Choosing storage for deep learning: a comprehensive guide

The rapid growth in the size and complexity of deep learning models has significantly increased demands on data management and storage infrastructure, prompting the use of the Good-Better-Best (GBB) framework to benchmark solutions. Key stages in deep learning pipelines, such as data preparation, tokenization, data streaming, and checkpointing, require distinct storage solutions tailored to their unique demands. Data preparation involves transforming raw data for model training, with techniques like pre-computed and on-the-fly augmentation and the use of heterogeneous clusters to optimize resource use. Efficient data streaming to GPU accelerators is crucial for minimizing training time, with storage solutions impacting overall performance based on dataset size and model requirements. Checkpointing, essential for resuming training, varies in complexity and resource demands depending on model size, with synchronous and asynchronous methods offering different trade-offs. Fine-tuning and inference stages shift focus towards rapid read access and reduced checkpoint sizes, necessitating flexible and adaptable storage infrastructures. The integration of storage solutions with deep learning infrastructure components, such as GPU accelerators and data processing frameworks, is critical for performance optimization. A tiered storage approach, combining high-performance shared filesystems, object storage, and local NVMe SSDs, can provide a balanced solution to meet the diverse demands across the machine learning lifecycle.

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