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From "Stian Soiland-Reyes (JIRA)" <j...@apache.org>
Subject [jira] [Created] (TAVERNA-901) Run Docker from Taverna
Date Wed, 17 Feb 2016 00:45:18 GMT
Stian Soiland-Reyes created TAVERNA-901:
-------------------------------------------

             Summary: Run Docker from Taverna
                 Key: TAVERNA-901
                 URL: https://issues.apache.org/jira/browse/TAVERNA-901
             Project: Apache Taverna
          Issue Type: Story
          Components: Taverna Common Activities
            Reporter: Stian Soiland-Reyes


h2. GSOC: Add Docker support to Taverna

The proposed GSOC project is to add support for invoking Docker containers within Taverna
by adding a Docker Activity plugin.

Tasks include:

* Propose JSON model for describing a {{docker run}} command
* (Optional) Validate Docker activity config, e.g. can the docker image be pulled?
* Investigate: New Docker activity, or modify existing External Tool activity?
* Make/modify a Taverna Activity plugin for executing Docker (may or may not be based on the
External Tool activity)
* (Optional) Capture docker metadata and add to workflow run provenance (e.g. which docker
image ID was pulled)
* (Optional) Add Bioboxes support
* (Optional) Integrate with CWL support (TAVERNA-900)

Other Taverna/Docker--related tasks can of course also be proposed by the students.


h2. Docker

[Docker|https://www.docker.com/] is a Linux container virtualization platform. A Linux _container_
is a special kernel feature, which similar to _chroot jails_ behave as a separate machine,
but unlike Virtual Machines do not have the overhead of virtualization of hardware. 

Docker is popular in the _devops_ movement as it provides an easy way to install dependencies
for software development and deployment, e.g. to run servers for mySQL, Apache Solr or node.js.

In brief a _Docker Image_ contains a virtual Linux file system (e.g. a miniature Debian installation).
A _Docker Container_ is a particular execution of a Docker Image, which typically runs a single
process as installed within the container, and may have network ports exposed to the world,
or have parts of the host computer's file system mounted within the inner container.

One great advantage of Docker is that it simplifies tool *installation*, as each Docker image
is a _self-contained Linux distribution_ which don't have to be compatible with the host computer
(beyond the kernel). 

For Windows and OS X users Docker automatically manage a virtual machine running the Linux
containers, but Docker containers can also be deployed on the cloud or a local cluster, e,g.
using _Docker Machine_.

Docker images can be created from a {{Dockerfile}}, which basically lists the commands to
run to prepare the image. Docker images can be chained together using _base images_ - for
instance to build on an image with mySQL, the Dockerfile says {{FROM mysql}}.

Thus Docker is also an important tool for *reproducibility*, as these images can be automatically
kept up to date and are distributed through the [Docker hub|https://hub.docker.com/]. In bioinformatics,
this has led to [Bioboxes|http://bioboxes.org/], a standard for creating interchangable bioinformatics
software containers.


h2. Taverna

[Apache Taverna|http://taverna.incubator.apache.org/] (incubating) is a Java-based workflow
system with a graphical design interface. Taverna workflows can combine many different service
types, including REST and WSDL services, command line tools, scripts (e.g. BeanShell, R) and
custom plugins (e.g. BioMart).

Taverna workflows can be executed on the desktop, on the command line, or on a Taverna server
installation, which can be controlled from a web portal, a mobile app, or integrated into
third-party applications.

Taverna is used in a [wide range of sciences|http://taverna.incubator.apache.org/introduction/taverna-in-use/]
for data analysis and processing, including bioinformatics, cheminformatics, biodiversity
and musicology. Workflow engine features include provenance tracking, implicit parallelism/iterations,
retry/failover and looping. 

Taverna workflows are commonly shared on [myExperiment|http://www.myexperiment.org], and can
either be created graphically in the [Taverna workbench|http://taverna.incubator.apache.org/download/workbench/],
programmatically using the [Taverna Language API|http://taverna.incubator.apache.org/download/language/]
or by generating workflow definitions in the [SCUFL2|http://taverna.incubator.apache.org/documentation/scufl2/]
 format.


h2. Community engagement

Interested GSOC students are requested to engage early with the [dev@taverna|http://taverna.incubator.apache.org/community/lists#devtaverna]
mailing list to describe their ideas for approaching this project, to clarify the tasks and
for any questions and issues.

As a first step, the prospective applicant should leave a comment on this Jira issue to indicate
their interest, and the GSOC mentors would be happy to assist on any questions. 

As the project starts we are expecting the student to become part of the dev@taverna community
to regularly discuss their progress. 


h2. Mentors

An important part of GSOC is the personal mentoring from existing  members of the open source
community. Our job is not just to teach you how to successfully get through the GSOC programme,
but also to motivate you and make sure you progress. We will show you how to contribute to
open source, debug, improve, document, test and release your code as part of Apache Taverna.


The GSOC mentors for Apache Taverna have experience from guiding multiple earlier GSOC students
and local students, and can be contacted privately for day-to-day interaction and trouble-shooting.


Mentors for this GSOC project:

* Stian Soiland-Reyes



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