def ncfmodel(istrain=False): ncf_common.define_ncf_flags() infermodel,trainmodel = run_ncf(FLAGS) if istrain: model=trainmodel else: model = infermodel return model
def _setup(self): """Sets up and resets flags before each test.""" tf.compat.v1.logging.set_verbosity(tf.compat.v1.logging.DEBUG) if KerasNCFBenchmarkBase.local_flags is None: ncf_common.define_ncf_flags() # Loads flags to get defaults to then override. List cannot be empty. flags.FLAGS(['foo']) core.set_defaults(**self.default_flags) saved_flag_values = flagsaver.save_flag_values() KerasNCFBenchmarkBase.local_flags = saved_flag_values else: flagsaver.restore_flag_values(KerasNCFBenchmarkBase.local_flags)
def _setup(self): """Sets up and resets flags before each test.""" tf.compat.v1.logging.set_verbosity(tf.compat.v1.logging.DEBUG) if NCFKerasBenchmarkBase.local_flags is None: ncf_common.define_ncf_flags() # Loads flags to get defaults to then override. List cannot be empty. flags.FLAGS(['foo']) core.set_defaults(**self.default_flags) saved_flag_values = flagsaver.save_flag_values() NCFKerasBenchmarkBase.local_flags = saved_flag_values else: flagsaver.restore_flag_values(NCFKerasBenchmarkBase.local_flags)
def __init__(self, output_dir=None, root_data_dir=None, default_flags=None, **kwargs): self.output_dir = output_dir self.default_flags = default_flags or {} ncf_common.define_ncf_flags() if root_data_dir: FLAGS.data_dir = os.path.join(root_data_dir, 'movielens_data')
def __init__(self, output_dir=None, root_data_dir=None, default_flags=None, **kwargs): self.output_dir = output_dir self.default_flags = default_flags or {} ncf_common.define_ncf_flags() if root_data_dir: FLAGS.data_dir = os.path.join(root_data_dir, 'movielens_data')
def _setup(self): """Sets up and resets flags before each test.""" assert tf.version.VERSION.startswith('2.') logging.set_verbosity(logging.INFO) if NCFKerasBenchmarkBase.local_flags is None: ncf_common.define_ncf_flags() # Loads flags to get defaults to then override. List cannot be empty. flags.FLAGS(['foo']) core.set_defaults(**self.default_flags) saved_flag_values = flagsaver.save_flag_values() NCFKerasBenchmarkBase.local_flags = saved_flag_values else: flagsaver.restore_flag_values(NCFKerasBenchmarkBase.local_flags)
if eval_result: stats["eval_loss"] = eval_result[0] stats["eval_hit_rate"] = eval_result[1] if time_callback: timestamp_log = time_callback.timestamp_log stats["step_timestamp_log"] = timestamp_log stats["train_finish_time"] = time_callback.train_finish_time if len(timestamp_log) > 1: stats["avg_exp_per_second"] = ( time_callback.batch_size * time_callback.log_steps * (len(time_callback.timestamp_log)-1) / (timestamp_log[-1].timestamp - timestamp_log[0].timestamp)) return stats def main(_): with logger.benchmark_context(FLAGS), \ mlperf_helper.LOGGER(FLAGS.output_ml_perf_compliance_logging): mlperf_helper.set_ncf_root(os.path.split(os.path.abspath(__file__))[0]) if FLAGS.tpu: raise ValueError("NCF in Keras does not support TPU for now") run_ncf(FLAGS) if __name__ == "__main__": ncf_common.define_ncf_flags() absl_app.run(main)
def setUpClass(cls): # pylint: disable=invalid-name super(NcfTest, cls).setUpClass() ncf_common.define_ncf_flags()
if eval_result: stats['eval_loss'] = eval_result[0] stats['eval_hit_rate'] = eval_result[1] if time_callback: timestamp_log = time_callback.timestamp_log stats['step_timestamp_log'] = timestamp_log stats['train_finish_time'] = time_callback.train_finish_time if len(timestamp_log) > 1: stats['avg_exp_per_second'] = ( time_callback.batch_size * time_callback.log_steps * (len(time_callback.timestamp_log)-1) / (timestamp_log[-1].timestamp - timestamp_log[0].timestamp)) return stats def main(_): with logger.benchmark_context(FLAGS), \ mlperf_helper.LOGGER(FLAGS.output_ml_perf_compliance_logging): mlperf_helper.set_ncf_root(os.path.split(os.path.abspath(__file__))[0]) if FLAGS.tpu: raise ValueError("NCF in Keras does not support TPU for now") run_ncf(FLAGS) if __name__ == "__main__": ncf_common.define_ncf_flags() absl_app.run(main)
def setUpClass(cls): # pylint: disable=invalid-name super(NcfTest, cls).setUpClass() ncf_common.define_ncf_flags()