by mitulgarg
Debug your GPU, CUDA, and AI stacks across local, Docker, and CI/CD (CLI and MCP server)
# Add to your Claude Code skills
git clone https://github.com/mitulgarg/env-doctor"Why does my PyTorch crash with CUDA errors when I just installed it?"
Because your driver supports CUDA 11.8, but
pip install torchgave you CUDA 12.4 wheels.
Env-Doctor diagnoses and fixes the #1 frustration in GPU computing: mismatched CUDA versions between your NVIDIA driver, system toolkit, cuDNN, and Python libraries.
It takes 5 seconds to find out if your environment is broken - and exactly how to fix it.

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| Feature | What It Does |
|---------|--------------|
| One-Command Diagnosis | Check compatibility: GPU Driver → CUDA Toolkit → cuDNN → PyTorch/TensorFlow/JAX |
| Compute Capability Check | Detect GPU architecture mismatches — catches why torch.cuda.is_available() returns False on new GPUs (e.g. Blackwell) even when driver and CUDA are healthy |
| Python Version Compatibility | Detect Python version conflicts with AI libraries and dependency cascade impacts |
| CUDA Installation Guide | Get platform-specific, copy-paste CUDA installation commands for your system |
| Safe Install Commands | Get the exact pip install command that works with YOUR driver |
| Extension Library Support | Install compilation packages (flash-attn, SageAttention, auto-gptq, apex, xformers) with CUDA version matching |
| AI Model Compatibility | Check if LLMs, Diffusion, or Audio models fit on your GPU before downloading |
| WSL2 GPU Support | Validate GPU forwarding, detect driver conflicts within WSL2 env for Windows users |
| **Deep CUDA Ana...