Task programming

This section covers the essential programming patterns and techniques for developing robust Flyte workflows. Once you understand the basics of task configuration, these guides will help you build sophisticated, production-ready data pipelines and machine learning workflows.

What you’ll learn

The task programming section covers key patterns for building effective Flyte workflows:

Data handling and types

• Files and directories: Work with large datasets using Flyte’s efficient file and directory types that automatically handle data upload, storage, and transfer between tasks.
• Dataclasses and structures: Use Python dataclasses and Pydantic models as task inputs and outputs to create well-structured, type-safe workflows.

Execution patterns

• Fanout: Scale your workflows by running many tasks in parallel, perfect for processing large datasets or running hyperparameter sweeps.
• Grouping actions: Organize related task executions into logical groups for better visualization and management in the UI.

Development and debugging

• Notebooks: Write and iterate on workflows directly in Jupyter notebooks for interactive development and experimentation.
• Reports: Generate custom HTML reports during task execution to display progress, results, and visualizations in the UI.
• Traces: Add fine-grained observability to helper functions within your tasks for better debugging and resumption capabilities.
• Error handling: Implement robust error recovery strategies, including automatic resource scaling and graceful failure handling.

When to use these patterns

These programming patterns become essential as your workflows grow in complexity:

• Use fanout when you need to process multiple items concurrently or run parameter sweeps.
• Implement error handling for production workflows that need to recover from infrastructure failures.
• Apply grouping to organize complex workflows with many task executions.
• Leverage files and directories when working with large datasets that don’t fit in memory.
• Use traces to debug non-deterministic operations like API calls or ML inference.
• Create reports to monitor long-running workflows and share results with stakeholders.

Each guide includes practical examples and best practices to help you implement these patterns effectively in your own workflows.