Editing Climate Impact Of AI

Warning: You are not logged in. Your IP address will be publicly visible if you make any edits. If you log in or create an account, your edits will be attributed to your username, along with other benefits.

The edit can be undone. Please check the comparison below to verify that this is what you want to do, and then publish the changes below to finish undoing the edit.

Latest revision Your text
Line 4: Line 4:
   
 
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.
 
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.
  +
 
Efforts to quantify the CO2 impact of ML have been undertaken<ref name=":0">{{Cite journal|last=Lacoste|first=Alexandre|last2=Luccioni|first2=Alexandra|last3=Schmidt|first3=Victor |last4=Dandres|first4=Thomas|date=2019-10-21|title=Quantifying the Carbon Emissions of Machine Learning|url=https://arxiv.org/abs/1910.09700|journal=arXiv:1910.09700 [cs, stat]}}</ref><ref>{{Cite journal|last=Henderson|first=Peter|last2=Hu|first2=Jieru|last3=Romoff|first3=Joshua|last4=Brunskill|first4=Emma|last5=Jurafsky|first5=Dan|last6=Pineau|first6=Joelle|date=2020-01-31|title=Towards the Systematic Reporting of the Energy and Carbon Footprints of Machine Learning|url=http://arxiv.org/abs/2002.05651|journal=arXiv:2002.05651 [cs]}}</ref>.
   
 
== Machine Learning Application Areas ==
 
== Machine Learning Application Areas ==
Line 30: Line 32:
   
 
== Background Readings ==
 
== Background Readings ==
*''' Efforts to quantify the CO2 impact of ML''' <ref name=":0">{{Cite journal|last=Lacoste|first=Alexandre|last2=Luccioni|first2=Alexandra|last3=Schmidt|first3=Victor |last4=Dandres|first4=Thomas|date=2019-10-21|title=Quantifying the Carbon Emissions of Machine Learning|url=https://arxiv.org/abs/1910.09700|journal=arXiv:1910.09700 [cs, stat]}}</ref><ref>{{Cite journal|last=Henderson|first=Peter|last2=Hu|first2=Jieru|last3=Romoff|first3=Joshua|last4=Brunskill|first4=Emma|last5=Jurafsky|first5=Dan|last6=Pineau|first6=Joelle|date=2020-01-31|title=Towards the Systematic Reporting of the Energy and Carbon Footprints of Machine Learning|url=http://arxiv.org/abs/2002.05651|journal=arXiv:2002.05651 [cs]}}</ref>.
 
   
 
== Online Courses and Course Materials ==
 
== Online Courses and Course Materials ==
Line 37: Line 38:
   
 
== Libraries and Tools ==
 
== Libraries and Tools ==
*'''ML CO2 Impact''': A tool to calculate Machine Learning CO2 emissions, available [https://mlco2.github.io/impact/ here].
+
*'''ML CO2 Impac''': A tool to calculate Machine Learning CO2 emissions, available [https://mlco2.github.io/impact/ 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 [https://github.com/Breakend/experiment-impact-tracker 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 [https://github.com/Breakend/experiment-impact-tracker here].
*'''Microsoft Emissions Impact Dashboard''': A tool by Microsoft to track carbon emissions related to Microsoft cloud services usage, [https://www.microsoft.com/en-us/sustainability/emissions-impact-dashboard 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, [https://codecarbon.io/ here]
 
   
 
== Data ==
 
== Data ==
 
* '''Hugging Face Model Cards''': Several model cards of trained Hugging Face models report the amount of CO2 it took to train them, [https://huggingface.co/models?other=co2_eq_emissions here].
 
   
 
== References ==
 
== References ==
Please note that all contributions to Climate Change AI Wiki are considered to be released under the Creative Commons Attribution-ShareAlike 4.0 International (CC BY-SA 4.0) (see Climate Change AI Wiki:Copyrights for details). If you do not want your writing to be edited mercilessly and redistributed at will, then do not submit it here.
You are also promising us that you wrote this yourself, or copied it from a public domain or similar free resource. Do not submit copyrighted work without permission!
Cancel Editing help (opens in new window)