Towards Green Automated Machine Learning: Status Quo and Future Directions

Abstract

Automated machine learning (AutoML) strives for the automatic configuration of machine learning algorithms and their composition into an overall (software) solution -a machine learning pipeline -tailored to the learning task (dataset) at hand. Over the last decade, AutoML has become a hot research topic with hundreds of contributions. While AutoML offers many prospects, it is also known to be quite resource-intensive, which is one of its major points of criticism. The primary cause for a high resource consumption is that many approaches rely on the (costly) evaluation of many ML pipelines while searching for good candidates. This problem is amplified in the context of research on AutoML methods, due to large scale experiments conducted with many datasets and approaches, each of them being run with several repetitions to rule out random effects. In the spirit of recent work on Green AI, this paper is written in an attempt to raise the awareness of AutoML researchers for the problem and to elaborate on possible remedies. To this end, we identify four categories of actions the community may take towards more sustainable research on AutoML, namely approach design, benchmarking, research incentives, and transparency.

Machine Learning methods in climate finance: a systematic review

“…A big challenge remains on new methods being currently developed to make ML trustworthy and scalable. For instance, challenges like model interpretability require computationally expensive ad-hoc techniques like SHAP (Lundberg and Lee, 2017), which is a key concern for financial supervisors (Alonso Robisco and Carbó Martínez, 2022;Dupont et al, 2020) or the cost of differential privacy is often a reduced model accuracy and a lowered convergence speed producing a higher carbon footprint due to either longer run-times or extensive experiments (Tornede et al, 2021). Similarly, this happens with Automated ML (AutoML), a discipline that provides methods and processes to make ML available for non-Machine Learning experts, where this problem is amplified due to large scale 12 experiments conducted with many datasets and approaches, each of them being run with several repetitions to rule out random effects (Naidu et al, 2021).…”

Improvement of Computational Performance of Evolutionary AutoML in a Heterogeneous Environment

“…It can be considered a bottleneck for any existing AutoML solution. This issue raises various problems from different fields: from integration of AutoML to business processes [31] to carbon emission and sustainability concerns [35].…”

Fair and Green Hyperparameter Optimization via Multi-objective and Multiple Information Source Bayesian Optimization

“…When these figures are converted into approximate carbon emissions it comes out that the carbon footprint of training a single large NLP model is equal to the amount of CO 2 emitted by 125 round-trip flights between New York and Beijing or, equivalently, five American average cars in their lifetimes, including their manufacturing processes. Consequently, the research community has been focusing on the Green-AI topic and also starting to propose novel optimization techniques to make the HPO task "greener" (Tornede et al, 2021), for instance by using smaller portions of the available databases/datasets, as proposed in the seminal work of (Swersky et al, 2013) to the most recent ones, such as in (Klein et al, 2017;Candelieri et al, 2021).…”