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Turi Create

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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:

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 modelmodel = tc.image_classifier.create(data, target='photoLabel')# Make predictionspredictions = model.predict(data)# Export to Core MLmodel.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 2.10+)

• Windows 10 (via WSL)

System Requirements

Turi Create requires:

• Python 2.7, 3.5, 3.6, 3.7 (macOS only)

• 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 thestandard python package installation steps. To create and activate a Python virtual environment called venv follow these steps:

# Create a Python virtual environmentcd ~virtualenv venv# Activate your virtual environmentsource ~/venv/bin/activate

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

conda create -n virtual_environment_name anaconda
conda activate virtual_environment_name

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

Prior to contributing, please review CONTRIBUTING.md and do not provide any contributions unless you agree with the terms and conditions set forth in CONTRIBUTING.md.

We want the Turi Create community to be as welcoming and inclusive as possible, and have adopted a Code of Conduct that we expect all community members, including contributors, to read and observe.