Memento: Facilitating Effortless, Efficient, and Reliable ML Experiments
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.
Cite
Text
Pullar-Strecker et al. "Memento: Facilitating Effortless, Efficient, and Reliable ML Experiments." European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, 2023. doi:10.1007/978-3-031-43430-3_21Markdown
[Pullar-Strecker et al. "Memento: Facilitating Effortless, Efficient, and Reliable ML Experiments." European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, 2023.](https://mlanthology.org/ecmlpkdd/2023/pullarstrecker2023ecmlpkdd-memento/) doi:10.1007/978-3-031-43430-3_21BibTeX
@inproceedings{pullarstrecker2023ecmlpkdd-memento,
title = {{Memento: Facilitating Effortless, Efficient, and Reliable ML Experiments}},
author = {Pullar-Strecker, Zac and Chang, Xinglong and Brydon, Liam and Ziogas, Ioannis and Dost, Katharina and Wicker, Jörg},
booktitle = {European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases},
year = {2023},
pages = {310-314},
doi = {10.1007/978-3-031-43430-3_21},
url = {https://mlanthology.org/ecmlpkdd/2023/pullarstrecker2023ecmlpkdd-memento/}
}