env-doctor 0.2.7

A CLI tool to verify and fix AI/ML environment compatibility (Driver <-> CUDA <-> Wheels) with platform-specific installation guides.

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 pip install torch gave 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.

Doctor "Check" (Diagnosis)

Features

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 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 and proper exit codes for automation

Installation

pip install env-doctor

MCP Server (AI Assistant Integration)

Env-Doctor includes a built-in Model Context Protocol (MCP) server that exposes diagnostic tools to AI assistants like Claude Code and Claude Desktop.

Quick Setup for Claude Desktop

1. Install env-doctor:

   pip install env-doctor
   

2. Add to Claude Desktop config (~/Library/Application Support/Claude/claude_desktop_config.json):

   {
     "mcpServers": {
       "env-doctor": {
         "command": "env-doctor-mcp"
       }
     }
   }
   

3. Restart Claude Desktop - the tools will be available automatically.

Available Tools (11 Total)

• env_check - Full GPU/CUDA environment diagnostics
• env_check_component - Check specific component (driver, CUDA, cuDNN, etc.)
• python_compat_check - Check Python version compatibility with installed AI libraries
• cuda_info - Detailed CUDA toolkit information
• cudnn_info - Detailed cuDNN library information
• cuda_install - Step-by-step CUDA installation instructions
• install_command - Get safe pip install commands for AI libraries
• model_check - Analyze if AI models fit on your GPU
• model_list - List all available models in database
• dockerfile_validate - Validate Dockerfiles for GPU issues
• docker_compose_validate - Validate docker-compose.yml for GPU configuration

Demo — Claude Code using env-doctor MCP tools

Example Usage

Ask your AI assistant:

• "Check my GPU environment"
• "Is my Python version compatible with my installed AI libraries?"
• "How do I install CUDA Toolkit on Ubuntu?"
• "Get me the pip install command for PyTorch"
• "Can I run Llama 3 70B on my GPU?"
• "Validate this Dockerfile for GPU issues"
• "What CUDA version does my PyTorch require?"
• "Show me detailed CUDA toolkit information"

Learn more: MCP Integration Guide

---

Usage

Diagnose Your Environment

env-doctor check

Example output:

🩺 ENV-DOCTOR DIAGNOSIS
============================================================

🖥️  Environment: Native Linux

🎮 GPU Driver
   ✅ NVIDIA Driver: 535.146.02
   └─ Max CUDA: 12.2

🔧 CUDA Toolkit
   ✅ System CUDA: 12.1.1

📦 Python Libraries
   ✅ torch 2.1.0+cu121

✅ All checks passed!

On new-generation GPUs (e.g. RTX 5070 / Blackwell), env-doctor catches architecture mismatches and distinguishes between two failure modes:

Hard failure — torch.cuda.is_available() returns False:

🎯  COMPUTE CAPABILITY CHECK
    GPU: NVIDIA GeForce RTX 5070 (Compute 12.0, Blackwell, sm_120)
    PyTorch compiled for: sm_50, sm_60, sm_70, sm_80, sm_90, compute_90
    ❌ ARCHITECTURE MISMATCH: Your GPU needs sm_120 but PyTorch 2.5.1 doesn't include it.

    This is likely why torch.cuda.is_available() returns False even though
    your driver and CUDA toolkit are working correctly.

    FIX: Install PyTorch nightly with sm_120 support:
       pip install --pre torch torchvision torchaudio --index-url https://download.pytorch.org/whl/nightly/cu126

Soft failure — torch.cuda.is_available() returns True via NVIDIA's PTX JIT, but complex ops may silently degrade:

🎯  COMPUTE CAPABILITY CHECK
    GPU: NVIDIA GeForce RTX 5070 (Compute 12.0, Blackwell, sm_120)
    PyTorch compiled for: sm_50, sm_60, sm_70, sm_80, sm_90, compute_90
    ⚠️  ARCHITECTURE MISMATCH (Soft): Your GPU needs sm_120 but PyTorch 2.5.1 doesn't include it.

    torch.cuda.is_available() returned True via NVIDIA's driver-level PTX JIT,
    but you may experience degraded performance or failures with complex CUDA ops.

