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()
