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#include <glog/logging.h>
#include <iostream>
#include "singa/singa.h"
/**
* \file main.cc provides an example main function.
*
* Like the main func of Hadoop, it prepares the job configuration and submit it
* to the Driver which starts the training.
*
* Users can define their own main func to prepare the job configuration in
* different ways other than reading it from a configuration file. But the main
* func must call Driver::Init at the beginning, and pass the job configuration
* and resume option to the Driver for job submission.
*
* Optionally, users can register their own implemented subclasses of Layer,
* Updater, etc. through the registration function provided by the Driver.
*
* Users must pass at least one argument to the singa-run.sh, i.e., the job
* configuration file which includes the cluster topology setting. Other fields
* e.g, neuralnet, updater can be configured in main.cc.
*
* TODO
* Add helper functions for users to generate configurations for popular models
* easily, e.g., MLP(layer1_size, layer2_size, tanh, loss);
*/
int main(int argc, char **argv) {
if (argc < 4) {
std::cout << "Args: -conf JOB_CONF -singa SINGA_CONF -job_id JOB_ID "
<< " [-resume|-test]\n"
<< "-resume\t resume training from latest checkpoint files\n"
<< "-test\t test performance or extract features\n";
return 0;
}
// initialize glog before creating the driver
google::InitGoogleLogging(argv[0]);
// must create driver at the beginning and call its Init method.
singa::Driver driver;
driver.Init(argc, argv);
// users can register new subclasses of layer, updater, etc.
// get the job conf, and custmize it if need
singa::JobProto jobConf = driver.job_conf();
if (singa::ArgPos(argc, argv, "-test") != -1) {
driver.Test(jobConf);
} else {
// if -resume in argument list, set resume to true; otherwise false
int resume_pos = singa::ArgPos(argc, argv, "-resume");
bool resume = (resume_pos != -1);
// submit the job for training
driver.Train(resume, jobConf);
}
return 0;
}