Distributed Machine Learning Training Platform for Bottleneck Reduction
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Solution Overview
Problem
Training a machine learning model on multiple processors in a distributed system is resource-intensive and time-consuming, requiring significant modifications to the code and often results in bottlenecks that slow down the process.
Innovation Solution
A distributed system automatically selects and configures processors for training and result aggregation, generating model copies with unique identifiers, and dynamically adjusts processing threads based on progress, allowing for seamless and efficient training without user intervention.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If multiple processors are used to train a machine learning model, then the training throughput and speed are improved, but the code complexity and difficulty of implementation increase significantly
Solution Approach 1:
The system enables self-service by allowing the model training process to automatically manage its own distribution across multiple processors. The framework handles processor selection, model copying, training coordination, and result aggregation without requiring developer intervention or code modifications, thus improving productivity while avoiding increased code complexity.
Solution Approach 2:
The patent introduces an intermediary training platform that acts as a mediator between the developer and the distributed processing system. This intermediary automatically manages the complexity of multi-processor training by handling model instantiation, processor coordination, and result aggregation, allowing developers to work with simplified code while achieving high throughput training.
2Speed
If multiple processors are used for model training, then the training speed is improved, but bottlenecks in resource allocation and coordination slow down the process
Solution Approach 1:
The system applies dynamics by automatically adjusting resource allocation and processing configurations during training based on real-time system state. The training platform dynamically selects appropriate processors, adjusts the number of model copies, and optimizes data distribution to maintain high training speed while avoiding bottlenecks through adaptive resource management.
Solution Approach 2:
The patent implements preliminary action by pre-configuring and pre-allocating training resources before the actual training process begins. The system automatically selects and prepares the necessary processors, pre-distributes training data, and sets up the training environment in advance, which eliminates setup bottlenecks and allows training to start immediately at high speed without coordination delays during execution.
3Adaptability or versatility
If manual configuration of multi-processor training is performed, then training can be customized, but the time and effort required for setup and maintenance increase
Solution Approach 1:
The training platform enables self-service by automatically managing all aspects of distributed training configuration and execution. It autonomously selects processors, copies models to appropriate processors, coordinates training operations, and aggregates results without requiring manual setup or maintenance, thus providing adaptable training while eliminating the time investment required for manual configuration.
Solution Approach 2:
The patent implements universality by creating a multi-functional training platform that handles diverse training scenarios through a single unified system. The platform can automatically adapt to different model types, processor configurations, and training requirements without requiring separate manual setup procedures, providing versatile customization capabilities while reducing setup and maintenance time through automated multi-functionality.
Data Source
AI summary
Aspects of the disclosure relate to training a machine learning model on a distributed computing system. The model can be trained using selected processors of the training platform. The distributed system automatically modifies the model for instantiation on each processor, adjusts an input pipeline to accommodate the capabilities of selected processors, and coordinates the training between those processors. Simultaneous processing at each stage can be scaled to reduce or eliminate bottlenecks in the distributed system. In addition, autonomous monitoring and re-allocating of resources can further reduce or eliminate bottlenecks. The training results may be aggregated by the distributed system, and a final model may then be transmitted to a user device.