Distributed Cluster Training Gradient Scheduling
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Solution Overview
Problem
Distributed cluster training methods face challenges with excessive network traffic and potential network congestion due to frequent gradient summarization and communication in existing technologies, which can affect the entire cluster's performance.
Innovation Solution
A distributed cluster training method where training servers iteratively calculate gradients locally before receiving a collection instruction from a scheduling server, which monitors the cluster environment to control when gradients are sent to an aggregation server for updating weights, reducing unnecessary network traffic and communication.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If gradients are summarized after each round of calculation in distributed cluster training, then training accuracy is improved through comprehensive data processing, but network traffic becomes huge and frequent causing network congestion
Solution Approach 1:
The system performs preliminary actions by having each node calculate gradients locally before receiving collection instructions, and by pre-establishing a scheduling server that monitors network conditions. This allows the system to prepare computational work in advance and trigger data transmission only when network conditions are appropriate, reducing overall network traffic while maintaining training accuracy.
Solution Approach 2:
Instead of continuous gradient summarization, the system implements periodic action through a scheduling server that monitors network conditions and issues collection instructions at appropriate intervals. The training servers only send gradients when instructed, transforming continuous network communication into periodic, condition-based transmission, thereby reducing network traffic volume.
2Productivity
If frequent gradient summarization is performed in distributed cluster training, then training convergence is accelerated, but network switches are overwhelmed and cluster performance is affected
Solution Approach 1:
The scheduling server implements feedback mechanisms by monitoring network conditions and using this information to control when collection instructions are issued. This feedback loop allows the system to adjust gradient summarization frequency based on actual network state, maintaining training convergence speed while preventing network switch overload.
Solution Approach 2:
The system introduces dynamics by making the gradient collection process adaptive rather than static. The scheduling server dynamically adjusts when collection instructions are based on real-time network condition monitoring, allowing the system to optimize between training convergence speed and network load management.
3Quantity of substance
If all training data is processed in distributed cluster, then big data processing capability is achieved, but communication cost increases significantly
Solution Approach 1:
The system segments the data processing task by distributing data across multiple nodes and having each node process its local data independently before gradients are collected. This segmentation allows big data processing capability while reducing the communication cost by minimizing the volume of data that needs to be transmitted between nodes.
Data Source
AI summary
A distributed cluster training method and an apparatus thereof are provided. The method includes reading a sample set, the sample set including at least one piece of sample data; using the sample data and current weights to substitute into a target model training function for iterative training to obtain a first gradient before receiving a collection instruction, the collection instruction being issued by a scheduling server when a cluster system environment meets a threshold condition; sending the first gradient to an aggregation server if a collection instruction is received, wherein the aggregation server collects each first gradient and calculates second weights; and receiving the second weights sent by the aggregation server to update current weights. The present disclosure reduces an amount of network communications and an impact on switches, and avoids the use of an entire cluster from being affected.