DataParallel basics in PyTorch - Practice Problems & Coding Challenges

Challenge - 5 Problems

🧠 Conceptual

intermediate

Why use DataParallel in PyTorch?

You have a deep learning model and a machine with multiple GPUs. What is the main reason to use DataParallel in PyTorch?

A To reduce the size of the model so it fits into a single GPU memory.

B To split the input data across multiple GPUs and run the model in parallel to speed up training.

C To automatically tune hyperparameters during training.

D To convert the model into a CPU-only version for faster inference.

💡 Hint

Think about how multiple GPUs can be used to handle more data at once.

DataParallel splits the input batch across multiple GPUs, runs the model on each part in parallel, and then combines the results. This speeds up training by using all GPUs efficiently.

❓ Predict Output

intermediate

Output of DataParallel model forward pass

Consider this PyTorch code snippet using DataParallel:

PyTorch

import torch
import torch.nn as nn

class SimpleModel(nn.Module):
    def __init__(self):
        super().__init__()
        self.linear = nn.Linear(2, 1)
    def forward(self, x):
        return self.linear(x)

model = SimpleModel()
model = nn.DataParallel(model)
input_tensor = torch.tensor([[1.0, 2.0], [3.0, 4.0]])
output = model(input_tensor)
print(output.shape)

A RuntimeError due to input on CPU but model on GPU

B torch.Size([1, 2])

C torch.Size([2, 1])

D torch.Size([4, 1])

💡 Hint

DataParallel splits the batch dimension but output shape matches input batch size.

The input has batch size 2 and feature size 2. The linear layer outputs size 1 per input. DataParallel splits the batch but output shape remains (2,1).

❓ Hyperparameter

advanced

Choosing batch size with DataParallel

You want to train a model using DataParallel on 4 GPUs. Your original batch size is 64. What is the best way to set the batch size when using DataParallel?

A Keep batch size 64; DataParallel will split it automatically across GPUs.

B Set batch size to 16 because DataParallel requires batch size per GPU.

C Increase batch size to 256 to fully use all GPUs.

D Set batch size to 1 because DataParallel only supports batch size 1.

💡 Hint

Think about how DataParallel handles the batch dimension internally.

DataParallel automatically splits the batch across GPUs. So you can keep the original batch size, and it will divide it evenly among GPUs.

🔧 Debug

advanced

Why does this DataParallel code raise an error?

Look at this code snippet:

PyTorch

import torch
import torch.nn as nn

model = nn.Linear(10, 5)
model = nn.DataParallel(model)
input_tensor = torch.randn(3, 10).cuda()
output = model(input_tensor)
print(output)

A Raises AttributeError because DataParallel is not imported.

B Runs fine and prints output tensor.

C Raises TypeError because input tensor shape is wrong.

D Raises RuntimeError because model is on CPU but input is on GPU.

💡 Hint

Check where the model parameters are located before wrapping with DataParallel.

The model is created on CPU by default. DataParallel expects the model to be on GPU before wrapping. Input is on GPU, so mismatch causes RuntimeError.

❓ Model Choice

expert

Best model wrapping for multi-GPU training

You have a complex model with custom layers and want to train on multiple GPUs. Which approach is best to ensure correct gradient updates and efficient training?

A Use nn.parallel.DistributedDataParallel with proper setup for multi-GPU training.

B Train the model on CPU and manually split data across GPUs.

C Wrap the model with nn.DataParallel before moving it to GPU.

D Wrap the model with nn.DataParallel after moving it to GPU.

💡 Hint

Consider scalability and correctness for multi-GPU training.

DistributedDataParallel is recommended for multi-GPU training because it handles gradient synchronization efficiently and avoids common pitfalls of DataParallel.