check
01Command
Diagnose your environment for GPU/CUDA compatibility issues.
172 3d ago B 0 tokens
original MIT
172Stars on the repository
14Mods indexed here, across every type
3d agoLast push, which is what freshness is scored on
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cuda-info
02Command
Get detailed information about your CUDA toolkit installations.
172 3d ago A 0 tokens
original MIT
cuda-install
03Command
Get step-by-step CUDA Toolkit installation instructions tailored to your platform.
172 3d ago B 0 tokens
original MIT
cudnn-info
04Command
Detect and validate cuDNN library installations.
172 3d ago B 0 tokens
original MIT
dashboard
05Command
The dashboard and report commands are part of the optional fleet monitoring add-on. The core CLI works without them.
172 3d ago A 0 tokens
original MIT
debug
06Command
Get detailed information from all detectors for troubleshooting.
172 3d ago A 0 tokens
original MIT
Command
Validate docker-compose.yml files for proper GPU device configuration.
172 3d ago B 0 tokens
original MIT
dockerfile
08Command
Validate Dockerfiles for GPU/CUDA configuration issues before building.
172 3d ago A 0 tokens
original MIT
init
09Command
Generate CI/CD integration files for your project.
172 3d ago A 0 tokens
original MIT
install
10Command
Get the safe install command for GPU libraries that matches your driver.
172 3d ago A 0 tokens
original MIT
model
11Command
Check if an AI model fits on your GPU before downloading.
172 3d ago A 0 tokens
original MIT
Command
Check Python version compatibility with installed AI libraries and detect dependency cascades.
172 3d ago A 0 tokens
original MIT
scan
13Command
Scan your project for deprecated AI library imports and suggest fixes. [BETA].
172 3d ago A 0 tokens
original MIT
wsl
14Command
Display comprehensive WSL environment information and GPU forwarding diagnostics.
172 3d ago B 0 tokens
original MIT