https://greenai-uppa.github.io/AIPowerMeter/

https://greenai-uppa.github.io/AIPowerMeter/

Last synced: 3 months ago
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• Host: GitHub
• URL: https://greenai-uppa.github.io/AIPowerMeter/
• Owner: GreenAI-Uppa
• Created: 2021-06-15T15:40:32.000Z (over 3 years ago)
• Default Branch: main
• Last Pushed: 2024-03-25T14:55:38.000Z (8 months ago)
• Last Synced: 2024-08-02T06:11:48.017Z (3 months ago)
• Language: Jupyter Notebook
• Size: 7.15 MB
• Stars: 22
• Watchers: 1
• Forks: 3
• Open Issues: 1
• Metadata Files:
  • Readme: README.md

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• green-ai - [Website - Uppa/AIPowerMeter) (Tools / Tools for measuring and quantifying footprint)

README

        
# Measure the efficiency of your deep learning

Record the energy consumption of your cpu and gpu. Check [our documentation](https://greenai-uppa.github.io/AIPowerMeter/) for usage.

This repo is largely inspired from this [experiment Tracker](https://github.com/Breakend/experiment-impact-tracker) 

## Requirements

Running Average Power Limit (RAPL) and its linux interface : powercap 

RAPL is introduced in the Intel processors starting with the Sandy bridge architecture in 2011. 

Your linux os supports RAPL if the following folder is not empty:

```

/sys/class/powercap/intel-rapl/

```

Empty folder? If your cpu is very recent, it is worth to check the most recent linux kernels.

## Installation

```

pip install -r requirements.txt

pip install --force-reinstall --no-cache-dir .

```

You need to authorize the reading of the rapl related files: 

```

sudo chmod -R 755 /sys/class/powercap/intel-rapl/

```

> some examples requires pytorch or tensorflow.

## Usage

See `examples/example_exp_deep_learning.py`.

Essentially, you instantiate an experiment and place the code you want to measure between a start and stop signal.

```

from deep_learning_power_measure.power_measure import experiment, parsers

driver = parsers.JsonParser("output_folder")

exp = experiment.Experiment(driver)

p, q = exp.measure_yourself(period=2)

###################

#  place here the code that you want to profile

################

q.put(experiment.STOP_MESSAGE)

``` 

This will save the recordings as json file in the `output_folder`. You can display them with: 

```

from deep_learning_power_measure.power_measure import experiment, parsers

driver = parsers.JsonParser(output_folder)

exp_result = experiment.ExpResults(driver)

exp_result.print()

```