AWS Kaggle Machine – turnkey data science “Lab in a Box”

The advancement in data science and machine learning has not only brought breakthroughs like AlphaGO, but also starting to have broad impact in our everyday lives (Airbnb uses data to predict traveler’s destinations). Gone are the days when data science is only accessible by those in the ivory tower with million dollar proprietary software, new trends have emerged:

• open source software and tools
• compute capacity at cloud scale, with dramatic cost reduction
• public data set, community based problem solving (Kaggle)

AWS provides both cost efficiency and scalable capabilities. It makes sense for data scientist to tap into the power of public cloud. An AWS image is developed here which:

• Automates the installation and configuration of a comprehensive set of open source data science tools
• Allows instance sizing based on needs
• Control cost (shut down or terminate when done, launch in a few minutes)

What it is

An AWS AMI which provides “data science server in a box” with current open source toolkit (RStudio, Jupyter Notebook, Anaconda, Xgboost…). Builds automatically, fully configured ready for use in less than five minutes.

How to build a Kaggle Machine

Using the community AMI named “kaggle machine”,  build your Kaggle Machine in AWS, with one of the following method. Note the AMIs are currently available in us-east-1 and us-west-2. For other regions, you can build your machine in the above two regions, and copy AMI across regions.

Build Kaggle Machine from AWS console
Launch EC2 instance, search for “Kaggle-machine” in Community AMIs, specify a key pair. After instance creation, add a Security Group which allows port 8787, 9999 and 22 for ssh.

Build Kaggle Machine using CloudFormation Stack
A CloudFormation template can be used to build Kaggle Machine and Security Group automatically. Download the template and use it to build your stack in us-east-1 or us-west-2. The template can be found at http://github.com/seanxwang/kagglemachine/.

How to use it

After instance creation, note public DNS name of the machine, from any client on the internet, access services by pointing your browser to:

Rstudio: http://:8787 (default ruser/ruser)

Jupyter: http://:9999 (default password jupyter)

Change the default password immediately. The EC2 instance runs on Ubuntu, you can ssh to it

Cost

The cost is based on AWS EC2 usage. You only pay hourly when instance running, shutdown the instance when done. When you are done with your project and no longer need data to be saved on server, terminate the instance.

The development of Kaggle Machine originates from the needs of data scientists participating in Kaggle challenges, hope it will be provide you a useful toolset as well.