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<h1 align="center">Env-Doctor</h1>
<p align="center">
<strong>The missing link between your GPU and Python AI libraries</strong>
</p>
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---
> **"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 |
| **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
```bash
pip install env-doctor
```
## Usage
### Diagnose Your Environment
```bash
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!
```
### Check Python Version Compatibility
```bash
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
```bash
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
```bash
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:
```bash
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
```bash
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.
```bash
# 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
```bash
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
```bash
# 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:**
```yaml
- run: pip install env-doctor
- run: env-doctor check --ci
```
## MCP Server (AI Assistant Integration)
Env-Doctor includes a built-in [Model Context Protocol (MCP)](https://modelcontextprotocol.io) server that exposes diagnostic tools to AI assistants like Claude Desktop.
### Quick Setup for Claude Desktop
1. **Install env-doctor:**
```bash
pip install env-doctor
```
2. **Add to Claude Desktop config** (`~/Library/Application Support/Claude/claude_desktop_config.json`):
```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
### Example Usage
Ask Claude Desktop:
- "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](docs/guides/mcp-integration.md)
## Documentation
**Full documentation:** https://mitulgarg.github.io/env-doctor/
- [Getting Started](docs/getting-started.md)
- [Command Reference](docs/commands/check.md)
- [MCP Integration Guide](docs/guides/mcp-integration.md)
- [WSL2 GPU Guide](docs/guides/wsl2.md)
- [CI/CD Integration](docs/guides/ci-cd.md)
- [Architecture](docs/architecture.md)
**Video Tutorial:** [Watch Demo on YouTube](https://youtu.be/mGAwxGuLpxk?si=Buf9yzNTSJmoirMU)
## Contributing
Contributions welcome! See [CONTRIBUTING.md](CONTRIBUTING.md) for details.
## License
MIT License - see [LICENSE](LICENSE)
