https://github.com/samermakni/cuda-selector

A simple tool to select the optimal CUDA device based custom criteria.
https://github.com/samermakni/cuda-selector

automation gpu-monitoring machine-learning

Last synced: 6 months ago
JSON representation

A simple tool to select the optimal CUDA device based custom criteria.

• Host: GitHub
• URL: https://github.com/samermakni/cuda-selector
• Owner: SamerMakni
• Created: 2024-10-17T11:45:53.000Z (over 1 year ago)
• Default Branch: main
• Last Pushed: 2025-03-10T10:22:01.000Z (over 1 year ago)
• Last Synced: 2026-01-07T14:36:43.919Z (6 months ago)
• Topics: automation, gpu-monitoring, machine-learning
• Language: Python
• Homepage: https://samermakni.github.io/cuda-selector/
• Size: 69.3 KB
• Stars: 8
• Watchers: 1
• Forks: 1
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

Awesome Lists containing this project

README

          
![PyPI - Version](https://img.shields.io/pypi/v/cuda_selector)

![PyPI - License](https://img.shields.io/pypi/l/cuda_selector)

![PyPI - Python Version](https://img.shields.io/pypi/pyversions/cuda_selector)

![PyPI - Downloads](https://img.shields.io/pypi/dm/cuda_selector)

# Auto Cuda Selector

A simple tool to select the optimal CUDA device based on memory, power, temperature, or utilization. It supports fallback to CPU and custom sorting functions. Supports CUDA devices on Linux and MPS devices on macOS. Full documentation be found [here](https://samermakni.github.io/cuda-selector/)

### Install

```bash

pip install cuda-selector

```

### Usage

```python

from cuda_selector import auto_cuda

# Select the CUDA device with the most memory available

device = auto_cuda()

# Select the CUDA device with the lowest power usage

device = auto_cuda('power')

# Select the CUDA device with the lowest GPU utilization

device = auto_cuda('utilization')

# Select the CUDA device with the lowest temperature

device = auto_cuda('temperature')

# Select multiple devices with the most free memory

devices = auto_cuda(n=3)

# Exclude specific devices by their index

devices = auto_cuda(exclude=[0, 1])

# Apply thresholds for power usage and utilization

devices = auto_cuda(thresholds={'power': 150, 'utilization': 70})

# Use a custom ranking function for selecting devices

devices = auto_cuda(sort_fn=lambda d: d['memory_free'] * 0.7 + d['utilization'] * 0.3)

```