CUDA availability check in PyTorch - Practice Problems & Coding Challenges

Challenge - 5 Problems

❓ Predict Output

intermediate

Check CUDA availability output

What is the output of this PyTorch code snippet that checks if CUDA is available?

PyTorch

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

A True if a CUDA-enabled GPU is available, otherwise False

B The number of CUDA devices available as an integer

C Raises a RuntimeError if CUDA is not installed

D Always prints False regardless of hardware

💡 Hint

This function returns a boolean indicating hardware availability.

torch.cuda.is_available() returns True if PyTorch detects a CUDA-capable GPU and the CUDA drivers are properly installed; otherwise, it returns False.

🧠 Conceptual

intermediate

Understanding CUDA device count

Which PyTorch function correctly returns the number of CUDA devices available on the system?

A torch.cuda.current_device()

B torch.cuda.is_available()

C torch.cuda.get_device_name()

D torch.cuda.device_count()

💡 Hint

This function returns an integer count of GPUs.

torch.cuda.device_count() returns the number of CUDA-capable devices detected by PyTorch.

🔧 Debug

advanced

Diagnose CUDA availability error

What error will this code raise if CUDA is not available on the system?

PyTorch

import torch
print(torch.cuda.current_device())

A IndexError: list index out of range

B RuntimeError: CUDA error: invalid device ordinal

C AssertionError: CUDA not available

D No error, prints 0

💡 Hint

current_device() requires at least one CUDA device.

If no CUDA device is available, torch.cuda.current_device() raises a RuntimeError indicating an invalid device ordinal.

❓ Model Choice

advanced

Choosing device for model training

Given this code snippet, which device will the model be moved to if CUDA is available?

PyTorch

import torch
model = torch.nn.Linear(10, 2)
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
model.to(device)
print(next(model.parameters()).device)

A cuda:0

B cpu

C cuda:1

D Raises an error

💡 Hint

The device string 'cuda' defaults to the first GPU.

If CUDA is available, 'cuda' refers to the first GPU device (cuda:0), so the model parameters move there.

❓ Metrics

expert

Interpreting CUDA memory usage

What does the following PyTorch code output represent?

PyTorch

import torch
print(torch.cuda.memory_allocated())

A The peak memory usage during the program run

B The total GPU memory available on the system in bytes

C The number of bytes currently allocated by tensors on the default CUDA device

D The number of CUDA devices available

💡 Hint

This function reports memory used by tensors, not total or peak memory.

torch.cuda.memory_allocated() returns the current number of bytes allocated by tensors on the default CUDA 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