Diagnose and Fix CUDA / GPU environments compatibility issues locally, in Docker, and CI/CD. CLI + MCP server available.
Config is the same across clients — only the file and path differ.
{
"mcpServers": {
"env-doctor": {
"args": [
"torch"
],
"command": "uvx"
}
}
}Are you the author?
Add this badge to your README to show your security score and help users find safe servers.
"Why does my PyTorch crash with CUDA errors when I just installed it?" > Because your driver supports CUDA 11.8, but pip install torch gave you CUDA 12.4 wheels.
This server supports HTTP transport. Be the first to test it — help the community know if it works.
Five weighted categories — click any category to see the underlying evidence.
PYSEC-2026-139
A vulnerability was identified in PyTorch 2.10.0. The affected element is an unknown function of the component pt2 Loading Handler. The manipulation leads to deserialization. The attack can only be performed from a local environment. The exploit is publicly available and might be used. The project was informed of the problem early through a pull request but has not reacted yet.
>= 0source →PYSEC-2025-210
An issue was discovered in PyTorch v2.5 and v2.7.1. Omission of profiler.stop() can cause torch.profiler.profile (PythonTracer) to crash or hang during finalization, leading to a Denial of Service (DoS).
>= 0source →PYSEC-2025-209
An issue in pytorch v2.7.0 can lead to a Denial of Service (DoS) when a PyTorch model consists of torch.Tensor.to_sparse() and torch.Tensor.to_dense() and is compiled by Inductor.
PYSEC-2025-208
A buffer overflow occurs in pytorch v2.7.0 when a PyTorch model consists of torch.nn.Conv2d, torch.nn.functional.hardshrink, and torch.Tensor.view-torch.mv() and is compiled by Inductor, leading to a Denial of Service (DoS).
PYSEC-2025-207
A Name Error occurs in pytorch v2.7.0 when a PyTorch model consists of torch.cummin and is compiled by Inductor, leading to a Denial of Service (DoS).
Click any tool to inspect its schema.
Be the first to review
Have you used this server?
Share your experience — it helps other developers decide.
Sign in to write a review.
Others in developer-tools / devops
A Model Context Protocol (MCP) server and CLI that provides tools for agent use when working on iOS and macOS projects.
Workspace template + MCP server for Claude Code, Codex CLI, Cursor & Windsurf. Multi-agent knowledge engine (ag-refresh / ag-ask) that turns any codebase into a queryable AI assistant.
MCP server for accessing Figma plugin console logs and screenshots via Cloudflare Workers or local mode
MCP server for using the GitLab API
MCP Security Weekly
Get CVE alerts and security updates for Env Doctor and similar servers.
Start a conversation
Ask a question, share a tip, or report an issue.
Sign in to join the discussion.
The missing link between your GPU and Python AI libraries
"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.

| 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 Auto-Installer | Execute CUDA Toolkit installation directly with --run; CI-friendly with --yes; preview with --dry-run |
| 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 Analysis | Find multiple installations, PATH issues, environment misconfigurations |
| Container Validation | Catch GPU config errors in Dockerfiles before you build |
| MCP Server | Expose diagnostics to AI assistants (Claude Desktop, Zed) via Model Context Protocol |
| CI/CD Ready | JSON output, proper exit codes, and CI-aware env-var persistence (GitHub Actions, GitLab CI, CircleCI, Azure Pipelines, Jenkins) |
| Fleet Dashboard (optional) | Web UI for monitoring multiple GPU machines — aggregate status, drill-down diagnostics, history timeline. Install with pip install "env-doctor[dashboard]" |
The core CLI has no heavy dependencies — installs in seconds.
pip install env-doctor
# Or with uv (faster, isolated)
uv
... [View full README on GitHub](https://github.com/mitulgarg/env-doctor#readme)