def __init__(
self,
model: types.Channel = None,
model_blessing: Optional[types.Channel] = None,
infra_blessing: Optional[types.Channel] = None,
push_destination: Optional[Union[pusher_pb2.PushDestination,
Dict[Text, Any]]] = None,
custom_config: Optional[Dict[Text, Any]] = None,
custom_executor_spec: Optional[executor_spec.ExecutorSpec] = None,
output: Optional[types.Channel] = None,
model_export: Optional[types.Channel] = None,
instance_name: Optional[Text] = None):
"""Construct a Pusher component.
Args:
model: A Channel of type `standard_artifacts.Model`, usually produced by
a Trainer component.
model_blessing: An optional Channel of type
`standard_artifacts.ModelBlessing`, usually produced from an Evaluator
component.
infra_blessing: An optional Channel of type
`standard_artifacts.InfraBlessing`, usually produced from an
InfraValidator component.
push_destination: A pusher_pb2.PushDestination instance, providing info
for tensorflow serving to load models. Optional if executor_class
doesn't require push_destination. If any field is provided as a
RuntimeParameter, push_destination should be constructed as a dict with
the same field names as PushDestination proto message.
custom_config: A dict which contains the deployment job parameters to be
passed to cloud-based training platforms. The [Kubeflow example](
https://github.com/tensorflow/tfx/blob/6ff57e36a7b65818d4598d41e584a42584d361e6/tfx/examples/chicago_taxi_pipeline/taxi_pipeline_kubeflow_gcp.py#L278-L285)
contains an example how this can be used by custom executors.
custom_executor_spec: Optional custom executor spec.
output: Optional output `standard_artifacts.PushedModel` channel with
result of push.
model_export: Backwards compatibility alias for the 'model' argument.
instance_name: Optional unique instance name. Necessary if multiple Pusher
components are declared in the same pipeline.
"""
if model_export:
absl.logging.warning(
'The "model_export" argument to the Pusher component has '
'been renamed to "model" and is deprecated. Please update your '
'usage as support for this argument will be removed soon.')
model = model_export
output = output or types.Channel(type=standard_artifacts.PushedModel)
if push_destination is None and not custom_executor_spec:
raise ValueError(
'push_destination is required unless a '
'custom_executor_spec is supplied that does not require '
'it.')
spec = PusherSpec(model=model,
model_blessing=model_blessing,
infra_blessing=infra_blessing,
push_destination=push_destination,
custom_config=json_utils.dumps(custom_config),
pushed_model=output)
super(Pusher, self).__init__(spec=spec,
custom_executor_spec=custom_executor_spec,
instance_name=instance_name)
def __init__(
self,
model: Optional[types.Channel] = None,
model_blessing: Optional[types.Channel] = None,
infra_blessing: Optional[types.Channel] = None,
push_destination: Optional[Union[pusher_pb2.PushDestination,
Dict[Text, Any]]] = None,
custom_config: Optional[Dict[Text, Any]] = None,
custom_executor_spec: Optional[executor_spec.ExecutorSpec] = None,
pushed_model: Optional[types.Channel] = None):
"""Construct a Pusher component.
Args:
model: An optional Channel of type `standard_artifacts.Model`, usually
produced by a Trainer component.
model_blessing: An optional Channel of type
`standard_artifacts.ModelBlessing`, usually produced from an Evaluator
component.
infra_blessing: An optional Channel of type
`standard_artifacts.InfraBlessing`, usually produced from an
InfraValidator component.
push_destination: A pusher_pb2.PushDestination instance, providing info
for tensorflow serving to load models. Optional if executor_class
doesn't require push_destination. If any field is provided as a
RuntimeParameter, push_destination should be constructed as a dict with
the same field names as PushDestination proto message.
custom_config: A dict which contains the deployment job parameters to be
passed to cloud-based training platforms. The [Kubeflow example](
https://github.com/tensorflow/tfx/blob/6ff57e36a7b65818d4598d41e584a42584d361e6/tfx/examples/chicago_taxi_pipeline/taxi_pipeline_kubeflow_gcp.py#L278-L285)
contains an example how this can be used by custom executors.
custom_executor_spec: Optional custom executor spec. This is experimental
and is subject to change in the future.
pushed_model: Optional output `standard_artifacts.PushedModel` channel
with result of push.
"""
pushed_model = pushed_model or types.Channel(
type=standard_artifacts.PushedModel)
if push_destination is None and not custom_executor_spec:
raise ValueError(
'push_destination is required unless a '
'custom_executor_spec is supplied that does not require '
'it.')
if model is None and infra_blessing is None:
raise ValueError(
'Either one of model or infra_blessing channel should be given. '
'If infra_blessing is used in place of model, it must have been '
'created with InfraValidator with RequestSpec.make_warmup = True. '
'This cannot be checked during pipeline construction time but will '
'raise runtime error if infra_blessing does not contain a model.'
)
spec = PusherSpec(model=model,
model_blessing=model_blessing,
infra_blessing=infra_blessing,
push_destination=push_destination,
custom_config=json_utils.dumps(custom_config),
pushed_model=pushed_model)
super(Pusher, self).__init__(spec=spec,
custom_executor_spec=custom_executor_spec)
def __init__(
self,
model: Optional[types.Channel] = None,
model_blessing: Optional[types.Channel] = None,
infra_blessing: Optional[types.Channel] = None,
push_destination: Optional[Union[pusher_pb2.PushDestination,
Dict[Text, Any]]] = None,
custom_config: Optional[Dict[Text, Any]] = None,
custom_executor_spec: Optional[executor_spec.ExecutorSpec] = None):
"""Construct a Pusher component.
