cuda initialization_error ai_generated true

CUDA error: initialization error (cudaErrorInitializationError)

ID: cuda/not-initialized

Also available as: JSON · Markdown
85%Fix Rate
88%Confidence
50Evidence
2023-01-01First Seen

Version Compatibility

VersionStatusIntroducedDeprecatedNotes
12 active — — —

Root Cause

CUDA runtime failed to initialize. Typically caused by driver not loaded, no GPU present, driver/toolkit version mismatch, or insufficient permissions to access /dev/nvidia* devices. Also occurs in containers without GPU passthrough.

generic

Workarounds

  1. 90% success Verify GPU driver is loaded: nvidia-smi 
    Run nvidia-smi. If it fails, reload driver: modprobe nvidia. Check dmesg for driver errors.
  2. 85% success Fix device permissions: add user to 'video' group or run with appropriate privileges 
    sudo usermod -aG video $USER && newgrp video. Or check: ls -la /dev/nvidia*
  3. 92% success In Docker, use --gpus all or nvidia-container-toolkit 
    docker run --gpus all <image>. Ensure nvidia-container-toolkit is installed on host.

Dead Ends

Common approaches that don't work:

  1. Reinstall PyTorch or TensorFlow to fix CUDA initialization 80% fail

    The issue is at the driver/hardware level, not in the ML framework. Reinstalling frameworks does not fix driver or permission issues.

  2. Set CUDA_HOME environment variable 85% fail

    CUDA_HOME is for compile-time toolkit location; it does not affect runtime initialization or driver loading

Error Chain

Leads to:
Preceded by:
Frequently confused with: