Examples
Worked examples
- Is an instance
A research team publishes the kg CO2e of training a deep-learning model alongside hyperparameters in a model card.
- Is an instance
A cluster scheduler reports per-user compute carbon to drive optimisation behaviour.
Counter-examples
Looks similar, but isn't
- Not an instance
Reporting only run-time hours without converting to kWh or CO2e is incomplete.
- Not an instance
A laptop's electricity bill in isolation is not a useful compute footprint metric.
Editorial commentary
Compute footprint is calculated as (energy used in kWh) x (regional grid carbon intensity in kg CO2e per kWh), with adjustments for power usage effectiveness (PUE) of the data centre and embodied emissions of the hardware. Tools such as Green Algorithms, CodeCarbon, and the ML CO2 Impact calculator estimate footprint at job or experiment level. Large machine-learning training runs have become emblematic, with single transformer pre-training campaigns reported at hundreds of tonnes CO2e. Researchers are encouraged to choose low-carbon regions, schedule for low-carbon hours, and right-size compute jobs.
References
- Lannelongue, Grealey, and Inouye 2021 Green Algorithms; CodeCarbon project documentation.
Also known as
Compute GHG footprint · ML carbon footprint · HPC carbon footprint
Machine-readable encodings
Use in your systems
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