GraphLab Create™ Quick Start

Get started with GraphLab Create for free, right now.

The GraphLab Create Discover Plan allows you to experience the full power of GraphLab Create today. When you are ready to build business-class predictive applications based on scalable, advanced machine learning models, upgrade to Developer or Deploy Plans and let the GraphLab Team help you go from inspiration to production with additional support and deployment options.


1
Get Your Product Key

Sign up and instantly receive a GraphLab Create product key for your individual use. We will also send you a confirmation email. Already have GraphLab Create and want to get the latest version? Follow these upgrade instructions .

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By signing up, I agree to the GraphLab Create EULA .

Your product key has been generated. To make configuration easy, we have provided a shell command that will insert your product key in a GraphLab Create configuration file located in your home directory. Before running Python, paste the following code into your terminal window and execute. GraphLab Create will reference this file upon import.

(mkdir -p ~/.graphlab && echo -e "[Product]\nproduct_key={key}" > ~/.graphlab/config && echo "Configuration file written") || echo "Configuration file not written"

Successful completion of this step will show "Configuration file written" in your terminal window. Now, proceed to Step 2.

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The confirmation email has been sent to your registered email address. Check your email to view your product key. Follow the link in the email to return to this Quick Start and proceed with the installation.
2
Install GraphLab Create

GraphLab Create is easy to install.

Supported operating systems:


What do I need to do?

  1. Ensure your system is running Python 2.7.x (execute python -V in terminal)
  2. Install pip , a Python tool for installing Python packages
  3. Choose the below installation method that works best for you

You can install GraphLab Create system-wide (recommended) or in a Python virtual environment (virtualenv).

Copy and paste the following code into your terminal window and hit "Enter".

sudo pip install graphlab-create==1.1

If you are unsure about installing or upgrading these libraries system-wide we recommend installation with virtualenv .

Installing with in a virtualenv contains the installation of GraphLab Create and allows you to customize this virtualenv with other Python packages you may want for your data science projects.

  1. Ensure your system has virtualenv installed. To verify, execute pip freeze . To install, execute sudo pip install virtualenv in your terminal before proceeding
  2. Copy and execute the following commands in your terminal. This will create a virtual environment called 'graphlab' and install GraphLab Create version 1.1
virtualenv graphlab
. graphlab/bin/activate
pip install graphlab-create==1.1
Resource: Learn how to use virtualenv


See detailed installation instructions for Mac OS X , Linux and Windows .

3

Start Creating: Hello World

Now you can start using GraphLab Create. Let's build a recommender. Copy and paste the following code into your Python console.

import graphlab as gl
url = 'http://s3.amazonaws.com/GraphLab-Datasets/movie_ratings/training_data.csv'
data = gl.SFrame.read_csv(url, column_type_hints={"rating":int})
data.show()
model = gl.recommender.create(data, user_id="user", item_id="movie", target="rating")
results = model.recommend(users=None, k=5)

You've just used the fundamentals of GraphLab Create! To learn more about this recommender see this notebook .

What's next?

Get started by visiting our Learn section where you will find our user guide, API documents, How To sample code, a syntax translator and more.

The Getting Started with GraphLab Create is a good IPython notebook for beginners. Download the code and have fun. It will give you a broad overview of how to use GraphLab Create. The notebook introduces the SFrame and SGraph, data structures ideal for working with very large tabular and graph datasets. The Getting Started notebook also introduces our machine learning toolkits. In the demo, you’ll ingest data, build a graph, and create a model to generate insight. Check out all of our notebooks, there's a lot to see!