Distributed Deep Learning Training with Heterogeneous Task Segmentation
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Solution Overview
Problem
Existing methods for training large-scale deep learning models face inefficiencies due to the challenges of handling oversized model parameters and training data, necessitating improved distributed training techniques to optimize resource utilization and performance across heterogeneous hardware environments.
Innovation Solution
A method and apparatus for distributed training on deep learning models that generate a distributed computation view and cluster resource view, determine a target segmentation strategy, and perform training based on this strategy, supporting heterogeneous hardware and optimizing resource utilization through elastic management.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If distributed training is applied to large-scale models, then training capability is improved, but system complexity increases
Solution Approach 1:
The patent segments the distributed training system into distinct functional modules: a configuration management module that handles segmentation strategy selection, a computation view generation module that creates distributed computation graphs, and a training execution module that coordinates multi-device training. This modular segmentation reduces system complexity by organizing complex distributed training operations into manageable, independent components that can be configured and executed separately.
Solution Approach 2:
The patent introduces an intermediary configuration file that serves as a mediator between user requirements and system execution. This configuration file contains pre-defined segmentation strategies that abstract the complexity of distributed training setup, allowing users to select appropriate strategies without directly managing the underlying computational graph segmentation and device coordination complexity.
2Adaptability or versatility
If heterogeneous hardware resources are utilized, then resource utilization is improved, but compatibility challenges increase
Solution Approach 1:
The patent implements a universal configuration file format that can describe segmentation strategies applicable to multiple types of heterogeneous hardware devices. The computation view generation process automatically adapts the segmentation strategy to the specific hardware topology, whether involving GPUs, TPUs, or other accelerators, making the system universally applicable across different hardware platforms without requiring hardware-specific implementation details.
3Ease of operation
If automatic segmentation strategy determination is implemented, then ease of operation is improved, but computational overhead increases
Solution Approach 1:
The patent performs preliminary action by pre-defining multiple segmentation strategies in the configuration file before training execution. These strategies are prepared in advance with all necessary segmentation parameters and device mappings, allowing the system to quickly select and apply an appropriate strategy without performing complex real-time optimization during training setup, thus reducing computational overhead while maintaining ease of operation.
Data Source
AI summary
The present disclosure provides a method and apparatus for performing distributed training on a deep learning model. The method may include: generating a distributed computation view based on data information of a to-be-trained deep learning model; generating a cluster resource view based on property information of a cluster hardware resource corresponding to the to-be-trained deep learning model; determining a target segmentation strategy of a distributed training task based on the distributed computation view and the cluster resource view; and performing distributed training on the to-be-trained deep learning model based on the target segmentation strategy.