Description

Introduction

Distributed Training and Model Parallelism in SageMaker is a specialized course designed for ML practitioners working with large datasets and complex models that cannot be trained efficiently on a single instance. The course explores SageMaker’s capabilities for horizontal scaling, including data parallelism, model parallelism, and multi-GPU/multi-node training. You will learn how to leverage SageMaker’s built-in libraries, such as SageMaker Distributed Data Parallel and Model Parallel, to accelerate training and manage computational resources effectively.

Prerequisites

Participants should have:

• Strong understanding of machine learning model training.

• Familiarity with AWS SageMaker and Jupyter Notebooks.

• Experience with ML frameworks like TensorFlow or PyTorch.

• Basic knowledge of distributed computing concepts.

Table of Contents

1. Understanding Distributed Training

   • 1.1 What is Distributed Training?

   • 1.2 Use Cases and Performance Gains

   • 1.3 Overview of SageMaker Training Clusters

2. Data Parallelism in SageMaker

   • 2.1 Concepts of Data Parallelism

   • 2.2 Using SageMaker Data Parallel Library

   • 2.3 Setting Up and Running Data Parallel Jobs

3. Model Parallelism in SageMaker

   • 3.1 Introduction to Model Parallelism

   • 3.2 When to Use Model Parallel vs. Data Parallel

   • 3.3 Using SageMaker Model Parallel Library

4. Multi-GPU and Multi-Node Training

   • 4.1 Configuring Training with Multiple GPUs

   • 4.2 Distributed Training Across Multiple Instances

   • 4.3 Best Practices for Efficient Scaling

5. Advanced Configuration and Optimization

   • 5.1 Managing Network Bandwidth and Latency

   • 5.2 Checkpointing and Fault Tolerance

   • 5.3 Monitoring Resource Utilization with SageMaker Profiler

6. Practical Hands-On: Training a Deep Neural Network

   • 6.1 Dataset Preparation and Sharding

   • 6.2 Launching a Distributed Training Job (PyTorch/TensorFlow)

   • 6.3 Debugging and Profiling the Job

7. Cost Optimization Strategies

   • 7.1 Using Spot Instances in Distributed Jobs

   • 7.2 Scaling Down with Elastic Training Jobs

   • 7.3 Benchmarking for Performance vs. Cost Trade-offs

8. Real-World Case Study

   • 8.1 Applying Distributed Training in a Production Scenario

   • 8.2 Lessons Learned and Performance Metrics

   • 8.3 Managing Model Artifacts and Deployment Post-Training

SageMaker’s distributed training features make it possible to train large-scale models faster and more cost-effectively. By mastering both data parallelism and model parallelism, you can tackle compute-heavy deep learning problems, reduce training times, and scale across multiple GPUs and nodes. This knowledge is essential for organizations building high-performance AI systems that demand scalability and speed.