GPU tensors (to, cuda) in PyTorch - Interactive Code Practice

Practice - 5 Tasks

1fill in blank

easy

Complete the code to move the tensor to GPU using the recommended method.

PyTorch

import torch
x = torch.tensor([1, 2, 3])
x_gpu = x.[1]('cuda')

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

Acpu

Bcuda

Cdevice

Dto

Attempts:

3 left

💡 Hint

Common Mistakes

Using .cuda() directly instead of .to('cuda')

Using .cpu() which moves tensor to CPU

The to method is the recommended way to move tensors to a device like GPU by specifying 'cuda'.

2fill in blank

medium

Complete the code to check if CUDA is available before moving the tensor to GPU.

PyTorch

import torch
x = torch.tensor([4, 5, 6])
if torch.cuda.is_available():
    x_gpu = x.[1]('cuda')

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

Acuda

Bto

Cdevice

Dcopy

Attempts:

3 left

💡 Hint

Common Mistakes

Using .cuda() without checking availability

Using .copy() which is not a tensor method

Using to('cuda') moves the tensor to GPU if CUDA is available.

3fill in blank

hard

Fix the error in the code to move the tensor to GPU correctly.

PyTorch

import torch
x = torch.tensor([7, 8, 9])
x_gpu = x.[1]()

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

Ato

Bdevice

Ccuda

Dto('cuda')

Attempts:

3 left

💡 Hint

Common Mistakes

Using to without arguments

Using to('cuda') with parentheses inside blank

The cuda() method moves the tensor to the default CUDA device without arguments.

4fill in blank

hard

Fill both blanks to create a tensor on GPU and then move it back to CPU.

PyTorch

import torch
x_gpu = torch.tensor([10, 11, 12], device=[1])
x_cpu = x_gpu.[2]()

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

A'cuda'

Bcpu

Dcuda

Attempts:

3 left

💡 Hint

Common Mistakes

Using device='cpu' to create GPU tensor

Using to('cpu') instead of cpu() method

Specify device='cuda' to create tensor on GPU, then use cpu() to move it back to CPU.

5fill in blank

hard

Fill all three blanks to create a tensor on CPU, then move it to GPU.

PyTorch

import torch
x = torch.tensor([13, 14, 15], device=[1])
x_gpu = x.[2]([3])

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

A'cpu'

Bto

C'cuda'

Dcuda

Attempts:

3 left

💡 Hint

Common Mistakes

Using device='cuda' initially

Using cuda() method with argument

Using to without argument

Create tensor on CPU with device='cpu', move to GPU with to('cuda').

Practice

1. What does the PyTorch method tensor.to('cuda') do?

easy

A. Moves the tensor to the GPU for faster computation

B. Converts the tensor to a CPU tensor

C. Changes the tensor data type to float

D. Deletes the tensor from memory

Solution

1. Step 1: Understand the purpose of to('cuda')

   This method moves the tensor from CPU memory to GPU memory to speed up calculations.

2. Step 2: Recognize the effect on computation

   By moving the tensor to GPU, PyTorch can use GPU cores for faster processing.

3. Final Answer:

   Moves the tensor to the GPU for faster computation -> Option A

4. Quick Check:

   to('cuda') = move tensor to GPU [OK]

Hint: to('cuda') means move tensor to GPU memory [OK]

Common Mistakes:

• Thinking it converts data type
• Assuming it moves tensor to CPU
• Believing it deletes the tensor

2. Which of the following is the correct syntax to move a tensor named x to GPU in PyTorch?

easy

A. x.to('cpu')

B. x.gpu()

C. x.to('cuda')

D. x.cuda('cpu')

Solution

1. Step 1: Identify correct method to move tensor to GPU

   The method to('cuda') moves tensor to GPU.

2. Step 2: Check each option

   x.to('cuda') uses to('cuda'), which is correct. Others either move to CPU or use invalid methods.

3. Final Answer:

   x.to('cuda') -> Option C

4. Quick Check:

   Correct syntax for GPU move = x.to('cuda') [OK]

Hint: Use .to('cuda') to move tensor to GPU [OK]

Common Mistakes:

• Using .to('cpu') instead of 'cuda'
• Calling .cuda('cpu') which is invalid
• Using non-existent .gpu() method

3. What will be the output device of the tensor after running this code?

import torch
x = torch.tensor([1, 2, 3])
if torch.cuda.is_available():
    x = x.cuda()
print(x.device)

medium

A. cuda:0

B. cuda:1

C. cpu

D. None

Solution

1. Step 1: Check if GPU is available

   The code checks torch.cuda.is_available(). If True, it moves tensor to GPU device 0 by default.

2. Step 2: Understand x.device output

   After x.cuda(), the tensor is on GPU device 0, so x.device prints cuda:0.

3. Final Answer:

   cuda:0 -> Option A

4. Quick Check:

   cuda() moves tensor to cuda:0 device [OK]

Hint: cuda() moves tensor to GPU device 0 by default [OK]

Common Mistakes:

• Assuming device is 'cpu' after cuda()
• Thinking cuda() moves to cuda:1 by default
• Expecting None as device

4. Identify the error in this code snippet that tries to move a tensor to GPU:

import torch
x = torch.tensor([1, 2, 3])
x = x.to('gpu')
print(x.device)

medium

A. Tensor cannot be moved to GPU

B. Tensor must be converted to float before moving

C. Missing .cuda() call after .to()

D. The device string 'gpu' is invalid; should be 'cuda'

Solution

1. Step 1: Check device string in to() method

   The string 'gpu' is not recognized by PyTorch; the correct string is 'cuda'.

2. Step 2: Understand correct usage

   Using 'cuda' moves tensor to GPU. Using 'gpu' causes a runtime error.

3. Final Answer:

   The device string 'gpu' is invalid; should be 'cuda' -> Option D

4. Quick Check:

   Use 'cuda' not 'gpu' in to() [OK]

Hint: Use 'cuda' not 'gpu' as device string [OK]

Common Mistakes:

• Using 'gpu' instead of 'cuda'
• Thinking tensor must be float to move
• Missing .cuda() call after .to()

5. You want to write code that safely moves a tensor x to GPU if available, otherwise keeps it on CPU. Which code snippet correctly does this?

hard

A. x = x.to('cuda') if torch.cuda.is_available() else x.to('gpu')

B. if torch.cuda.is_available():
x = x.to('cuda')
else:
x = x.to('cpu')

C. x = x.cuda() if torch.cuda.is_available() else x.cuda()

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

Solution

1. Step 1: Understand device check logic

   We must check if GPU is available using torch.cuda.is_available().

2. Step 2: Apply correct device move

   If GPU is available, move tensor to 'cuda', else keep on 'cpu'. if torch.cuda.is_available(): x = x.to('cuda') else: x = x.to('cpu') does this correctly.

3. Final Answer:

   if torch.cuda.is_available(): x = x.to('cuda') else: x = x.to('cpu') -> Option B

4. Quick Check:

   Check GPU availability before moving tensor [OK]

Hint: Check cuda availability before moving tensor [OK]

Common Mistakes:

• Using invalid device string 'gpu'
• Moving to cuda even if unavailable
• Swapping cpu and cuda in else