  With the rapid increase in the research, development, and application of
neural networks in the current era, there is a proportional increase in the
energy needed to train and use models. Crucially, this is accompanied by the
increase in carbon emissions into the environment. A sustainable and socially
beneficial approach to reducing the carbon footprint and rising energy demands
associated with the modern age of AI/deep learning is the adaptive and
continuous reuse of models with regard to changes in the environment of model
deployment or variations/changes in the input data. In this paper, we propose
PreIndex, a predictive index to estimate the environmental and compute
resources associated with model retraining to distributional shifts in data.
PreIndex can be used to estimate environmental costs such as carbon emissions
and energy usage when retraining from current data distribution to new data
distribution. It also correlates with and can be used to estimate other
resource indicators associated with deep learning, such as epochs, gradient
norm, and magnitude of model parameter change. PreIndex requires only one
forward pass of the data, following which it provides a single concise value to
estimate resources associated with retraining to the new distribution shifted
data. We show that PreIndex can be reliably used across various datasets, model
architectures, different types, and intensities of distribution shifts. Thus,
PreIndex enables users to make informed decisions for retraining to different
distribution shifts and determine the most cost-effective and sustainable
option, allowing for the reuse of a model with a much smaller footprint in the
environment. The code for this work is available here:
https://github.com/JEKimLab/AIES2024PreIndex
