env-doctor

by mitulgarg · MCP Server · ★ 172

About env-doctor

Env-Doctor 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 gave you CUDA 12.4 whee

cicdcompatibility-toolcudacuda-librarycuda-supportcuda-toolkitcudnndiagnosticsgpumcp-server

Quick Facts

Stars172
Forks10
LanguagePython
CategoryMCP Server
LicenseMIT
Quality Score63.21/100
Open Issues8
Last Updated2026-10-01
Created2025-11-22
Platformscli, docker, mcp, python
Est. Tokens~182k

Compatible Skills

These tools work well together with env-doctor for enhanced workflows:

  • remote-gpu-trainer — semantic(0.15)+complementary+rare_topics+similar_pop+shared_platform (54%)
  • ingero — semantic(0.52)+rare_topics+similar_pop+shared_platform (52%)
  • dstack — semantic(0.20)+complementary+rare_topics+same_lang+shared_platform (46%)

More MCP Server Tools

Explore other popular mcp server tools:

View all MCP Server tools →

Popular Python Agent Tools

Frequently Asked Questions

What is env-doctor?

env-doctor is Diagnose and Fix CUDA / GPU environments compatibility issues locally, in Docker, and CI/CD. CLI + MCP server available.. It is categorized as a MCP Server with 172 GitHub stars.

What programming language is env-doctor written in?

env-doctor is primarily written in Python. It covers topics such as cicd, compatibility-tool, cuda.

How do I install or use env-doctor?

You can find installation instructions and usage details in the env-doctor GitHub repository at github.com/mitulgarg/env-doctor. The project has 172 stars and 10 forks, indicating an active community.

What license does env-doctor use?

env-doctor is released under the MIT license, making it free to use and modify according to the license terms.

View on GitHub → Browse MCP Server tools