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Continue with grid_experiment refactor

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This commit is contained in:
Ricardo Montañana Gómez
2024-12-21 14:18:47 +01:00
parent 1cc19a7b19
commit 0318dcf8e5
7 changed files with 49 additions and 119 deletions
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108
src/grid/GridExperiment.cpp
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@@ -23,6 +23,16 @@ namespace platform {
}
json GridExperiment::build_tasks()
{
/*
* Each task is a json object with the following structure:
* {
* "dataset": "dataset_name",
* "idx_dataset": idx_dataset, // used to identify the dataset in the results
* // this index is relative to the list of used datasets in the actual run not to the whole datasets list
* "seed": # of seed to use,
* "fold": # of fold to process
* }
*/
auto tasks = json::array();
auto grid = GridData(Paths::grid_input(config.model));
auto datasets = Datasets(false, Paths::datasets());
@@ -57,104 +67,6 @@ namespace platform {
std::cout << separator << std::endl << separator << std::flush;
return tasks;
}
void GridExperiment::go(struct ConfigMPI& config_mpi)
{
/*
* Each task is a json object with the following structure:
* {
* "dataset": "dataset_name",
* "idx_dataset": idx_dataset, // used to identify the dataset in the results
* // this index is relative to the list of used datasets in the actual run not to the whole datasets list
* "seed": # of seed to use,
* "fold": # of fold to process
* }
*
* This way a task consists in process all combinations of hyperparameters for a dataset, seed and fold
*
* The overall process consists in these steps:
* 0. Create the MPI result type & tasks
* 0.1 Create the MPI result type
* 0.2 Manager creates the tasks
* 1. Manager will broadcast the tasks to all the processes
* 1.1 Broadcast the number of tasks
* 1.2 Broadcast the length of the following string
* 1.2 Broadcast the tasks as a char* string
* 2a. Producer delivers the tasks to the consumers
* 2a.1 Producer will loop to send all the tasks to the consumers and receive the results
* 2a.2 Producer will send the end message to all the consumers
* 2b. Consumers process the tasks and send the results to the producer
* 2b.1 Consumers announce to the producer that they are ready to receive a task
* 2b.2 Consumers receive the task from the producer and process it
* 2b.3 Consumers send the result to the producer
* 3. Manager select the bests scores for each dataset
* 3.1 Loop thru all the results obtained from each outer fold (task) and select the best
* 3.2 Save the results
*/
//
// 0.1 Create the MPI result type
//
Task_Result result;
int tasks_size;
MPI_Datatype MPI_Result;
MPI_Datatype type[5] = { MPI_UNSIGNED, MPI_UNSIGNED, MPI_INT, MPI_DOUBLE, MPI_DOUBLE };
int blocklen[5] = { 1, 1, 1, 1, 1 };
MPI_Aint disp[5];
disp[0] = offsetof(Task_Result, idx_dataset);
disp[1] = offsetof(Task_Result, idx_combination);
disp[2] = offsetof(Task_Result, n_fold);
disp[3] = offsetof(Task_Result, score);
disp[4] = offsetof(Task_Result, time);
MPI_Type_create_struct(5, blocklen, disp, type, &MPI_Result);
MPI_Type_commit(&MPI_Result);
//
// 0.2 Manager creates the tasks
//
char* msg;
json tasks;
if (config_mpi.rank == config_mpi.manager) {
timer.start();
tasks = build_tasks();
auto tasks_str = tasks.dump();
tasks_size = tasks_str.size();
msg = new char[tasks_size + 1];
strcpy(msg, tasks_str.c_str());
}
//
// 1. Manager will broadcast the tasks to all the processes
//
MPI_Bcast(&tasks_size, 1, MPI_INT, config_mpi.manager, MPI_COMM_WORLD);
if (config_mpi.rank != config_mpi.manager) {
msg = new char[tasks_size + 1];
}
MPI_Bcast(msg, tasks_size + 1, MPI_CHAR, config_mpi.manager, MPI_COMM_WORLD);
tasks = json::parse(msg);
delete[] msg;
auto env = platform::DotEnv();
auto datasets = Datasets(config.discretize, Paths::datasets(), env.get("discretize_algo"));
if (config_mpi.rank == config_mpi.manager) {
//
// 2a. Producer delivers the tasks to the consumers
//
auto datasets_names = std::vector<std::string>();
json all_results = MPI_EXPERIMENT::producer(datasets_names, tasks, config_mpi, MPI_Result);
std::cout << separator << std::endl;
//
// 3. Manager select the bests sccores for each dataset
//
auto results = initializeResults();
//select_best_results_folds(results, all_results, config.model);
//
// 3.2 Save the results
//
save(results);
} else {
//
// 2b. Consumers prostore_search_resultcess the tasks and send the results to the producer
//
MPI_EXPERIMENT::consumer(datasets, tasks, config, config_mpi, MPI_Result);
}
}
json GridExperiment::initializeResults()
{
// Load previous results if continue is set