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Tools that translate source code and computational graphs from one machine learning library to another.
Distinct from Deep Learning Frameworks: None of the candidates describe the act of transpiling code between frameworks; they focus on the frameworks themselves.
Explore 2 awesome GitHub repositories matching artificial intelligence & ml · Framework Transpilers. Refine with filters or upvote what's useful.
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
Converts machine learning code and computational graphs between different deep learning frameworks.
Kornia is a differentiable computer vision library and cross-framework tensor vision toolset. It implements vision operations as differentiable tensors to enable integration into deep learning pipelines and supports the transpilation of operations across PyTorch, TensorFlow, JAX, and NumPy. The project provides specialized toolsets for geometric vision and stereo depth, including algorithms for 3D scene reconstruction, camera calibration, and pose estimation. It further distinguishes itself as a differentiable image augmentation framework, applying random geometric and color transformations w
Translates vision operations to run on different backends including JAX, TensorFlow, and NumPy.