def create_hooks(
self, t2r_model, estimator,
export_generator
):
export_generator.set_specification_from_model(t2r_model)
warmup_requests_file = export_generator.create_warmup_requests_numpy(
batch_sizes=self._batch_sizes_for_export,
export_dir=estimator.model_dir)
def _export_fn(export_dir):
res = estimator.export_saved_model(
export_dir_base=export_dir,
serving_input_receiver_fn=export_generator
.create_serving_input_receiver_numpy_fn(),
assets_extra={'tf_serving_warmup_requests': warmup_requests_file})
return res
return [
tf.contrib.tpu.AsyncCheckpointSaverHook(
save_secs=self._save_secs,
checkpoint_dir=estimator.model_dir,
listeners=[
checkpoint_hooks.LaggedCheckpointListener(
export_fn=_export_fn,
num_versions=self._num_versions,
export_dir=self._export_dir,
lagged_export_dir=self._lagged_export_dir)
])
]
def create_hooks(
self,
t2r_model,
estimator,
):
if not self._export_dir and not self._lagged_export_dir:
return []
self._export_generator.set_specification_from_model(t2r_model)
warmup_requests_file = self._export_generator.create_warmup_requests_numpy(
batch_sizes=self._batch_sizes_for_export,
export_dir=estimator.model_dir)
in_feature_spec = t2r_model.get_feature_specification_for_packing(
mode=tf.estimator.ModeKeys.PREDICT)
in_label_spec = t2r_model.get_label_specification_for_packing(
mode=tf.estimator.ModeKeys.PREDICT)
t2r_assets = t2r_pb2.T2RAssets()
t2r_assets.feature_spec.CopyFrom(in_feature_spec.to_proto())
t2r_assets.label_spec.CopyFrom(in_label_spec.to_proto())
def _export_fn(export_dir, global_step):
"""The actual closure function creating the exported model and assets."""
t2r_assets.global_step = global_step
tmpdir = tempfile.mkdtemp()
t2r_assets_filename = os.path.join(
tmpdir, tensorspec_utils.T2R_ASSETS_FILENAME)
tensorspec_utils.write_t2r_assets_to_file(t2r_assets,
t2r_assets_filename)
res = estimator.export_saved_model(
export_dir_base=export_dir,
serving_input_receiver_fn=self._export_generator.
create_serving_input_receiver_numpy_fn(),
assets_extra={
'tf_serving_warmup_requests': warmup_requests_file,
tensorspec_utils.T2R_ASSETS_FILENAME: t2r_assets_filename
})
return res
return [
contrib_tpu.AsyncCheckpointSaverHook(
save_secs=self._save_secs,
checkpoint_dir=estimator.model_dir,
listeners=[
checkpoint_hooks.LaggedCheckpointListener(
export_fn=_export_fn,
num_versions=self._num_versions,
export_dir=self._export_dir,
lagged_export_dir=self._lagged_export_dir)
])
]
def create_hooks(
self, t2r_model, estimator,
export_generator
):
export_generator.set_specification_from_model(t2r_model)
warmup_requests_file = export_generator.create_warmup_requests_numpy(
batch_sizes=self._batch_sizes_for_export,
export_dir=estimator.model_dir)
def _export_fn(export_dir, global_step):
"""The actual closure function creating the exported model and assets."""
tmpdir = tempfile.mkdtemp()
global_step_pkl_filename = os.path.join(tmpdir, 'global_step.pkl')
tensorspec_utils.write_global_step_to_file(global_step,
global_step_pkl_filename)
res = estimator.export_saved_model(
export_dir_base=export_dir,
serving_input_receiver_fn=export_generator
.create_serving_input_receiver_numpy_fn(),
assets_extra={
'tf_serving_warmup_requests': warmup_requests_file,
'global_step.pkl': global_step_pkl_filename
})
return res
return [
tf.contrib.tpu.AsyncCheckpointSaverHook(
save_secs=self._save_secs,
checkpoint_dir=estimator.model_dir,
listeners=[
checkpoint_hooks.LaggedCheckpointListener(
export_fn=_export_fn,
num_versions=self._num_versions,
export_dir=self._export_dir,
lagged_export_dir=self._lagged_export_dir)
])
]
def DefaultLaggedCheckpointListener(self):
return checkpoint_hooks.LaggedCheckpointListener(
export_fn=self._ExportFn,
export_dir=self._export_dir,
lagged_export_dir=self._lagged_export_dir,
num_versions=3)
