Data parallelism vs model parallelism in MLOps - CLI Comparison

Introduction

When training large machine learning models, it can take a long time and use a lot of computer power. Data parallelism and model parallelism are two ways to split the work across multiple computers or processors to make training faster and more efficient.

When your dataset is very large and you want to split it across multiple GPUs to train faster.

When your model is too big to fit into the memory of a single GPU and needs to be split across multiple GPUs.

When you want to reduce training time by using multiple processors working together.

When you want to scale your training to use cloud resources efficiently.

When debugging how your model behaves when split across devices.

Commands

This Python code shows how to use data parallelism with PyTorch. It splits the input data batches across multiple GPUs to speed up training. The model is wrapped with nn.DataParallel to do this automatically.

Terminal

import torch
import torch.nn as nn
import torch.optim as optim
from torch.utils.data import DataLoader, TensorDataset

# Create a simple model
class SimpleModel(nn.Module):
    def __init__(self):
        super(SimpleModel, self).__init__()
        self.linear = nn.Linear(10, 5)
    def forward(self, x):
        return self.linear(x)

# Create dummy data
data = torch.randn(100, 10)
labels = torch.randn(100, 5)
dataset = TensorDataset(data, labels)
dataloader = DataLoader(dataset, batch_size=10)

# Initialize model and optimizer
model = SimpleModel()
optimizer = optim.SGD(model.parameters(), lr=0.01)

# Use Data Parallelism
model = nn.DataParallel(model)

# Training loop
for inputs, targets in dataloader:
    optimizer.zero_grad()
    outputs = model(inputs)
    loss = nn.MSELoss()(outputs, targets)
    loss.backward()
    optimizer.step()

print('Training with data parallelism complete')

Expected Output

Training with data parallelism complete

This code shows model parallelism by splitting parts of the model across two GPUs manually. The first layer runs on GPU 0 and the second on GPU 1. Data moves between GPUs during forward pass.

Terminal

import torch
import torch.nn as nn
import torch.optim as optim

# Define a model split across two GPUs
class ModelParallel(nn.Module):
    def __init__(self):
        super(ModelParallel, self).__init__()
        self.part1 = nn.Linear(10, 20).to('cuda:0')
        self.part2 = nn.Linear(20, 5).to('cuda:1')
    def forward(self, x):
        x = self.part1(x.to('cuda:0'))
        return self.part2(x.to('cuda:1'))

model = ModelParallel()
optimizer = optim.SGD(model.parameters(), lr=0.01)

inputs = torch.randn(10, 10)
labels = torch.randn(10, 5).to('cuda:1')

optimizer.zero_grad()
outputs = model(inputs)
loss = nn.MSELoss()(outputs, labels)
loss.backward()
optimizer.step()

print('Training with model parallelism complete')

Expected Output

Training with model parallelism complete

Key Concept

If you remember nothing else, remember: data parallelism splits the data across devices, while model parallelism splits the model itself across devices.

Code Example

MLOps

import torch
import torch.nn as nn
import torch.optim as optim
from torch.utils.data import DataLoader, TensorDataset

# Simple model
class SimpleModel(nn.Module):
    def __init__(self):
        super(SimpleModel, self).__init__()
        self.linear = nn.Linear(10, 5)
    def forward(self, x):
        return self.linear(x)

# Dummy data
data = torch.randn(100, 10)
labels = torch.randn(100, 5)
dataset = TensorDataset(data, labels)
dataloader = DataLoader(dataset, batch_size=10)

# Initialize model and optimizer
model = SimpleModel()
optimizer = optim.SGD(model.parameters(), lr=0.01)

# Use Data Parallelism if multiple GPUs available
if torch.cuda.device_count() > 1:
    model = nn.DataParallel(model)

model.to('cuda' if torch.cuda.is_available() else 'cpu')

# Training loop
for inputs, targets in dataloader:
    inputs = inputs.to('cuda' if torch.cuda.is_available() else 'cpu')
    targets = targets.to('cuda' if torch.cuda.is_available() else 'cpu')
    optimizer.zero_grad()
    outputs = model(inputs)
    loss = nn.MSELoss()(outputs, targets)
    loss.backward()
    optimizer.step()

print('Training complete')

Common Mistakes

Trying to use data parallelism when the model is too large to fit on one GPU.

Data parallelism requires the whole model to fit on each device, so it fails if the model is too big.

Use model parallelism to split the model across multiple GPUs instead.

Not moving inputs and outputs to the correct devices in model parallelism.

If tensors are not on the right GPU, the code will raise errors or run slowly due to data transfer overhead.

Explicitly move tensors to the correct device before computation.

Assuming data parallelism automatically speeds up training without checking GPU availability.

If only one GPU is available, data parallelism adds overhead without speed benefit.

Check the number of GPUs and use data parallelism only if multiple GPUs are present.

Summary

Data parallelism splits input data batches across multiple devices to speed up training when the model fits on each device.

Model parallelism splits the model itself across devices to handle models too large for one device.

