🩺 AI InfraDr

Diagnose broken PyTorch, CUDA, NVIDIA GPU, and NCCL environments with evidence instead of guesswork.

pip install ai-infradr
ai-infradr

Example output:

AI InfraDr

Area          Detected                         Status
System        Linux 6.8                       ✓
GPU           8 × NVIDIA A800                 ✓
Driver        570.86                          ✓
Driver CUDA   12.8                            ✓
PyTorch       2.7.1+cu126                     ✓
Torch CUDA    12.6                            ✓
NCCL          2.26.2                          ✓

Detected issues
LOW  CUDA_TOOLKIT_DIFFERS_FROM_TORCH_RUNTIME
System CUDA toolkit differs from PyTorch CUDA runtime
Evidence:
 • nvcc toolkit: 12.8
 • torch CUDA runtime: 12.6

AI InfraDr is not a version printer. It normalizes environment facts, applies deterministic compatibility checks, shows the evidence behind each finding, and gives cautious next steps.

Why

AI environments fail in ways that are hard to diagnose:

• PyTorch installs successfully but torch.cuda.is_available() is false.
• nvidia-smi, nvcc, and torch.version.cuda show different CUDA versions.
• A CPU-only PyTorch wheel is installed on a GPU machine.
• The host driver is too old for the CUDA runtime used by PyTorch.
• A container sees fewer GPUs than the host.
• NCCL is unavailable in a multi-GPU environment.

The project treats these as compatibility/debugging problems, not as a request to blindly reinstall everything.

v0.1 checks

• Linux/system information
• Python interpreter and environment
• NVIDIA GPUs and driver via nvidia-smi
• Driver-reported maximum CUDA support
• CUDA Toolkit / nvcc
• PyTorch version, bundled CUDA runtime, CUDA availability, visible devices
• cuDNN version when available
• NCCL version exposed by PyTorch
• GPU visibility mismatches
• Driver ↔ PyTorch CUDA runtime compatibility
• System CUDA Toolkit ↔ PyTorch CUDA runtime differences
• Graceful degradation when optional tools are missing

Usage

Human-readable report

ai-infradr

JSON for automation

ai-infradr --json

Fail CI on serious findings

ai-infradr --fail-on high

or:

ai-infradr --fail-on medium

More detail

ai-infradr --verbose

You can also run it as a Python module:

python -m ai_infradr

Important CUDA distinction

AI InfraDr keeps these concepts separate:

1. NVIDIA driver CUDA support — reported by nvidia-smi.
2. System CUDA Toolkit — usually reported by nvcc --version.
3. PyTorch CUDA runtime — reported by torch.version.cuda.

Different Toolkit and PyTorch runtime versions are not automatically a bug. PyTorch wheels commonly ship with their own CUDA runtime. The difference becomes more relevant when compiling/loading CUDA extensions.

Architecture

Probes
  ↓
Structured EnvironmentSnapshot
  ↓
Deterministic Diagnosis Engine
  ↓
Evidence-backed Issues
  ↓
Console / JSON reports

The core does not require an LLM or API key. Future AI explanations should remain an optional layer above deterministic evidence.

Python API

from ai_infradr import InfraDr

snapshot, issues = InfraDr().diagnose()

for issue in issues:
    print(issue.code, issue.severity.value)

Development

git clone https://github.com/xxPcy/ai-infradr.git
cd ai-infradr

python -m venv .venv
source .venv/bin/activate
pip install -e '.[dev]'

ruff check src tests
pytest

Roadmap

• v0.1 — Linux + NVIDIA + Python + PyTorch + CUDA + NCCL
• v0.2 — FlashAttention + Transformers + Triton
• v0.3 — vLLM + SGLang + DeepSpeed
• v0.4 — Docker / Conda / uv environment adapters and offline scans
• v0.5 — GitHub Action compatibility checks
• v1.0 — optional AI explanation, safe fix planning, community compatibility rules

Design principles

• Evidence before recommendations.
• Stable diagnostic error codes.
• Missing optional dependencies must not crash the scan.
• Never silently modify the user's environment.
• Do not treat every version difference as incompatibility.
• Core diagnostics work offline and without an API key.

Contributing

Contributions are welcome, especially reproducible compatibility cases and additional probes. See CONTRIBUTING.md.

License

MIT