removed the fix setting min-width to all select2 containers, I believe it is unnecessary with width: 'resolve'  and dropdownAutoWidth: true settings
ToolForm: Fix subsection creation
Visualizations registry: do not warn when a plugin is set to 'disabled' - use info
tweaks to UI for library import, also added ext types and genome selection
Merge
Ui: Fix option fields
Merge
Fix page title on tool shed categories page, as pointed out by Greg.
ToolForm/Ui: Improve and extend option fields, add dataset/collection selector
Charts: Fix selection grid
Reduce code duplication between job metrics and dependency resolution plugins.
Same code reused again downstream in workflow schedulers plugin framework.
Allow loading dependency resolvers and job metrics from YAML instead of XML.
Mostly for downstream Pulsar configuration improvements - but I don't see a reason not to allow this in Galaxy as well.

Besides simply being more pleasant to write - having everything configurable by Python dictionary should enable easier automation and programatic configuration of these job running systems.
Implement workflow requests (API only) and a scheduling 'plugin' framework to evaluate them.

The model changes and WorkQueue class are based on modifications to Kyle Ellrott's Galaxy farm work (https://bitbucket.org/kellrott/galaxy-farm/branch/workflow_migrate). I made heavy modifications to the model to enforce referential integrity on parameter to workflow step mappings and made some cosmetic changes various other details. I further filled out WorkQueue (adding ability to 'complete' requests, implementing tests, etc...). The work context (allowing tool's to be executed outside the context of a web thread), workflow module and API modifications, and the workflow scheduling framework are my own contributions.

I like some of this - I remain skeptical about much of it though. I think maintaining queues of generic requests and distributing them across multiple processes, machines, etc... is both a hard problem and one for which many adequete solutions already exist - some of which we are already using (if only optionally) in other parts of Galaxy - so I don't understand why we need/want to so explicitly replicate this in Galaxy and not just defer to an external message queue.

In internal discussions it has been argued that message queues are appropriate for short requests but long term requests are better tracked in the database. I know that intuitively these operations may feel different - but I am unconvienced they are from a technical standpoint. Delaying an action that would have happened in milliseconds to hours later is many order of magnitude larger than the additional delay when going from the hours to days or weeks.

My own preference would be that both this work and Galaxy farm further investigate using message queues before making such substantial changes to the data model. It certainly may prove that this approach is needed - but I personally would like to understand why maintain the work queue in a message queue would not work first.

However, if the Galaxy project is going to add a WorkRequest object to the database and something that looks like the WorkQueue class in the new galaxy.work module and store and distribute workflows that way for the farm project, then I believe that typical workflow requesting and scheduling should make use of it also instead of developing a competing implementation. (Because my interaction pattern with Galaxy has always been if you cannot beat them join them.)

Testing

One can issue a workflow request to the WorkQueue and scheduled via the scheduling framework by running the following test.

./run_tests.sh -api test/api/test_workflows.py:WorkflowsApiTestCase.test_workflow_request

Final Note: The workflow scheduling stuff is a bit rough right now - it is just enough to prove to myself that this approach can be taken and the farm-stuff and workflow scheduling stuff can utilize the same models. I wanted to try to spark a broader conversation on this (re-encounrage use of message queues) before putting more effort into this approach.
Implement pause module for workflows.

Extracts base class ouptut of InputModule for modules that are "simple" - i.e. their configuration state is represented as a dictionary and configuration form is rendered via the generic template.
Enable workflow recovery over mapping steps of workflows.
Rebase into pause.
Rebase into map reduce pause.
Implement explicit workflow invocation cancellation.
Infrastructure for supporting modules cancelling workflows.
Allow pause module to cancel workflow if module action is False.