Ivy

Ivy is a machine learning framework transpiler and model converter designed to translate code and computational graphs between different deep learning ecosystems. It serves as a portability tool for migrating model architectures and logic across competing frameworks to enable flexible deployment.

The system achieves cross-framework conversion by utilizing abstract syntax tree analysis to rewrite source code and by employing a computational graph tracer to capture tensor flows and operation sequences during live execution. This process allows for the translation of both high-level model definitions and trained models.

The project covers capabilities including deep learning code transpilation, machine learning framework migration, and the analysis of computational graphs to ensure model portability across different runtime environments.

Features

Machine Learning Transpilers - Converts high-level model definitions and logic between competing deep learning ecosystems using a common intermediate representation.

Framework Portability Tools - Provides a bridge for migrating model architectures and logic between competing deep learning ecosystems to enable flexible deployment.

Computational Graphs - Captures the sequence of operations and tensor flows by recording the live execution of machine learning code.

Deep Learning Frameworks - Translates machine learning code and computational graphs between different deep learning frameworks using AST analysis.

Framework Transpilers - Converts machine learning code and computational graphs between different deep learning frameworks.

Machine Learning Frameworks - Translates machine learning code and libraries between different frameworks to ensure software works across multiple environments.

Machine Learning Model Portability - Translates trained models and tools between different frameworks to maintain compatibility across platforms.

Model Format Converters - Translates trained machine learning models between frameworks to ensure compatibility across different runtime environments.

Operator Mappings - Connects individual mathematical functions from a source framework to their functionally equivalent implementations in a target framework.

AST Transformation Tools - Utilizes abstract syntax tree manipulation to rewrite framework-specific function calls for target library equivalents.

Source-To-Source Transpilers - Uses abstract syntax tree analysis to automatically rewrite machine learning source code for a different target library.

ML Operator Translations - Maps individual mathematical functions from one framework to the equivalent functional implementation in another target framework.

Cross-Framework Deployments - Enables moving models between different machine learning environments to facilitate deployment on platforms requiring specific frameworks.

Framework Migration Tools - Serves as a bridge for migrating model architectures and logic between competing machine learning ecosystems.

Intermediate Representations - Translates high-level framework operations into a neutral format to remove library-specific constraints for easier conversion.

Framework-Agnostic Lowering - Lowers high-level model definitions into a simplified intermediate representation that removes framework-specific constraints.

ML Runtime Interceptors - Hooks into the runtime execution of a model to analyze data flow and operation dependencies during a live pass.

Machine Learning - Unified machine learning framework.

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Features

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