Coremltools

coremltools este un toolkit de conversie și traducător conceput pentru a transforma modelele de machine learning din diverse framework-uri în formatul Core ML pentru execuție pe hardware Apple. Oferă o suită de instrumente pentru migrarea ponderilor și arhitecturilor din biblioteci externe într-un format de model implementabil.

Proiectul include un instrument de optimizare și o interfață programatică pentru editarea grafurilor de model și modificarea metadatelor pentru a îmbunătăți performanța pe hardware-ul țintă. De asemenea, dispune de o suită de validare utilizată pentru a verifica specificațiile modelului și compatibilitatea operațiunilor pentru a asigura execuția corectă în cadrul runtime-ului.

Toolkit-ul acoperă o gamă largă de capabilități de implementare, inclusiv editarea grafurilor de model, configurarea metadatelor și verificarea compatibilității cu specificațiile formale de format.

Features

Cross-Framework Model Conversion - Transforms machine learning models from various external frameworks into the Core ML format for Apple hardware.
Graph Model Transformations - Represents machine learning models as computational graphs to enable structural transformations and optimization.
Hardware-Specific Model Optimizations - Optimizes model graphs and metadata specifically to leverage Apple hardware accelerators and neural engines.
Model Graph Optimizers - Provides a programmatic interface to simplify and optimize model graphs for improved inference performance.
Model Conversion Toolkits - Provides a comprehensive toolkit for converting model checkpoints into lightweight libraries for specific hardware runtimes.
Model Conversion - Transforms trained models from various frameworks into optimized formats for target hardware deployment.
ML Operator Translations - Provides translation of individual mathematical operators between different machine learning frameworks.
High-Level Model APIs - Provides high-level APIs for the incremental programmatic construction of neural network architectures.
Model Deployment - Prepares and validates optimized models for production execution on target hardware.
Model Validation Schemas - Verifies model correctness by enforcing schema constraints on inputs and operations for the target runtime.
Model-to-Runtime Compatibility Verifications - Identifies unsupported operations to verify model compatibility with specific versions of the inference engine.
Model Compatibility Suites - Ships a validation suite to check model specifications and operation compatibility for correct runtime execution.
Deployment Specification Validators - Checks model specifications against formal schemas and runtime requirements before deployment.
Intermediate Representation Translation - Translates tensors and operators into a standardized internal format to decouple source frameworks from target conversion.
Model Conversion Tools - Tools for converting models to Apple's CoreML format.

Istoric stele

applecoremltools

Name: apple/coremltools
Author: apple

View on GitHub

5,333 stele806 fork-uriPythonBSD-3-Clause7 vizualizăricoremltools.readme.io

Coremltools

Features

Cross-Framework Model Conversion - Transforms machine learning models from various external frameworks into the Core ML format for Apple hardware.
Graph Model Transformations - Represents machine learning models as computational graphs to enable structural transformations and optimization.
Hardware-Specific Model Optimizations - Optimizes model graphs and metadata specifically to leverage Apple hardware accelerators and neural engines.
Model Graph Optimizers - Provides a programmatic interface to simplify and optimize model graphs for improved inference performance.
Model Conversion Toolkits - Provides a comprehensive toolkit for converting model checkpoints into lightweight libraries for specific hardware runtimes.
Model Conversion - Transforms trained models from various frameworks into optimized formats for target hardware deployment.
ML Operator Translations - Provides translation of individual mathematical operators between different machine learning frameworks.
High-Level Model APIs - Provides high-level APIs for the incremental programmatic construction of neural network architectures.
Model Deployment - Prepares and validates optimized models for production execution on target hardware.
Model Validation Schemas - Verifies model correctness by enforcing schema constraints on inputs and operations for the target runtime.
Model-to-Runtime Compatibility Verifications - Identifies unsupported operations to verify model compatibility with specific versions of the inference engine.
Model Compatibility Suites - Ships a validation suite to check model specifications and operation compatibility for correct runtime execution.
Deployment Specification Validators - Checks model specifications against formal schemas and runtime requirements before deployment.
Intermediate Representation Translation - Translates tensors and operators into a standardized internal format to decouple source frameworks from target conversion.
Model Conversion Tools - Tools for converting models to Apple's CoreML format.

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Întrebări frecvente

Ce face apple/coremltools?

Care sunt principalele funcționalități ale apple/coremltools?

Principalele funcționalități ale apple/coremltools sunt: 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.

Care sunt câteva alternative open-source pentru apple/coremltools?

Alternativele open-source pentru apple/coremltools includ: 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…

Coremltools

Features

Istoric stele

Coremltools

Features

Alternative open-source pentru Coremltools

onnx/onnxmltools

NVIDIA/TensorRT

pytorch/executorch

apple/corenet

Întrebări frecvente

Istoric stele

Alternative open-source pentru Coremltools

onnx/onnxmltools

NVIDIA/TensorRT

pytorch/executorch

apple/corenet

Întrebări frecvente