BioCloudCentral.org, CloudMan, and Galaxy API library
Project description
`Blend <http://blend.readthedocs.org/en/latest/>`_ is a Python (2.6 or higher)
library for interacting with `BioCloudCentral.org`_, `CloudMan`_, and `Galaxy`_'s
API. Conceptually, it makes it possible to script and automate the process of
cloud infrastrucutre provisioning and scaling, as well as running of analyses
within Galaxy. In reality, it makes it possible to do things like this:
- Create a CloudMan compute cluster, via an API and directly from your local machine::
from blend.cloudman.launch import CloudManLaunch
cml = CloudManLaunch('<your cloud access key>', '<your cloud secret key')
cml.launch('Blend CloudMan', 'ami-<ID>', 'm1.small', 'password')
cml.get_status()
- Manipulate your CloudMan instance and react to the current needs::
from blend.cloudman import CloudMan
cm = CloudMan("instance IP", "password")
cm.initialize(type="Galaxy")
cm.add_nodes(3)
cluster_status = cm.get_status()
cm.remove_nodes(2)
- Interact with Galaxy via a straighforward API::
from blend.galaxy import GalaxyInstance
gi = GalaxyInstance('<Galaxy IP>', key='your API key')
libs = gi.libraries.get_libraries()
gi.workflows.show_workflow('workflow ID')
gi.workflows.run_workflow('workflow ID', input_dataset_map)
.. note::
Although this library allows you to blend these three services into a cohesive unit,
the library itself can be used with any single service irrespective of the rest. For
example, you can use it to just manipulate CloudMan clusters or to script the
interactions with an instance of Galaxy running on your laptop.
library for interacting with `BioCloudCentral.org`_, `CloudMan`_, and `Galaxy`_'s
API. Conceptually, it makes it possible to script and automate the process of
cloud infrastrucutre provisioning and scaling, as well as running of analyses
within Galaxy. In reality, it makes it possible to do things like this:
- Create a CloudMan compute cluster, via an API and directly from your local machine::
from blend.cloudman.launch import CloudManLaunch
cml = CloudManLaunch('<your cloud access key>', '<your cloud secret key')
cml.launch('Blend CloudMan', 'ami-<ID>', 'm1.small', 'password')
cml.get_status()
- Manipulate your CloudMan instance and react to the current needs::
from blend.cloudman import CloudMan
cm = CloudMan("instance IP", "password")
cm.initialize(type="Galaxy")
cm.add_nodes(3)
cluster_status = cm.get_status()
cm.remove_nodes(2)
- Interact with Galaxy via a straighforward API::
from blend.galaxy import GalaxyInstance
gi = GalaxyInstance('<Galaxy IP>', key='your API key')
libs = gi.libraries.get_libraries()
gi.workflows.show_workflow('workflow ID')
gi.workflows.run_workflow('workflow ID', input_dataset_map)
.. note::
Although this library allows you to blend these three services into a cohesive unit,
the library itself can be used with any single service irrespective of the rest. For
example, you can use it to just manipulate CloudMan clusters or to script the
interactions with an instance of Galaxy running on your laptop.
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