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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.
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:
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
Configure the decorator specifying the device to monitor
You can easily configure which device and which socket to monitor using the parameters of the
For example, the following example only monitors the CPU power consumption on the CPU socket
By default, pyRAPL monitors all the available devices of the CPU sockets.
import pyRAPL pyRAPL.setup(devices=[pyRAPL.Device.PKG], socket_ids=) @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.
Running the test multiple times
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()
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
Output instance from the
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
Output classes exist to export data to MongoDB and Panda
You can also create your own Output class (see the
Measure the energy consumption of a piece of code
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
You can also use an output to handle this results, for example with the .csv output:
Measure the energy consumption of a block
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
import pyRAPL pyRAPL.setup() report = pyRAPL.outputs.DataFrameOutput() with pyRAPL.Measurement('bar',output=report): # ... # Instructions to be evaluated. # ... report.data.head()
The documentation is available here.
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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.
Copyright (c) 2018, INRIA Copyright (c) 2018, 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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