def export(self,
export_dir,
signature_fn=None,
input_fn=export.default_input_fn,
default_batch_size=1,
exports_to_keep=None):
"""Exports inference graph into given dir.
Args:
export_dir: A string containing a directory to write the exported graph
and checkpoints.
signature_fn: Function that returns a default signature and a named
signature map, given `Tensor` of `Example` strings, `dict` of `Tensor`s
for features and `Tensor` or `dict` of `Tensor`s for predictions.
input_fn: Function that given `Tensor` of `Example` strings, parses it
into features that are then passed to the model.
default_batch_size: Default batch size of the `Example` placeholder.
exports_to_keep: Number of exports to keep.
"""
export.export_estimator(estimator=self,
export_dir=export_dir,
signature_fn=signature_fn,
input_fn=input_fn,
default_batch_size=default_batch_size,
exports_to_keep=exports_to_keep)
def every_n_step_end(self, step, outputs):
super(ExportMonitor, self).every_n_step_end(step, outputs)
try:
export.export_estimator(self._estimator, self.export_dir,
exports_to_keep=self.exports_to_keep)
except RuntimeError:
# Currently we are not syncronized with saving checkpoints, which leads to
# runtime errors when we are calling export on the same global step.
logging.info("Skipping exporting for the same step. "
"Consider exporting less frequently.")
def every_n_step_end(self, step, outputs):
super(ExportMonitor, self).every_n_step_end(step, outputs)
try:
export.export_estimator(self._estimator,
self.export_dir,
exports_to_keep=self.exports_to_keep)
except RuntimeError:
# Currently we are not syncronized with saving checkpoints, which leads to
# runtime errors when we are calling export on the same global step.
logging.info("Skipping exporting for the same step. "
"Consider exporting less frequently.")
def end(self, session=None):
super(ExportMonitor, self).end(session=session)
latest_path = saver_lib.latest_checkpoint(self._estimator.model_dir)
if latest_path is None:
logging.info("Skipping export at the end since model has not been saved "
"yet.")
return
export.export_estimator(self._estimator,
self.export_dir,
exports_to_keep=self.exports_to_keep,
signature_fn=self.signature_fn,
default_batch_size=self._default_batch_size)
def end(self, session=None):
super(ExportMonitor, self).end(session=session)
latest_path = saver_lib.latest_checkpoint(self._estimator.model_dir)
if latest_path is None:
logging.info("Skipping export at the end since model has not been saved "
"yet.")
return
export.export_estimator(self._estimator,
self.export_dir,
exports_to_keep=self.exports_to_keep,
signature_fn=self.signature_fn,
default_batch_size=self._default_batch_size)
def export(self, export_dir, signature_fn=None,
input_fn=export.default_input_fn, default_batch_size=1,
exports_to_keep=None):
"""Exports inference graph into given dir.
Args:
export_dir: A string containing a directory to write the exported graph
and checkpoints.
signature_fn: Function that returns a default signature and a named
signature map, given `Tensor` of `Example` strings, `dict` of `Tensor`s
for features and `Tensor` or `dict` of `Tensor`s for predictions.
input_fn: Function that given `Tensor` of `Example` strings, parses it
into features that are then passed to the model.
default_batch_size: Default batch size of the `Example` placeholder.
exports_to_keep: Number of exports to keep.
"""
export.export_estimator(estimator=self,
export_dir=export_dir,
signature_fn=signature_fn,
input_fn=input_fn,
default_batch_size=default_batch_size,
exports_to_keep=exports_to_keep)
def end(self):
super(ExportMonitor, self).end()
export.export_estimator(self._estimator,
self.export_dir,
exports_to_keep=self.exports_to_keep)
def end(self, session=None):
super(ExportMonitor, self).end(session=session)
export.export_estimator(self._estimator,
self.export_dir,
exports_to_keep=self.exports_to_keep,
signature_fn=self.signature_fn)
def end(self, session=None):
super(ExportMonitor, self).end(session=session)
export.export_estimator(self._estimator,
self.export_dir,
exports_to_keep=self.exports_to_keep,
signature_fn=self.signature_fn)
def end(self):
super(ExportMonitor, self).end()
export.export_estimator(self._estimator, self.export_dir,
exports_to_keep=self.exports_to_keep)
