Eco4cast: Bridging predictive scheduling and cloud computing for reduction of carbon emissions for ML models training

We introduce eco4cast$^1$, an open-source package aimed to reduce carbon footprint of machine learning models via predictive cloud computing scheduling. The package is integrated with machine learning models and employs an advanced temporal convolution neural network to forecast daily carbon dioxide emissions stemming from electricity generation. The model attains remarkable predictive accuracy by accounting for weather conditions, acknowledged for their robust correlation with carbon energy intensity. The hallmark of eco4cast lies in its capability to identify periods of temporal minimal carbon intensity. This enables the package to manage cloud computing tasks only during these periods, significantly reducing the ecological impact. Our contribution represents a compelling fusion of sustainability and computational efficiency. The code and documentation of the package are hosted on Github under the Apache 2.0 license.