def __init__(self,
examples: types.Channel = None,
model: Optional[types.Channel] = None,
model_blessing: Optional[types.Channel] = None,
data_spec: Optional[Union[bulk_inferrer_pb2.DataSpec,
Dict[Text, Any]]] = None,
model_spec: Optional[Union[bulk_inferrer_pb2.ModelSpec,
Dict[Text, Any]]] = None,
output_example_spec: Optional[Union[
bulk_inferrer_pb2.OutputExampleSpec, Dict[Text,
Any]]] = None):
"""Construct an BulkInferrer component.
Args:
examples: A Channel of type `standard_artifacts.Examples`, usually
produced by an ExampleGen component. _required_
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.
data_spec: bulk_inferrer_pb2.DataSpec instance that describes data
selection. If any field is provided as a RuntimeParameter, data_spec
should be constructed as a dict with the same field names as DataSpec
proto message.
model_spec: bulk_inferrer_pb2.ModelSpec instance that describes model
specification. If any field is provided as a RuntimeParameter,
model_spec should be constructed as a dict with the same field names as
ModelSpec proto message.
output_example_spec: bulk_inferrer_pb2.OutputExampleSpec instance, specify
if you want BulkInferrer to output examples instead of inference result.
If any field is provided as a RuntimeParameter, output_example_spec
should be constructed as a dict with the same field names as
OutputExampleSpec proto message.
"""
if output_example_spec:
output_examples = types.Channel(type=standard_artifacts.Examples)
inference_result = None
else:
inference_result = types.Channel(
type=standard_artifacts.InferenceResult)
output_examples = None
spec = BulkInferrerSpec(examples=examples,
model=model,
model_blessing=model_blessing,
data_spec=data_spec
or bulk_inferrer_pb2.DataSpec(),
model_spec=model_spec
or bulk_inferrer_pb2.ModelSpec(),
output_example_spec=output_example_spec,
inference_result=inference_result,
output_examples=output_examples)
super(BulkInferrer, self).__init__(spec=spec)
def __init__(self,
examples: types.Channel = None,
model: Optional[types.Channel] = None,
model_blessing: Optional[types.Channel] = None,
data_spec: Optional[Union[bulk_inferrer_pb2.DataSpec,
Dict[Text, Any]]] = None,
model_spec: Optional[Union[bulk_inferrer_pb2.ModelSpec,
Dict[Text, Any]]] = None,
inference_result: Optional[types.Channel] = None,
instance_name: Optional[Text] = None,
enable_cache: Optional[bool] = None):
"""Construct an BulkInferrer component.
Args:
examples: A Channel of type `standard_artifacts.Examples`, usually
produced by an ExampleGen component. _required_
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.
data_spec: bulk_inferrer_pb2.DataSpec instance that describes data
selection. If any field is provided as a RuntimeParameter, data_spec
should be constructed as a dict with the same field names as DataSpec
proto message.
model_spec: bulk_inferrer_pb2.ModelSpec instance that describes model
specification. If any field is provided as a RuntimeParameter,
model_spec should be constructed as a dict with the same field names as
ModelSpec proto message.
inference_result: Channel of type `standard_artifacts.InferenceResult`
to store the inference results.
instance_name: Optional name assigned to this specific instance of
BulkInferrer. Required only if multiple BulkInferrer components are
declared in the same pipeline.
enable_cache: Optional boolean to indicate if cache is enabled for the
BulkInferrer component. If not specified, defaults to the value
specified for pipeline's enable_cache parameter.
"""
inference_result = inference_result or types.Channel(
type=standard_artifacts.InferenceResult,
artifacts=[standard_artifacts.InferenceResult()])
spec = BulkInferrerSpec(examples=examples,
model=model,
model_blessing=model_blessing,
data_spec=data_spec
or bulk_inferrer_pb2.DataSpec(),
model_spec=model_spec
or bulk_inferrer_pb2.ModelSpec(),
inference_result=inference_result)
super(BulkInferrer, self).__init__(spec=spec,
instance_name=instance_name,
enable_cache=enable_cache)
def __init__(self,
examples: types.Channel = None,
model_export: Optional[types.Channel] = None,
model_blessing: Optional[types.Channel] = None,
model_push: Optional[types.Channel] = None,
data_spec: Optional[bulk_inferrer_pb2.DataSpec] = None,
model_spec: Optional[bulk_inferrer_pb2.ModelSpec] = None,
output: Optional[types.Channel] = None,
instance_name: Optional[Text] = None):
"""Construct an BulkInferrer component.
