Why distributed training handles large models in PyTorch - Test Your Understanding

Practice - 5 Tasks

1fill in blank

easy

Complete the code to import the PyTorch distributed package.

PyTorch

import torch
import torch.[1] as dist

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Adistributed

Bdistribute

Cdistutils

Ddistlib

Attempts:

3 left

💡 Hint

Common Mistakes

Using 'distribute' instead of 'distributed'

Confusing with unrelated modules like 'distutils'

The correct module for distributed training in PyTorch is torch.distributed.

2fill in blank

medium

Complete the code to initialize the distributed process group.

PyTorch

dist.init_process_group(backend=[1], init_method='env://')

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A'nccl'

B'mpi'

C'gloo'

D'cuda'

Attempts:

3 left

💡 Hint

Common Mistakes

Using 'gloo' which is CPU-based

Using 'cuda' which is not a valid backend string

'nccl' is the recommended backend for distributed training on GPUs in PyTorch.

3fill in blank

hard

Fix the error in the code to wrap the model for distributed training.

PyTorch

model = torch.nn.[1](model)

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AParallelDataDistributed

BDataParallel

CDistributedDataParallel

DDistributedParallelData

Attempts:

3 left

💡 Hint

Common Mistakes

Using 'DataParallel' which is not for distributed multi-node training

Misspelling the class name

The correct wrapper for distributed training in PyTorch is DistributedDataParallel.

4fill in blank

hard

Fill both blanks to create a distributed sampler and data loader for training.

PyTorch

train_sampler = torch.utils.data.[1](dataset, num_replicas=dist.get_world_size(), rank=dist.get_rank())
train_loader = torch.utils.data.DataLoader(dataset, batch_size=32, sampler=[2])

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ADistributedSampler

BRandomSampler

Ctrain_sampler

DSequentialSampler

Attempts:

3 left

💡 Hint

Common Mistakes

Using 'RandomSampler' which does not split data across processes

Passing wrong sampler variable to DataLoader

Use DistributedSampler to split data across processes and pass it as the sampler to the DataLoader.

5fill in blank

hard

Fill all three blanks to perform a training step with distributed training.

PyTorch

optimizer.zero_grad()
outputs = model(inputs)
loss = criterion(outputs, labels)
loss.[1]()
optimizer.[2]()
dist.barrier()  # [3] synchronization

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Abackward

Bstep

Censures all processes wait for each other

Dzero_grad

Attempts:

3 left

💡 Hint

Common Mistakes

Calling optimizer.zero_grad() instead of optimizer.step()

Missing loss.backward() call

Not synchronizing processes with dist.barrier()

Call loss.backward() to compute gradients, optimizer.step() to update weights, and dist.barrier() to synchronize all processes.