Feeding large object-store data into training
WHAT IT TESTS: ML data loading efficiency. OUTLINE: stream data instead of copying it all to disk, use streaming/pipe modes, shard and prefetch in parallel, and pack many small images into larger files. RED FLAG: downloading the whole 1TB to local disk first.
WHAT IT TESTS: whether you can feed large object-store datasets into training without IO becoming the bottleneck. ANSWER OUTLINE: avoid copying all 1 TB to local disk before training; instead stream data with modes like SageMaker fast file or pipe mode, or mounted access, shard the dataset across workers, prefetch and overlap IO with compute, and consolidate many tiny image files into larger packed records like TFRecord or WebDataset to cut per-object overhead.
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- #cloud
- #machine-learning
- #data-loading
- #sagemaker
- #training
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