taskflow/taskflow/examples/calculate_in_parallel.py

56 lines
1.5 KiB
Python

import logging
import os
import sys
logging.basicConfig(level=logging.ERROR)
my_dir_path = os.path.dirname(os.path.abspath(__file__))
sys.path.insert(0, os.path.join(os.path.join(my_dir_path, os.pardir),
os.pardir))
from taskflow import blocks
from taskflow.engines.action_engine import engine as eng
from taskflow import task
# This examples shows how LinearFlow and ParallelFlow can be used
# together to execute calculations in parallel and then use the
# result for the next task. Adder task is used for all calculations
# and arguments' bindings are used to set correct parameters to the task.
class Provider(task.Task):
def __init__(self, name, *args):
super(Provider, self).__init__(name)
self._provide = args
def execute(self):
return self._provide
class Adder(task.Task):
def __init__(self, name):
super(Adder, self).__init__(name)
def execute(self, x, y):
return x + y
flow = blocks.LinearFlow().add(
# x1 = 2, y1 = 3, x2 = 5, x3 = 8
blocks.Task(Provider("provide-adder", 2, 3, 5, 8),
save_as=('x1', 'y1', 'x2', 'y2')),
blocks.ParallelFlow().add(
# z1 = x1+y1 = 5
blocks.Task(Adder("add"), save_as='z1', rebind_args=['x1', 'y1']),
# z2 = x2+y2 = 13
blocks.Task(Adder("add"), save_as='z2', rebind_args=['x2', 'y2'])),
# r = z1+z2 = 18
blocks.Task(Adder("add"), save_as='r', rebind_args=['z1', 'z2']))
engine = eng.MultiThreadedActionEngine(flow)
engine.run()
print engine.storage.fetch_all()