AI Infrastructure
Training data versioning: How to reproduce an AI run
A model that performed well in March cannot be rebuilt in September unless the data it learned from can be reconstructed exactly. Code and hyperparameters are ...
Topic
AI Infrastructure
Small-file performance: Why AI data loaders slow down
An AI training job can wait for data while the storage network has plenty of unused bandwidth. This happens when the job needs thousands of small files and ...
AI Infrastructure
AI data preprocessing: Where should it happen?
AI data preprocessing should happen where each transformation can run efficiently, within the data’s access and location constraints. A useful starting point ...
AI Infrastructure
GPU starvation: How to identify a storage bottleneck
GPU starvation occurs when an accelerator waits for the data or work it needs to continue processing. Storage can cause that wait, but so can image decoding, ...
AI Infrastructure
AI storage sizing: How much capacity do you need?
AI storage sizing starts with everything your workloads need to retain: source datasets, prepared training data, saved dataset versions, checkpoints and model ...
AI Infrastructure
What storage architecture is best for RAG?
Retrieval-augmented generation (RAG) has become the default way to put an organization’s own documents in front of a large language model without retraining ...
AI Infrastructure
How should you store large datasets for AI training?
A training cluster is only as productive as the storage behind it. When accelerators sit idle because the data loader cannot read samples fast enough, or a ...