hhy5277 / turicreate

Turi Create simplifies the development of custom machine learning models. 一个简化的机器学习库 它简化了机器学习模型的创建和部署，可用于复杂的任务，如对象检测、活动分类和推荐系统。

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Turi Create

Click here to check out our talk at WWDC 2018!

Turi Create simplifies the development of custom machine learning models. You don't have to be a machine learning expert to add recommendations, object detection, image classification, image similarity or activity classification to your app.

• Easy-to-use: Focus on tasks instead of algorithms
• Visual: Built-in, streaming visualizations to explore your data
• Flexible: Supports text, images, audio, video and sensor data
• Fast and Scalable: Work with large datasets on a single machine
• Ready To Deploy: Export models to Core ML for use in iOS, macOS, watchOS, and tvOS apps

With Turi Create, you can accomplish many common ML tasks:

ML Task	Description
Recommender	Personalize choices for users
Image Classification	Label images
Object Detection	Recognize objects within images
Style Transfer	Stylize images
Activity Classification	Detect an activity using sensors
Image Similarity	Find similar images
Classifiers	Predict a label
Regression	Predict numeric values
Clustering	Group similar datapoints together
Text Classifier	Analyze sentiment of messages

Example: Image classifier with a few lines of code

If you want your app to recognize specific objects in images, you can build your own model with just a few lines of code:

import turicreate as tc

# Load data 
data = tc.SFrame('photoLabel.sframe')

# Create a model
model = tc.image_classifier.create(data, target='photoLabel')

# Make predictions
predictions = model.predict(data)

# Export to Core ML
model.export_coreml('MyClassifier.mlmodel')

It's easy to use the resulting model in an iOS application:

Supported Platforms

Turi Create supports:

• macOS 10.12+
• Linux (with glibc 2.12+)
• Windows 10 (via WSL)

System Requirements

Turi Create requires:

• Python 2.7, 3.5, 3.6
• x86_64 architecture
• At least 4 GB of RAM

Installation

For detailed instructions for different varieties of Linux see LINUX_INSTALL.md. For common installation issues see INSTALL_ISSUES.md.

We recommend using virtualenv to use, install, or build Turi Create.

pip install virtualenv

The method for installing Turi Create follows the standard python package installation steps. To create and activate a Python virtual environment called venv follow these steps:

# Create a Python virtual environment
cd ~
virtualenv venv

# Activate your virtual environment
source ~/venv/bin/activate

Alternatively, if you are using Anaconda, you may use its virtual environment:

conda create -n venv python=2.7 anaconda
source activate venv

To install Turi Create within your virtual environment:

(venv) pip install -U turicreate

Version 5.0 (New)

Turi Create 5.0 includes:

• GPU Acceleration on Macs for:
  • Image Classification (macOS 10.13+)
  • Image Similarity (macOS 10.13+)
  • Object Detection (macOS 10.14+)
  • Activity Classification (macOS 10.14+)
• New Task: Style Transfer
• Recommender model deployment
• Vision Feature Print model deployment

Documentation

The package User Guide and API Docs contain more details on how to use Turi Create.

GPU Support

Turi Create does not require a GPU, but certain models can be accelerated 9-13x when utilizing a GPU.

Turi Create automatically utilizes Mac GPUs for the following tasks:

• Image Classification (macOS 10.13+)
• Image Similarity (macOS 10.13+)
• Object Detection (macOS 10.14+, discrete GPU only)
• Activity Classification (macOS 10.14+, discrete GPU only)

For linux GPU support, see LinuxGPU.md.

Building From Source

If you want to build Turi Create from source, see BUILD.md.

Contributing

See CONTRIBUTING.md.