Building Language Models on AWS

Description

In this course, you will learn:

• Apply best practices for storing and digesting a huge volume of text data to support distributed learning.
• Investigate data parallelism and model parallelism libraries to facilitate distributed training in SageMaker.
• Explain SageMaker's choices for improving training performance, such as Amazon SageMaker Training Compiler and Elastic Fabric Adapter (EFA).
• Investigate large language model (LLM) optimization methods for efficient model deployment.
• Show how to fine-tune the core models accessible in SageMaker. Jumpstart

Syllabus:

1. Addressing the Challenges of Building Language Models

• Section 1: Common Challenges
  • Common LLM Practitioner Challenges
• Section 2: Multi-Machine Training Solutions
  • Scaling LLMs with Distributed Training
  • Applying Data Parallelism Techniques
  • Applying Model Parallelism Techniques
• Section 3: Performance Optimization Solutions
  • Performance Optimization Techniques
  • Using Purpose-Built Infrastructure

2. Using Amazon SageMaker for Training Language Models

• Section 1: Configuring SageMaker Studio
  • SageMaker Basics
  • Setting up a SageMaker Studio Domain
• Section 2: SageMaker Infrastructure
  • Choosing Compute Instance Types
• Section 3: Working with the SageMaker Python SDK
  • SageMaker Python SDK Basics
  • Training and Deploying Language Models with the SageMaker Python SDK

3. Ingesting Language Model Data

• Section 1: Preparing Data
  • Data Management Overview
  • Preparing Data for Ingestion
• Section 2: Analyzing Data Ingestion Options
  • Loading Data with the SageMaker Python SDK
  • Ingesting Data from Amazon S3
  • Ingesting Data with FSx for Lustre
  • Additional Data Ingestion Options
  • Data Ingestion and Storage Considerations

4. Training Large Language Models

• Section 1: Creating a SageMaker Training Job
  • Launching SageMaker Training Jobs
  • Modifying Scripts for Script Mode
• Section 2: Optimizing Your SageMaker Training Job
  • Monitoring and Troubleshooting
  • Optimizing Computational Performance
  • SageMaker Training Features for Language Model Training
• Section 3: Using Distributed Training on SageMaker
  • SageMaker Distributed Training Support
  • Using the SageMaker Distributed Data Parallel Library
  • Using the SageMaker Model Parallel Library
  • Using the SageMaker Model Parallel Library and Sharded Data Parallelism
  • Training with the EFA
• Section 4: Compiling Your Training Code
  • Using the SageMaker Training Compiler

5. Deploying Language Models

• Section 1: Deploying a Model in SageMaker
  • Introduction to SageMaker Deployment
  • Choosing a SageMaker Deployment Option
• Section 2: Deploying Models for Inference
  • Real-Time Inference Overview
  • Using the SageMaker Python SDK for Model Deployment
  • Using the SageMaker Inference Recommender
• Section 3: Deploying Large Language Models for Inference
  • Optimization Techniques
  • Model Compression Techniques
  • Model Partitioning
• Optimized Kernels and Compilation
  • Deploying with SageMaker LMI Containers
• Section 4: Additional Considerations
  • Other Considerations When Deploying Models on SageMaker

6. Customizing Foundation Language Models for Generative AI Tasks

• Section 1: Introduction
  • Introduction to Foundation Models
• Section 2: Using SageMaker JumpStart
  • Getting Started with SageMaker JumpStart
  • Deploying SageMaker JumpStart Models with the SageMaker Python SDK
  • Selecting an FM
• Section 3: Customizing FMs
  • Prompt Engineering
  • Fine-tune JumpStart Models with the SageMaker Python SDK
• Section 4: Retrieval Augmented Generation (RAG)
  • Using Retrieval Augmented Generation (RAG)

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