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Course Outline
Introduction
Overview of ParlAI Features and Architecture
- ParlAI framework.
- Key capabilities and goals.
- Core concepts (agents, messages, teachers, and worlds).
Getting Started with ParlAI for Conversational AI
- Installation.
- Adding a simple model.
- Simple display data script.
- Validation and testing.
- Tasks.
- Agent training and evaluation.
- Interacting with models.
Working with Tasks and Datasets in ParlAI
- Adding datasets.
- Separating data into sets (train, valid, or test).
- Using JSON instead of a text file.
- Creating and executing tasks.
Exploring Worlds, Sharing, and Batching
- The concept of Worlds.
- Agent sharing.
- Implementing batching.
- Dynamic batching.
Using Torch Generator and Ranker Agents
- Torch generator agent.
- Torch ranker agent.
- Example models.
- Creating models.
- Training and evaluating models.
Adding Built-In and Custom Metrics
- Standard metrics.
- Adding custom metrics.
- Teacher metrics.
- Agent level metrics (global and local).
- List of metrics.
Speeding up Training Runs in ParlAI
- Setting a baseline.
- Skip generation command.
- Dynamic batching training command.
- Using FP16 and multiple GPUs.
- Background preprocessing.
Exploring Other ParlAI Topics
- Using and writing mutators.
- Running crowdsourcing tasks.
- Using existing chat services.
- Swapping out transformer subcomponents.
- Running and writing tests.
- ParlAI tips and tricks.
Troubleshooting
Summary and Conclusion
Requirements
- Knowledge of Python or other programming languages.
- General understanding of artificial intelligence (AI) concepts.
Audience
- Researchers.
- Developers.
14 Hours
