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Core ML FormatGithub
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Overview

  • Introduction
  • Installation
  • Quickstart Example
  • Migrating to This Version
  • Contributing
    • GitHub
    • Contribution Guidelines
  • Release Notes

Neural Networks

  • Unified Conversion API
  • TensorFlow 1 Conversion
    • Convert a TensorFlow 1 Image Classifier
    • Convert a TensorFlow 1 DeepSpeech Model
  • TensorFlow 2 Conversion
    • Convert TensorFlow 2 BERT Transformer Models
  • PyTorch Conversion
    • Model Tracing
    • Model Scripting
    • Convert a PyTorch Segmentation Model
  • Model Intermediate Language
  • Conversion Options
    • Image Inputs
    • Classifiers
    • Flexible Input Shapes
    • Composite Operators
    • Custom Operators
  • Quantization
  • Other Converters
    • Multi-backend Keras
    • ONNX
    • Caffe

Trees & Linear Models

  • LibSVM
  • Scikit-learn
  • XGBoost

MLModel

  • MLModel Overview
  • Model Prediction
  • Xcode Model Preview Types
  • MLModel Utilities

Updatable Models

  • Updatable Models Overview
  • Nearest Neighbor Classifier
  • Neural Network Classifier
  • Pipeline Classifier

XGBoost

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You can convert a trained XGBoost model to Core ML format.

# Convert it with default input and output names
import coremltools as ct
coreml_model = ct.converters.xgboost.convert(model)

# Saving the Core ML model to a file.
coreml_model.save('my_model.mlmodel')

For more information, see the API reference.

Updated about 4 years ago