CUDA availability check in PyTorch - ML Experiment: Train & Evaluate

Experiment - CUDA availability check

Problem:You want to check if your computer can use the GPU with CUDA to speed up PyTorch computations.

Current Metrics:N/A - This is a hardware/software environment check, not a model training task.

Issue:You are unsure if CUDA is available and properly configured for PyTorch on your system.

Your Task

Write a simple PyTorch script to check if CUDA is available and print the result clearly.

Use only PyTorch library functions.

Do not attempt to train any model.

Output must be clear and user-friendly.

Hint 1

Hint 2

Hint 3

Solution

PyTorch

import torch

def check_cuda():
    if torch.cuda.is_available():
        num_gpus = torch.cuda.device_count()
        print(f"CUDA is available! Number of GPUs detected: {num_gpus}")
        for i in range(num_gpus):
            print(f"GPU {i}: {torch.cuda.get_device_name(i)}")
    else:
        print("CUDA is not available on this system.")

if __name__ == "__main__":
    check_cuda()

Added a function to check CUDA availability using torch.cuda.is_available().

Printed the number of GPUs detected with torch.cuda.device_count().

Printed the name of each GPU detected with torch.cuda.get_device_name(i).

Added a clear message if CUDA is not available.

Results Interpretation

Before: User did not know if CUDA was available. After: User runs the script and sees a clear message about CUDA availability and GPU details.

Checking CUDA availability is a simple but important first step to ensure your PyTorch code can run on GPU hardware for faster training and inference.

Bonus Experiment

Modify the script to create a tensor on the GPU if CUDA is available, and on CPU otherwise, then print the device of the tensor.

💡 Hint

Use torch.device('cuda') or torch.device('cpu') and create a tensor with .to(device). Then print tensor.device.

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