Why PyTorch is preferred for research and production - Test Your Understanding

Practice - 5 Tasks

1fill in blank

easy

Complete the code to create a tensor in PyTorch.

PyTorch

import torch
x = torch.[1]([1, 2, 3])

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

Amatrix

Barray

Clist

Dtensor

Attempts:

3 left

💡 Hint

Common Mistakes

Using 'array' instead of 'tensor' causes an error.

In PyTorch, torch.tensor creates a tensor from a list or array.

2fill in blank

medium

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

PyTorch

device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
x = x.[1](device)

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

Amove

Bto

Ccuda

Dtransfer

Attempts:

3 left

💡 Hint

Common Mistakes

Using 'cuda' directly on tensor causes error if GPU not available.

The to method moves tensors to the specified device, like GPU or CPU.

3fill in blank

hard

Fix the error in the code to perform a forward pass in a PyTorch model.

PyTorch

output = model.[1](input_tensor)

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

Aforward

Brun

Cpredict

Dcall

Attempts:

3 left

💡 Hint

Common Mistakes

Using 'predict' causes attribute error.

In PyTorch, the forward method defines the model's computation. Calling model(input) runs forward internally, but explicitly calling forward is correct here.

4fill in blank

hard

Fill both blanks to create a dictionary comprehension that maps words to their lengths if length is greater than 3.

PyTorch

{word: [1] for word in words if len(word) [2] 3}

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

Alen(word)

B<

C>

Dword

Attempts:

3 left

💡 Hint

Common Mistakes

Using '<' instead of '>' changes the filter logic.

The dictionary comprehension maps each word to its length using len(word). The condition filters words with length greater than 3 using >.

5fill in blank

hard

Fill all three blanks to create a dictionary comprehension that maps uppercase words to their values if the value is positive.

PyTorch

result = [1]: [2] for k, v in data.items() if v [3] 0}

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

Ak.upper()

Bv

C>

Dk

Attempts:

3 left

💡 Hint

Common Mistakes

Using 'k' instead of 'k.upper()' misses uppercase conversion.

The comprehension maps uppercase keys k.upper() to their values v only if the value is greater than zero >.

Practice

1. Why is PyTorch often preferred by researchers for developing new machine learning models?

easy

A. Because it offers dynamic computation graphs that are easy to modify during runtime

B. Because it only supports static computation graphs which are faster

C. Because it requires no coding and works automatically

D. Because it is only designed for production, not research

Solution

1. Step 1: Understand computation graph types

   Dynamic graphs allow changes during execution, while static graphs are fixed before running.

2. Step 2: Relate to PyTorch's feature

   PyTorch uses dynamic graphs, making it flexible for experimenting and debugging models.

3. Final Answer:

   Because it offers dynamic computation graphs that are easy to modify during runtime -> Option A

4. Quick Check:

   Dynamic graphs = flexibility for research [OK]

Hint: Dynamic graphs mean easy model changes during research [OK]

Common Mistakes:

• Confusing dynamic with static graphs
• Thinking PyTorch has no coding
• Believing PyTorch is only for production

2. Which of the following is the correct way to import PyTorch in a Python script?

easy

A. from torch import pytorch

B. import torch

C. import pytorch

D. import torch as ptch

Solution

1. Step 1: Recall PyTorch import syntax

   The official PyTorch package is imported using 'import torch'.

2. Step 2: Check other options

   'pytorch' is not a valid package name, and aliasing as 'ptch' is uncommon and not standard.

3. Final Answer:

   import torch -> Option B

4. Quick Check:

   Standard import = import torch [OK]

Hint: PyTorch is imported as 'torch' in Python [OK]

Common Mistakes:

• Using 'import pytorch' instead of 'import torch'
• Trying to import submodules incorrectly
• Using non-standard aliases without explanation

3. What will be the output of this PyTorch code snippet?

import torch
x = torch.tensor([1.0, 2.0, 3.0], requires_grad=True)
y = x * 2
y.backward(torch.tensor([1.0, 1.0, 1.0]))
print(x.grad)

medium

A. tensor([0., 0., 0.])

B. tensor([1., 1., 1.])

C. tensor([2., 2., 2.])

D. Error: backward() argument must be a scalar

Solution

1. Step 1: Understand the computation and gradient

   y = x * 2 means dy/dx = 2 for each element.

2. Step 2: Apply backward with gradient vector

   Passing tensor([1.,1.,1.]) as gradient sums gradients element-wise, so x.grad becomes tensor([2.,2.,2.]).

3. Final Answer:

   tensor([2., 2., 2.]) -> Option C

4. Quick Check:

   Gradient of y=2x is 2 [OK]

Hint: Gradient of x*2 is 2, so grads are all 2 [OK]

Common Mistakes:

• Expecting scalar backward without gradient argument
• Confusing gradient values with inputs
• Ignoring requires_grad=True

4. Identify the error in this PyTorch code snippet and choose the correct fix:

import torch
x = torch.tensor([1, 2, 3])
y = x + 2.5
print(y)

medium

A. No error; code runs fine

B. Error because tensor must be on GPU; fix by moving x to cuda

C. Error because + operator is not supported; fix by using torch.add()

D. Error due to mixing int tensor with float; fix by defining x as float tensor

Solution

1. Step 1: Analyze tensor and scalar types

   x is integer tensor, 2.5 is float scalar; adding causes type mismatch error.

2. Step 2: Fix by converting x to float tensor

   Define x as torch.tensor([1,2,3], dtype=torch.float) to allow float addition.

3. Final Answer:

   Error due to mixing int tensor with float; fix by defining x as float tensor -> Option D

4. Quick Check:

   Int tensor + float scalar needs float tensor [OK]

Hint: Match tensor dtype with scalar type to avoid errors [OK]

Common Mistakes:

• Ignoring dtype mismatch errors
• Thinking + operator is unsupported
• Assuming GPU is required for basic ops

5. You want to train a PyTorch model on GPU for faster results but also keep it easy to switch to CPU for debugging. Which PyTorch feature helps you do this smoothly?

hard

A. Using .to(device) method to move tensors and models between CPU and GPU

B. Writing separate code for CPU and GPU versions

C. Using only CPU tensors because GPU is complex

D. Manually copying data with numpy arrays

Solution

1. Step 1: Understand device management in PyTorch

   PyTorch provides .to(device) to move tensors/models between CPU and GPU easily.

2. Step 2: Why .to(device) is preferred

   This method allows writing one codebase that works on both devices by changing device variable.

3. Final Answer:

   Using .to(device) method to move tensors and models between CPU and GPU -> Option A

4. Quick Check:

   .to(device) enables flexible device switching [OK]

Hint: Use .to(device) for easy CPU/GPU switching [OK]

Common Mistakes:

• Writing separate code for CPU and GPU
• Avoiding GPU due to complexity
• Using numpy instead of PyTorch device methods