    FIX: Install a newer PyTorch with native sm_120 support for full compatibility:
       pip install --pre torch torchvision torchaudio --index-url https://download.pytorch.org/whl/nightly/cu126

Check Python Version Compatibility

env-doctor python-compat

🐍  PYTHON VERSION COMPATIBILITY CHECK
============================================================
Python Version: 3.13 (3.13.0)
Libraries Checked: 2

❌  2 compatibility issue(s) found:

    tensorflow:
      tensorflow supports Python <=3.12, but you have Python 3.13
      Note: TensorFlow 2.15+ requires Python 3.9-3.12. Python 3.13 not yet supported.

    torch:
      torch supports Python <=3.12, but you have Python 3.13
      Note: PyTorch 2.x supports Python 3.9-3.12. Python 3.13 support experimental.

⚠️   Dependency Cascades:
    tensorflow [high]: TensorFlow's Python ceiling propagates to keras and tensorboard
      Affected: keras, tensorboard, tensorflow-estimator
    torch [high]: PyTorch's Python version constraint affects all torch ecosystem packages
      Affected: torchvision, torchaudio, triton

💡  Consider using Python 3.12 or lower for full compatibility

💡  Cascade: tensorflow constraint also affects: keras, tensorboard, tensorflow-estimator

💡  Cascade: torch constraint also affects: torchvision, torchaudio, triton

============================================================

Get Safe Install Command

env-doctor install torch

⬇️ Run this command to install the SAFE version:
---------------------------------------------------
pip install torch torchvision --index-url https://download.pytorch.org/whl/cu118
---------------------------------------------------

Get CUDA Installation Instructions

env-doctor cuda-install

============================================================
CUDA TOOLKIT INSTALLATION GUIDE
============================================================

Detected Platform:
    Linux (ubuntu 22.04, x86_64)

Driver: 535.146.02 (supports up to CUDA 12.2)
Recommended CUDA Toolkit: 12.1

============================================================
Ubuntu 22.04 (x86_64) - Network Install
============================================================

Installation Steps:
------------------------------------------------------------
    1. wget https://developer.download.nvidia.com/compute/cuda/repos/ubuntu2204/x86_64/cuda-keyring_1.1-1_all.deb
    2. sudo dpkg -i cuda-keyring_1.1-1_all.deb
    3. sudo apt-get update
    4. sudo apt-get -y install cuda-toolkit-12-1

Post-Installation Setup:
------------------------------------------------------------
    export PATH=/usr/local/cuda-12.1/bin${PATH:+:${PATH}}
    export LD_LIBRARY_PATH=/usr/local/cuda-12.1/lib64${LD_LIBRARY_PATH:+:${LD_LIBRARY_PATH}}

    TIP: Add the above exports to ~/.bashrc or ~/.zshrc

Verify Installation:
------------------------------------------------------------
    nvcc --version

Official Download Page:
    https://developer.nvidia.com/cuda-12-1-0-download-archive

Supported Platforms:

• Ubuntu 20.04, 22.04, 24.04
• Debian 11, 12
• RHEL 8, 9 / Rocky Linux / AlmaLinux
• Fedora 39+
• WSL2 (Ubuntu)
• Windows 10/11
• Conda (all platforms)

Install Compilation Packages (Extension Libraries)

For extension libraries like flash-attn, SageAttention, auto-gptq, apex, and xformers that require compilation from source, env-doctor provides special guidance to handle CUDA version mismatches:

env-doctor install flash-attn

Example output (with CUDA mismatch):

🩺  PRESCRIPTION FOR: flash-attn

⚠️   CUDA VERSION MISMATCH DETECTED
     System nvcc: 12.1.1
     PyTorch CUDA: 12.4.1

🔧  flash-attn requires EXACT CUDA version match for compilation.
    You have TWO options to fix this:

============================================================
📦  OPTION 1: Install PyTorch matching your nvcc (12.1)
============================================================

Trade-offs:
  ✅ No system changes needed
  ✅ Faster to implement
  ❌ Older PyTorch version (may lack new features)

