CUDA availability check in PyTorch - Interactive Code Practice

Practice - 5 Tasks

1fill in blank

easy

Complete the code to check if CUDA is available in PyTorch.

PyTorch

import torch

cuda_available = torch.cuda.[1]()
print(f"CUDA available: {cuda_available}")

Drag options to blanks, or click blank then click option'

Acuda_available

Bavailable

Cis_available

Dcheck

Attempts:

3 left

💡 Hint

Common Mistakes

Using torch.cuda.available() which does not exist.

Trying to access a property instead of calling a function.

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

2fill in blank

medium

Complete the code to get the name of the current CUDA device.

PyTorch

import torch

if torch.cuda.is_available():
    device_name = torch.cuda.get_device_[1](torch.cuda.current_device())
    print(f"Current CUDA device: {device_name}")

Drag options to blanks, or click blank then click option'

Adevice

Binfo

Cid

Dname

Attempts:

3 left

💡 Hint

Common Mistakes

Using torch.cuda.get_device_id() which does not exist.

Using torch.cuda.get_device_info() which is incorrect.

The function torch.cuda.get_device_name(device_id) returns the name of the CUDA device.

3fill in blank

hard

Fix the error in the code to correctly check CUDA availability and print a message.

PyTorch

import torch

if torch.cuda.[1]:
    print("CUDA is available")
else:
    print("CUDA is not available")

Drag options to blanks, or click blank then click option'

Ais_available

Bis_available()

Cavailable()

Davailable

Attempts:

3 left

💡 Hint

Common Mistakes

Forgetting parentheses after is_available.

Using a non-existent attribute like available.

torch.cuda.is_available() is a function and must be called with parentheses to get a boolean result.

4fill in blank

hard

Fill both blanks to create a tensor on the CUDA device if available, otherwise on CPU.

PyTorch

import torch

device = torch.device("cuda" if torch.cuda.[1]() else "[2]")
tensor = torch.tensor([1, 2, 3], device=device)
print(tensor.device)

Drag options to blanks, or click blank then click option'

Ais_available

Bcpu

Ccuda

Dis_ready

Attempts:

3 left

💡 Hint

Common Mistakes

Using 'cuda' instead of 'cpu' as fallback device.

Using a non-existent function like is_ready.

Use torch.cuda.is_available() to check CUDA, and 'cpu' as fallback device string.

5fill in blank

hard

Fill all three blanks to print the number of CUDA devices and the name of the first device if available.

PyTorch

import torch

if torch.cuda.[1]():
    count = torch.cuda.[2]()
    name = torch.cuda.get_device_[3](0)
    print(f"CUDA devices: {count}, First device: {name}")
else:
    print("No CUDA devices found.")

Drag options to blanks, or click blank then click option'

Ais_available

Bdevice_count

Cname

Dcount

Attempts:

3 left

💡 Hint

Common Mistakes

Using torch.cuda.count() which does not exist.

Using torch.cuda.get_device_id() instead of get_device_name().

Use torch.cuda.is_available() to check availability, torch.cuda.device_count() to get number of devices, and torch.cuda.get_device_name() to get device name.

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