env-doctor

"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 Auto-Installer | Execute CUDA Toolkit installation directly with --run; CI-friendly with --yes; preview with --dry-run | | 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, proper exit codes, and CI-aware env-var persistence (GitHub Actions, GitLab CI, CircleCI, Azure Pipelines, Jenkins) | | Fleet Dashboard (optional) | Web UI for monitoring multiple GPU machines — aggregate status, drill-down diagnostics, history timeline. Install with pip install "env-doctor[dashboard]" |

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

| | pip install env-doctor | pip install "env-doctor[dashboard]" | |---|---|---| | env-doctor check | ✅ | ✅ | | All CLI commands | ✅ | ✅ | | MCP server | ✅ | ✅ | | env-doctor check --report-to | ✅ | ✅ | | env-doctor report install/status | ✅ | ✅ | | env-doctor dashboard (web UI) | ✗ | ✅ |

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

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