Commands:
  # Uninstall current PyTorch
  pip uninstall torch torchvision torchaudio -y

  # Install PyTorch for CUDA 12.1
  pip install torch --index-url https://download.pytorch.org/whl/cu121

  # Install flash-attn
  pip install flash-attn --no-build-isolation

============================================================
⚙️   OPTION 2: Upgrade nvcc to match PyTorch (12.4)
============================================================

Trade-offs:
  ✅ Keep latest PyTorch
  ✅ Better long-term solution
  ❌ Requires system-level changes
  ❌ Verify driver supports CUDA 12.4

Steps:
  1. Check driver compatibility:
     env-doctor check

  2. Download CUDA Toolkit 12.4:
     https://developer.nvidia.com/cuda-12-4-0-download-archive

  3. Install CUDA Toolkit (follow NVIDIA's platform-specific guide)

  4. Verify installation:
     nvcc --version

  5. Install flash-attn:
     pip install flash-attn --no-build-isolation

============================================================

Check Model Compatibility

env-doctor model llama-3-8b

🤖  Checking: LLAMA-3-8B (8.0B params)

🖥️   Your Hardware: RTX 3090 (24GB)

💾  VRAM Requirements:
  ✅  FP16: 19.2GB - fits with 4.8GB free
  ✅  INT4:  4.8GB - fits with 19.2GB free

✅  This model WILL FIT on your GPU!

List all models: env-doctor model --list

Automatic HuggingFace Support (New ✨) If a model isn't found locally, env-doctor automatically checks the HuggingFace Hub, fetches its parameter metadata, and caches it locally for future runs — no manual setup required.

# Fetches from HuggingFace on first run, cached afterward
env-doctor model bert-base-uncased
env-doctor model sentence-transformers/all-MiniLM-L6-v2

Output:

🤖  Checking: BERT-BASE-UNCASED
    (Fetched from HuggingFace API - cached for future use)
    Parameters: 0.11B
    HuggingFace: bert-base-uncased

🖥️   Your Hardware:
    RTX 3090 (24GB VRAM)

💾  VRAM Requirements & Compatibility
  ✅  FP16:  264 MB - Fits easily!

💡  Recommendations:
1. Use fp16 for best quality on your GPU

Validate Dockerfiles

env-doctor dockerfile

🐳  DOCKERFILE VALIDATION

❌  Line 1: CPU-only base image: python:3.10
    Fix: FROM nvidia/cuda:12.1.0-runtime-ubuntu22.04

❌  Line 8: PyTorch missing --index-url
    Fix: pip install torch --index-url https://download.pytorch.org/whl/cu121

More Commands

Command	Purpose
env-doctor check	Full environment diagnosis
env-doctor python-compat	Check Python version compatibility with AI libraries
env-doctor cuda-install	Step-by-step CUDA Toolkit installation guide
env-doctor install <lib>	Safe install command for PyTorch/TensorFlow/JAX, extension libraries (flash-attn, auto-gptq, apex, xformers, SageAttention, etc.)
env-doctor model <name>	Check model VRAM requirements
env-doctor cuda-info	Detailed CUDA toolkit analysis
env-doctor cudnn-info	cuDNN library analysis
env-doctor dockerfile	Validate Dockerfile
env-doctor docker-compose	Validate docker-compose.yml
env-doctor scan	Scan for deprecated imports
env-doctor debug	Verbose detector output

CI/CD Integration

# JSON output for scripting
env-doctor check --json

# CI mode with exit codes (0=pass, 1=warn, 2=error)
env-doctor check --ci

GitHub Actions example:

- run: pip install env-doctor
- run: env-doctor check --ci

Documentation

Full documentation: https://mitulgarg.github.io/env-doctor/

• Getting Started
• Command Reference
• MCP Integration Guide
• WSL2 GPU Guide
• CI/CD Integration
• Architecture

Video Tutorial: Watch Demo on YouTube

Contributing

Contributions welcome! See CONTRIBUTING.md for details.

License

MIT License - see LICENSE