This Ansible playbook is used to build the components required to run The Genomics Virtual Laboratory (GVL). If you do not plan to make any customisations of the GVL build, and you are using an OpenStack based cloud, you do not need to build the image yourself and can download a publicly available image instead. Otherwise, see the "Build your Own Image" section for instructions on creating a customised build.
GVL 4.4.0 - Download
GVL 4.3.0 - Download
GVL 4.2.0 - Download
GVL 4.1.0 - Download
GVL 4.0.0 - Download
See the "Launching" section below for information on how to make your image launchable on your private cloud.
The playbook is heavily reliant on the Galaxy CloudMan playbook and is intended for anyone wanting to deploy a customised version of the GVL on a public or private cloud. The overall process for building the GVL follows very closely the one for building Galaxy on the Cloud and hence it is recommended to first read this page that describes the high-level concepts of the build process - just use this playbook instead of the one mentioned in that document.
There are several roles contained in this playbook; the roles manage the build process of different components:
GVL-Image: Installs components required for a GVL image snapshot. Implements only the differences from a base CloudMan image.
GVL-FS: Installs components required for a GVL filesystem snapshot. Implements only the differences from a base CloudMan filesystem.
These roles are intended to be run on an Ubuntu (16.04) system.
To get going, it is necessary to clone this repository and then pull in all the dependent roles:
git clone https://github.com/gvlproject/gvl.ansible.playbook.git
ansible-galaxy install -r requirements.yml -p roles
If targeting AWS instances and want to have Elastic Network Adapter (ENA)
enabled, you will also need to install boto (v2) and awscli Python packages
as well as export the following environment vars with their appropriate values:
EC2_REGION, AWS_ACCESS_KEY, AWS_SECRET_KEY. If you have multiple profiles
defined for your aws command (i.e., in ~/aws/credentials) and you don't want
to use the default one, also do export AWS_PROFILE=<profile name>. The target
instance will need to have an elastic IP associated with it and you will also
need to set the path to your Python interpreter in the inventory file under the
localhost host definition.
The easiest method for building the base machine image is to use Packer.
Once you have it installed, check any variables specified at the top of
packer.json, check the formatting of the file with packer validate packer.json,
and run it with packer build packer.json. The command will provision an instance,
run the Ansible image build role, and create a Machine Image. By default, images will be
created on both AWS and the NeCTAR cloud (OpenStack-based). Custom options
can be set by editing packer.json, under the extra_arguments section.
Alternatively, to build an image without Packer, make sure the default values
provided in the gvl.ansible.image role and gvl.ansible.filesystem role suite
you. Create a copy of inventory/builders.sample as inventory/builders, launch
a new instance and set the instance IP address under gvl-image-hosts host
group in the builders file.
ansible-playbook -i inventory/builders playbook.yml --tags "gvl-image" --extra-vars vnc_password=<choose a password> --extra-vars psql_galaxyftp_password=<choose a password> --extra-vars cleanup=yes [--extra-vars cm_aws_instance_id=<aws inst id>]
On average, the build time takes about 1 hour. Note that after the playbook
has run to completion, you will no longer be able to ssh into the instance! If
you still need to ssh, set --extra-vars cleanup=no in the above command.
If you run with that flag, before creating the image, you must rerun the
playbook with that flag set.
A configuration file exposing adjustable options is available under
group_vars/image-builder.yml. Besides allowing you to set some
of the image configuration options, this file allows you to easily control which
steps of the image building process run. This can be quite useful if a step fails
and you want to rerun only it or if you're just trying to run a certain steps.
Launch an instance of the machine image built in the previous step and when
CloudMan comes up, choose the Cluster only with transient storage option
(under Additional startup options). Insert the instance IP address in
inventory/builders file under galaxyFS-builder host group and change the value
of psql_galaxyftp_password in group_vars/all; run the role with
ansible-playbook -i inventory/builders playbook.yml --tags "gvl-fs" --extra-vars psql_galaxyftp_password=<choose a password>
Running above command will automatically install a number of Galaxy tools. The list of
tools that can be installed can be changed by editing shed_tool_list.yaml.gvl.
You may also want to update the default container
to which the GVL filesystem archive will be uploaded.
This playbook requires a number of configuration options for the Galaxy application,
CloudMan application, PostgreSQL the database, as well as the glue linking those.
The configuration options have been aggregated under
defaults/main.yml in each role, and represent reasonable defaults.
Keep in mind that changing the options that influence how the system is deployed
and/or managed may also require changes in CloudMan.
Once the Machine Image has been built (or downloaded from our public images) and made available on your private cloud, the next step is to configure the GVL launcher so that it can launch the built image. You cannot directly launch the image without the Launcher because the launcher passes in some "user data" to contextualize the image, and manually performing this process is error-prone, inconvenient and therefore not recommended. To get your cloud/image added to the launcher, simply mail us at help@genome.edu.au and we'll help you with the process.
Alternatively, you can also setup your own private launch server.