The deployment method contained in this repo has been superseeded with with method available at https://github.com/galaxyproject/cloudman-image-playbook
As a result, this repo is no longer maintained.
mi-deployment project is a set of scripts that orchestrate and automate the process of customizing a machine image (MI). Its primary applicability is for the Galaxy CloudMan project (http://userwww.service.emory.edu/~eafgan/projects.html) where it sets up the necessary environment. The project is currently used to create Galaxy deployments on the Amazon Elastic Compute Cloud (EC2) as well as the Galaxy VM (http://usegalaxy.org/vm). The provided set of scripts should also be applicable in other environments, namely a local cloud deployment, a single server setup, or for deploying other applications in a similar environment.
**** Overview *** NOTE: In order to use the scripts provided within the mi-deployment project, Python Fabric v1.0 (http://docs.fabfile.org/) and boto (http://github.com/boto/boto) need to be available on the system from where mi-deployments scripts are run.
There are two basic scripts that can be run as part of mi-deployment (the rest of the scripts in the repository are used automatically by these two main scripts): - mi_fabfile.py: this script sets up the machine image and automates the process of image rebundling - tools_fabfile.py: this script installs a range of bioinformatics tools exposed by the Galaxy application
~ ~ ~ ~ mi_fabfile.py ~ ~ ~~
When run, the mi_fabfile.py script performs the following set of operations:
- update the system
- install packages required for running Galaxy CloudMan
- setup additional system users
- install required programs for running Galaxy CloudMan
- install required python libraries for running Galaxy CloudMan
- customize and configure the system environment
At the completion of the machine image customization, the script offers an
option to rebundle and register the new image with the cloud provider.
USAGE: Before running this script, in the context of Amazon EC2, you will either have to define the following two environment variables or edit the script and specify your AWS account keys in the code (in rebundle() method): export AWS_ACCESS_KEY_ID=<Your AWS Access Key ID> export AWS_SECRET_ACCESS_KEY=<Your AWS Secret Access Key>
In order to run the script, one should: 1. Start a machine instance based on a compatible AMI 2. Run the script, specifying the instance as one of the arguments The mi_deployment and Galaxy CloudMan projects target Ubuntu 10.04 operating system; however, any comparable derivative of the given OS should result in a compatible AMI. In the specific case of the Galaxy CloudMan, in order to deliver a broad set of bioinformatics tools to our users, we use a Cloud BioLinux AMI (http://cloudbiolinux.com/) and build on top of it. For the rebundling process to work, the instance used must be based on an EBS AMI.
Specifically, once an instance of the given AMI is running, from the local machine, run the following command: fab -f mi_fabfile.py -i <full_path_to_private_key_file> -H <instance_public_dns> configure_MI
NOTE: When the script finishes with the system configuration, it is going to prompt whether a new AMI should be created from the given instance. If the AMI rebundling is to take place, depending on the amount of system updates, it is very likely that the remote instance will need to be rebooted. If that is the case, the mi_fabfile.py will automatically reboot the instance and exit. You should then run the script again, using 'rebundle' as the last argument, like so: fab -f mi_fabfile.py -H <instance_public_dns> -i <full_path_to_private_key_file> rebundle
The script will proceed with instance rebundling, prompting you for couple more options. Once done, a new AMI will have been created under your account.
~ ~ ~ ~ tools_fabfile.py ~ ~ ~~
When run, the tools_fablile.py script installs a set of (bioinformatics) tools.
These tools and their installation properties are primarily intended for use
with the Galaxy application but can easily be adopted for other uses as well.
The list of tools being installed is available under method '_install_tools'.
This script expects an environment to be configured before it is run. This environment can be specified in a method corresponding to a given machine and thus the script can be reusable in a variety of scenarios. In the most generic case, and for use with Amazon EC2, the method 'amazon_ec2' sets up a sample environment. Specifically, all this entails is availability of a file system at the location specified in the environment setup method.
For example, for use with Amazon EC2, the act of using this script would assume starting an EC2 instance, attaching an EBS volume to it, creating a file system on the newly created volume, and mounting it at paths specified in the 'amazon_ec2' method (e.g., /mnt/galaxyTools/).
Once the environment is ready, the script can be invoked from a local machine using the following command: fab -f tools_fabfile.py -i <full_path_to_private_key_file> -H <instance_public_dns> install_tools
As a reminder, for the Amazon EC2 case, unless all of the tools are being installed on the root volume/file system and a new AMI will be (manually) created in order to persist the changes, you will need to unmount the file system where the tools were installed, detach the given EBS volume and create a snapshot of it. Then, the next time you plan on using the tools or the volume, you can create a new volume based on the created snapshot, attach it to a new instance, and mount it (at the same location that was used for installing the tools).