unifyai/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 defini

MMdnn is a deep learning model converter and migrator designed to translate neural network architectures and weights between different frameworks such as TensorFlow, PyTorch, and Keras. It utilizes a standardized intermediate representation to decouple network structures and weights from specific framework implementations, enabling the transformation of pre-trained models across different environments. The project distinguishes itself by generating native Python reconstruction code from its intermediate representations, allowing models to be rebuilt and fine-tuned in target environments. It a

ONNX is an open-source standard for machine learning interoperability that provides a unified format for representing neural network models. By defining a common set of operators and a standardized file structure, it enables models to be shared, exported, and executed consistently across different training frameworks and software ecosystems. The project functions as an intermediate representation layer that decouples model development from deployment. It utilizes a language-neutral binary serialization format to store model structures and weights, ensuring that computational graphs remain por

coremltools is a conversion toolkit and translator designed to transform machine learning models from various frameworks into the Core ML format for execution on Apple hardware. It provides a suite of tools for migrating weights and architectures from external libraries into a deployable model format. The project includes an optimization tool and a programmatic interface for editing model graphs and modifying metadata to improve performance on target hardware. It also features a validation suite used to check model specifications and operation compatibility to ensure correct execution within