Examples

This section contains practical examples showing how to use Mammoth Lite for continual learning research. Each example includes both conceptual explanations and runnable code.

Basic Examples

• Basic Usage
  • Learning Objectives
  • Core Functions Overview
  • Exploring Available Arguments
  • Loading Models and Datasets
  • Running the Training Process
  • Understanding the Results
  • Complete Example Script
  • Variations to Try
  • Next Steps

Creating Custom Components

• Creating Custom Models
  • Learning Objectives
  • Understanding Continual Learning Models
  • Creating a Simple Custom Model
  • Testing Your Custom Model
  • Advanced Custom Model Example
  • Next Steps
• Creating Custom Datasets
  • Learning Objectives
  • Understanding Dataset Architecture
  • Data Source Requirements
  • Creating a Custom Data Source
  • Creating a Custom Continual Dataset
  • Testing Your Custom Dataset
  • Data Augmentation Strategies
  • Validation and Testing
  • Next Steps
• Creating Custom Backbones
  • Learning Objectives
  • Understanding Backbone Architecture
  • Backbone Requirements
  • Creating a Simple Custom Backbone
  • Registering Backbone Variants
  • Using Custom Backbones
  • Testing and Validation
  • Next Steps

Running the Examples

Jupyter Notebooks

All examples are available as interactive Jupyter notebooks in the examples/notebooks/ directory.

Python Scripts

You can also run the examples as standalone Python scripts.

Online Viewing

Examples can be viewed directly in the documentation without running code.

Getting Started

If this is your first time using Mammoth Lite, start with:

1. Basic Usage - Learn the fundamental API and workflow

2. Creating Custom Models - Create your first custom continual learning algorithm

Prerequisites

To run the examples, you need:

• Mammoth Lite installed (see Installation)

• Basic Python knowledge

• Understanding of PyTorch fundamentals

• Familiarity with machine learning concepts

Some advanced examples may require additional packages that will be noted in the individual example descriptions.