Skip to main content

configure virtual (or real) machines with tools for biological analyses

Project description

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][v2], cloud providers such as [Amazon EC2][0] or desktop machines.
This works equally well for other virtual machines and private cloud
environments, including [XEN][XEN], Linux [KVM][KVM], [Eucalyptus][eucalyptus]
and [Openstack][openstack].

# Using pre-built images

## Amazon

See the 'Getting Started with CloudBioLinux' guide on the
[CloudBioLinux website][1] for a detailed description. The short
version for users familiar with Amazon is:

* Login to the [Amazon EC2 console][2].
* Click Launch Instance, and choose the latest CloudBioLinux AMI from
the [website][1] 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)

# Installing CloudBioLinux on a local machine

The install process for CloudBioLinux is fully automated through a
[Fabric build file][3] 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.

## Installation

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

## Usage

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.

## Customization with flavors

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.

## Specific install targets

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.

## Specific package installation

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][3] 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.

## Biological data

We manage a repository of useful public biological data on an
[Amazon S3 bucket][bd1]. 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][bd2],
`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][bd3] Galaxy server.

[bd1]: http://s3.amazonaws.com/biodata
[bd2]: https://github.com/chapmanb/cloudbiolinux/blob/master/config/biodata.yaml
[bd3]: https://bitbucket.org/galaxy/galaxy-central/wiki/cloud

# Supported virtual environments

## Vagrant VirtualBox

Vagrant allows easy deploying and connecting to VirtualBox images. The
setup is ideal for runnig CloudBioLinux on a desktop computer.
Install [VirtualBox 4.0][v2] and [vagrant][v1]. 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][doc], as well as the
documentation on the [Vagrant website][v1].

## Amazon

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.

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][4]
- [Camptocamp Debian images][4b]

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][2] to create an AMI.
Thereafter make it public so it can be used by others.

## Virtualbox

See [the VirtualBox and Vagrant documentation][vb1] for details on creating a
local virtual machine from scratch with CloudBioLinux.

[vb1]: https://github.com/chapmanb/cloudbiolinux/blob/master/doc/virtualbox.md

## OpenStack/XEN/KVM/Eucalyptus private Cloud

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.

[0]: http://aws.amazon.com/ec2/
[1]: http://cloudbiolinux.org/
[2]: https://console.aws.amazon.com/ec2/home
[3]: http://fabfile.org/
[4]: http://alestic.com/
[4b]: http://www.camptocamp.com/en/infrastructure-solutions/amazon-images
[v1]: http://vagrantup.com/
[v2]: http://digitizor.com/2011/01/07/virtualbox-4-0-install-ubuntu/
[XEN]: http://xen.org/
[KVM]: http://www.linux-kvm.org/
[eucalyptus]: http://open.eucalyptus.com/
[openstack]: http://www.openstack.org/

# EC2 quickstart

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

## Initial set up

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][qs1].

Login to your [Amazon EC2 account][qs2] 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][qs3], 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

[qs1]: http://docs.amazonwebservices.com/AWSEC2/latest/GettingStartedGuide/
[qs2]: http://aws.amazon.com/account/
[qs3]: http://developer.amazonwebservices.com/connect/entry.jspa?externalID=351&categoryID=88

## Starting an instance

Each time you'd like to use EC2, you need to create a remote instance to work
with; the [AWS console][4] 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.

# Testing

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

# Documentation

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

[doc]: https://github.com/chapmanb/cloudbiolinux

# LICENSE

The code is freely available under the [MIT license][l1].

[l1]: http://www.opensource.org/licenses/mit-license.html

Project details


Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

cloudbiolinux-0.3a.tar.gz (106.1 kB view hashes)

Uploaded source

Supported by

AWS AWS Cloud computing Datadog Datadog Monitoring Facebook / Instagram Facebook / Instagram PSF Sponsor Fastly Fastly CDN Google Google Object Storage and Download Analytics Huawei Huawei PSF Sponsor Microsoft Microsoft PSF Sponsor NVIDIA NVIDIA PSF Sponsor Pingdom Pingdom Monitoring Salesforce Salesforce PSF Sponsor Sentry Sentry Error logging StatusPage StatusPage Status page