Use data parallelism by wrapping the model with nn.DataParallel and model parallelism by manually assigning model parts to devices.

Practice

1. What is the main difference between data parallelism and model parallelism in machine learning training?

easy

A. Data parallelism splits the data across workers, while model parallelism splits the model across workers.

B. Data parallelism splits the model across workers, while model parallelism splits the data across workers.

C. Data parallelism uses only one worker, model parallelism uses multiple workers.

D. Data parallelism trains different models, model parallelism trains the same model multiple times.

Solution

1. Step 1: Understand data parallelism

   Data parallelism means dividing the input data into parts and sending each part to a different worker. Each worker runs the full model on its data part.

2. Step 2: Understand model parallelism

   Model parallelism means splitting the model itself into parts and assigning each part to a different worker. The data flows through these parts sequentially.

3. Final Answer:

   Data parallelism splits the data across workers, while model parallelism splits the model across workers. -> Option A

4. Quick Check:

   Data vs Model split [OK]

Hint: Data parallelism splits data; model parallelism splits model [OK]

Common Mistakes:

• Confusing which is split: data or model
• Thinking both split data only
• Assuming model parallelism uses one worker

2. Which of the following is the correct way to describe data parallelism in a distributed training setup?

easy

A. The data is duplicated on one worker and processed sequentially.

B. Each worker trains a different part of the model on the full dataset.

C. The model is split into layers, each trained by a different worker on the full data.

D. Each worker trains the full model on a subset of the data.

Solution

1. Step 1: Analyze data parallelism setup

   In data parallelism, the full model is copied to each worker. Each worker trains on a different subset of the data.

2. Step 2: Evaluate options

   Each worker trains the full model on a subset of the data. correctly states that each worker trains the full model on a subset of data. Other options describe model splitting or incorrect data handling.

3. Final Answer:

   Each worker trains the full model on a subset of the data. -> Option D

4. Quick Check:

   Full model + data subset [OK]

Hint: Data parallelism = full model per worker, split data [OK]

Common Mistakes:

• Thinking model is split in data parallelism
• Assuming data is duplicated on one worker
• Confusing model layers with data chunks

3. Consider a model split into 3 parts for model parallelism across 3 workers. If input data batch size is 90, how is the data processed?

medium

A. Each worker processes 30 data samples independently on the full model.

B. All 90 samples flow sequentially through the 3 model parts on different workers.

C. Each worker processes all 90 samples on its model part independently.

D. The data is split into 3 parts, each processed by a different worker on the full model.

Solution

1. Step 1: Understand model parallelism data flow

   In model parallelism, the model is split into parts on different workers. The full data batch flows through these parts sequentially.

2. Step 2: Analyze data processing

   All 90 samples pass through the first model part on worker 1, then output flows to worker 2's model part, and so on.

3. Final Answer:

   All 90 samples flow sequentially through the 3 model parts on different workers. -> Option B

4. Quick Check:

   Model split, data flows through [OK]

Hint: Model parallelism splits model; data flows through all parts [OK]

Common Mistakes:

• Assuming data is split in model parallelism
• Thinking each worker processes full data independently
• Confusing data parallelism with model parallelism

4. You tried to implement model parallelism but noticed workers are idle waiting for data. What is the likely cause?

medium

A. Model parts are not connected properly causing data flow delays.

B. Data is not being split correctly across workers.

C. Each worker is running the full model on the full data.

D. Data parallelism was used instead of model parallelism.

Solution

1. Step 1: Identify symptoms of idle workers in model parallelism

   Idle workers waiting for data usually mean data flow between model parts is blocked or delayed.

2. Step 2: Analyze model part connections

   If model parts are not connected properly, data cannot flow smoothly, causing some workers to wait.

3. Final Answer:

   Model parts are not connected properly causing data flow delays. -> Option A

4. Quick Check:

   Idle workers = broken model part connections [OK]

Hint: Idle workers? Check model part connections in model parallelism [OK]

Common Mistakes:

• Blaming data splitting in model parallelism
• Confusing full model runs with model splitting
• Mixing up data and model parallelism issues

5. You have a very large model that does not fit into one GPU memory. Which approach is best to train it efficiently?

hard

A. Use data parallelism by splitting data across GPUs, each with full model copy.

B. Train the model on CPU only to avoid GPU memory limits.

C. Use model parallelism by splitting the model across GPUs, each handling part of the model.

D. Reduce batch size and train on a single GPU.

Solution

1. Step 1: Understand GPU memory limits

   If the model is too large to fit in one GPU, copying full model to each GPU (data parallelism) is not possible.

2. Step 2: Choose model parallelism

   Splitting the model across GPUs allows each GPU to hold only a part of the model, enabling training of large models.

3. Final Answer:

   Use model parallelism by splitting the model across GPUs, each handling part of the model. -> Option C

4. Quick Check:

   Large model fits by splitting model [OK]

Hint: Large model? Split model across GPUs (model parallelism) [OK]

Common Mistakes:

• Trying data parallelism with too large model
• Ignoring GPU memory limits
• Reducing batch size instead of splitting model