const std::string kOptimizerId = "optimizerId";
const std::string kLevel = "level";
const std::string kSummary = "summary";
+const std::string kSavePath = "savePath";
+const std::string kSaveFormat = "saveFormat";
} // namespace
using namespace common;
REGISTER_METHOD(MLTrainerModelAddLayer);
REGISTER_METHOD(MLTrainerModelRun);
REGISTER_METHOD(MLTrainerModelSummarize);
+ REGISTER_METHOD(MLTrainerModelSave);
REGISTER_METHOD(MLTrainerModelSetDataset);
REGISTER_METHOD(MLTrainerModelSetOptimizer);
REGISTER_METHOD(MLTrainerDatasetCreateGenerator);
ReportSuccess(out);
}
+void MlInstance::MLTrainerModelSave(const picojson::value& args,
+ picojson::object& out) {
+ ScopeLogger("args: %s", args.serialize().c_str());
+ CHECK_ARGS(args, kId, double, out);
+ CHECK_ARGS(args, kSavePath, std::string, out);
+ CHECK_ARGS(args, kSaveFormat, std::string, out);
+
+ auto id = static_cast<int>(args.get(kId).get<double>());
+ auto path = args.get(kSavePath).get<std::string>();
+
+ ml_train_model_format_e model_format = ML_TRAIN_MODEL_FORMAT_INI_WITH_BIN;
+ PlatformResult result = types::ModelSaveFormatEnum.getValue(
+ args.get(kSaveFormat).get<std::string>(), &model_format);
+ if (!result) {
+ LogAndReportError(result, &out);
+ return;
+ }
+
+ result = trainer_manager_.ModelSave(id, path, model_format);
+ if (!result) {
+ ReportError(result, &out);
+ return;
+ }
+ ReportSuccess(out);
+}
+
void MlInstance::MLTrainerModelSetDataset(const picojson::value& args, picojson::object& out) {
ScopeLogger("args: %s", args.serialize().c_str());
CHECK_ARGS(args, kId, double, out);