Principle:Huggingface Datasets Dataset Shuffling
| Knowledge Sources | |
|---|---|
| Domains | Data_Engineering, ML_Preprocessing |
| Last Updated | 2026-02-14 18:00 GMT |
Overview
Randomly reordering dataset rows to prevent training on ordered data and improve model convergence.
Description
Dataset Shuffling is the process of randomly permuting the order of examples in a dataset. When datasets are loaded from storage, they often have an inherent ordering (e.g., sorted by class, by collection time, or by source). Training on ordered data can cause gradient descent to converge to poor solutions because the model sees correlated batches of similar examples in sequence, leading to unstable updates and biased gradients.
Shuffling breaks this ordering by creating a random permutation of the row indices. The operation supports reproducibility through seed-based random number generation, allowing the same shuffle order to be recreated across runs. In the HuggingFace Datasets library, shuffling creates an indices mapping rather than physically reordering the data, which is fast but may reduce sequential read performance.
Usage
Use Dataset Shuffling when:
- You are preparing data for training and the dataset has an inherent ordering that could bias gradient updates.
- You need reproducible random orderings across multiple training runs using a fixed seed.
- You are creating randomized subsets or want to vary the order of examples for data augmentation.
- You need to break any correlation between adjacent examples before batching.
Theoretical Basis
Dataset Shuffling is motivated by stochastic gradient descent (SGD) theory. SGD assumes that each mini-batch is an independent sample from the data distribution. When data is ordered, consecutive batches are correlated, violating this assumption and potentially causing the optimizer to oscillate or converge slowly. Shuffling produces a random permutation that decorrelates consecutive examples, approximating the i.i.d. sampling assumption. Theoretical results show that SGD with random shuffling converges at least as fast as SGD with i.i.d. sampling, and empirical evidence consistently demonstrates that shuffling improves training stability and final model quality.