CUDA availability check in PyTorch - Cheat Sheet & Quick Revision

Recall & Review

beginner

What does CUDA stand for and why is it important in PyTorch?

CUDA stands for Compute Unified Device Architecture. It allows PyTorch to use NVIDIA GPUs to speed up computations, making training and running models much faster than using only the CPU.

beginner

How do you check if CUDA is available in PyTorch?

You use the command torch.cuda.is_available(). It returns True if a compatible GPU is ready to use, otherwise False.

intermediate

What does torch.cuda.device_count() tell you?

It tells you how many CUDA-capable GPUs are available on your system. This helps you know if you can use multiple GPUs for training.

beginner

Why should you check CUDA availability before running a PyTorch model?

Because if CUDA is not available and you try to run your model on GPU, your code will fail. Checking first lets you decide to run on CPU or GPU safely.

beginner

Write a simple PyTorch code snippet to print if CUDA is available and how many GPUs are detected.

import torch

if torch.cuda.is_available():
    print(f"CUDA is available. Number of GPUs: {torch.cuda.device_count()}")
else:
    print("CUDA is not available. Using CPU.")

Which PyTorch function checks if CUDA is available?

A torch.cuda.status()

B torch.cuda.check()

C torch.is_cuda()

D torch.cuda.is_available()

The correct function to check CUDA availability is torch.cuda.is_available().

What does torch.cuda.device_count() return?

A Number of CPU cores

B Total memory of GPU

C Number of CUDA GPUs available

D CUDA version installed

torch.cuda.device_count() returns the number of CUDA-capable GPUs available.

If torch.cuda.is_available() returns False, what should you do?

A Run your model on CPU

B Run your model on GPU anyway

C Restart your computer

D Install more RAM

If CUDA is not available, you should run your model on CPU to avoid errors.

Why is using CUDA beneficial for training models?

A It speeds up training by using GPU power

B It uses less electricity

C It reduces model size

D It makes training slower

CUDA allows PyTorch to use GPU power, which speeds up training significantly.

What will this code print if no GPU is available? if torch.cuda.is_available(): print("GPU ready") else: print("Using CPU")

A GPU ready

B Using CPU

C Error

D Nothing

If no GPU is available, torch.cuda.is_available() returns False, so it prints "Using CPU".

Explain how to check if your PyTorch code can use a GPU and why this check is important.

Think about a simple yes/no question your code asks before using GPU.

Describe what information torch.cuda.device_count() provides and how it can help in training models.

It tells you how many GPUs your computer has.