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Helios GPU Controller dynamically reduces Graphics Processing Unit (GPU) clock speeds by leveraging insights from the current energy mix and meteorological data. Data is obtained by the SMARD API and Forecast.Solar. The goal is to reduce power consumption of non-renewable sources.
This is the code for the Helios GPU Controller
Developers:
Lasse Müller, Bao Tran Nguyen, Berkehan Ünal
Our solution was developed using Python. To run the software, these are the prerequisites:
A development copy of the project's repository or software can be created using the following command.
$ git clone https://github.com/automl-private/MLProject_Mueller-Nguyen-Uenal_Helios
To include the package into a virtual environment directly, consider installing with:
$ pip install HeliosGPUController
Include in the code: from helios_gpu_controller.HeliosGPUController import HeliosGPUController
An example config file can be found in src/helios_gpu_controller/conf.yaml These are the configuration parameters
Helios GPU controller runs in a subprocess and does not interfere with the main program loop. When use_helios_on_init is set to True, the Controller is executed automatically on init. To run Helios, use the following code snippets:
When use_helios_on_init is set to True:
Execute: gpu_controller = HeliosGPUController("conf.yaml")
After the program finishes: gpu_controller.stop_helios()
When use_helios_on_init is set to False:
Execute: gpu_controller = HeliosGPUController("conf.yaml")
To throttle GPU manually: gpu_controller.throttle_gpu()
After the program finishes: gpu_controller.stop_helios()
This project is licensed under the MIT License.
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FAQs
Helios GPU Controller dynamically reduces Graphics Processing Unit (GPU) clock speeds by leveraging insights from the current energy mix and meteorological data. Data is obtained by the SMARD API and Forecast.Solar. The goal is to reduce power consumption of non-renewable sources.
We found that HeliosGPUController demonstrated a healthy version release cadence and project activity because the last version was released less than a year ago. It has 3 open source maintainers collaborating on the project.
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