Distributed training basics in MLOps - Mini Project: Build & Apply

Distributed training basics

📖 Scenario: You are working on a machine learning project that needs to train a model faster by using multiple machines. This is called distributed training. You will create a simple setup to simulate how training data is split and processed across different workers.

🎯 Goal: Build a basic Python script that simulates splitting training data across multiple workers, processes each part, and then combines the results. This will help you understand the core idea of distributed training.

📋 What You'll Learn

Create a list of training data samples

Define the number of workers to split the data

Split the data evenly among workers

Simulate processing each worker's data by doubling the values

Combine and print the processed results

💡 Why This Matters

🌍 Real World

Distributed training helps machine learning models learn faster by sharing the work across multiple machines or processors.

💼 Career

Understanding distributed training basics is important for roles in machine learning operations (MLOps), data engineering, and AI development where scaling training is common.

1

Create training data samples

Create a list called training_data with these exact integer values: 10, 20, 30, 40, 50, 60, 70, 80.

MLOps

# Create the list training_data with the exact values
# Your code here

Need a hint?

Use square brackets to create a list and separate values with commas.

2

Set number of workers

Create a variable called num_workers and set it to 4 to represent four workers for distributed training.

MLOps

training_data = [10, 20, 30, 40, 50, 60, 70, 80]
# Set num_workers to 4
# Your code here

Need a hint?

Just assign the number 4 to the variable num_workers.

3

Split and process data per worker

Create a list called processed_parts that contains the processed data for each worker. Split training_data evenly into num_workers parts. For each part, create a new list where each number is doubled (multiplied by 2). Use a for loop with the variable i to iterate over the range of num_workers.

MLOps

training_data = [10, 20, 30, 40, 50, 60, 70, 80]
num_workers = 4
# Split training_data into equal parts and double each number in each part
# Your code here

Need a hint?

Calculate part_size by dividing the length of training_data by num_workers. Use slicing to get each part. Use a list comprehension to double each number.

4

Combine and print processed results

Create a list called combined_results by joining all lists inside processed_parts into one list. Then print combined_results.

MLOps

training_data = [10, 20, 30, 40, 50, 60, 70, 80]
num_workers = 4
part_size = len(training_data) // num_workers
processed_parts = []
for i in range(num_workers):
    part = training_data[i * part_size:(i + 1) * part_size]
    processed_part = [x * 2 for x in part]
    processed_parts.append(processed_part)
# Combine all processed parts into one list and print it
# Your code here

Need a hint?

Use a loop to add each processed part to combined_results. Then print combined_results.