Papers › GraB-sampler: Optimal Permutation-based SGD Data Sampler for PyTorch
GraB-sampler: Optimal Permutation-based SGD Data Sampler for PyTorch
Guanghao Wei
The online Gradient Balancing (GraB) algorithm greedily choosing the examples ordering by solving the herding problem using per-sample gradients is proved to be the theoretically optimal solution that guarantees to outperform Random Reshuffling. However, there is currently no efficient implementation of GraB for the community to easily use it. This work presents an efficient Python library, GraB-sampler, that allows the community to easily use GraB algorithms and proposes 5 variants of the GraB algorithm. The best performance result of the GraB-sampler reproduces the training loss and test accuracy results while only in the cost of 8.7% training time overhead and 0.85% peak GPU memory usage overhead.
Code
Repository list and official/mentioned flags are the archive's, frozen 2025-07-28. Reachability, where shown, is from one Syntology probe window (2026-09-16 to 2026-09-18); repositories not probed show nothing. GitHub stars are not tracked.
Code Syntology ran Syntology
Not run by Syntology. Nothing on this page verifies that the listed code works.
Results from the paper archive 2025-07-28
No leaderboard rows for this paper in the archive.
Report a problem or propose a change · a person checks every report against the paper or source before anything changes; decisions are listed on /corrections