3 repositorios
Awesome GitHub RepositoriesModel-to-Runtime Compatibility Verifications
Validation of model compatibility with specific versions of an inference engine.
Distinct from Compatibility Verification: Distinct from Compatibility Verification: specifically validates AI models against target inference runtimes.
Explore 3 awesome GitHub repositories matching development tools & productivity · Model-to-Runtime Compatibility Verifications. Refine with filters or upvote what's useful.
Awesome Model-to-Runtime Compatibility Verifications GitHub Repositories
Encuentra los mejores repositorios con IA.Buscaremos los repositorios que mejor coincidan usando IA.
- apple/coremltools
apple/coremltools
5,333Ver en GitHubcoremltools es un kit de herramientas de conversión y traductor diseñado para transformar modelos de machine learning de varios frameworks al formato Core ML para su ejecución en hardware de Apple. Proporciona un conjunto de herramientas para migrar pesos y arquitecturas de librerías externas a un formato de modelo desplegable. El proyecto incluye una herramienta de optimización y una interfaz programática para editar grafos de modelos y modificar metadatos con el fin de mejorar el rendimiento en el hardware de destino. También cuenta con una suite de validación utilizada para verificar las especificaciones del modelo y la compatibilidad de las operaciones para asegurar una ejecución correcta dentro del runtime. El kit de herramientas cubre una amplia gama de capacidades de despliegue, incluyendo la edición de grafos de modelos, configuración de metadatos y verificación de compatibilidad con especificaciones formales de formato.
Identifies unsupported operations to verify model compatibility with specific versions of the inference engine.
Pythoncoremlcoremltoolsmachine-learning - onnx/onnx-tensorrt
onnx/onnx-tensorrt
3,187Ver en GitHubThis project is a deep learning model compiler and parser that converts ONNX models into optimized TensorRT engines. It functions as a bridge that maps standardized ONNX operators to vendor-specific kernels to enable high-performance inference on NVIDIA GPUs. The system operates as a GPU inference optimizer, selecting hardware-specific kernels and tuning memory allocation to maximize throughput. It transforms neural network graphs into serialized binary execution plans to reduce runtime overhead. The toolset covers deep learning model deployment and edge AI performance tuning. It includes ca
Tests whether an ONNX model is compatible with specific TensorRT versions before deployment.
C++deep-learningnvidiaonnx - onnx/onnxmltools
onnx/onnxmltools
1,160Ver en GitHubThis project is a machine learning interoperability tool designed to translate models from various training frameworks into the standardized open neural network exchange format. It functions as a model deployment pipeline that enables consistent execution across diverse inference engines and hardware environments. The tool utilizes graph-based translation and an operator mapping layer to convert framework-specific mathematical functions into a common intermediate representation. It distinguishes itself through a pluggable converter architecture, which allows developers to register custom tran
Validates converted models against target inference runtimes to ensure numerical accuracy and structural integrity.
Pythonkerasmachine-learningonnx
