AI Training Checkpoint Caching for Non-Blocking Remote Persistence
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Solution Overview
Problem
Existing AI model training processes are inefficient due to the need to suspend training until checkpoint files are completely written to remote storage systems, leading to computational resource waste and prolonged downtime.
Innovation Solution
A model training and checkpoint file storage system that locally caches checkpoint files and concurrently performs notification and storage operations, allowing the training task to resume without waiting for persistence to complete.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If checkpoint files are written to remote storage systems sequentially, then data persistence is ensured, but training efficiency is reduced and computational resources are wasted
Solution Approach 1:
The patent segments the checkpoint file storage process into two independent parts: local caching (for immediate availability) and remote storage (for persistence). By dividing the storage function into local and remote components, the system can ensure persistence through remote storage while maintaining high training efficiency through local caching, resolving the contradiction between reliability and productivity.
Solution Approach 2:
The patent introduces a remote storage system as an intermediary between the training module and the final destination for checkpoint files. The remote storage system handles the persistence requirement independently, allowing the training module to continue without waiting for complete remote storage, thus resolving the contradiction between ensuring data persistence and maintaining training efficiency.
2Reliability
If training is suspended until checkpoint files are completely written, then data safety is ensured, but downtime increases and computational resources are wasted
Solution Approach 1:
The patent applies preliminary action by first caching the checkpoint file locally before requiring remote storage completion. The training module can resume training based on the locally cached file, which is prepared in advance, thereby reducing downtime while maintaining data safety through subsequent remote storage operations.
Solution Approach 2:
The patent makes the training process dynamic by allowing the training module to resume based on local cache availability rather than waiting for remote storage completion. This dynamic approach enables the system to adapt to different storage speeds and conditions, reducing downtime while ensuring data safety through the remote storage system.
3Reliability
If checkpoint files are stored remotely, then data backup is improved, but write operations block training and reduce overall efficiency
Solution Approach 1:
The patent segments the storage function into local caching and remote storage operations. The local cache handles immediate write operations without blocking training, while the remote storage system independently handles data backup. This segmentation allows both data backup and training efficiency to be improved simultaneously.
Solution Approach 2:
The patent ensures continuity of useful action by allowing the training module to continue training based on locally cached checkpoint files without waiting for remote storage completion. The remote storage operation continues in the background, ensuring data backup while maintaining continuous training progress and improving overall efficiency.
Data Source
AI summary
One or more implementations of this specification provide model training and checkpoint file storage systems and methods. In an implementation, a method includes executing, by a model training module of a storage system, a training task of an artificial intelligence model, during execution of the training task, suspending, by the model training module, the training task if a first checkpoint file is generated and sending a request to a checkpoint file processing module of the storage system to cache the first checkpoint file, locally caching, by the checkpoint file processing module, the first checkpoint file based on the request, and concurrently performing, by the check file processing module, a notification operation and a storage operation.