Turi Create simplifies the development of custom machine learning models.
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README.md

Turi Create

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:

Turi Create

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

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

Supported Platforms

Turi Create supports:

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

System Requirements

  • Python 2.7 (Python 3.5+ support coming soon)
  • x86_64 architecture

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

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 by the use of a GPU. To enable GPU support after installation of the turicreate package, please perform the following steps:

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.