This page is part of the Climate Change AI Wiki, which aims provide resources at the intersection of climate change and machine learning.
🌎 This article is a stub, and is currently under construction. You can help by adding to it!
AI and machine learning has a climate impact from the energy used at all stages from development to deployment, as well as considerations around embodied energy in hardware from GPUs to data centres. The majority of emissions come from CO2 released from electricity generation powering AI processes. The carbon intensity of any particular project is highly dependent on its location and the electricity mix that it consumes.
Machine Learning Application Areas
- Models and training: Many recent projects in deep learning have used increasingly large computational resources to create models. The compute resources used for the largest model training runs was shown to have doubled in size every 3.4 months from 2012-2018.
- Inference: Some estimates show the vast majority of energy (80-90%) used by deep learning models is spent on inference.
- Efficiency gains. Redundant training. Experiment tracking. Smart hyperparameter selection
Data centres accounted for almost 1% of global energy demand in 2019, at around 200TWh, and while demand increases, efficiency gains mean this may stay flat for now. AI's current total impact can be estimated as a fraction of this, though growing extremely quickly. AI may itself offer efficiency gains for data centres by optimising control systems.
Estimates of electricity use of AI
- Cloud comparison: The major cloud computing providers, Amazon, Google and Microsoft, have varying targets and carbon intensities for their services. Google now publishes hourly estimates of the proportion of carbon-free energy (CFE) and the carbon intensity for all its cloud regions.
- Energy buying
- Targets: Several Cloud Providers have published CO2 emission targets:
Development of other chip types - TPUs, IPUs etc
Online Courses and Course Materials
Libraries and Tools
- ML CO2 Impact: A tool to calculate Machine Learning CO2 emissions, available here.
- Experiment Impact Tracker: Anther CO2 emissions calculator providing information about power draw from CPU and GPU, hardware information, python package versions, estimated carbon emissions information, and in California realtime carbon emission information, available here.
- Microsoft Emissions Impact Dashboard: A tool by Microsoft to track carbon emissions related to Microsoft cloud services usage, here
- Codecarbon: A software package that integrates into Python codebase to estimate the amount of carbon dioxide produced by the cloud or personal computing resources used to execute the code, here
- "AI and Compute". OpenAI. 2018-05-16. Retrieved 2021-03-27.
- Strategy, Moor Insights and. "Google Cloud Doubles Down On NVIDIA GPUs For Inference". Forbes. Retrieved 2021-03-27.
- Jassy, Andy. "AWS re:Invent 2018 - Keynote with Andy Jassy". YouTube. Retrieved 2021-03-27.
- Biewald, Lukas (2019-06-24). "Deep Learning and Carbon Emissions". Medium. Retrieved 2021-03-27.
- "Data Centres and Data Transmission Networks – Analysis". IEA. Retrieved 2021-03-27.
- Masanet, Eric; Shehabi, Arman; Lei, Nuoa; Smith, Sarah; Koomey, Jonathan (2020-02-28). "Recalibrating global data center energy-use estimates". Science. 367 (6481): 984–986. doi:10.1126/science.aba3758. ISSN 0036-8075. PMID 32108103.
- "DeepMind AI reduces energy used for cooling Google data centers by 40%". Google. 2016-07-20. Retrieved 2021-03-27.
- "Carbon free energy for Google Cloud regions". Google Cloud. Retrieved 2021-03-27.
- "Amazon becomes the world's largest corporate purchaser of renewable energy". UK Day One Blog. 2020-12-10. Retrieved 2021-03-27.
- "Google Cloud aims for carbon-free energy for its data centers". Google Cloud Blog. Retrieved 2021-03-27.
- "Microsoft will be carbon negative by 2030". The Official Microsoft Blog. 2020-01-16. Retrieved 2021-03-27.
- Lacoste, Alexandre; Luccioni, Alexandra; Schmidt, Victor; Dandres, Thomas (2019-10-21). "Quantifying the Carbon Emissions of Machine Learning". arXiv:1910.09700 [cs, stat].
- Henderson, Peter; Hu, Jieru; Romoff, Joshua; Brunskill, Emma; Jurafsky, Dan; Pineau, Joelle (2020-01-31). "Towards the Systematic Reporting of the Energy and Carbon Footprints of Machine Learning". arXiv:2002.05651 [cs].