CloudBioLinux is a build and deployment system which installs a large selection of Bioinformatics and machine learning libraries on a bare virtual machine (VM) image, freshly installed PC, or in the Cloud. By default CloudBioLinux includes a large suite of tools installed through the default distribution installer, native installers, and libraries for Perl, R, Python, Java and Ruby.

CloudBioLinux included software packages are fully customizable. In addition to the default configuration, we support custom configuration builds through flavors. Flavors support overriding default package installations, making it simple to create derived installs for specific purposes.

CloudBioLinux is a single install route both for desktop VMs such as VirtualBox, cloud providers such as Amazon EC2 or desktop machines. This works equally well for other virtual machines and private cloud environments, including XEN, Linux KVM, Eucalyptus and Openstack.

See the 'Getting Started with CloudBioLinux' guide on the CloudBioLinux website for a detailed description. The short version for users familiar with Amazon is:

• Login to the Amazon EC2 console.
• Click Launch Instance, and choose the latest CloudBioLinux AMI from the website in the community AMI section (search for 'CloudBioLinux').
• After launching the instance, find the host details of your running instance from the Instances section.
• Connect to your machine via ssh or VNC (using the Amazon PEM keys)

The install process for CloudBioLinux is fully automated through a Fabric build file written in Python. The Fabric build files are useful for automating installation of scientific software on local systems as well as Amazon cloud servers. Everything is fully configurable through plain text YAML configuration files, and custom build targets allow installation of a subset of the total available packages.

Retrieve the CloudBioLinux code base and install libraries and dependencies:

    git clone git://github.com/chapmanb/cloudbiolinux.git
    cd cloudbiolinux
    python setup.py build
    sudo python setup.py install

The basic usage specifies the hostname of a machine accessible via ssh:

  fab -f fabfile.py -H localhost install_biolinux

Fabric contains some other useful commandline arguments for customizing this to your environments:

• -c your_fabricrc.txt -- Specify the path to a fabricrc configuration files. This allows customization of install directories and other server specific details. See the default config/fabricrc.txt for a full list of options.

• -u username -- The username on the remote machine, overriding the default of your current username.

CloudBioLinux normally creates a full system for bioinformatics, but can be easily configured to install only a subset of tools through flavors:

  fab -f fabfile.py -H localhost install_biolinux:flavor=my_flavor

my_flavor can be the name of an existing flavor in contrib/flavor or the path to a directory with customization information. The files in your flavor directory replace those in the standard config directory, allowing replacement of any of the configuration files like main.yaml with customized copies.

If you desire even more control, flavors allow custom python hooks. See doc/hacking.md for more details.

You can substitute install_biolinux with more specific targets to only build portions of CloudBioLinux:

• install_biolinux:packages -- Install all of the defined system packages.
• install_biolinux:libraries -- Install all libraries for various programming languages.
• install_libraries:language -- Install libraries for a specific language.
• install_biolinux:custom -- Install all custom programs.
• install_custom:a_package_name -- Install a specific custom program.

The custom directory contains installation instructions for programs that are not available from standard package repositories. These instructions are written in Python using the Fabric remote deployment tool and can also be used for installing individual packages locally on your machine. To do this, run:

  fab -f fabfile.py -H localhost install_custom:your_package_name

To build and install your_package_name on the local machine. We welcome additional custom bioinformatics package definitions for inclusion in CloudBioLinux. custom/shared.py contains a number of higher level functions which make it easier to write installation instructions.

We manage a repository of useful public biological data on an Amazon S3 bucket. Currently this includes whole genomes pre-indexed for a number of popular aligners. Downloading and installing these saves a ton of time over running the indexing steps yourself, and eases running next-generation analyses on cloud machines.

A Fabric build script is provided to install this data on your local machine. A biodata configuration file in YAML format, config/biodata.yaml, specifies the genomes of interest and the aligner indexes to use. The config/fabricrc.txt file specifies details about the system and where to install the data.

The basic commandline is:

fab -f data_fabfile.py -H your_machine install_data_s3

and you can pass in custom biodata and fabricrc files with:

fab -f data_fabfile.py -H your_machine -c your_fabricrc.txt install_data_s3:your_biodata.yaml

In addition to downloading and preparing the data, the script will integrate these files with a Galaxy instance by updating appropriate Galaxy configuration files. This makes it useful for installing data to a local or cloud-based Galaxy server.

