cgpu-info 0.1.0

Quick CUDA/GPU status summary for ML engineers

cgpu

Quick CUDA/GPU status summary for ML engineers. One import, one call, all the info you need.

Installation

pip install cgpu-info

or with uv:

uv pip install cgpu-info

Note: You need PyTorch installed separately. For temperature/utilization info, also install pynvml:

pip install torch pynvml

Usage

from cgpu import cgpu

device = cgpu()
# Now use `device` in your code
model.to(device)

That's it! You'll see a colorful summary like:

═══════════════════════════════════════
          GPU Status Summary
═══════════════════════════════════════
✓ CUDA Available
  Device: cuda
  GPU Count: 1
  [0] NVIDIA GeForce RTX 4090
      VRAM: 24.0 GB
      Allocated: 0.00 GB
      Reserved: 0.00 GB
      Temp: 42°C
      GPU Util: 0%
      Mem Util: 0%
  CUDA Version: 12.1
  cuDNN Version: 8902
  PyTorch: 2.1.0
═══════════════════════════════════════

What it shows

• CUDA availability status
• Device string (cuda or cpu)
• GPU name and count
• VRAM total and usage
• Temperature (color-coded: green < 50°C, yellow < 70°C, red >= 70°C)
• GPU/Memory utilization
• CUDA, cuDNN, and PyTorch versions

License

MIT