17bf3db06a
Remove line containing comment - # vim: tabstop=4 shiftwidth=4 softtabstop=4 Change-Id: I7581cc88b8de433d5609ed06c6570b0b45c13573 Closes-Bug:#1229324
92 lines
2.8 KiB
Python
92 lines
2.8 KiB
Python
# -*- coding: utf-8 -*-
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# Copyright (C) 2012-2013 Yahoo! Inc. All Rights Reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License"); you may
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# not use this file except in compliance with the License. You may obtain
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# a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS, WITHOUT
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# WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the
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# License for the specific language governing permissions and limitations
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# under the License.
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import logging
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import os
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import sys
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logging.basicConfig(level=logging.ERROR)
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top_dir = os.path.abspath(os.path.join(os.path.dirname(__file__),
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os.pardir,
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os.pardir))
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sys.path.insert(0, top_dir)
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import taskflow.engines
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from taskflow.patterns import graph_flow as gf
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from taskflow.patterns import linear_flow as lf
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from taskflow import task
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# In this example there are complex dependencies between tasks that are used to
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# perform a simple set of linear equations.
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#
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# As you will see below the tasks just define what they require as input
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# and produce as output (named values). Then the user doesn't care about
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# ordering the TASKS (in this case the tasks calculate pieces of the overall
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# equation).
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#
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# As you will notice graph_flow resolves dependencies automatically using the
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# tasks requirements and provided values and no ordering dependency has to be
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# manually created.
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#
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# Also notice that flows of any types can be nested into a graph_flow; subflows
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# dependencies will be resolved too!! Pretty cool right!
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class Adder(task.Task):
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def execute(self, x, y):
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return x + y
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flow = gf.Flow('root').add(
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lf.Flow('nested_linear').add(
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# x2 = y3+y4 = 12
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Adder("add2", provides='x2', rebind=['y3', 'y4']),
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# x1 = y1+y2 = 4
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Adder("add1", provides='x1', rebind=['y1', 'y2'])
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),
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# x5 = x1+x3 = 20
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Adder("add5", provides='x5', rebind=['x1', 'x3']),
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# x3 = x1+x2 = 16
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Adder("add3", provides='x3', rebind=['x1', 'x2']),
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# x4 = x2+y5 = 21
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Adder("add4", provides='x4', rebind=['x2', 'y5']),
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# x6 = x5+x4 = 41
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Adder("add6", provides='x6', rebind=['x5', 'x4']),
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# x7 = x6+x6 = 82
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Adder("add7", provides='x7', rebind=['x6', 'x6']))
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# Provide the initial variable inputs using a storage dictionary.
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store = {
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"y1": 1,
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"y2": 3,
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"y3": 5,
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"y4": 7,
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"y5": 9,
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}
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result = taskflow.engines.run(
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flow, engine_conf='serial', store=store)
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print("Single threaded engine result %s" % result)
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result = taskflow.engines.run(
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flow, engine_conf='parallel', store=store)
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print("Multi threaded engine result %s" % result)
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