Multi-GPU training in PyTorch - Interactive Code Practice

Practice - 5 Tasks

1fill in blank

easy

Complete the code to move the model to the GPU device.

PyTorch

model = MyModel()
model = model.[1]('cuda')

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

Acuda_device

Bcpu

Cto

Ddevice

Attempts:

3 left

💡 Hint

Common Mistakes

Using 'cpu' instead of 'cuda' moves the model to the CPU, not GPU.

Trying to use a non-existent method like 'cuda_device' or 'device'.

Use model.to('cuda') to move the model to the GPU device.

2fill in blank

medium

Complete the code to wrap the model for multi-GPU training.

PyTorch

model = MyModel().to('cuda')
model = torch.nn.[1](model)

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

ADataParallel

BDistributedDataParallel

CParallelData

DMultiGPUWrapper

Attempts:

3 left

💡 Hint

Common Mistakes

Using DistributedDataParallel without setting up distributed environment.

Using made-up wrappers like 'MultiGPUWrapper'.

Use torch.nn.DataParallel to wrap the model for multi-GPU training easily.

3fill in blank

hard

Fix the error in the code to correctly move input data to the GPU.

PyTorch

inputs = inputs.[1]('cuda')

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

Ato

Bmove

Cdevice

Dcuda_device

Attempts:

3 left

💡 Hint

Common Mistakes

Trying to use non-existent methods like 'cuda_device' or 'move'.

Forgetting to move inputs to the same device as the model.

Use inputs.to('cuda') to move the input tensor to the GPU device.

4fill in blank

hard

Fill both blanks to create a dictionary comprehension that stores squared values for even numbers only.

PyTorch

squares = {x: x[1]2 for x in range(1, 11) if x [2] 2 == 0}

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

A**

B%

C//

D+

Attempts:

3 left

💡 Hint

Common Mistakes

Using // instead of % for checking even numbers.

Using + instead of ** for squaring.

The operator ** is used for power, and % is the modulo operator to check even numbers.

5fill in blank

hard

Fill all three blanks to create a filtered dictionary with uppercase keys and values greater than zero.

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.lower()

Attempts:

3 left

💡 Hint

Common Mistakes

Using k.lower() instead of k.upper().

Using < instead of > for filtering.

Use k.upper() to uppercase keys, keep values as v, and filter with v > 0.