gpu-keepalive-with-keepgpu

What problem does it solve?

This Skill prevents shared GPUs from being reclaimed or silently shared during preparatory tasks like data preprocessing, debugging, or multi-stage pipeline coordination, ensuring your GPU resources remain available.

Core Features & Use Cases

• Resource Reservation: Allocates minimal VRAM and issues lightweight CUDA work to signal an "active" device to schedulers.
• Polite Resource Usage: Uses NVML to monitor utilization and backs off when the GPU is actively in use by another process.
• Flexible Operation: Supports both blocking CLI mode for manual control and non-blocking service mode for agent workflows.
• Use Case: When running a long data preprocessing job on a shared cluster, use this Skill to ensure your allocated GPU isn't taken by another user or process while you wait.

Quick Start

Install KeepGPU and start a non-blocking keep-alive session for GPU 0, holding 1GiB of VRAM and backing off if utilization exceeds 25%, by running pip install keep-gpu then keep-gpu start --gpu-ids 0 --vram 1GiB --busy-threshold 25.