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Refactor grid classes and add summary of tasks at the end

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This commit is contained in:
Ricardo Montañana Gómez
2025-01-14 18:53:11 +01:00
parent 28894004c8
commit 386faf960e
13 changed files with 761 additions and 658 deletions
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232
src/grid/GridBase.cpp
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@@ -1,3 +1,5 @@
#include <random>
#include <cstddef>
#include "common/DotEnv.h"
#include "common/Paths.h"
#include "GridBase.h"
@@ -18,5 +20,235 @@ namespace platform {
exit(1);
}
}
std::string GridBase::get_color_rank(int rank)
{
auto colors = { Colors::WHITE(), Colors::RED(), Colors::GREEN(), Colors::BLUE(), Colors::MAGENTA(), Colors::CYAN(), Colors::YELLOW(), Colors::BLACK() };
std::string id = "0123456789ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklmnopqrstuvwxyz";
auto idx = rank % id.size();
return *(colors.begin() + rank % colors.size()) + id[idx];
};
json GridBase::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());
auto all_datasets = datasets.getNames();
auto datasets_names = filterDatasets(datasets);
for (int idx_dataset = 0; idx_dataset < datasets_names.size(); ++idx_dataset) {
auto dataset = datasets_names[idx_dataset];
for (const auto& seed : config.seeds) {
auto combinations = grid.getGrid(dataset);
for (int n_fold = 0; n_fold < config.n_folds; n_fold++) {
json task = {
{ "dataset", dataset },
{ "idx_dataset", idx_dataset},
{ "seed", seed },
{ "fold", n_fold},
};
tasks.push_back(task);
}
}
}
// Shuffle the array so heavy datasets are eas ier spread across the workers
std::mt19937 g{ 271 }; // Use fixed seed to obtain the same shuffle
std::shuffle(tasks.begin(), tasks.end(), g);
std::cout << "* Number of tasks: " << tasks.size() << std::endl;
std::cout << separator << std::flush;
for (int i = 0; i < tasks.size(); ++i) {
if ((i + 1) % 10 == 0)
std::cout << separator;
else
std::cout << (i + 1) % 10;
}
std::cout << separator << std::endl << separator << std::flush;
return tasks;
}
void GridBase::summary(json& all_results, json& tasks, struct ConfigMPI& config_mpi)
{
// Report the tasks done by each worker, showing dataset number, seed, fold and time spent
// The format I want to show is:
// worker, dataset, seed, fold, time
// with headers
std::cout << Colors::RESET() << "* Summary of tasks done by each worker" << std::endl;
json worker_tasks = json::array();
for (int i = 0; i < config_mpi.n_procs; ++i) {
worker_tasks.push_back(json::array());
}
int max_dataset = 7;
for (const auto& [key, results] : all_results.items()) {
auto dataset = key;
if (dataset.size() > max_dataset)
max_dataset = dataset.size();
for (const auto& result : results) {
int n_task = result["task"].get<int>();
json task = tasks[n_task];
auto seed = task["seed"].get<int>();
auto fold = task["fold"].get<int>();
auto time = result["time"].get<double>();
auto worker = result["process"].get<int>();
json line = {
{ "dataset", dataset },
{ "seed", seed },
{ "fold", fold },
{ "time", time }
};
worker_tasks[worker].push_back(line);
}
}
std::cout << Colors::MAGENTA() << " W " << setw(max_dataset) << std::left << "Dataset";
std::cout << " Seed Fold Time" << std::endl;
std::cout << "=== " << std::string(max_dataset, '=') << " ==== ==== " << std::string(15, '=') << std::endl;
for (int worker = 0; worker < config_mpi.n_procs; ++worker) {
auto color = (worker % 2) ? Colors::CYAN() : Colors::BLUE();
std::cout << color << std::right << setw(3) << worker << " ";
if (worker == config_mpi.manager) {
std::cout << "Manager" << std::endl;
continue;
}
if (worker_tasks[worker].empty()) {
std::cout << "No tasks" << std::endl;
continue;
}
bool first = true;
double total = 0.0;
int num_tasks = 0;
for (const auto& task : worker_tasks[worker]) {
num_tasks++;
if (!first)
std::cout << std::string(4, ' ');
else
first = false;
std::cout << std::left << setw(max_dataset) << task["dataset"].get<std::string>();
std::cout << " " << setw(4) << std::right << task["seed"].get<int>();
std::cout << " " << setw(4) << task["fold"].get<int>();
std::cout << " " << setw(15) << std::setprecision(7) << std::fixed << task["time"].get<double>() << std::endl;
total += task["time"].get<double>();
}
if (num_tasks > 1) {
std::cout << Colors::MAGENTA() << setw(3) << std::right << num_tasks;
std::cout << setw(max_dataset) << " Total..." << std::string(10, '.');
std::cout << setw(15) << std::setprecision(7) << std::fixed << total << std::endl;
}
}
}
void GridBase::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
* 3.3 Summary of jobs done
*/
//
// 0.1 Create the MPI result type
//
Task_Result result;
int tasks_size;
MPI_Datatype MPI_Result;
MPI_Datatype type[10] = { MPI_UNSIGNED, MPI_UNSIGNED, MPI_INT, MPI_DOUBLE, MPI_DOUBLE, MPI_DOUBLE, MPI_DOUBLE, MPI_DOUBLE, MPI_INT, MPI_INT };
int blocklen[10] = { 1, 1, 1, 1, 1, 1, 1, 1, 1, 1 };
MPI_Aint disp[10];
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);
disp[5] = offsetof(Task_Result, nodes);
disp[6] = offsetof(Task_Result, leaves);
disp[7] = offsetof(Task_Result, depth);
disp[8] = offsetof(Task_Result, process);
disp[9] = offsetof(Task_Result, task);
MPI_Type_create_struct(10, 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 = filterDatasets(datasets);
json all_results = 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);
//
// 3.3 Summary of jobs done
//
if (!config.quiet)
summary(all_results, tasks, config_mpi);
} else {
//
// 2b. Consumers process the tasks and send the results to the producer
//
consumer(datasets, tasks, config, config_mpi, MPI_Result);
}
}
}