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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 main features of apple/coremltools are: Cross-Framework Model Conversion, Graph Model Transformations, Hardware-Specific Model Optimizations, Model Graph Optimizers, Model Conversion Toolkits, Model Conversion, ML Operator Translations, High-Level Model APIs.
Open-source alternatives to apple/coremltools include: onnx/onnxmltools — This project is a machine learning interoperability tool designed to translate models from various training frameworks… nvidia/tensorrt — TensorRT is a deep learning inference engine and software development kit designed to optimize and deploy neural… pytorch/executorch — ExecuTorch is a lightweight C++ runtime for deploying PyTorch models on mobile, embedded, and edge hardware. It… apple/corenet — Corenet is a deep learning training framework and computer vision model library designed for developing neural… unifyai/ivy — Ivy is a machine learning framework transpiler and model converter designed to ensure deep learning portability. It… mozilla/tts — This project is a comprehensive suite for neural speech synthesis, featuring a deep learning text-to-speech engine, a…
This 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
TensorRT is a deep learning inference engine and software development kit designed to optimize and deploy neural networks for high-performance execution on NVIDIA GPUs. It functions as a GPU acceleration framework that reduces latency and increases throughput for trained models during production deployment. The toolkit imports models from the Open Neural Network Exchange format and transforms them into optimized engines. It utilizes graph-based model optimization, layer-fusion kernel generation, and precision-based quantization to convert floating point weights into lower precision formats.
ExecuTorch is a lightweight C++ runtime for deploying PyTorch models on mobile, embedded, and edge hardware. It provides an ahead-of-time compilation pipeline that exports, quantizes, and lowers model graphs into compact serialized programs, then executes them through a minimal runtime with hardware acceleration and on-device large language model inference capabilities. The project distinguishes itself through a hardware accelerator delegate system that partitions model subgraphs and offloads computation to specialized backends including NPUs, GPUs, and DSPs from Apple, Arm, Intel, MediaTek,
Corenet is a deep learning training framework and computer vision model library designed for developing neural networks across vision, text, and audio modalities. It functions as a distributed training orchestrator for scaling workloads across multiple compute nodes and provides a multimodal data pipeline for processing image, text, and video data. The project includes a model conversion toolkit for transforming weights and architectures between different machine learning frameworks. It also provides tools for optimizing model performance on Apple Silicon and reducing response latency in gene