Args:
examples: A Channel of 'ExamplesPath' type, usually produced by ExampleGen
component. _required_
model_export: A Channel of 'ModelExportPath' type, usually produced by
Trainer component.
model_blessing: A Channel of 'ModelBlessingPath' type, usually produced by
Model Validator component.
model_push: A Channel of 'PushedModel' type, usually produced by Pusher
component.
data_spec: bulk_inferrer_pb2.DataSpec instance that describes data
selection.
model_spec: bulk_inferrer_pb2.ModelSpec instance that describes model
specification.
output: Channel of `InferenceResult` to store the inference results.
instance_name: Optional name assigned to this specific instance of
BulkInferrer. Required only if multiple BulkInferrer components are
declared in the same pipeline.
"""
output = output or types.Channel(
type=standard_artifacts.InferenceResult,
artifacts=[standard_artifacts.InferenceResult()])
spec = BulkInferrerSpec(examples=examples,
model_export=model_export,
model_blessing=model_blessing,
model_push=model_push,
data_spec=data_spec
or bulk_inferrer_pb2.DataSpec(),
model_spec=model_spec
or bulk_inferrer_pb2.ModelSpec(),
output=output)
super(BulkInferrer, self).__init__(spec=spec,
instance_name=instance_name)
def __init__(self,
examples: types.Channel = None,
model: Optional[types.Channel] = None,
model_blessing: Optional[types.Channel] = None,
data_spec: Optional[Union[bulk_inferrer_pb2.DataSpec,
Dict[Text, Any]]] = None,
model_spec: Optional[Union[bulk_inferrer_pb2.ModelSpec,
Dict[Text, Any]]] = None,
output_example_spec: Optional[Union[
bulk_inferrer_pb2.OutputExampleSpec, Dict[Text,
Any]]] = None,
inference_result: Optional[types.Channel] = None,
output_examples: Optional[types.Channel] = None,
instance_name: Optional[Text] = None):
"""Construct an BulkInferrer component.
Args:
examples: A Channel of type `standard_artifacts.Examples`, usually
produced by an ExampleGen component. _required_
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.
data_spec: bulk_inferrer_pb2.DataSpec instance that describes data
selection. If any field is provided as a RuntimeParameter, data_spec
should be constructed as a dict with the same field names as DataSpec
proto message.
model_spec: bulk_inferrer_pb2.ModelSpec instance that describes model
specification. If any field is provided as a RuntimeParameter,
model_spec should be constructed as a dict with the same field names as
ModelSpec proto message.
output_example_spec: bulk_inferrer_pb2.OutputExampleSpec instance, specify
if you want BulkInferrer to output examples instead of inference result.
If any field is provided as a RuntimeParameter, output_example_spec
should be constructed as a dict with the same field names as
OutputExampleSpec proto message.
inference_result: Channel of type `standard_artifacts.InferenceResult`
to store the inference results, must not be specified when
output_example_spec is set.
output_examples: Channel of type `standard_artifacts.Examples`
to store the output examples, must not be specified when
output_example_spec is unset. Check output_example_spec for details.
instance_name: Optional name assigned to this specific instance of
BulkInferrer. Required only if multiple BulkInferrer components are
declared in the same pipeline.
Raises:
ValueError: Must not specify inference_result or output_examples depends
on whether output_example_spec is set or not.
"""
if output_example_spec:
if inference_result:
raise ValueError(
'Must not specify inference_result when output_example_spec is set.'
)
output_examples = output_examples or types.Channel(
type=standard_artifacts.Examples)
else:
if output_examples:
raise ValueError(
'Must not specify output_examples when output_example_spec is unset.'
)
inference_result = inference_result or types.Channel(
type=standard_artifacts.InferenceResult)
spec = BulkInferrerSpec(examples=examples,
model=model,
model_blessing=model_blessing,
data_spec=data_spec
or bulk_inferrer_pb2.DataSpec(),
model_spec=model_spec
or bulk_inferrer_pb2.ModelSpec(),
output_example_spec=output_example_spec,
inference_result=inference_result,
output_examples=output_examples)
super(BulkInferrer, self).__init__(spec=spec,
instance_name=instance_name)