Record energy consumption of your cpu and gpu as well as cpu/gpu memory and usage. Check our documentation for usage.

This repo is largely inspired from this experiment Tracker. A popular package is also Code Carbon

Running Average Power Limit (RAPL) and its linux interface : powercap

RAPL is introduced in the Intel processors starting with the Sandy bridge architecture in 2011.

Your linux os supports RAPL if the following folder is not empty:

/sys/class/powercap/intel-rapl/

Empty folder? If your cpu is very recent, it is worth to check the most recent linux kernels.

git clone https://github.com/GreenAI-Uppa/AIPowerMeter.git
cd AIPowerMeter
pip install -r requirements.txt
pip install --force-reinstall --no-cache-dir .

You need to authorize the reading of the rapl related files:

sudo chmod -R 755 /sys/class/powercap/intel-rapl/

some examples requires pytorch or tensorflow.

Essentially, you instantiate an experiment and place the code you want to measure between a start and stop signal.

from deep_learning_power_measure.power_measure import experiment, parsers

driver = parsers.JsonParser("output_folder")
exp = experiment.Experiment(driver)

p, q = exp.measure_yourself(period=2)
###################
#  place here the code that you want to profile
################
q.put(experiment.STOP_MESSAGE)

This will save the recordings as json file in the output_folder. You can display them with:

from deep_learning_power_measure.power_measure import experiment, parsers
driver = parsers.JsonParser(output_folder)
exp_result = experiment.ExpResults(driver)
exp_result.print()

See also examples/example_exp_deep_learning.py and other tips to monitor a bash command, get some energy plots on this page