The Hidden Environmental Cost of Poor Coding Practices in TensorFlow and Keras Applications: A Study on Resource Leaks and Carbon Emissions
A new research paper from arXiv investigates the environmental impact of inefficient coding practices in machine learning applications, specifically focusing on TensorFlow and Keras. The study quantifies how resource leaks, such as improper model reuse and unreleased tensor references, lead to increased energy consumption and carbon emissions. Preliminary results indicate that these coding smells can elevate electricity usage by approximately 32% to 46%, highlighting the need for better resource lifecycle management in ML development. AI
IMPACT Highlights the significant environmental cost of inefficient ML code, urging developers to prioritize sustainability.