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WangmerlynWangmerlynCommunity·1 Agent Skills Included

KeepGPU

Keep shared GPUs alive with lightweight VRAM sessions

Keeps CUDA, ROCm, and Apple Silicon GPUs active on shared machines by holding a small VRAM signal. Prevents schedulers and idle timeouts from releasing your GPU between workloads without manual babysitting. Offers blocking and background service modes with start, status, and stop commands plus a live dashboard. Backs off automatically when GPU utilization is high so real workloads are never disturbed.
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npx skills add Wangmerlyn/KeepGPU --all -g -y

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WangmerlynWangmerlyn

gpu-keepalive-with-keepgpu

Install and operate KeepGPU to maintain GPU availability via VRAM allocation and utilization monitoring.

Community
Intermediate

Frequently Asked Questions

FAQPage Schema
How to install KeepGPU?▼

Run `npx skills add Wangmerlyn/KeepGPU --all -g -y` in your terminal to install the skill globally. You also need PyTorch and the keep-gpu Python package installed for the underlying CLI to work.

How to stop a GPU from being released on shared servers?▼

Run `keep-gpu start --gpu-ids 0 --vram 1GiB --interval 60` to hold a lightweight VRAM signal that keeps the GPU active. Stop it anytime with `keep-gpu stop --all`.

Does KeepGPU work on Mac and AMD GPUs?▼

Yes. It supports NVIDIA CUDA, AMD ROCm/HIP, and Apple Silicon MPS, as long as PyTorch can see the device on your machine.

Will KeepGPU slow down other users on a shared machine?▼

No. It monitors GPU utilization and automatically backs off when the card is busy, only running keepalive cycles when the GPU is idle.

Can I monitor my KeepGPU sessions in a browser?▼

Yes. KeepGPU ships with a local web dashboard at http://127.0.0.1:8765/ where you can view active sessions and GPU status.

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