HeliosGPUController 1.2

Helios GPU Controller dynamically reduces Graphics Processing Unit (GPU) clock speeds by leveraging insights from the current energy mix and meteorological data. The goal is to reduce power consumption of non-renewable sources.

Helios version: 1.1

This is the code for the Helios GPU Controller

Developers:

Lasse Müller, Bao Tran Nguyen, Berkehan Ünal

Usage

Requirements

Our solution was developed using Python. To run the software, these are the prerequisites:

• Operating system distribution of Windows or Linux
• Python >=3.8
• pip package installer (https://pip.pypa.io/en/stable/)
• Preferably a virtual environment

Installation of Software and Dependencies

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

Python-specific extensions and modules are needed and can be found in the Requirements.txt. To install the modules, execute the following command from the /post-hoc-cbm folder:

$ pip install -r requirements.txt

To run PyTorch with cuda (NVIDIAs software for GPU management), PyTorch has to be installed from their website.

Code Execution

Execute: gpu_controller = HeliosGPUController("conf.yaml")

Testing

We used testpcbm.py and testheliosgpucontroller.py for unit testing.

License

This project is licensed under the MIT License.

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