Boost graph flow example comments
Change-Id: Ibcfc2cd8e1b516d6481d3ae7e2c4fc753014fb53
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@ -33,12 +33,20 @@ 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
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# tasks. User shouldn't care about ordering the Tasks.
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# GraphFlow resolves dependencies automatically using tasks'
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# requirements and provided values.
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# Flows of any types can be nested into Graph flow. Subflows
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# dependencies will be resolved too.
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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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@ -65,6 +73,7 @@ flow = gf.Flow('root').add(
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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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