Args:
model: An optional Channel of type `standard_artifacts.Model`, usually
produced by a Trainer component.
model_blessing: An optional Channel of type
`standard_artifacts.ModelBlessing`, usually produced from an Evaluator
component.
infra_blessing: An optional Channel of type
`standard_artifacts.InfraBlessing`, usually produced from an
InfraValidator component.
push_destination: A pusher_pb2.PushDestination instance, providing info
for tensorflow serving to load models. Optional if executor_class
doesn't require push_destination. If any field is provided as a
RuntimeParameter, push_destination should be constructed as a dict with
the same field names as PushDestination proto message.
custom_config: A dict which contains the deployment job parameters to be
passed to Cloud platforms.
custom_executor_spec: Optional custom executor spec. This is experimental
and is subject to change in the future.
"""
pushed_model = types.Channel(type=standard_artifacts.PushedModel)
if (push_destination is None and not custom_executor_spec
and self.EXECUTOR_SPEC.executor_class == executor.Executor):
raise ValueError(
'push_destination is required unless a '
'custom_executor_spec is supplied that does not require '
'it.')
if custom_executor_spec:
logging.warning(
'`custom_executor_spec` is going to be deprecated.')
if model is None and infra_blessing is None:
raise ValueError(
'Either one of model or infra_blessing channel should be given. '
'If infra_blessing is used in place of model, it must have been '
'created with InfraValidator with RequestSpec.make_warmup = True. '
'This cannot be checked during pipeline construction time but will '
'raise runtime error if infra_blessing does not contain a model.'
)
spec = PusherSpec(model=model,
model_blessing=model_blessing,
infra_blessing=infra_blessing,
push_destination=push_destination,
custom_config=json_utils.dumps(custom_config),
pushed_model=pushed_model)
super(Pusher, self).__init__(spec=spec,
custom_executor_spec=custom_executor_spec)
def __init__(
self,
model: types.Channel = None,
model_blessing: types.Channel = None,
push_destination: Optional[Union[pusher_pb2.PushDestination,
Dict[Text, Any]]] = None,
custom_config: Optional[Dict[Text, Any]] = None,
custom_executor_spec: Optional[executor_spec.ExecutorSpec] = None,
model_push: Optional[types.Channel] = None,
model_export: Optional[types.Channel] = None,
instance_name: Optional[Text] = None):
"""Construct a Pusher component.
Args:
model: A Channel of type `standard_artifacts.Model`, usually produced by
a Trainer component.
model_blessing: A Channel of type `standard_artifacts.ModelBlessing`,
usually produced by a ModelValidator component. _required_
push_destination: A pusher_pb2.PushDestination instance, providing info
for tensorflow serving to load models. Optional if executor_class
doesn't require push_destination. If any field is provided as a
RuntimeParameter, push_destination should be constructed as a dict with
the same field names as PushDestination proto message.
custom_config: A dict which contains the deployment job parameters to be
passed to cloud-based training platforms. The [Kubeflow
example](https://github.com/tensorflow/tfx/blob/master/tfx/examples/chicago_taxi_pipeline/taxi_pipeline_kubeflow.py#L211)
contains an example how this can be used by custom executors.
custom_executor_spec: Optional custom executor spec.
model_push: Optional output 'ModelPushPath' channel with result of push.
model_export: Backwards compatibility alias for the 'model' argument.
instance_name: Optional unique instance name. Necessary if multiple Pusher
components are declared in the same pipeline.
"""
model = model or model_export
model_push = model_push or types.Channel(
type=standard_artifacts.PushedModel,
artifacts=[standard_artifacts.PushedModel()])
if push_destination is None and not custom_executor_spec:
raise ValueError(
'push_destination is required unless a '
'custom_executor_spec is supplied that does not require '
'it.')
spec = PusherSpec(model_export=model,
model_blessing=model_blessing,
push_destination=push_destination,
custom_config=custom_config,
model_push=model_push)
super(Pusher, self).__init__(spec=spec,
custom_executor_spec=custom_executor_spec,
instance_name=instance_name)
def __init__(
self,
model_export: types.Channel = None,
model_blessing: types.Channel = None,
push_destination: Optional[pusher_pb2.PushDestination] = None,
custom_config: Optional[Dict[Text, Any]] = None,
executor_class: Optional[Type[base_executor.BaseExecutor]] = None,
model_push: Optional[types.Channel] = None,
model: Optional[types.Channel] = None,
name: Optional[Text] = None):
"""Construct a Pusher component.
Args:
model_export: A Channel of 'ModelExportPath' type, usually produced by
Trainer component (required).
model_blessing: A Channel of 'ModelBlessingPath' type, usually produced by
ModelValidator component (required).
push_destination: A pusher_pb2.PushDestination instance, providing
info for tensorflow serving to load models. Optional if executor_class
doesn't require push_destination.
custom_config: A dict which contains the deployment job parameters to be
passed to Google Cloud ML Engine. For the full set of parameters
supported by Google Cloud ML Engine, refer to
https://cloud.google.com/ml-engine/reference/rest/v1/projects.models
executor_class: Optional custom python executor class.
model_push: Optional output 'ModelPushPath' channel with result of push.
model: Forwards compatibility alias for the 'model_exports' argument.
name: Optional unique name. Necessary if multiple Pusher components are
declared in the same pipeline.
"""
model_export = model_export or model
model_push = model_push or types.Channel(
type=standard_artifacts.PushedModel,
artifacts=[standard_artifacts.PushedModel()])
if push_destination is None and not executor_class:
raise ValueError(
'push_destination is required unless a custom '
'executor_class is supplied that does not require '
'it.')
spec = PusherSpec(model_export=model_export,
model_blessing=model_blessing,
push_destination=push_destination,
custom_config=custom_config,
model_push=model_push)
super(Pusher, self).__init__(spec=spec,
custom_executor_class=executor_class,
name=name)