Installation and GPU setup in PyTorch - Model Metrics & Evaluation

Metrics & Evaluation - Installation and GPU setup

Which metric matters for Installation and GPU setup and WHY

For installation and GPU setup, the key metric is successful environment readiness. This means PyTorch and GPU drivers are correctly installed and working together. We check this by running simple tests like torch.cuda.is_available() which returns True if the GPU is ready. This metric matters because without a working GPU setup, training models will be slow or fail.

Confusion matrix or equivalent visualization

Instead of a confusion matrix, we use a simple test output to confirm GPU setup:

GPU Available: True
Number of GPUs: 1
CUDA Version: 12.1
    

If torch.cuda.is_available() is False, it means the setup failed.

Tradeoff: Installation ease vs GPU performance

Sometimes, installing the latest PyTorch version is easy but may not support your GPU. Older versions might support your GPU better but require more setup effort. The tradeoff is between quick installation and getting full GPU speed. Testing torch.cuda.is_available() helps find the right balance.

What "good" vs "bad" setup looks like

• Good: torch.cuda.is_available() returns True, GPU count matches your hardware, and training runs faster than CPU.
• Bad: torch.cuda.is_available() returns False, or errors appear when running GPU code, meaning no GPU acceleration.

Common pitfalls in installation and GPU setup

• Mismatch between CUDA version and PyTorch version causing errors.
• Missing or outdated GPU drivers.
• Not restarting the system after installation.
• Using CPU-only PyTorch by mistake.
• Confusing multiple Python environments causing wrong PyTorch version.

Self-check question

Your PyTorch installation shows torch.cuda.is_available() is False but your computer has a GPU. Is your setup good? Why or why not?

Answer: No, the setup is not good. It means PyTorch cannot use the GPU, possibly due to missing drivers, wrong CUDA version, or installation issues. You need to fix these to get GPU acceleration.

Key Result

The key metric is whether PyTorch detects the GPU (torch.cuda.is_available() == True) to ensure GPU acceleration is ready.