Installation and GPU setup in PyTorch - Interactive Code Practice

Practice - 5 Tasks

1fill in blank

easy

Complete the code to check if a GPU is available in PyTorch.

PyTorch

import torch
is_gpu = torch.cuda.[1]()

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

AisAvailable()

Bis_available

Cis_available()

Davailable()

Attempts:

3 left

💡 Hint

Common Mistakes

Forgetting the parentheses () after the function name.

Using incorrect function names like 'isAvailable' or 'available'.

The correct function to check GPU availability in PyTorch is torch.cuda.is_available() which returns True if a GPU is available.

2fill in blank

medium

Complete the code to move a tensor to GPU if available.

PyTorch

device = torch.device('cuda' if torch.cuda.[1]() else 'cpu')
tensor = torch.tensor([1, 2, 3]).to(device)

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

Ais_available()

Bavailable()

Cis_available

DisAvailable()

Attempts:

3 left

💡 Hint

Common Mistakes

Using the function without parentheses.

Using wrong function names.

To check if CUDA GPU is available, use torch.cuda.is_available(). This helps decide the device for tensor operations.

3fill in blank

hard

Fix the error in the code to print the name of the current GPU device.

PyTorch

if torch.cuda.is_available():
    print(torch.cuda.get_device_[1]())

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

Anames

Bname

Cdevice_name

Ddevice

Attempts:

3 left

💡 Hint

Common Mistakes

Using incorrect function names like 'get_device' or 'get_device_names'.

Missing parentheses after the function.

The correct function to get the GPU device name is torch.cuda.get_device_name().

4fill in blank

hard

Fill both blanks to create a tensor on GPU and print its device.

PyTorch

tensor = torch.tensor([1, 2, 3], device=[1])
print(tensor.[2])

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

A'cuda'

Bdevice

D'cpu'

Attempts:

3 left

💡 Hint

Common Mistakes

Using 'cpu' instead of 'cuda' for GPU device.

Trying to call device as a function instead of attribute.

To create a tensor on GPU, set device='cuda'. To print the device of a tensor, use tensor.device.

5fill in blank

hard

Fill all three blanks to move a model to GPU and print its device of first parameter.

PyTorch

model = MyModel()
model.to([1])
first_param = next(model.parameters())
print(first_param.[2])
print(torch.cuda.[3]())

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

A'cuda'

Bdevice

Cis_available()

D'cpu'

Attempts:

3 left

💡 Hint

Common Mistakes

Using 'cpu' instead of 'cuda' to move model.

Calling device as a function instead of attribute.

Forgetting parentheses in is_available function.

Move model to GPU with model.to('cuda'). The device of a parameter is accessed by param.device. Check GPU availability with torch.cuda.is_available().

Practice

1. What is the correct command to install PyTorch using pip?

easy

A. pip install torch-gpu-support

B. pip install pytorch-gpu

C. pip install torch-cuda

D. pip install torch torchvision torchaudio

Solution

1. Step 1: Understand PyTorch installation command

   The official PyTorch installation command is pip install torch torchvision torchaudio.

2. Step 2: Check other options

   Other options like pytorch-gpu or torch-cuda are not valid package names for PyTorch installation.

3. Final Answer:

   pip install torch torchvision torchaudio -> Option D

4. Quick Check:

   Official PyTorch install command = pip install torch torchvision torchaudio [OK]

Hint: Use official PyTorch install command with torch and torchvision [OK]

Common Mistakes:

• Trying to install non-existing packages like pytorch-gpu
• Forgetting to include torchvision or torchaudio
• Using conda command instead of pip

2. Which of the following Python code snippets correctly checks if a GPU is available in PyTorch?

easy

A. torch.gpu.available()

B. torch.cuda.is_available()

C. torch.cuda.check_gpu()

D. torch.device('gpu').is_available()

Solution

1. Step 1: Recall PyTorch GPU availability function

   PyTorch provides torch.cuda.is_available() to check if a GPU is accessible.

2. Step 2: Verify other options

   Functions like torch.gpu.available(), torch.cuda.check_gpu(), and torch.device('gpu').is_available() do not exist in PyTorch.

3. Final Answer:

   torch.cuda.is_available() -> Option B

4. Quick Check:

   GPU check function = torch.cuda.is_available() [OK]

Hint: Use torch.cuda.is_available() to check GPU presence [OK]

Common Mistakes:

• Using incorrect function names like torch.gpu.available()
• Trying to check GPU with torch.device('gpu')
• Confusing GPU check with device creation

3. What will be the output of the following code if a GPU is available?

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

medium

A. True

B. False

C. None

D. Error

Solution

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

   This function returns a boolean: True if a GPU is available, otherwise False.

2. Step 2: Given GPU is available

   The code prints the result of torch.cuda.is_available(), so it will print True.

3. Final Answer:

   True -> Option A

4. Quick Check:

   GPU available? = True [OK]

Hint: If GPU exists, torch.cuda.is_available() returns True [OK]

Common Mistakes:

• Expecting string output instead of boolean
• Confusing True/False values
• Assuming function raises error if no GPU

4. Identify the error in this code snippet for checking GPU availability:

import torch
if torch.cuda.is_available:
    print("GPU is ready")
else:
    print("GPU not found")

medium

A. Missing parentheses after is_available

B. Incorrect import statement

C. Wrong print syntax

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 other parts of code

   Import and print statements are correct. The only error is missing parentheses.

3. Final Answer:

   Missing parentheses after is_available -> Option A

4. Quick Check:

   Function call needs () = Missing parentheses [OK]

Hint: Remember to add () when calling functions like is_available [OK]

Common Mistakes:

• Forgetting parentheses on function calls
• Assuming is_available is a property, not a method
• Misreading indentation or print syntax

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

hard

A. device = torch.device('cuda:0' if torch.cuda.is_available else 'cpu:0')

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

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

D. device = torch.device('cuda:1' if torch.cuda.is_available() else 'cpu')

Solution

1. Step 1: Understand device selection syntax

   PyTorch uses torch.device('cuda') for GPU and torch.device('cpu') for CPU.

2. Step 2: Check conditional device assignment

   The correct way is to use 'cuda' if GPU is available, else 'cpu'. device = torch.device('cuda' if torch.cuda.is_available() else 'cpu') uses this correctly.

3. Step 3: Analyze other options

   device = torch.device('cuda:0' if torch.cuda.is_available else 'cpu:0') omits parentheses after torch.cuda.is_available, so the function is not called (function object is truthy), always selecting GPU even if unavailable at runtime. device = torch.device('gpu' if torch.cuda.is_available() else 'cpu') uses invalid 'gpu' string. device = torch.device('cuda:1' if torch.cuda.is_available() else 'cpu') forces 'cuda:1' which may not exist.

4. Final Answer:

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

5. Quick Check:

   Use 'cuda' or 'cpu' strings correctly for device [OK]

Hint: Use 'cuda' string, not 'gpu', for device selection [OK]

Common Mistakes:

• Forgetting parentheses after torch.cuda.is_available()
• Using 'gpu' instead of 'cuda' in device string
• Hardcoding GPU index without checking availability