Distributed training basics in MLOps - Interactive Code Practice

Practice - 5 Tasks

1fill in blank

easy

Complete the code to initialize the distributed training environment using PyTorch.

MLOps

import torch.distributed as dist

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

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Ampi

Bnccl

Cgloo

Dtcp

Attempts:

3 left

💡 Hint

Common Mistakes

Using 'gloo' backend on GPU clusters causes slower communication.

The nccl backend is optimized for NVIDIA GPUs and is commonly used for distributed training on GPU clusters.

2fill in blank

medium

Complete the code to wrap the model for distributed training in PyTorch.

MLOps

import torch.nn as nn
import torch.distributed as dist

model = nn.Linear(10, 2)
model = [1](model)

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Ann.parallel.DistributedDataParallel

Bnn.Sequential

Cnn.ModuleList

Dnn.DataParallel

Attempts:

3 left

💡 Hint

Common Mistakes

Using DataParallel instead of DistributedDataParallel for multi-node training.

DistributedDataParallel wraps the model to synchronize gradients across multiple processes during distributed training.

3fill in blank

hard

Fix the error in the code to correctly set the device for distributed training.

MLOps

import torch
import os

local_rank = int(os.environ['LOCAL_RANK'])
torch.cuda.set_device([1])

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A0

Brank

Clocal_rank

Ddevice

Attempts:

3 left

💡 Hint

Common Mistakes

Hardcoding device 0 causes all processes to use the same GPU.

Setting the CUDA device to local_rank ensures each process uses the correct GPU.

4fill in blank

hard

Fill both blanks to create a distributed sampler for the training dataset.

MLOps

from torch.utils.data import DataLoader, [1]

train_sampler = [2](dataset, num_replicas=world_size, rank=rank)

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ADistributedSampler

BRandomSampler

DSequentialSampler

Attempts:

3 left

💡 Hint

Common Mistakes

Using RandomSampler causes data overlap between processes.

DistributedSampler ensures each process gets a unique subset of the dataset during distributed training.

5fill in blank

hard

Fill all three blanks to correctly configure the DataLoader for distributed training.

MLOps

train_loader = DataLoader(dataset, batch_size=[1], sampler=[2], shuffle=[3])

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A64

Btrain_sampler

CFalse

DTrue

Attempts:

3 left

💡 Hint

Common Mistakes

Setting shuffle to True with a sampler causes errors.

When using a sampler, shuffle should be set to False because the sampler controls data shuffling.