347 citations · 1k across the 30 of their papers we have counts for
5 papers · 1 filter
Counting Carbon: A Survey of Factors Influencing the Emissions of Machine Learning
Alexandra Sasha Luccioni, Alex Hernandez-Garcia
Machine learning (ML) requires using energy to carry out computations during the model training process. The generation of this energy comes with an environmental cost in terms of…
Estimating the Carbon Footprint of BLOOM, a 176B Parameter Language Model
Alexandra Sasha Luccioni, Sylvain Viguier, Anne-Laure Ligozat
Progress in machine learning (ML) comes with a cost to the environment, given that training ML models requires significant computational resources, energy and materials. In the pre…
Evaluate & Evaluation on the Hub: Better Best Practices for Data and Model Measurements
Leandro von Werra, Lewis Tunstall, Abhishek Thakur +16
Evaluation is a key part of machine learning (ML), yet there is a lack of support and tooling to enable its informed and systematic practice. We introduce Evaluate and Evaluation o…
Measuring the Carbon Intensity of AI in Cloud Instances
Jesse Dodge, Taylor Prewitt, Remi Tachet Des Combes +7
By providing unprecedented access to computational resources, cloud computing has enabled rapid growth in technologies such as machine learning, the computational demands of which…
Establishing an Evaluation Metric to Quantify Climate Change Image Realism
Sharon Zhou, Alexandra Luccioni, Gautier Cosne +2
With success on controlled tasks, generative models are being increasingly applied to humanitarian applications [1,2]. In this paper, we focus on the evaluation of a conditional ge…