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:
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. 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
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:
- 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.