.. _adapt_ta:

*************************************
Adaptive applications: Task-attribute
*************************************

We encourage you to take a look at the Pipeline of Ensembles example on the
next page and compare it with the above pattern.

.. note:: The reader is assumed to be familiar with the :ref:`PST Model <app_model>` and to have read through the :ref:`introduction` of Ensemble Toolkit.

.. note:: This chapter assumes that you have successfully installed Ensemble Toolkit, if not see :ref:`Installation`.


You can download the complete code discussed in this section :download:`here
<../../../../examples/advanced/adapt_ta.py>` or find it in your virtualenv under
``share/radical.entk/advanced/scripts``.

For any adaptive capability within a Pipeline, we need to use the post
execution property of a Stage object. Decisions can only be performed once all
tasks of a Stage reached a final state (running tasks cannot be interrupted by
design).  The post execution property of a Stage requires a callable function
that can influence the next stages of a workflow.

.. code-block:: python

        CUR_NEW_STAGE=0
        MAX_NEW_STAGE=4

        s1.post_exec = func_post

        def func_post():

            global CUR_NEW_STAGE, MAX_NEW_STAGE

            if CUR_NEW_STAGE <= MAX_NEW_STAGE:
                CUR_NEW_STAGE += 1
                s = Stage()

                for i in range(10):
                    t = Task()
                    t.executable = '/bin/sleep'
                    t.arguments = ['30']
                    s.add_tasks(t)

                s.post_exec = func_condition
                p.add_stages(s)


In the following example, we create 1 Pipeline with five stages. There are 10
tasks in each Stage, each running 'sleep 30'. After a Stage is DONE (i.e. all
tasks in the Stage have completed execution), a condition is evaluated that
checks whether the current stage is less than the max number of stages in the
Pipeline. If yes, then the arguments of the tasks of the next Stage are
modified. If not, no operation is performed, and the pipeline will terminate.



.. code-block:: bash

    python adapt_ta.py

Let's take a look at the complete code in the example. Note: You can generate
a more verbose output by setting the environment variable
``RADICAL_ENTK_VERBOSE=DEBUG``.

A look at the complete code in this section:

.. literalinclude:: ../../../../examples/advanced/adapt_ta.py