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Guide to Core ML Tools

API Reference

  • coremltools API Reference
  • Core ML Model Format

Overview

  • What Is Core ML Tools?
  • Installing Core ML Tools
  • Getting Started
  • New Features
  • Core ML Tools FAQs
  • Examples
  • Contributing

Unified Conversion

  • Core ML Tools API Overview
  • Converting Deep Learning Models
    • Source and Conversion Formats
    • Load and Convert Model Workflow
    • Convert Models to ML Programs
    • Convert Models to Neural Networks
    • Comparing ML Programs and Neural Networks
    • Typed Execution
    • Typed Execution Workflow Example
  • Converting from TensorFlow
    • TensorFlow 1 Workflow
    • Converting a TensorFlow 1 Image Classifier
    • Converting a TensorFlow 1 DeepSpeech Model
    • TensorFlow 2 Workflow
    • Converting TensorFlow 2 BERT Transformer Models
  • Converting from PyTorch
    • PyTorch Conversion Workflow
    • Model Tracing
    • Model Exporting
    • Converting a torchvision Model from PyTorch
    • Converting a PyTorch Segmentation Model
    • Converting an Open Efficient Language Model
  • Conversion Options
    • New Conversion Options
    • Model Input and Output Types
    • Image Input and Output
    • Stateful Models
    • Classifiers
    • Flexible Input Shapes
    • Composite Operators
    • Custom Operators
    • Graph Passes
  • Model Intermediate Language

Optimization

  • Overview
  • What’s New
  • Examples
    • Optimizing ResNet50 Model
    • Optimizing OPT Model
    • Optimizing StableDiffusion Model
  • Optimization Workflow
  • Palettization
    • Palettization Overview
    • Performance
    • Palettization Algorithms
    • API Overview
  • Linear Quantization
    • Quantization Overview
    • Performance
    • Quantization Algorithms
    • API Overview
  • Pruning
    • Overview
    • Performance
    • Pruning Algorithms
    • API Overview
  • Combining Compression Types
  • Conversion
  • Compressing Neural Network Weights

Other Converters

  • LibSVM
  • Scikit-learn
  • XGBoost

MLModel

  • MLModel Overview
  • Multifunction Models
  • Xcode Model Preview Types
  • MLModel Utilities
  • Model Prediction
  • Updatable Models
    • Neural Network Classifier
    • Pipeline Classifier
    • Nearest Neighbor Classifier
  • Debugging And Performance Utilities
  • .rst

Core ML Tools

Core ML Tools#

Core ML Tools logo

Convert models from TensorFlow, PyTorch, and other libraries to Core ML.

This guide includes instructions and examples. For details about using the API classes and methods, see the coremltools API Reference.

Index | Search Page


API Reference

  • coremltools API Reference
  • Core ML Model Format

Overview

  • What Is Core ML Tools?
  • Installing Core ML Tools
  • Getting Started
  • New Features
  • Core ML Tools FAQs
  • Examples
  • Contributing

Unified Conversion

  • Core ML Tools API Overview
  • Converting Deep Learning Models
  • Converting from TensorFlow
  • Converting from PyTorch
  • Conversion Options
  • Model Intermediate Language

Optimization

  • Overview
  • What’s New
  • Examples
  • Optimization Workflow
  • Palettization
  • Linear Quantization
  • Pruning
  • Combining Compression Types
  • Conversion
  • Compressing Neural Network Weights

Other Converters

  • LibSVM
  • Scikit-learn
  • XGBoost

MLModel

  • MLModel Overview
  • Multifunction Models
  • Xcode Model Preview Types
  • MLModel Utilities
  • Model Prediction
  • Updatable Models
  • Debugging And Performance Utilities

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What Is Core ML Tools?

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