https://github.com/jaxony/pynvidia

⚙️ NVIDIA GPU utilities for Python 🔧
https://github.com/jaxony/pynvidia

cuda deep-learning nvidia-gpu pip python utility

Last synced: about 1 month ago
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⚙️ NVIDIA GPU utilities for Python 🔧

• Host: GitHub
• URL: https://github.com/jaxony/pynvidia
• Owner: jaxony
• License: mit
• Created: 2017-12-22T04:55:22.000Z (about 7 years ago)
• Default Branch: master
• Last Pushed: 2017-12-23T05:37:36.000Z (about 7 years ago)
• Last Synced: 2024-11-13T08:46:00.123Z (2 months ago)
• Topics: cuda, deep-learning, nvidia-gpu, pip, python, utility
• Language: Python
• Homepage:
• Size: 6.84 KB
• Stars: 4
• Watchers: 2
• Forks: 1
• Open Issues: 0
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

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README

        
# pynvidia

A simple, lightweight Python library that provides NVIDIA GPU utility functions.

## Why?

I found it annoying that my continuous integration tests for deep learning default to using a fixed GPU. This default behaviour leads to problems when memory-intensive tests are being done on GPU 0 when GPU 0 is at 90% memory usage. The consequence is failed tests and, in turn, merge/pull requests that cannot pass because of `CUDA out of memory` errors.

## Solution

`pynvidia` is a simple Python library that parses and processes the command line output of `nvidia-smi` so that GPUs can be dynamically selected at runtime. Specifically, the least utilised GPU(s) can be selected for a machine learning job.

## Install me

```

$ pip install pynvidia

```

## Requirements

`nvidia-smi` must be installed on your system.

## Example usage

### Dynamically select the most appropriate GPU to use at runtime

```python

import os

import pynvidia as pyn

gpu_id = pyn.gpu_using_least_memory()

...

os.environ["CUDA_VISIBLE_DEVICES"] = str(gpu_id)

```

## API

### memory

Utilities for getting GPU memory usage.

- `pynvidia.memory.gpu_using_least_memory()`

- `pynvidia.memory.gpus_using_least_memory(num_gpus=1)` (future)