Turi Create

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

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:

With Turi Create, you can can tackle a number of common scenarios:

• Recommender systems
• Image classification
• Image similarity
• Object detection
• Activity classifier
• Text classifier

You can also work with essential machine learning models, organized into algorithm-based toolkits:

• Classifiers
• Regression
• Graph analytics
• Clustering
• Nearest Neighbors
• Topic models

System Requirements

• Python 2.7 (Python 3.5+ support coming soon)
• x86_64 architecture
• macOS 10.11+, Linux with glibc 2.12+ (including WSL on Windows 10)

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. Be sure to install virtualenv using your system pip.

pip install virtualenv

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

# Create a Python virtual environment
cd ~
virtualenv venv

To activate your new virtual environment and install Turi Create in this environment, follow these steps:

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

# Install Turi Create in the new virtual environment, pythonenv
(venv) pip install -U turicreate

Documentation

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

GPU Support

By default, turicreate takes a dependency on the default installation of mxnet. To enable GPU support after installation of the turicreate package, please perform the following steps:

• Install CUDA 8.0 (instructions)
• Install cuDNN 5 for CUDA 8.0 (instructions)

Make sure to add the CUDA library path to your LD_LIBRARY_PATH environment variable. In the typical case, this means adding the following line to your ~/.bashrc file:

export LD_LIBRARY_PATH=/usr/local/cuda/lib64:$LD_LIBRARY_PATH

If you installed the cuDNN files into a separate directory, make sure to separately add it as well. Next step is to uninstall mxnet and install the CUDA-enabled mxnet-cu80 package:

(venv) pip uninstall -y mxnet
(venv) pip install mxnet-cu80==0.11.0

Make sure you install the same version of MXNet as the one turicreate depends on (currently 0.11.0). If you have trouble setting up the GPU, the MXNet installation instructions may offer additional help.

Building From Source

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

Contributing

See CONTRIBUTING.md.