ElastiCluster aims to provide a user-friendly command line tool to create, manage and setup computing clusters hosted on cloud infrastructures (like Amazon's Elastic Compute Cloud EC2, Google Compute Engine, or a private OpenStack cloud). Its main goal is to get your own private cluster up and running with just a few commands; this video demoes ElastiCluster setting up a computational batch-queueing cluster.

Complete documentation for ElastiCluster is available on the Read The Docs website. General discussion over ElastiCluster's usage, features, and bugs takes place on the [email protected] mailing-list (only subscribers can post).

The ElastiCluster project is an effort of the Services and Support for Science IT (S3IT) unit at the University of Zurich, licensed under the GNU General Public License version 3.

ElastiCluster is in active development, and offers the following features at the moment:

• INI-style configuration file to define cluster templates
• Can start and manage multiple independent clusters at the same time

* Automated setup of:

• HPC clusters using SLURM or GridEngine;
• Spark / Hadoop clusters with HDFS and Hive/SQL;
• distributed storage clusters using GlusterFS, OrangeFS, or Ceph;
• ...or anything that you can install with an Ansible playbook!

• Growing and shrinking a running cluster.

ElastiCluster is currently in active development: please use the GitHub issue tracker to file enhancement requests and ideas, or the mailing-list for discussion.

We appreciate pull requests for new features and enhancements. Please use the master branch as starting point.

ElastiCluster is a Python program; Python version 2.6 or 2.7 is required to run it. Python 3 is not (yet) supported.

It's quite easy to install ElastiCluster using pip; the command below is all you need to install ElastiCluster on your system:

pip install elasticluster

If you want to try out the development version instead, please read the Installing development code from GitHub section in the manual.

Note: The 1.3 release is nearing, which has seen quite some changes from the 1.2 code that's on PyPI. For the moment, you are therefore encouraged to run the development code from GitHub and report on any bugs you find!