Windows Ml Troubleshooting

Windows ML Troubleshooting

Diagnose and fix Python ML/AI tool crashes on Windows: CUDA version mismatches, native DLL failures, and environment contamination when calling subprocess Python.

When to use

• Python ML tool crashes with 0xC0000005 (access violation) or 0xC0000135 (DLL not found)
• torch.cuda.is_available() returns False but nvidia-smi shows a working GPU
• cudaGetDeviceCount() returns cudaErrorNotSupported
• Running a different venv's Python but it loads packages from the wrong environment

1. CUDA / PyTorch Version Mismatch → 0xC0000005 Crash

Symptom: ComfyUI (or any PyTorch app) crashes on startup with:

Windows fatal exception: access violation
Process exited with code 3221225477 / 0xC0000005
Stack (most recent call first):
  File "...torch\cuda\__init__.py", line 491 in _lazy_init

And/or:

cudaGetDeviceCount() returned cudaErrorNotSupported, likely using older driver

Root cause: PyTorch was installed with a CUDA build (e.g., cu130) that is newer than the driver's supported CUDA version. The NVIDIA driver reports its max CUDA version via nvidia-smi; PyTorch MUST be built with a CUDA version ≤ that number.

Diagnosis:

# Check driver's max CUDA version
nvidia-smi | grep "CUDA Version"
# → "CUDA Version: 12.9"   ← this is the MAX allowed

# Check what PyTorch was built with
python -c "import torch; print('CUDA built:', torch.version.cuda)"
# → "CUDA built: 13.0"     ← HIGHER than driver → CRASH

Fix: Uninstall and reinstall PyTorch with a compatible CUDA build. cu124 (CUDA 12.4) is the safest choice for most drivers (CUDA 12.0–12.9):

pip uninstall torch torchvision torchaudio -y
pip install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cu124

Verify:

python -c "import torch; print(torch.cuda.is_available(), torch.cuda.get_device_name(0))"
# → "True NVIDIA GeForce GTX 1080 Ti"

CUDA version compatibility table

2. PYTHONPATH Contamination (Wrong venv Packages Loaded)

Symptom: You call a different venv's Python (e.g., ComfyUI's .venv/Scripts/python.exe), but it loads packages from the Hermes venv instead.

Root cause: Hermes sets PYTHONPATH, PYTHONHOME, and VIRTUAL_ENV environment variables that can bleed into subprocess calls and override the target venv's isolation.

Fix: When running Python from a different venv via subprocess.run() or terminal(), always clear these environment variables first:

import subprocess, os

env = os.environ.copy()
env.pop('PYTHONPATH', None)
env.pop('PYTHONHOME', None)
env.pop('VIRTUAL_ENV', None)

result = subprocess.run(
    [r"E:\target\.venv\Scripts\python.exe", "-c", "import torch; print(torch.__file__)"],
    capture_output=True, text=True,
    env=env
)

Verification: The loaded torch.__file__ should point to the target venv's site-packages, not the Hermes venv.

3. General DLL Load Failures

Symptom: OSError: [WinError 126] 지정된 모듈을 찾을 수 없습니다 when importing torch or other native libraries.

Common causes:

• Missing Visual C++ Redistributable (install from https://aka.ms/vs/17/release/vc_redist.x64.exe)
• Missing CUDA toolkit DLLs (check CUDA_PATH environment variable)
• cuDNN not in PATH (should be in CUDA_PATH\bin)

Pitfalls

• Never assume cu130 is safe — many driver versions don't support CUDA 13.0 yet. Always check nvidia-smi first, then pick the matching PyTorch build.
• nvidia-smi working ≠ PyTorch CUDA working — the driver can be healthy but the PyTorch build can still be incompatible. Always verify with torch.cuda.is_available().
• Meta Virtual Monitor (Oculus/Meta Quest) can interfere with CUDA device enumeration. If nvidia-smi shows the GPU but PyTorch can't see it, the virtual display adapter may be confusing CUDA initialization.