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

Installation

Core CLI

The core CLI has no heavy dependencies — installs in seconds.

pip install env-doctor

# Or with uv (faster, isolated)
uv tool install env-doctor
uvx env-doctor check

With Fleet Dashboard

If you want to manage a distributed system of multiple GPU nodes, this dashboard can help you from a observability POV. It adds a web UI for monitoring multiple GPU machines and has no effect on the core CLI. You will be able to soon take action directly from the dashboard via distributed env-doctor cli instances on each VM!

pip install "env-doctor[dashboard]"

This adds: fastapi, uvicorn, sqlalchemy, aiosqlite

MCP Server (AI Assistant Integration)

Env-Doctor includes a built-in Model Context Protocol (MCP) server that exposes 11 diagnostic tools to AI assistants like Claude Code and Claude Desktop.

Quick Setup

// Claude Desktop config (~/.config/Claude/claude_desktop_config.json)
{
  "mcpServers": {
    "env-doctor": {
      "command": "env-doctor-mcp"
    }
  }
}

Ask your AI assistant things like "Check my GPU environment", "Can I run Llama 3 70B on my GPU?", or "Validate this Dockerfile for GPU issues".

Learn more: MCP Integration Guide

Fleet Dashboard (optional)

The core CLI works standalone. The dashboard is an observability layer for teams running multiple GPU machines.

pip install "env-doctor[dashboard]" unlocks a web UI that aggregates diagnostic results from every machine in your fleet into a single view — no SSH required.

Quick Start

1. Start the dashboard (any machine — no GPU needed):

pip install "env-doctor[dashboard]"
env-doctor dashboard
# → Serving at http://localhost:8765
# → Generated API token at ~/.env-doctor/api-token

2. Report from each GPU machine:

pip install env-doctor

# One-time report
env-doctor check --report-to http://<dashboard-host>:8765 --token <token>

# Automatic reporting every 2 minutes (cron on Linux, Task Scheduler on Windows)
env-doctor report install --url http://<dashboard-host>:8765 --token <token> --interval 2m

What You Get

• Fleet overview — sortable table with status, GPU, driver, CUDA, torch, and group filtering
• Topology view — force-directed graph of all machines, colour-coded by health, grouped into clusters
• Activity log — cross-fleet command log with status, output, and filtering
• Machine detail — full diagnostics + snapshot history timeline
• Remote remediation — queue env-doctor commands from the UI, executed on next check-in (no SSH needed)

Smart change detection means stable machines only POST ~1 heartbeat every 30 minutes, not on every poll.

Learn more: Fleet Monitoring 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 compute capability mismatches — the reason torch.cuda.is_available() returns False even when your driver and CUDA are healthy:

🎯  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.

    FIX: Install PyTorch nightly with sm_120 support:
       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)

❌  2 compatibility issue(s) found:

    tensorflow: supports Python <=3.12, but you have Python 3.13
    torch: supports Python <=3.12, but you have Python 3.13

⚠️   Dependency Cascades:
    tensorflow [high]: propagates to keras, tensorboard
    torch [high]: propagates to torchvision, torchaudio, triton

💡  Consider using Python 3.12 or lower for full compatibility
============================================================

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
---------------------------------------------------

Install CUDA Toolkit

Display instructions or execute the installation directly:

# Show platform-specific steps (default)
env-doctor cuda-install

# Preview what would run — no changes made
env-doctor cuda-install --dry-run

# Execute interactively (asks [y/N] before running)
env-doctor cuda-install --run

# Execute headlessly — great for CI/scripts
env-doctor cuda-install --run --yes

# Install a specific version, headless
env-doctor cuda-install 12.6 --run --yes

Example dry-run output (Windows):

[DRY RUN] [1/1] winget install Nvidia.CUDA --version 12.2

[DRY RUN] [1/1] nvcc --version

CUDA 12.2 installation completed successfully.
Verification: PASSED

Full log: C:\Users\you\.env-doctor\install.log

Every run writes a timestamped log to ~/.env-doctor/install.log for debugging.

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 (via winget)
• Conda (all platforms)

Exit codes for CI pipelines:

Install Compilation Packages (Extension Libraries)

For packages like flash-attn, SageAttention, auto-gptq, apex, and xformers that compile from source, env-doctor detects CUDA mismatches and provides two fix paths:

env-doctor install flash-attn

🩺  PRESCRIPTION FOR: flash-attn

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

🔧  Two options:
    📦 OPTION 1: Downgrade PyTorch to match nvcc (12.1) — no system changes
    ⚙️  OPTION 2: Upgrade nvcc to match PyTorch (12.4) — better long-term

    (Full step-by-step commands shown for both options)

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

Cloud GPU Recommendations:

# Get cloud GPU recommendations for a model that doesn't fit
env-doctor model llama-3-70b --recommend

# Direct VRAM lookup (no model name needed)
env-doctor model --vram 80000 --recommend

☁️   Cloud GPU Recommendations

  FP16 (~140.0 GB):
    $27.20 /hr  azure  ND96asr_v4              8x A100 (40GB each)          180.0GB free
    $29.39 /hr  gcp    a2-highgpu-8g            8x A100 (40GB each)          180.0GB free
    ...

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

CI/CD Integration

Generate a GitHub Actions workflow with one command:

env-doctor init --github-actions

This creates .github/workflows/env-doctor.yml — review, commit, and push. Your CI will validate the ML environment on every push and PR.

Or add manually:

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

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

Exit code semantics for check --json / 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

Platform Support

Env-Doctor is built for Linux and Windows — the platforms where NVIDIA GPUs and CUDA are available. All GPU diagnostics (driver, CUDA, cuDNN, library compatibility) target these platforms.

macOS is supported for non-GPU features: Fleet Dashboard hosting, model memory checks (env-doctor model), Python compatibility, project import scanning, and the MCP server. This makes a Mac a great centralised dashboard host while your Linux/Windows VMs handle the GPU workloads.

macOS uses zsh, which treats [] as a glob pattern. Quote extras when installing: pip install "env-doctor[dashboard]"

Contributing

Contributions welcome! See CONTRIBUTING.md for details.

License

MIT License - see LICENSE