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[docs] misc: add DeepWiki badge #54

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Merged
Wangmerlyn merged 2 commits into from
Nov 26, 2025
Merged

[docs] misc: add DeepWiki badge #54

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f51f487
Add DeepWiki badge to README
Wangmerlyn Nov 26, 2025
c45a650
Update DeepWiki badge link
Wangmerlyn Nov 26, 2025
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1 change: 1 addition & 0 deletions README.md
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Expand Up @@ -3,6 +3,7 @@
[![PyPI Version](https://img.shields.io/pypi/v/keep-gpu.svg)](https://pypi.python.org/pypi/keep-gpu)
[![Docs Status](https://readthedocs.org/projects/keepgpu/badge/?version=latest)](https://keepgpu.readthedocs.io/en/latest/?version=latest)
[![DOI](https://zenodo.org/badge/987167271.svg)](https://doi.org/10.5281/zenodo.17129114)
[![Ask DeepWiki](https://deepwiki.com/badge.svg)](https://deepwiki.com/Wangmerlyn/KeepGPU)
Comment on lines 3 to +6

@gemini-code-assist gemini-code-assist Bot Nov 26, 2025

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medium

For better readability of the raw markdown and to group the badges semantically, consider placing them all on a single line, separated by spaces. While the current multi-line approach renders correctly, a single line is a common convention for badge blocks and makes the grouping more explicit in the source.

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[![PyPI Version](https://img.shields.io/pypi/v/keep-gpu.svg)](https://pypi.python.org/pypi/keep-gpu)
[![Docs Status](https://readthedocs.org/projects/keepgpu/badge/?version=latest)](https://keepgpu.readthedocs.io/en/latest/?version=latest)
[![DOI](https://zenodo.org/badge/987167271.svg)](https://doi.org/10.5281/zenodo.17129114)
[![Ask DeepWiki](https://deepwiki.com/badge.svg)](https://deepwiki.com/Wangmerlyn/KeepGPU)
[![PyPI Version](https://img.shields.io/pypi/v/keep-gpu.svg)](https://pypi.python.org/pypi/keep-gpu) [![Docs Status](https://readthedocs.org/projects/keepgpu/badge/?version=latest)](https://keepgpu.readthedocs.io/en/latest/?version=latest) [![DOI](https://zenodo.org/badge/987167271.svg)](https://doi.org/10.5281/zenodo.17129114) [![Ask DeepWiki](https://deepwiki.com/badge.svg)](https://deepwiki.com/Wangmerlyn/KeepGPU)


**Keep GPU** keeps shared GPUs from being reclaimed while you prep data, debug, or coordinate multi-stage pipelines. It allocates just enough VRAM and issues lightweight CUDA work so schedulers observe an “active” device—without running a full training job.

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