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# -*- coding: utf-8 -*-
# Copyright (C) 2012-2013 Yahoo! Inc. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License"); you may
# not use this file except in compliance with the License. You may obtain
# a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS, WITHOUT
# WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the
# License for the specific language governing permissions and limitations
# under the License.
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)
import taskflow.engines
from taskflow.patterns import graph_flow as gf
from taskflow.patterns import linear_flow as lf
from taskflow import task
# In this example there are complex *inferred* dependencies between tasks that
# are used to perform a simple set of linear equations.
#
# As you will see below the tasks just define what they require as input
# and produce as output (named values). Then the user doesn't care about
# ordering the tasks (in this case the tasks calculate pieces of the overall
# equation).
#
# As you will notice a graph flow resolves dependencies automatically using the
# tasks symbol requirements and provided symbol values and no orderin
# dependency has to be manually created.
#
# Also notice that flows of any types can be nested into a graph flow; showing
# that subflow dependencies (and associated ordering) will be inferred too.
class Adder(task.Task):
def execute(self, x, y):
return x + y
flow = gf.Flow('root').add(
lf.Flow('nested_linear').add(
# x2 = y3+y4 = 12
Adder("add2", provides='x2', rebind=['y3', 'y4']),
# x1 = y1+y2 = 4
Adder("add1", provides='x1', rebind=['y1', 'y2'])
),
# x5 = x1+x3 = 20
Adder("add5", provides='x5', rebind=['x1', 'x3']),
# x3 = x1+x2 = 16
Adder("add3", provides='x3', rebind=['x1', 'x2']),
# x4 = x2+y5 = 21
Adder("add4", provides='x4', rebind=['x2', 'y5']),
# x6 = x5+x4 = 41
Adder("add6", provides='x6', rebind=['x5', 'x4']),
# x7 = x6+x6 = 82
Adder("add7", provides='x7', rebind=['x6', 'x6']))
# Provide the initial variable inputs using a storage dictionary.
store = {
"y1": 1,
"y2": 3,
"y3": 5,
"y4": 7,
"y5": 9,
}
# This is the expected values that should be created.
unexpected = 0
expected = [
('x1', 4),
('x2', 12),
('x3', 16),
('x4', 21),
('x5', 20),
('x6', 41),
('x7', 82),
]
result = taskflow.engines.run(
flow, engine='serial', store=store)
print("Single threaded engine result %s" % result)
for (name, value) in expected:
actual = result.get(name)
if actual != value:
sys.stderr.write("%s != %s\n" % (actual, value))
unexpected += 1
result = taskflow.engines.run(
flow, engine='parallel', store=store)
print("Multi threaded engine result %s" % result)
for (name, value) in expected:
actual = result.get(name)
if actual != value:
sys.stderr.write("%s != %s\n" % (actual, value))
unexpected += 1
if unexpected:
sys.exit(1)
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