuration.
4Loss of time
If dynamic epoch stop control is implemented, then the training time is reduced, but the control complexity and monitoring requirements increase
Solution Approach 1:
The system implements feedback mechanisms that continuously monitor training progress, convergence metrics, and system performance across distributed partitions. Based on this feedback, the dynamic epoch stop control determines when training can be prematurely terminated without sacrificing model quality, thereby reducing overall training time while maintaining effectiveness.
Data Source
AI summary
Methods and systems for training a distributed Artificial Intelligence (AI) model over a distributed network are disclosed, and where a function is configured to: in response to a convergence level of an entity meeting a first threshold, transmit a message for transferring the entity from an unfrozen to a frozen state; in response to training data volume meeting a second threshold, transmit a second message and a third message for stopping a training epoch by the entity and a non-top-level partition entity; in response to a difference between local batch and local distributions being above a third threshold, transmit a fourth message to the client for stopping of feeding training batch; and in response to a difference between the local batch and the overall distributions being above a fourth threshold, transmit a fifth message to the client for decreasing data feeding frequency of the training batch.