pyRAPL is a toolkit that measures the energy consumption of a machine during the execution of a python code.

pyRAPL uses the intel "Running Average Power Limit" (RAPL) technology that estimate global power consumption of internal devices. This technology is only available on Intel CPU with Sandy Bridge architecture or higher.

pyRAPL can measure the energy consumption of the following devices :

• CPU socket package
• RAM (only on server CPU)

You can install pyRAPL with pip : pip install pyRAPL

Here are some basic usages of pyRAPL. Please understand that the measured energy consumption is not only the energy consumption of the code you are running. It's the global energy consumption of all the process running on the machine during this period. This includes also the operating system and other applications. That's why we recommend eliminating any extra programs that may alter the energy consumption of the machine where we run the experiments and keep only the code we want to measure its energy consumption (no extra applications such as graphical interface, background running task ...). This will give the closest measure to the real energy consumption of the measured code.

To measure the energy consumed by the machine during the execution of the function fun() run the following code :

import pyRAPL

pyRAPL.setup() 

@pyRAPL.measure
def fun():
	# Some stuff ...

fun()

This will print the recorded energy consumption of all the monitorable devices of the machine during the execution of function fun.

You can easly specify which device and which socket to monitor using the parameters of the pyRAPL.setup function. For example, here, we only monitor the CPU power consumption on the socket 1. By default, pyRAPL monitors all the available devices of the machine's sockets.

import pyRAPL

pyRAPL.setup(devices=[pyRAPL.Device.PKG], socket_ids=[1])

@pyRAPL.measure
def fun():
	# Some stuff ...

fun()	

You can append the device pyRAPL.Device.DRAM to the devices parameter list to monitor RAM device too.

If you want to handle data with different output than the standard one, you can configure the decorator with an Output instance from the pyRAPL.outputs module.

As an example if you want to write the recorded energy consumption in a csv file :

import pyRAPL

pyRAPL.setup()

csv_output = pyRAPL.outputs.CSVOutput('result.csv')

@pyRAPL.measure(output=csv_output)
def fun():
	# Some stuff ...

for _ in range(100):
	fun()
	
csv_output.save() 

This will produce a csv file of 100 lines. Each line containing the energy consumption recorded during one execution of the function fun. Other predefined Output classes exist to export data to Mongodb and Panda dataframe. You can also create your own Output class (see the documentation)

To measure the energy consumed by the machine during the execution of a given piece of code, run the following code :

import pyRAPL

pyRAPL.setup()
measure = pyRAPL.Measurement('toto')
measure.begin()

# ...
# Piece of code to measure energy consumption 
# ...

measure.end()

You can get the result of the measures using the property : measure.result this will return a Result instance.

You can also use an output to handle this results, for example with the csv output : measure.export(csv_output)

PyRAPL is an open-source project developed by the Spirals research group (University of Lille and Inria) that take part of the powerapi project.

The documentation is available here.

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