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arXiv · 2304.09175

Memento: Facilitating Effortless, Efficient, and Reliable ML Experiments

Zac Pullar-Strecker·Xinglong Chang·Liam Brydon·Ioannis Ziogas·Katharina Dost·Jörg Wicker
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Abstract

Running complex sets of machine learning experiments is challenging and time-consuming due to the lack of a unified framework. This leaves researchers forced to spend time implementing necessary features such as parallelization, caching, and checkpointing themselves instead of focussing on their project. To simplify the process, in this paper, we introduce Memento, a Python package that is designed to aid researchers and data scientists in the efficient management and execution of computationally intensive experiments. Memento has the capacity to streamline any experimental pipeline by providing a straightforward configuration matrix and the ability to concurrently run experiments across multiple threads. A demonstration of Memento is available at: https://wickerlab.org/publication/memento.

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Zac Pullar-Strecker, Xinglong Chang, Liam Brydon, Ioannis Ziogas, Katharina Dost, Jörg Wicker. 2023-04-17. Memento: Facilitating Effortless, Efficient, and Reliable ML Experiments. https://doi.org/10.1007/978-3-031-43430-3_21

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