Data parallelism vs model parallelism in MLOps - Hands-On Comparison

Data Parallelism vs Model Parallelism in MLOps

📖 Scenario: You are working on a machine learning project where you want to speed up training by splitting the work across multiple devices. There are two main ways to do this: data parallelism and model parallelism.Data parallelism means copying the whole model on each device and splitting the data among them. Model parallelism means splitting the model itself across devices.

🎯 Goal: Build a simple Python example to show how data parallelism and model parallelism can be represented using lists and dictionaries. You will create data batches, define model parts, and then combine results to understand the difference.

📋 What You'll Learn

Create a list of data batches

Create a dictionary representing model parts

Use a loop to simulate processing data batches with model parts

Print the combined results

💡 Why This Matters

🌍 Real World

In machine learning projects, splitting data or models across devices helps speed up training and handle large models or datasets.

💼 Career

Understanding data and model parallelism is important for MLOps engineers to optimize resource use and reduce training time.

1

Create data batches for parallel processing

Create a list called data_batches with these exact values: ["batch1", "batch2", "batch3"]

MLOps

# Create a list called data_batches with ['batch1', 'batch2', 'batch3']
# Your code here

Need a hint?

Think of data_batches as small pieces of your training data split for parallel work.

2

Define model parts for model parallelism

Create a dictionary called model_parts with these exact entries: {"part1": "layerA", "part2": "layerB"}

MLOps

data_batches = ["batch1", "batch2", "batch3"]
# Create a dictionary called model_parts with {'part1': 'layerA', 'part2': 'layerB'}
# Your code here

Need a hint?

Model parts represent splitting the model into pieces to run on different devices.

3

Simulate processing data batches with model parts

Use a for loop with variables batch and part to iterate over data_batches and model_parts.values(). Inside the loop, create a list called results and append strings combining batch and part separated by a dash.

MLOps

data_batches = ["batch1", "batch2", "batch3"]
model_parts = {"part1": "layerA", "part2": "layerB"}
# Create an empty list called results
# Use nested for loops to combine each batch with each model part
# Append combined strings like 'batch1-layerA' to results
# Your code here

Need a hint?

This simulates how data batches are processed by different parts of the model.

4

Print the combined processing results

Write print(results) to display the list of combined batch and model part strings.

MLOps

data_batches = ["batch1", "batch2", "batch3"]
model_parts = {"part1": "layerA", "part2": "layerB"}
results = []
for batch in data_batches:
    for part in model_parts.values():
        results.append(f"{batch}-{part}")
# Print the results list
# Your code here

Need a hint?

This output shows how each data batch is combined with each model part, illustrating parallelism.