2024-09-26 –, Louis Armand 1 - Est
Rising concerns over IT's carbon footprint necessitate tools that gauge and mitigate these impacts. This session introduces CodeCarbon, an open-source tool that estimates computing's carbon emissions by measuring energy use across hardware components. Aimed at AI researchers and data scientists, CodeCarbon provides actionable insights into the environmental costs of computational projects, supporting efforts towards sustainability without requiring deep technical expertise.
This talk from the main contributors of Code Carbon will cover the environmental impact of IT, the possibilities to estimate it and a demo of CodeCarbon.
Background: The IT sector's environmental impact is significant and growing, driven by the surge in data processing and computational demands, particularly within the realms of AI and data science. Addressing this requires tools that not only measure but also help manage and reduce these impacts.
Introducing CodeCarbon: CodeCarbon is a Python package that offers a straightforward method for estimating the carbon emissions of computing tasks. It calculates energy consumption (GPU, CPU, RAM) and translates this into carbon emissions based on the regional carbon intensity where the computation occurs. It's designed for both technical and non-technical users, with a command line interface and easy integration into existing code bases.
Session Objectives:
- Environmental Impact Awareness: Highlight the urgency of acknowledging and acting on the carbon footprint associated with IT projects.
- Tool Demonstration: Walk through CodeCarbon's features, including setup, integration into different environments (command line, Jupyter notebook, and direct code integration), and how to utilize the dashboard for visualizing emissions data.
- Broad Applicability: Emphasize CodeCarbon's utility beyond AI and data science, demonstrating its relevance for any computational work seeking to minimize environmental impact.
- Community Engagement : Discuss how developers and researchers can contribute to CodeCarbon, enhancing its accuracy, usability, and impact.
- Takeaway : Participants will gain a thorough understanding of how to leverage CodeCarbon to make informed decisions about the environmental implications of their work.
The session underscores the importance of measurement in environmental sustainability, echoing Niels Bohr: "Nothing exists until it is measured." By quantifying the carbon footprint of computational tasks, CodeCarbon empowers the community to drive change towards more sustainable practices in technology.
This talk aims to bridge the gap between awareness and action, providing the PyData community with the tools to actively reduce the carbon footprint of their computational endeavors.
I am a freelance Machine Learning Engineer
I have applied data science and machine learning in various industries. I have grown to enjoy taking things from idea to production and I now have a strong grip on most of the satellite fields of data science (data engineering, devops/mlops, unit testing, containers, git and CI/CD, cloud, product development).
I am mostly interested in work that have a neutral if not positive impact on climate change and biodiversity. I spare some time off missions so I can dedicate it to open source project like Code Carbon
Benoît Courty is a data scientist with over 20 years of experience in the tech industry. He began his career as an inside subcontractor at a large French company, where he worked on a variety of projects. In 2015, he co-founded a UAV startup that developed technology to automatically fly around buildings to detect cracks. He then worked as a freelance data scientist for banking and TV companies. Three years ago, he became an internal data scientist at the French National Assembly. He is also a member of Data For Good France, where he discovered CodeCarbon in 2020 and became its main contributor.