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.

Practice

1. What does the function torch.cuda.is_available() check in PyTorch?

easy

A. If the CPU supports multi-threading

B. If a CUDA-capable GPU is available for PyTorch to use

C. If the PyTorch version is up to date

D. If the current script has internet access

Solution

1. Step 1: Understand the purpose of torch.cuda.is_available()

   This function checks if PyTorch can access a CUDA-enabled GPU on the machine.

2. Step 2: Differentiate from other hardware checks

   It does not check CPU features, PyTorch version, or internet access.

3. Final Answer:

   If a CUDA-capable GPU is available for PyTorch to use -> Option B

4. Quick Check:

   torch.cuda.is_available() = GPU availability [OK]

Hint: Check GPU presence with torch.cuda.is_available() [OK]

Common Mistakes:

• Confusing CUDA availability with CPU features
• Assuming it checks PyTorch version
• Thinking it checks internet connection

2. Which of the following is the correct syntax to check CUDA availability in PyTorch?

easy

A. torch.cuda.is_available()

B. torch.cuda.available()

C. torch.is_cuda_available()

D. torch.cuda.check_available()

Solution

1. Step 1: Recall the exact function name

   The correct function is torch.cuda.is_available() with this exact spelling and structure.

2. Step 2: Identify incorrect options

   Other options have wrong function names or missing parts.

3. Final Answer:

   torch.cuda.is_available() -> Option A

4. Quick Check:

   Correct function name = torch.cuda.is_available() [OK]

Hint: Remember exact function: torch.cuda.is_available() [OK]

Common Mistakes:

• Misspelling the function name
• Omitting 'is_' prefix
• Using incorrect method names

3. What will be the output of this code snippet if the machine has no CUDA GPU?

import torch
print(torch.cuda.is_available())

medium

A. False

B. Raises an error

C. null

D. True

Solution

1. Step 1: Understand torch.cuda.is_available() output

   This function returns a boolean: true if CUDA GPU is available, false otherwise.

2. Step 2: Consider no CUDA GPU present

   If no CUDA GPU exists, the function returns false without error.

3. Final Answer:

   False -> Option A

4. Quick Check:

   No GPU means torch.cuda.is_available() = False [OK]

Hint: No GPU means torch.cuda.is_available() returns False [OK]

Common Mistakes:

• Expecting null instead of False
• Thinking it raises an error without GPU
• Confusing output with string 'False'

4. Identify the error in this code snippet that checks for CUDA availability:

import torch
if torch.cuda.is_available:
    device = torch.device('cuda')
else:
    device = torch.device('cpu')

medium

A. Should import 'torch.cuda' separately

B. Wrong device string 'cuda' instead of 'gpu'

C. Missing parentheses after 'is_available' function call

D. No error, code is correct

Solution

1. Step 1: Check function call syntax

   torch.cuda.is_available is a function and must be called with parentheses: torch.cuda.is_available()

2. Step 2: Verify device string correctness

   'cuda' is the correct device string, so no error there.

3. Final Answer:

   Missing parentheses after 'is_available' function call -> Option C

4. Quick Check:

   Function call needs () = Missing parentheses [OK]

Hint: Always add () when calling torch.cuda.is_available [OK]

Common Mistakes:

• Forgetting parentheses on is_available
• Using 'gpu' instead of 'cuda' as device string
• Thinking torch.cuda needs separate import

5. You want your PyTorch model to run on GPU if available, otherwise on CPU. Which code snippet correctly sets the device?

hard

A. device = torch.device('gpu' if torch.cuda.is_available() else 'cpu')

B. device = torch.device('cpu' if torch.cuda.is_available() else 'cuda')

C. device = torch.device('cuda') if torch.cuda.is_available else torch.device('cpu')

D. device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')

Solution

1. Step 1: Use torch.cuda.is_available() with parentheses

   The function must be called with parentheses to get a boolean result.

2. Step 2: Correct device string and conditional expression

   Use 'cuda' when GPU is available, else 'cpu'. device = torch.device('cuda' if torch.cuda.is_available() else 'cpu') uses correct syntax and logic.

3. Final Answer:

   device = torch.device('cuda' if torch.cuda.is_available() else 'cpu') -> Option D

4. Quick Check:

   Correct device setting = device = torch.device('cuda' if torch.cuda.is_available() else 'cpu') [OK]

Hint: Use device = torch.device('cuda' if torch.cuda.is_available() else 'cpu') [OK]

Common Mistakes:

• Swapping 'cpu' and 'cuda' in conditional
• Missing parentheses on is_available
• Using 'gpu' instead of 'cuda' as device string