carbonai.PowerMeter.measure_power

PowerMeter.measure_power(package, algorithm, step='other', data_type='', data_shape='', algorithm_params='', comments='')

A decorator to measure the power consumption of a given function

Parameters

packagestr

A string describing the package used by this function (e.g. sklearn, Pytorch, …)

algorithmstr

A string describing the algorithm used in the function monitored (e.g. RandomForestClassifier, ResNet121, …)

step{‘inference’, ‘training’, ‘other’, ‘test’, ‘run’, ‘preprocessing’}, optional

A string to provide useful information on the current stage of the algorithm

data_type{‘tabular’, ‘image’, ‘text’, ‘time series’, ‘other’}, optional

A string describing the type of data used for training

data_shapestr or tuple, optional

A string or tuple describing the quantity of data used

algorithm_paramsstr, optional

A string describing the parameters used by the algorithm

commentsstr, optional

A string to provide any useful information

See also

PowerMeter.from_config

Create a PowerMeter object from a config file

PowerMeter.__call__

Measure the power usage using a with statement

Examples

First, create a PowerMeter (you only do to this step once).

>>> power_meter = PowerMeter.from_config("config.json")

Decorate the function you wish to monitor.

>>> @power_meter.measure_power(
...     package="pandas, numpy",
...     algorithm="data cleaning",
...     step="preprocessing",
...     data_type="tabular",
...     comments="Cleaning of csv files + train-test splitting"
... )
... def example_func():
...     # do something

Each time this function will be called, the PowerMeter will monitor the power usage and log the function’s carbon footprint.

>>> example_func()
result_of_your_function

carbonai.PowerMeter.from_config carbonai.PowerMeter.start_measure