env-doctor
Active·★ 155·MIT·Updated 2026-05-16
★ Trending★ Observability★ LLM Infra
Debug your GPU, CUDA, and AI stacks across local, Docker, and CI/CD (CLI and MCP server)
Env-Doctor is a crucial tool that diagnoses and resolves common compatibility issues between your GPU, NVIDIA CUDA versions, and Python AI libraries like PyTorch and TensorFlow. It helps users quickly identify and fix mismatches, ensuring a smooth deep learning development experience.
#GPU Diagnostics#CUDA Version Management#Python Environment#AI/ML Libraries#System Compatibility#Deep Learning#Dockerfile Validation#VRAM Management
01
Features
01One-Command Diagnosis of GPU, CUDA, and AI Library compatibility
02Generates safe `pip install` commands tailored to your system's CUDA
03Checks AI model (LLM, Diffusion) VRAM requirements against your GPU
04Provides platform-specific CUDA Toolkit installation guides
05Validates Dockerfiles for GPU configuration errors
02
Compatibility
Python
Runtime
Verified via docs
Linux
OS
Verified via docs
Windows
OS
Verified via docs
WSL2
Environment
Verified via docs
Conda
Environment
Verified via docs
03
Quick start
1
$ pip install env-doctor
04
Use cases
↳Diagnosing GPU, CUDA, and Python AI library version conflicts
↳Obtaining correct `pip install` commands for AI libraries compatible with local environment
↳Checking if an AI model (e.g., LLM) will fit into a GPU's VRAM
↳Getting platform-specific CUDA Toolkit installation instructions
↳Validating Dockerfiles or `docker-compose.yml` for GPU configuration errors
05
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Comments
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- SScout LewisMay 16, 2026
Catches the environment mismatches that are the most frustrating debugging scenarios.
- BBlake WhiteMay 6, 2026
CLI and MCP dual interface covers both interactive debugging and automated checks.
- Kai RiveraMay 1, 2026
GPU, CUDA, and AI stack debugging across local, Docker, and CI/CD environments.
- FFinley WhiteApr 3, 2026
Good for teams debugging AI environment issues across different compute environments.