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taskflow各种场景案例

taskflow各种场景案例

作者: hugoren | 来源:发表于2018-03-30 16:45 被阅读0次

    HelloWorld

    import logging
    import os
    import sys
    
    logging.basicConfig(level=logging.ERROR)
    
    top_dir = os.path.abspath(os.path.join(os.path.dirname(__file__),
                                           os.pardir,
                                           os.pardir))
    sys.path.insert(0, top_dir)
    
    from taskflow import engines
    from taskflow.patterns import linear_flow as lf
    from taskflow.patterns import unordered_flow as uf
    from taskflow import task
    
    
    # INTRO: This is the defacto hello world equivalent for taskflow; it shows how
    # an overly simplistic workflow can be created that runs using different
    # engines using different styles of execution (all can be used to run in
    # parallel if a workflow is provided that is parallelizable).
    
    class PrinterTask(task.Task):
        def __init__(self, name, show_name=True, inject=None):
            super(PrinterTask, self).__init__(name, inject=inject)
            self._show_name = show_name
    
        def execute(self, output):
            if self._show_name:
                print("%s: %s" % (self.name, output))
            else:
                print(output)
    
    
    # This will be the work that we want done, which for this example is just to
    # print 'hello world' (like a song) using different tasks and different
    # execution models.
    song = lf.Flow("beats")
    
    # Unordered flows when ran can be ran in parallel; and a chorus is everyone
    # singing at once of course!
    hi_chorus = uf.Flow('hello')
    world_chorus = uf.Flow('world')
    for (name, hello, world) in [('bob', 'hello', 'world'),
                                 ('joe', 'hellooo', 'worllllld'),
                                 ('sue', "helloooooo!", 'wooorllld!')]:
        hi_chorus.add(PrinterTask("%s@hello" % name,
                                  # This will show up to the execute() method of
                                  # the task as the argument named 'output' (which
                                  # will allow us to print the character we want).
                                  inject={'output': hello}))
        world_chorus.add(PrinterTask("%s@world" % name,
                                     inject={'output': world}))
    
    # The composition starts with the conductor and then runs in sequence with
    # the chorus running in parallel, but no matter what the 'hello' chorus must
    # always run before the 'world' chorus (otherwise the world will fall apart).
    song.add(PrinterTask("conductor@begin",
                         show_name=False, inject={'output': "*ding*"}),
             hi_chorus,
             world_chorus,
             PrinterTask("conductor@end",
                         show_name=False, inject={'output': "*dong*"}))
    
    # Run in parallel using eventlet green threads...
    try:
        import eventlet as _eventlet  # noqa
    except ImportError:
        # No eventlet currently active, skip running with it...
        pass
    else:
        print("-- Running in parallel using eventlet --")
        e = engines.load(song, executor='greenthreaded', engine='parallel',
                         max_workers=1)
        e.run()
    
    
    # Run in parallel using real threads...
    print("-- Running in parallel using threads --")
    e = engines.load(song, executor='threaded', engine='parallel',
                     max_workers=1)
    e.run()
    
    
    # Run in parallel using external processes...
    print("-- Running in parallel using processes --")
    e = engines.load(song, executor='processes', engine='parallel',
                     max_workers=1)
    e.run()
    
    
    # Run serially (aka, if the workflow could have been ran in parallel, it will
    # not be when ran in this mode)...
    print("-- Running serially --")
    e = engines.load(song, engine='serial')
    e.run()
    print("-- Statistics gathered --")
    print(e.statistics)
    

    Passing values from and to tasks

    import logging
    import os
    import sys
    
    logging.basicConfig(level=logging.ERROR)
    
    self_dir = os.path.abspath(os.path.dirname(__file__))
    top_dir = os.path.abspath(os.path.join(os.path.dirname(__file__),
                                           os.pardir,
                                           os.pardir))
    sys.path.insert(0, top_dir)
    sys.path.insert(0, self_dir)
    
    from taskflow import engines
    from taskflow.patterns import linear_flow
    from taskflow import task
    
    # INTRO: This example shows how a task (in a linear/serial workflow) can
    # produce an output that can be then consumed/used by a downstream task.
    
    
    class TaskA(task.Task):
        default_provides = 'a'
    
        def execute(self):
            print("Executing '%s'" % (self.name))
            return 'a'
    
    
    class TaskB(task.Task):
        def execute(self, a):
            print("Executing '%s'" % (self.name))
            print("Got input '%s'" % (a))
    
    
    print("Constructing...")
    wf = linear_flow.Flow("pass-from-to")
    wf.add(TaskA('a'), TaskB('b'))
    
    print("Loading...")
    e = engines.load(wf)
    
    print("Compiling...")
    e.compile()
    
    print("Preparing...")
    e.prepare()
    
    print("Running...")
    e.run()
    
    print("Done...")
    
    Using listeners
    import logging
    import os
    import sys
    
    logging.basicConfig(level=logging.DEBUG)
    
    top_dir = os.path.abspath(os.path.join(os.path.dirname(__file__),
                                           os.pardir,
                                           os.pardir))
    sys.path.insert(0, top_dir)
    
    from taskflow import engines
    from taskflow.listeners import logging as logging_listener
    from taskflow.patterns import linear_flow as lf
    from taskflow import task
    
    # INTRO: This example walks through a miniature workflow which will do a
    # simple echo operation; during this execution a listener is associated with
    # the engine to receive all notifications about what the flow has performed,
    # this example dumps that output to the stdout for viewing (at debug level
    # to show all the information which is possible).
    
    
    class Echo(task.Task):
        def execute(self):
            print(self.name)
    
    
    # Generate the work to be done (but don't do it yet).
    wf = lf.Flow('abc')
    wf.add(Echo('a'))
    wf.add(Echo('b'))
    wf.add(Echo('c'))
    
    # This will associate the listener with the engine (the listener
    # will automatically register for notifications with the engine and deregister
    # when the context is exited).
    e = engines.load(wf)
    with logging_listener.DynamicLoggingListener(e):
        e.run()
    
    

    转自:
    https://docs.openstack.org/taskflow/latest/user/examples.html#hello-world

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