Vagrant allows easy deploying and connecting to VirtualBox images. The setup is ideal for runnig CloudBioLinux on a desktop computer. Install VirtualBox 4.0 and vagrant. Then add a pre-built CloudLinux VirtualBox images and start it up:

    vagrant box add biolinux_$VERSION https://s3.amazonaws.com/cloudbiolinux/biolinux_$VERSION.box
    mkdir tmp/biolinux
    cd tmp/biolinux
    vagrant init biolinux_version

(note with vagrant you need disk space - at least 3x the image size). The created ./Vagrantfile can be edited to get a full GUI, extra RAM, and a local IP address. Next, fire up the image with

    vagrant up

Once you have a running virtual machine with CloudBioLinux, connect to it with:

    vagrant ssh

no passwords needed! Get root with

    sudo bash

Through Vagrant additional facilities are available, such as a shared network drive. It is also possible to tweak the image (e.g. RAM/CPU settings, and getting the all important guest additions) by firing up virtualbox itself. For more information, see the BioLinux Vagrant documentation, as well as the documentation on the Vagrant website.

A bare Linux image launched in Amazon EC2 is configured from another machine, i.e. your local desktop, using ssh and cloudbiolinux. See the Installation section for installing CloudBioLinux with fabric.

Any cloudbiolinux distribution can be used, including Ubuntu, Debian Linux and CentOS. We recommend using m1.medium or better instance for building a CloudBioLinux image from scratch, due to resource usage while compiling software.

1. Go to the cloudbiolinux source and edit the config/fabricrc.txt, to match the system you plan to install on. Specifically, distribution and dist_name parameters specify details about the type of target.

2. Start an Amazon EC2 base instance and retrieve it's DNS hostname:

   • Alestic Ubuntu images
   • Camptocamp Debian images

3. From your local machine, have CloudBioLinux install your Amazon instance:

    fab -f fabfile.py -H hostname -u username -i private_key_file install_biolinux
   

4. When finished, use the Amazon console to create an AMI. Thereafter make it public so it can be used by others.

See the VirtualBox and Vagrant documentation for details on creating a local virtual machine from scratch with CloudBioLinux.

As long as there is an 'ssh' entry to an running VM, CloudBioLinux can install itself.

For more on private Cloud and CloudBioLinux see ./doc/private_cloud.md.

This provides a quick cheat sheet of commands for getting up and running on EC2 using Amazon's command line tools.

The first time using EC2, you'll need to install the toolkit and credentials for connecting on your local machine, following the getting started guide.

Login to your Amazon EC2 account and go to Security Credentials/X.509. Create a new certificate and download the public cert-*.pem and private pk-*.pem files. Put these in ~.ec2.

Install the ec2 api tools, which require java.

Set up .zshrc/.bashrc:

   export EC2_PRIVATE_KEY=~/.ec2/pk-UBH43XTAWVNQMIZRAV3RP5IIBAPBIFVP.pem
   export EC2_CERT=~/.ec2/cert-UBH43XTAWVNQMIZRAV3RP5IIBAPBIFVP.pem
   export AWS_ACCESS_KEY_ID=<your access key>
   export AWS_SECRET_ACCESS_KEY=<your secret access key>

To test, you should be able to run the command:

   % ec2-describe-regions

Now generate a privatekey for logging in:

   % ec2-add-keypair yourmachine-keypair

This will produce an RSA private key. You should copy and paste this to your .ec2 directory for future use:

   % vim ~/.ec2/id-yourmachine.keypair
   % chmod 600 ~/.ec2/id-yourmachine.keypair

Allow ssh and web access to your instances:

   % ec2-authorize default -p 22
   % ec2-authorize default -p 80

Each time you'd like to use EC2, you need to create a remote instance to work with; the AWS console is useful for managing this process.

When building from scratch with Alestic images, you will need to increase the size of the root filesystem to fit all of the CloudBioLinux data and libraries. This is done by starting the instance from the commandline with:

   % ec2-run-instances ami-1aad5273 -k kunkel-keypair -t m1.large
                       -b /dev/sda1=:20
   % ec2-describe-instances i-0ca39764

On Ubuntu 10.04, you then need to ssh into the instance and resize the filesystem with:

   % sudo resize2fs /dev/sda1

On 11.04 the resize happens automatically and this is not required.

BioLinux comes with an integration testing frame work - currently based on Vagrant. Try:

    cd test
    ./testing_vagrant --help

Target VMs can be listed with

    ./testing_vagrant --list

Build a minimal VM

    ./testing_vagrant Minimal

Additional documentation can be found in the ./doc directory in the BioLinux source tree.

The code is freely available under the MIT license.