A specialized utility designed to prevent GPU idle timeouts by maintaining active VRAM allocations and simulating workload activity.
Install & Download
1. ClawHub CLI
The fastest way to install a skill directly from the registry.
npx clawhub@latest install gpu-keepalive-with-keepgpu
2. Manual Installation
Copy the skill folder to one of these locations
~/.openclaw/skills/ <project>/skills/ Priority: Workspace > Local > Bundled
3. Prompt Installation
Copy this prompt to OpenClaw to install it automatically.
Help me install gpu-keepalive-with-keepgpu using Clawhub. If Clawhub is not installed, install it first (npm i -g clawhub).
Prefer to download?
Get the raw skill files in a ZIP archive.
What is GPU Keep-Alive with KeepGPU?
KeepGPU is a technical utility focused on ensuring high-performance hardware remains active and available during intermittent workloads. By leveraging Openclaw Skills, developers can automate the process of holding specific amounts of VRAM and simulating activity to bypass auto-termination policies on cloud platforms or shared clusters. The tool supports both NVIDIA CUDA and AMD ROCm environments, making it a versatile choice for diverse AI infrastructure setups.
The core value of this skill lies in its ability to manage GPU states through either foreground blocking processes or background service daemons. This flexibility allows users to integrate keep-alive logic into complex agentic pipelines or simple manual shell sessions, ensuring that expensive compute resources are not reclaimed due to inactivity while preprocessing data or waiting for model deployments.
GPU Keep-Alive with KeepGPU Use Cases
- Prevent cloud-based GPU instances from timing out or being reclaimed during long-running data preprocessing tasks.
- Maintain active memory allocation in multi-GPU environments to ensure immediate readiness for inference requests.
- Automate GPU activity monitoring in shared server environments using configurable busy-threshold backoff logic.
- Manage persistent GPU sessions across remote SSH connections using background service modes.
How GPU Keep-Alive with KeepGPU Works
- Environment Verification: The tool checks for CUDA or ROCm driver availability and confirms PyTorch connectivity to the hardware.
- Mode Selection: Users choose between blocking mode for direct terminal visibility or non-blocking service mode for persistent background tasks.
- Parameter Configuration: Operational bounds are set, including target GPU IDs, the specific amount of VRAM to hold, and the cycle interval.
- Active Monitoring: KeepGPU monitors the hardware's utilization percentage; if it falls below the busy-threshold, it triggers a keep-alive cycle.
- Lifecycle Management: Users can monitor job statuses via the CLI or a web dashboard and terminate sessions to release resources once tasks are complete.
GPU Keep-Alive with KeepGPU Setup
First, ensure PyTorch is installed for your specific hardware platform. Then, install the KeepGPU package.
# Install for CUDA environments
pip install --index-url https://download.pytorch.org/whl/cu121 torch
pip install keep-gpu
# Install for ROCm environments
pip install --index-url https://download.pytorch.org/whl/rocm6.1 torch
pip install keep-gpu[rocm]
# Alternative: Direct installation from Git
pip install "git+https://github.com/Wangmerlyn/KeepGPU.git"
Verify the installation by checking the help documentation:
keep-gpu --help
GPU Keep-Alive with KeepGPU Data Schema & Taxonomy
The skill manages GPU sessions through a structured set of CLI arguments and service states. Data is organized as follows:
| Command Option | Type | Purpose |
|---|---|---|
--gpu-ids |
String | Comma-separated list (e.g., 0,1) of target hardware. |
--vram |
String | Amount of memory to hold (e.g., 512MB, 1GiB). |
--interval |
Integer | Seconds between keep-alive checks. |
--busy-threshold |
Integer | Utilization % at which the tool will back off. |
start/stop/status |
Command | Lifecycle management for non-blocking service jobs. |
Local monitoring is available via a web dashboard at http://127.0.0.1:8765/.
GPU Keep-Alive with KeepGPU Advanced Features
- Non-blocking daemon support allowing GPU sessions to persist without an active terminal connection.
- Integrated web dashboard for real-time telemetry and hardware utilization monitoring.
- Intelligent backoff mechanics that prevent keep-alive cycles from interfering with actual compute-heavy workloads.
- Full compatibility with tmux and nohup for advanced remote session management within Openclaw Skills environments.
- Cross-vendor hardware support covering both NVIDIA (CUDA) and AMD (ROCm) ecosystems.
SKILL.md
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