def testTee(): graph = WorkflowGraph() prod = TestProducer() prev = prod cons1 = TestOneInOneOut() cons2 = TestOneInOneOut() graph.connect(prod, 'output', cons1, 'input') graph.connect(prod, 'output', cons2, 'input') args.num = 3 process(graph, inputs={prod: [{}, {}, {}, {}, {}]}, args=args)
def testTee(): graph = WorkflowGraph() prod = TestProducer() prev = prod cons1 = TestOneInOneOut() cons2 = TestOneInOneOut() graph.connect(prod, 'output', cons1, 'input') graph.connect(prod, 'output', cons2, 'input') args.num = 3 process(graph, inputs={prod: [{}, {}, {}, {}, {}]}, args=args)
def testPipeline(): prod = TestProducer() cons1 = TestOneInOneOut() cons2 = TestOneInOneOut() graph = WorkflowGraph() graph.connect(prod, 'output', cons1, 'input') graph.connect(cons1, 'output', cons2, 'input') args = argparse.Namespace args.num = 5 args.simple = False process(graph, inputs={prod: [{}, {}, {}]}, args=args)
def testPipeline(): prod = TestProducer() cons1 = TestOneInOneOut() cons2 = TestOneInOneOut() graph = WorkflowGraph() graph.connect(prod, 'output', cons1, 'input') graph.connect(cons1, 'output', cons2, 'input') args = argparse.Namespace args.num = 5 args.simple = False process(graph, inputs={ prod : [ {}, {}, {} ] }, args=args )
def testSquare(): graph = WorkflowGraph() prod = TestProducer(2) cons1 = TestOneInOneOut() cons2 = TestOneInOneOut() last = TestTwoInOneOut() graph.connect(prod, 'output0', cons1, 'input') graph.connect(prod, 'output1', cons2, 'input') graph.connect(cons1, 'output', last, 'input0') graph.connect(cons2, 'output', last, 'input1') args.num = 4 process(graph, inputs={prod: [{}]}, args=args)
def testSquare(): graph = WorkflowGraph() prod = TestProducer(2) cons1 = TestOneInOneOut() cons2 = TestOneInOneOut() last = TestTwoInOneOut() graph.connect(prod, 'output0', cons1, 'input') graph.connect(prod, 'output1', cons2, 'input') graph.connect(cons1, 'output', last, 'input0') graph.connect(cons2, 'output', last, 'input1') args.num = 4 process(graph, inputs={ prod : [{}]}, args=args )
def testTee(): graph = WorkflowGraph() prod = TestProducer() prev = prod cons1 = TestOneInOneOut() cons2 = TestOneInOneOut() graph.connect(prod, 'output', cons1, 'input') graph.connect(prod, 'output', cons2, 'input') args.num = 3 process(graph, inputs={prod: [{}, {}, {}, {}, {}]}, args=args) #print '='*20 + 'PIPELINE' + '='*20 #testPipeline() #print '='*20 + 'SQUARE ' + '='*20 #testSquare() #print '='*20 + 'TEE ' + '='*20 #testTee()
def testTee(): graph = WorkflowGraph() prod = TestProducer() prev = prod cons1 = TestOneInOneOut() cons2 = TestOneInOneOut() graph.connect(prod, 'output', cons1, 'input') graph.connect(prod, 'output', cons2, 'input') args.num = 3 process(graph, inputs={prod: [{}, {}, {}, {}, {}]}, args=args) #print '='*20 + 'PIPELINE' + '='*20 #testPipeline() #print '='*20 + 'SQUARE ' + '='*20 #testSquare() #print '='*20 + 'TEE ' + '='*20 #testTee()
import processor from dispel4py.workflow_graph import WorkflowGraph from dispel4py.examples.graph_testing.testing_PEs import TestProducer, TestOneInOneOut prod = TestProducer() cons1 = TestOneInOneOut() cons2 = TestOneInOneOut() graph = WorkflowGraph() graph.connect(prod, 'output', cons1, 'input') graph.connect(cons1, 'output', cons2, 'input') graph.partitions = [[prod], [cons1, cons2]] ubergraph = processor.create_partitioned(graph) processes, inputmappings, outputmappings = processor.assign_and_connect( ubergraph, 2) print processes print inputmappings print outputmappings import multi_process inputs = {prod: [{}]} mapped_inputs = processor.map_inputs_to_partitions(ubergraph, inputs) print 'MAPPED INPUTS: %s' % mapped_inputs multi_process.process(ubergraph, 2, inputs=mapped_inputs)
import processor from dispel4py.workflow_graph import WorkflowGraph from dispel4py.examples.graph_testing.testing_PEs import TestProducer, TestOneInOneOut prod = TestProducer() cons1 = TestOneInOneOut() cons2 = TestOneInOneOut() graph = WorkflowGraph() graph.connect(prod, 'output', cons1, 'input') graph.connect(cons1, 'output', cons2, 'input') graph.partitions= [ [prod], [cons1, cons2]] ubergraph = processor.create_partitioned(graph) processes, inputmappings, outputmappings = processor.assign_and_connect(ubergraph, 2) print processes print inputmappings print outputmappings import multi_process inputs= { prod : [{}] } mapped_inputs=processor.map_inputs_to_partitions(ubergraph, inputs) print 'MAPPED INPUTS: %s' % mapped_inputs multi_process.process(ubergraph, 2, inputs = mapped_inputs)