AI Training Checkpoint Storage for Faster Resume After Writes
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Solution Overview
Problem
The cache node in existing AI model training systems takes an excessively long time to write checkpoint files, leading to prolonged suspension of the training task and reduced efficiency.
Innovation Solution
A system with a first cache module that identifies the type of data as either a dataset or a checkpoint file, using a buffer mechanism for datasets and direct hard disk storage for checkpoint files, optimizing storage strategies to enhance reading and writing efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Speed
If the cache node uses a buffer mechanism to store datasets, then the reading efficiency of datasets is improved, but the writing time of checkpoint files increases due to buffer management overheads
Solution Approach 1:
The cache node applies different storage strategies to different data types: datasets are stored in the buffer for fast reading during training, while checkpoint files are directly written to the hard disk to avoid buffer management overhead. This differentiated approach resolves the contradiction by optimizing for each data type's specific access patterns.
Solution Approach 2:
The storage system is segmented into two independent paths: one for datasets (through the buffer) and one for checkpoint files (direct hard disk writing). This segmentation allows each path to be optimized independently, enabling fast dataset reading without compromising checkpoint writing speed.
2Productivity
If the cache node directly stores all data in the hard disk, then the writing process is simple and fast, but the reading speed of datasets becomes slow
Solution Approach 1:
Different storage mechanisms are applied to different data types based on their access characteristics: datasets are cached in the buffer for rapid reading during training iterations, while checkpoint files use direct hard disk writing since they are written infrequently and do not benefit from buffering.
3Speed
If the cache node uses a buffer for all data storage, then reading speed is improved, but the complexity of buffer management increases
Solution Approach 1:
The buffer is selectively applied only to datasets that require frequent reading during training, while checkpoint files bypass the buffer and write directly to the hard disk. This selective buffering approach maintains high reading speeds for datasets while avoiding the complexity of managing buffers for all data types.
Data Source
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AI summary
One or more embodiments of this specification provide a system for training an artificial intelligence model and a checkpoint file storage method. The system includes a model training module and a first cache module. The model training module is configured to: read a dataset needed for training from the first cache module, to execute a training task of an artificial intelligence model, where computation of the training task is performed by a GPU chip, and in a process of executing the training task, generate a checkpoint file and send the checkpoint file to the first cache module. The first cache module is configured to: identify a type of obtained to-be-stored data; and if the type of the to-be-stored data is a dataset, first write the to-be-stored data into a local buffer, and then store the to-be-stored data in a local hard disk from the local buffer; or if the type of the to-be-stored data is a checkpoint file, directly store the to-be-stored data in the local hard disk.