pyRAPL is a software toolkit to measure the energy footprint of a host machine along the execution of a piece of Python code.

pyRAPL uses the Intel "Running Average Power Limit" (RAPL) technology that estimates power consumption of a CPU. This technology is available on Intel CPU since the Sandy Bridge generation.

More specifically, pyRAPL can measure the energy consumption of the following CPU domains:

• CPU socket package
• DRAM (for server architectures)
• GPU (for client architectures)

You can install pyRAPL with pip: pip install pyRAPL

Here are some basic usages of pyRAPL. Please note that the reported energy consumption is not only the energy consumption of the code you are running. This includes the global energy consumption of all the process running on the machine during this period, thus including the operating system and other applications. That is why we recommend to eliminate any extra programs that may alter the energy consumption of the machine hosting experiments and to keep only the code under measurement (i.e., 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 foo() run the following code:

	import pyRAPL

	pyRAPL.setup() 

	@pyRAPL.measure
	def foo():
		# Instructions to be evaluated.

	foo()

This will print in the console the recorded energy consumption of all the CPU domains during the execution of function foo.

You can easily configure which device and which socket to monitor using the parameters of the pyRAPL.setup function. For example, the following example only monitors the CPU power consumption on the CPU socket 1. By default, pyRAPL monitors all the available devices of the CPU sockets.

	import pyRAPL

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

	@pyRAPL.measure
	def foo():
		# Instructions to be evaluated.

	foo()	

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

For short functions, you can configure the number of runs and it will calculate the mean energy consumption of all runs. As an example, if you want to run the evaluation 100 times:

	import pyRAPL

	pyRAPL.setup()
	
	
	@pyRAPL.measure(number=100)
	def foo():
		# Instructions to be evaluated.

	for _ in range(100):
		foo()

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 foo():
		# Instructions to be evaluated.

	for _ in range(100):
		foo()
		
	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()
	meter = pyRAPL.Measurement('bar')
	meter.begin()
	# ...
	# Instructions to be evaluated.
	# ...
	meter.end()

You can also access the result of the measurements by using the property meter.result, which returns a Result object.

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

pyRAPL allows developers to measure a block of instructions using the keyword with as the example below:

	import pyRAPL
	pyRAPL.setup()
	
	with pyRAPL.Measurement('bar'):
		# ...
		# Instructions to be evaluated.
		# ...

This will report the energy consumption of the block. To process the measurements instead of printing them, you can use any Output class that you pass to the Measurement object:

	import pyRAPL
	pyRAPL.setup()
	
	report = pyRAPL.outputs.DataFrameOutput()

	with pyRAPL.Measurement('bar',output=report):
		# ...
		# Instructions to be evaluated.
		# ...

	report.data.head()

pyRAPL is an open-source project developed by the Spirals research group (University of Lille and Inria) that is part of the PowerAPI initiative.

The documentation is available here.

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