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Turi Create simplifies the development of custom machine learning models.
README at capture
Quick Links: [Installation](#supported-platforms) | [Documentation](#documentation)
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[<img align="right" src="https://docs-assets.developer.apple.com/turicreate/turi-dog.svg" alt="Turi Create" width="100">](#)
# 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
With Turi Create, you can accomplish many common ML tasks:
| ML Task | Description |
|:------------------------:|:--------------------------------:|
| [Recommender](https://apple.github.io/turicreate/docs/userguide/recommender/) | Personalize choices for users |
| [Image Classification](https://apple.github.io/turicreate/docs/userguide/image_classifier/) | Label images |
| [Drawing Classification](https://apple.github.io/turicreate/docs/userguide/drawing_classifier) | Recognize Pencil/Touch Drawings and Gestures |
| [Sound Classification](https://apple.github.io/turicreate/docs/userguide/sound_classifier) | Classify sounds |
| [Object Detection](https://apple.github.io/turicreate/docs/userguide/object_detection/) | Recognize objects within images |
| [One Shot Object Detection](https://apple.github.io/turicreate/docs/userguide/one_shot_object_detection/) | Recognize 2D objects within images using a single example |
| [Style Transfer](https://apple.github.io/turicreate/docs/userguide/style_transfer/) | Stylize images |
| [Activity Classification](https://apple.github.io/turicreate/docs/userguide/activity_classifier/) | Detect an activity using sensors |
| [Image Similarity](https://apple.github.io/turicreate/docs/userguide/image_similarity/) | Find similar images |
| [Classifiers](https://apple.github.io/turicreate/docs/userguide/supervised-learning/classifier.html) | Predict a label |
| [Regression](https://apple.github.io/turicreate/docs/userguide/supervised-learning/regression.html) | Predict numeric values |
| [Clustering](https://apple.github.io/turicreate/docs/userguide/clustering/) | Group similar datapoints together|
| [Text Classifier](https://apple.github.io/turicreate/docs/userguide/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:
```python
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](https://developer.apple.com/documentation/vision/classifying_images_with_vision_and_core_ml):
<p align="center"><img src="https://docs-assets.developer.apple.com/published/a2c37bce1f/689f61a6-1087-4112-99d9-bbfb326e3138.png" alt="Turi Create" width="600"></p>
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, 3.8
* x86\_64 architecture
* At least 4 GB of RAM
Installation
------------
For detailed instructions for different varieties of Linux see [LINUX\_INSTALL.md](LINUX_INSTALL.md).
For common installation issues see [INSTALL\_ISSUES.md](INSTALL_ISSUES.md).
We recommend using virtualenv to use, install, or build Turi Create.
```shell
pip install virtualenv
```
The method for installing *Turi Create* follows the
[standard python package installation steps](https://packaging.python.org/installing/).
To create and activate a Python virtual environment called `venv` follow these steps:
```shell
# Create a Python virtual environment
cd ~
virtualenv venv
# Activate your virtual environment
source ~/venv/bin/activate
```
Alternatively, if you are using [Anaconda](https://www.anaconda.com/what-is-anaconda/), you may use its virtual environment:
```shell
conda create -n virtual_environment_name anaconda
conda activate virtual_environment_name
```
To install `Turi Create` within your virtual environment:
```shell
(venv) pip install -U turicreate
```
Documentation
-------------
The package [User Guide](https://apple.github.io/turicreate/docs/userguide) and [API Docs](https://apple.github.io/turicreate/docs/api) 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 by utilizing a GPU.
| Linux | macOS 10.13+ | macOS 10.14+ discrete GPUs, macOS 10.15+ integrated GPUs |
| :-------------------------|:---------------------|:---------------------------------------------------------|
| Activity Classification | Image Classification | Activity Classification |
| Drawing Classification | Image Similarity | Object Detection |
| Image Classification | Sound Classification | One Shot Object Detection |
| Image Similarity | | Style Transfer |
| Object Detection | | |
| One Shot Object Detection | | |
| Sound Classification | | |
| Style Transfer | | |
macOS GPU support is automatic. For Linux GPU support, see [LinuxGPU.md](LinuxGPU.md).
Building From Source
---------------------
If you want to build Turi Create from source, see [BUILD.md](BUILD.md).
Contributing
------------
Prior to contributing, please review [CONTRIBUTING.md](CONTRIBUTING.md) and do
not provide any contributions unless you agree with the terms and conditions
set forth in [CONTRIBUTING.md](CONTRIBUTING.md).
We want the Turi Create community to be as welcoming and inclusive as possible, and have adopted a [Code of Conduct](CODE_OF_CONDUCT.md) that we expect all community members, including contributors, to read and observe.