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PyJoules

License: MIT Build Status

About

pyJoules is a software toolkit to measure the energy footprint of a host machine along the execution of a piece of Python code. It monitors the energy consumed by specific device of the host machine such as :

  • intel CPU socket package
  • RAM (for intel server architectures)
  • intel integrated GPU (for client architectures)
  • nvidia GPU

Limitation

CPU, RAM and integrated GPU

pyJoules uses the Intel "Running Average Power Limit" (RAPL) technology that estimates power consumption of the CPU, ram and integrated GPU. This technology is available on Intel CPU since the Sandy Bridge generation(2010).

Nvidia GPU

pyJoules uses the nvidia "Nvidia Management Library" technology to measure energy consumption of nvidia devices. The energy measurement API is only available on nvidia GPU with Volta architecture(2018)

Installation

You can install pyJoules with pip: pip install pyJoules

if you want to use pyJoule to also measure nvidia GPU energy consumption, you have to install it with nvidia driver support using this command : pip install pyJoules[nvidia]. You need also to install the nvml library.

Basic usage

Here are some basic usages of pyJoules. 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.

Decorate a function to measure its energy consumption

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

from pyJoules.energy_meter import measureit

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

foo()

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

Configure the decorator specifying the device to monitor

You can easily configure which device to monitor using the parameters of the measureit decorator. For example, the following example only monitors the CPU power consumption on the CPU socket 1 and the Nvidia GPU 0. By default, pyJoules monitors all the available devices of the CPU sockets.

from pyJoules.energy_meter import measureit
from pyJoules.energy_device.rapl_device import RaplPackageDomain
from pyJoules.energy_device.nvidia_device import NvidiaGPUDomain

@measureit(domains=[RaplPackageDomain(1), NvidiaGPUDomain(0)])
def foo():
	# Instructions to be evaluated.

foo()	

You can append the following domain list to monitor them :

  • pyJoules.energy_device.rapl_device.RaplPackageDomain : CPU (specify the socket id in parameter)
  • pyJoules.energy_device.rapl_device.RaplDramDomain : RAM (specify the socket id in parameter)
  • pyJoules.energy_device.rapl_device.RaplUncoreDomain : integrated GPU (specify the socket id in parameter)
  • pyJoules.energy_device.rapl_device.RaplCoreDomain : RAPL Core domain (specify the socket id in parameter)
  • pyJoules.energy_device.nvidia_device.NvidiaGPUDomain : Nvidia GPU (specify the socket id in parameter)

Configure the output of the decorator

If you want to handle data with different output than the standard one, you can configure the decorator with an EnergyHandler instance from the pyJoules.energy_handler module.

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

from pyJoules.energy_meter import measureit
from pyJoules.energy_handler import CsvHandler

csv_handler = CsvHandler('result.csv')

@measureit(handler=csv_handler)
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 foo. Other predefined Handler classes exist to export data to MongoDB and Panda dataframe. You can also create your own Output class (see the documentation)

Use a context manager to add tagged "breakpoint" in your measurment

If you want to know where is the "hot spots" where your python code consume the most energy you can add "breakpoints" during the measurement process and tag them to know amount of energy consumed between this breakpoints.

For this, you have to use a context manager to measure the energy consumption. It is configurable as the decorator. For example, here we use an EnergyContext to measure the power consumption of CPU 1 and nvidia gpu 0 and report it in a csv file :

from pyJoules.energy_meter import EnergyContext
from pyJoules.energy_device.rapl_device import RaplPackageDomain
from pyJoules.energy_device.nvidia_device import NvidiaGPUDomain
from pyJoules.energy_handler import CsvHandler

csv_handler = CsvHandler('result.csv')

with EnergyContext(handler=csv_handler, domains=[RaplPackageDomain(1), NvidiaGPUDomain(0)], start_tag='foo') as ctx:
	foo()
	ctx.record(tag='bar')
	bar()

csv_handler.save()

This will record the energy consumed :

  • between the beginning of the EnergyContext and the call of the ctx.record method
  • between the call of the ctx.record method and the end of the EnergyContext

Each measured part will be written in the csv file. One line per part.

Miscellaneous

About

pyJoules 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.

Mailing list

You can follow the latest news and asks questions by subscribing to our mailing list.

Contributing

If you would like to contribute code, you can do so via GitHub by forking the repository and sending a pull request.

When submitting code, please make every effort to follow existing coding conventions and style in order to keep the code as readable as possible.

MIT License

Copyright (c) 2019, INRIA Copyright (c) 2019, University of Lille All rights reserved.

Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions:

The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software.

THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.

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