Release-v0.2.0

New Features

1. Ray Integration: Successfully implemented Ray for distributed task execution.
2. RayExecutor Class: Designed a RayExecutor class that adheres to the Future interface, enabling asynchronous task execution.
3. Optional Ray Dependency Handling: Provided optional handling for Ray dependencies, ensuring functionality in environments without Ray.

Example Code

1. Comprehensive example code has been included to demonstrate how to use the RayExecutor for distributed task management.

Testing

1. Added test cases for the RayExecutor to ensure functionality correctness and comprehensive error handling.

Documentation Updates

1. Updated the README.md to detail the new features and usage instructions, ensuring users can quickly get started.

Version Update

The package version has been bumped from 0.1.5 to 0.2.0 to reflect this significant feature addition

Release-v0.1.5

Release Notes for Version 0.1.5

Overview

This release introduces several important updates to the Liteflow framework, enhancing its functionality and ensuring compatibility with the latest tools.

Changes

Version Update: The project version has been incremented to 0.1.5, reflecting the latest improvements and fixes.

Testing Framework Update: Updated the development dependencies to include pytest (version >=8.3.3). This ensures that the testing environment is equipped with the latest features and improvements for better test coverage and reliability.

Notes

Ensure to run poetry install to update your environment with the new dependencies.

Release-v0.1.3

LiteFlow v0.1.3

LiteFlow is a lightweight Python task flow framework that enables building flexible and efficient workflows.

Key Features

• Simple Task Management: Easy task definition and execution using decorators or function registration
• Parallel Execution: Built-in support for concurrent task execution using thread pool executors
• Task Chaining: Seamless task sequencing with automatic result passing
• Streaming Results: Stream intermediate results during task execution
• Dynamic Workflows: Support for conditional branching and dynamic task routing
• State Management: Built-in context management for sharing state between tasks
• Map Reduce Pattern: Support for spawning multiple parallel tasks and collecting results
• Flexible Input Handling: Pass parameters to tasks through context

Highlights

• Lightweight and intuitive API
• Thread pool execution out of the box
• No external dependencies
• Pythonic implementation
• Extensive documentation and examples