MLtuner: System Support for Automatic Machine Learning Tuning

• Main Authors: Cui, Henggang, Ganger, Gregory R, Gibbons, Phillip B
• Format: Journal Article
• Language: English
• Published: 20-03-2018
• Subjects: Computer Science - Learning; Statistics - Machine Learning
• DOI: 10.48550/arxiv.1803.07445

MLtuner automatically tunes settings for training tunables (such as the
learning rate, the momentum, the mini-batch size, and the data staleness bound)
that have a significant impact on large-scale machine learning (ML)
performance. Traditionally, these tunables are set manually, which is
unsurprisingly error-prone and difficult to do without extensive domain
knowledge. MLtuner uses efficient snapshotting, branching, and
optimization-guided online trial-and-error to find good initial settings as
well as to re-tune settings during execution. Experiments show that MLtuner can
robustly find and re-tune tunable settings for a variety of ML applications,
including image classification (for 3 models and 2 datasets), video
classification, and matrix factorization. Compared to state-of-the-art ML
auto-tuning approaches, MLtuner is more robust for large problems and over an
order of magnitude faster.