4.3. Adding Stages

In this section, we will take a look at how we can add more tasks to our script from the Adding Tasks section.

Note

The reader is assumed to be familiar with the PST Model and to have read through the Introduction of Ensemble Toolkit.

Note

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

You can download the complete code discussed in this section here or find it in your virtualenv under share/radical.entk/user_guide/scripts.

Below, you can see the code snippet that shows how you can add more Stages to a Pipeline. You simple create more Stage objects, populate them with Tasks and add them to the Pipeline using the add_stage() method.

password = os.environ.get('RMQ_PASSWORD')

if __name__ == '__main__':

    # Create a Pipeline object
    p = Pipeline()

    # Create a Stage object
    s1 = Stage()
    s1.name = 'Stage 1'

    for cnt in range(10):

        # Create a Task object
        t = Task()
        t.name = 'my-task'        # Assign a name to the task (optional)
        t.executable = '/bin/echo'   # Assign executable to the task
        t.arguments = ['I am task %s in %s' % (cnt, s1.name)]  # Assign arguments for the task executable

        # Add the Task to the Stage
        s1.add_tasks(t)

    # Add Stage to the Pipeline
    p.add_stages(s1)


    # Create another Stage object
    s2 = Stage()
    s2.name = 'Stage 2'

    for cnt in range(5):

        # Create a Task object
        t = Task()
        t.name = 'my-task'        # Assign a name to the task (optional, do not use ',' or '_')
        t.executable = '/bin/echo'   # Assign executable to the task
        t.arguments = ['I am task %s in %s' % (cnt, s2.name)]  # Assign arguments for the task executable

        # Add the Task to the Stage
python add_stages.py

Let’s take a look at the complete code in the example. 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:

#!/usr/bin/env python

from radical.entk import Pipeline, Stage, Task, AppManager
import os

# ------------------------------------------------------------------------------
# Set default verbosity
if os.environ.get('RADICAL_ENTK_VERBOSE') is None:
    os.environ['RADICAL_ENTK_REPORT'] = 'True'


# Description of how the RabbitMQ process is accessible
# No need to change/set any variables if you installed RabbitMQ has a system
# process. If you are running RabbitMQ under a docker container or another
# VM, set "RMQ_HOSTNAME" and "RMQ_PORT" in the session where you are running
# this script.
hostname = os.environ.get('RMQ_HOSTNAME', 'localhost')
port = os.environ.get('RMQ_PORT', 5672)
username = os.environ.get('RMQ_USERNAME')
password = os.environ.get('RMQ_PASSWORD')

if __name__ == '__main__':

    # Create a Pipeline object
    p = Pipeline()

    # Create a Stage object
    s1 = Stage()
    s1.name = 'Stage 1'

    for cnt in range(10):

        # Create a Task object
        t = Task()
        t.name = 'my-task'        # Assign a name to the task (optional)
        t.executable = '/bin/echo'   # Assign executable to the task
        t.arguments = ['I am task %s in %s' % (cnt, s1.name)]  # Assign arguments for the task executable

        # Add the Task to the Stage
        s1.add_tasks(t)

    # Add Stage to the Pipeline
    p.add_stages(s1)


    # Create another Stage object
    s2 = Stage()
    s2.name = 'Stage 2'

    for cnt in range(5):

        # Create a Task object
        t = Task()
        t.name = 'my-task'        # Assign a name to the task (optional, do not use ',' or '_')
        t.executable = '/bin/echo'   # Assign executable to the task
        t.arguments = ['I am task %s in %s' % (cnt, s2.name)]  # Assign arguments for the task executable

        # Add the Task to the Stage
        s2.add_tasks(t)

    # Add Stage to the Pipeline
    p.add_stages(s2)


    # Create Application Manager
    appman = AppManager(hostname=hostname, port=port, username=username,
            password=password)

    # Create a dictionary describe four mandatory keys:
    # resource, walltime, and cpus
    # resource is 'local.localhost' to execute locally
    res_dict = {

        'resource': 'local.localhost',
        'walltime': 10,
        'cpus': 1
    }

    # Assign resource request description to the Application Manager
    appman.resource_desc = res_dict

    # Assign the workflow as a set or list of Pipelines to the Application Manager
    # Note: The list order is not guaranteed to be preserved
    appman.workflow = set([p])

    # Run the Application Manager
    appman.run()