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Turi Create

Check out our talks at WWDC 2019 and 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, Drawing Classification, Recognize Pencil/Touch Drawings and Gestures, Sound Classification, Classify sounds, Object Detection, Recognize objects within images, One Shot Object Detection, Recognize 2D objects within images using a single example, 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 2.10+)
• Windows 10 (via WSL)

System Requirements

Turi Create requires:

• Python 2.7, 3.5, 3.6, 3.7
• 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 virtual_environment_name anaconda
conda activate virtual_environment_name

To install Turi Create within your virtual environment:

(venv) pip install -U turicreate

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.