Show gCO2eq emissions information with nvidia-smi, at the top right corner. For example: 79.2gCO2eq/h or 23.76mm^2/h sea ice.
Copies code from experiment-impact-tracker for mapping geolocations to energy usage, which can be used to monitor and report on longer-running experiments.
This script doesn't take into account:
- Carbon intensity changes with time of day.
- Datacenters often have unique energy sources.
experiment-impact-trackertracks this information, and it can be accessed with theirscripts/lookup-cloud-region-info. I would be happy to add this info if the script can automatically detect the provider and region, possibly from the IP address. - The state of California has more detailed information available via California ISO and this script does not use that data.
- CPU usage is only monitored if it is tracked at
/sys/class/powercap/intel-rapl. Doing this in a hardware-independent way requires a lot more code, with some first steps inexperiment-impact-tracker.
When running the first time at an IP address, the script will geolocate your IP address and estimate the local carbon intensity. This information will be cached between runs in /tmp/nvidia-co2-cache.(dir|bak|dat). The first run might take 1 second, additional runs should take 200ms.
This script won't work by default on Google Cloud because I'm using dig to quickly get a public IP address. Permissions are also set up in a way where you would need to install it to --user and call python -m nvidia-co2 or similar. But with a little work it could be done :)
pip install git+https://github.com/kylemcdonald/nvidia-co2.git
$ nvidia-co2 -m ice
Sun Feb 16 14:44:50 2020 23.76mm^2/h sea ice
+-----------------------------------------------------------------------------+
| NVIDIA-CO2 435.21 Driver Version: 435.21 CUDA Version: 10.1 |
|-------------------------------+----------------------+----------------------+
| GPU Name Persistence-M| Bus-Id Disp.A | Volatile Uncorr. ECC |
| Fan Temp Perf Pwr:Usage/Cap| Memory-Usage | GPU-Util Compute M. |
|===============================+======================+======================|
| 0 GeForce RTX 208... Off | 00000000:05:00.0 On | N/A |
| 45% 59C P2 206W / 260W | 10975MiB / 11016MiB | 89% Default |
+-------------------------------+----------------------+----------------------+
| 1 GeForce RTX 208... Off | 00000000:09:00.0 Off | N/A |
| 26% 34C P8 19W / 260W | 166MiB / 11019MiB | 0% Default |
+-------------------------------+----------------------+----------------------+
+-----------------------------------------------------------------------------+
| Processes: GPU Memory |
| GPU PID Type Process name Usage |
|=============================================================================|
| 0 1149 G /usr/lib/xorg/Xorg 85MiB |
| 0 1359 G /usr/bin/gnome-shell 91MiB |
| 0 21752 C ...e/kyle/anaconda3/envs/tf2gpu/bin/python 10787MiB |
| 1 21752 C ...e/kyle/anaconda3/envs/tf2gpu/bin/python 155MiB |
+-----------------------------------------------------------------------------+
$ nvidia-co2 --help
usage: nvidia-co2 [-h] [--mode MODE]
Show gCO2eq emissions information with nvidia-smi. Combines CPU and GPU usage.
Emissions are corrected for location using IP address geolocation.
optional arguments:
-h, --help show this help message and exit
--mode MODE, -m MODE [ice|beef|tofu|car-mph|car-kph|bulb|cfl|watt|gco2eqph]
`ice` shows how much sea ice is lost per hour due to
your emissions. `beef` and `tofu` shows how many grams
of each it takes to produce the same emissions. `car-
mph` and `car-kph` show how fast a car would have to
drive to produce the same emissions. `bulb` and `cfl`
show how many incandescent lightbulbs or CFLs are
required to use the same power. `watt` shows how many
watts used, and `gco2eqph` shows gCOeq/hour used.
(default: gco2eqph)