taskflow/examples/parallel_data_pipeline.cpp at master · feengg/taskflow · GitHub

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// This program demonstrates how to use tf::DataPipeline to create

// a pipeline with in-pipe data automatically managed by the Taskflow

// library.

#include <taskflow/taskflow.hpp>

#include <taskflow/algorithm/data_pipeline.hpp>

int main() {

// dataflow => void -> int -> std::string -> float -> void

tf::Taskflow taskflow("pipeline");

tf::Executor executor;

const size_t num_lines = 3;

// create a pipeline graph

tf::DataPipeline pl(num_lines,

tf::make_data_pipe<void, int>(tf::PipeType::SERIAL, [&](tf::Pipeflow& pf) -> int{

if(pf.token() == 5) {

pf.stop();

return 0;

}

else {

printf("first pipe returns %lu\n", pf.token());

return pf.token();

}

}),

tf::make_data_pipe<int, std::string>(tf::PipeType::SERIAL, [](int& input) {

printf("second pipe returns a strong of %d\n", input + 100);

return std::to_string(input + 100);

}),

tf::make_data_pipe<std::string, void>(tf::PipeType::SERIAL, [](std::string& input) {

printf("third pipe receives the input string %s\n", input.c_str());

})

);

// build the pipeline graph using composition

taskflow.composed_of(pl).name("pipeline");

// dump the pipeline graph structure (with composition)

taskflow.dump(std::cout);

// run the pipeline

executor.run(taskflow).wait();

return 0;

}

taskflow/examples/parallel_data_pipeline.cpp at master · feengg/taskflow · GitHub

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