fairDMS: Rapid Model Training by Data and Model Reuse
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Extracting actionable information from data sources such as the Linac Coherent Light Source (LCLS-II) and Advanced Photon Source Upgrade (APS-U) is becoming more challenging due to the fast-growing data generation rate. The rapid analysis possible with ML methods can enable fast feedback loops that can be used to adjust experimental setups in real-time, for example when errors occur or interesting events are detected. However, to avoid degradation in ML performance over time due to changes in an instrument or sample, we need a way to update ML models rapidly while an experiment is running. We present here a data service and model service to accelerate deep neural network training with a focus on ML-based scientific applications. Our proposed data service achieves 100x speedup in terms of data labeling compare to the current state-of-the-art. Further, our model service achieves up to 200x improvement in training speed. Overall, fairDMS achieves up to 92x speedup in terms of end-to-end model updating time.
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