Centralized Data-Parallel Training for State and Error Recovery
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Solution Overview
Problem
The training of artificial intelligence and machine learning frameworks is resource-intensive and costly, with large amounts of data required for accuracy, making the process inefficient and difficult to manage, especially in distributed data parallel training systems where error recovery and state management are challenging.
Innovation Solution
A centralized distributed data parallel training architecture is implemented, where a centralized process manages tasks, state information, and decision-making across training workers, enabling efficient data parallelism, improved error recovery, and state management, reducing the complexity of training AI/ML models.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If distributed data parallel training is implemented to train AI/ML models, then training capacity and speed are improved, but system complexity and difficulty of management increase
Solution Approach 1:
The patent introduces a centralized process as an intermediary between training workers and the training data. This centralized process manages task distribution, state tracking, and error recovery coordination, thereby reducing the complexity of direct peer-to-peer communication in distributed systems while maintaining high training throughput through coordinated data parallelism
2Productivity
If distributed data parallel training is implemented to train AI/ML models, then training capacity and speed are improved, but error recovery and state management become more difficult
Solution Approach 1:
The centralized process continuously monitors the state of each training worker and receives feedback about training progress and errors. Based on this feedback, the centralized process can coordinate error recovery actions, redistribute tasks, and manage the overall training state, making error recovery systematic rather than ad-hoc in distributed environments
Solution Approach 2:
The centralized process serves as a mediator that handles error recovery coordination between training workers. When errors occur in the distributed system, the centralized process manages the recovery logic centrally, simplifying what would otherwise be complex distributed error handling across multiple independent training nodes
3Measurement precision
If large amounts of training data are used to achieve high accuracy, then model performance is improved, but resource requirements and training costs increase
Solution Approach 1:
The training data is segmented and distributed across multiple training workers in the data parallel training system. Each worker processes a portion of the training data independently, enabling the system to handle large volumes of data efficiently by dividing the computational burden across multiple nodes while maintaining the ability to achieve high model accuracy through comprehensive data coverage
Data Source
AI summary
Systems and techniques are provided for a centralized architecture for distributed data parallel training. An example method can determine, by a centralized process in a distributed data parallel training environment used to train a model via data parallelism, a respective state of each training worker process from a plurality of training worker processes in the distributed data parallel training environment, the model comprising an artificial intelligence (AI) or machine learning (ML) model; determining, by the centralized process based on the respective state of each training worker process, a respective task that one or more training worker processes should perform with respect to a local replica of the model and/or training data associated with the local replica; and sending, by the centralized process to the one or more training worker processes, an instruction to perform the respective task with respect to the local replica of the model and/or the training data.