苹果公开Turi Create框架，推动机器学习

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苹果公开Turi Create框架，推动机器学习

12月9日消息，苹果公司本周在开源项目托管平台GitHub上分享了Turi Create的框架。苹果表示，这一框架应该会让开发者更容易构建机器学习模型。

正如苹果所描述的，Turi Create是为那些在机器学习方面不一定是专家的人设计的。根据苹果的说法，Turi Create简化了定制机器学习模型的开发。你不需要成为一个机器学习专家，也可以在你的应用中添加建议、对象检测、图像分类、图像相似性或活动分类。

苹果解释说，Turi Create的设计意图是简单易用，具有视觉界面，灵活和快速。此外，它还可以部署在iOS、macOS、watchOS 和tvOS 上面。

易于使用：专注于任务而不是算法

可视化：内置的流媒体可视化技术探索你的数据

灵活：支持文本、图像、音频、视频和传感器数据

快速和可扩展：在一台机器上处理大型数据集

准备部署：将模型导出到Core ML，用于iOS、macOS、watchOS和tvOS应用

苹果最初是在2016年收购了位于西雅图的Turi公司，当时该公司正努力推动自己的机器学习。不久之后，苹果将Turi转变为内部机器学习部门，致力于将技术整合到现有和未来的产品线中。尽管苹果从未证实收购Turi公司的交易条款，但有报道称，该公司为这笔交易支付了超过2 亿美元。

苹果向公众发布Turi Create的目标似乎是为了增加开发机器学习功能的开发者数量。该框架支持多种常见场景的构建，包括推荐系统、图像分类、图像相似性、对象检测等等。所有关于Turi Create的细节都可以在GitHub上找到。

https://github.com/apple/turicreate

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 modelmodel = tc.image_classifier.create(data, target='photoLabel')# Make predictionspredictions = model.predict(data)# Export to Core MLmodel.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
  

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 environmentcd ~virtualenv venv

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

# Active your virtual environmentsource ~/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/lib64LD_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.