tfx.components.InfraValidator

A TFX component to validate the model against the serving infrastructure.

Inherits From: BaseComponent, BaseNode

View aliases

Main aliases

tfx.components.infra_validator.component.InfraValidator

tfx.components.InfraValidator(
    model: tfx.types.Channel,
    serving_spec: infra_validator_pb2.ServingSpec,
    examples: Optional[tfx.types.Channel] = None,
    blessing: Optional[tfx.types.Channel] = None,
    request_spec: Optional[infra_validator_pb2.RequestSpec] = None,
    validation_spec: Optional[infra_validator_pb2.ValidationSpec] = None,
    instance_name: Optional[Text] = None
)

An infra validation is done by loading the model to the exactly same serving binary that is used in production, and additionaly sending some requests to the model server. Such requests can be specified from Examples artifact.

Examples

Full example using TensorFlowServing binary running on local docker.

infra_validator = InfraValidator(
    model=trainer.outputs['model'],
    examples=test_example_gen.outputs['examples'],
    serving_spec=ServingSpec(
        tensorflow_serving=TensorFlowServing(  # Using TF Serving.
            tags=['latest']
        ),
        local_docker=LocalDockerConfig(),  # Running on local docker.
    ),
    validation_spec=ValidationSpec(
        max_loading_time_seconds=60,
        num_tries=5,
    ),
    request_spec=RequestSpec(
        tensorflow_serving=TensorFlowServingRequestSpec(),
        num_examples=1,
    )
)

Minimal example when running on Kubernetes.

infra_validator = InfraValidator(
    model=trainer.outputs['model'],
    examples=test_example_gen.outputs['examples'],
    serving_spec=ServingSpec(
        tensorflow_serving=TensorFlowServing(
            tags=['latest']
        ),
        kubernetes=KubernetesConfig(),  # Running on Kubernetes.
    ),
)

Args
model	A Channel of ModelExportPath type, usually produced by Trainer component. required
serving_spec	A ServingSpec configuration about serving binary and test platform config to launch model server for validation. required
examples	A Channel of ExamplesPath type, usually produced by ExampleGen component. If not specified, InfraValidator does not issue requests for validation.
blessing	Output Channel of InfraBlessingPath that contains the validation result.
request_spec	Optional RequestSpec configuration about making requests from examples input. If not specified, InfraValidator does not issue requests for validation.
validation_spec	Optional ValidationSpec configuration.
instance_name	Optional name assigned to this specific instance of InfraValidator. Required only if multiple InfraValidator components are declared in the same pipeline.

Attributes
component_id
component_type
downstream_nodes
exec_properties
id	Node id, unique across all TFX nodes in a pipeline. If id is set by the user, return it directly. otherwise, if instance name (deprecated) is available, node id will be: . otherwise, node id will be:
inputs
outputs
type
upstream_nodes

Child Classes

class DRIVER_CLASS

class SPEC_CLASS

Methods

add_downstream_node

View source

add_downstream_node(
    downstream_node
)

Experimental: Add another component that must run after this one.

This method enables task-based dependencies by enforcing execution order for synchronous pipelines on supported platforms. Currently, the supported platforms are Airflow, Beam, and Kubeflow Pipelines.

Note that this API call should be considered experimental, and may not work with asynchronous pipelines, sub-pipelines and pipelines with conditional nodes. We also recommend relying on data for capturing dependencies where possible to ensure data lineage is fully captured within MLMD.

It is symmetric with add_upstream_node.

Args
downstream_node	a component that must run after this node.

add_upstream_node

View source

add_upstream_node(
    upstream_node
)

Experimental: Add another component that must run before this one.

This method enables task-based dependencies by enforcing execution order for synchronous pipelines on supported platforms. Currently, the supported platforms are Airflow, Beam, and Kubeflow Pipelines.

Note that this API call should be considered experimental, and may not work with asynchronous pipelines, sub-pipelines and pipelines with conditional nodes. We also recommend relying on data for capturing dependencies where possible to ensure data lineage is fully captured within MLMD.

It is symmetric with add_downstream_node.

Args
upstream_node	a component that must run before this node.

from_json_dict

View source

@classmethod
from_json_dict(
    dict_data: Dict[Text, Any]
) -> Any

Convert from dictionary data to an object.

get_class_type

View source

@classmethod
get_class_type() -> Text

get_id

View source

@classmethod
get_id(
    instance_name: Optional[Text] = None
)

Gets the id of a node.

This can be used during pipeline authoring time. For example: from tfx.components import Trainer

resolver = ResolverNode(..., model=Channel( type=Model, producer_component_id=Trainer.get_id('my_trainer')))

Args
instance_name	(Optional) instance name of a node. If given, the instance name will be taken into consideration when generating the id.

Returns
an id for the node.

to_json_dict

View source

to_json_dict() -> Dict[Text, Any]

Convert from an object to a JSON serializable dictionary.

with_id

View source

with_id(
    id: Text
) -> "BaseNode"

with_platform_config

View source

with_platform_config(
    config: message.Message
) -> "BaseComponent"

Attaches a proto-form platform config to a component.

The config will be a per-node platform-specific config.

Args
config	platform config to attach to the component.

Returns
the same component itself.

Class Variables
EXECUTOR_SPEC	Instance of tfx.dsl.components.base.executor_spec.ExecutorClassSpec