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TFX proto module.
Modules
orchestration module: TFX orchestrator proto imports.
Classes
class ClassifyOutput: One type of output_type under proto.OutputColumnsSpec.
class CustomConfig: Optional specified configuration for ExampleGen components.
class DataSpec: Indicates which splits of examples should be processed incomponents.BulkInferrer.
class DistributionValidatorConfig: Configurations related to Distribution Validator.
class EnvVar: EnvVar represents an environment variable present in a Container.
class EnvVarSource: EnvVarSource represents a source for the value of an EnvVar.
class EvalArgs: Args specific to eval in components.Trainer.
class ExampleDiffConfig: Configurations related to Example Diff.
class FeatureComparator: Per feature configuration in Distribution Validator.
class FeatureSlicingSpec: Slices corresponding to data set in components.Evaluator.
class Filesystem: File system based destination definition.
class Input: Specification of the input of the ExampleGen components.
class KubernetesConfig: Kubernetes configuration. We currently only support the use case when infra validator is run by orchestration.KubeflowDagRunner.
class LocalDockerConfig: Docker runtime in a local machine. This is useful when you're running pipeline with infra validator component in your your local machine.
class ModelSpec: Specifies the signature name to run the inference in components.BulkInferrer.
class Output: Specification of the output of the ExampleGen components.
class OutputColumnsSpec: The signature_name should exist in ModelSpec.model_signature_name.
class OutputExampleSpec: Defines how the inferrence results map to columns in output example in components.BulkInferrer.
class PairedExampleSkew: Configurations related to Example Diff on feature pairing level.
class PodOverrides: Flattened collections of overridable variables for Pod and its submessages.
class PredictOutput: One type of output_type under proto.OutputColumnsSpec.
class PredictOutputCol: Proto type of output_columns under proto.PredictOutput.
class PushDestination: Defines the destination of pusher in components.Pusher.
class RangeConfig: RangeConfig is an abstract proto which can be used to describe ranges for different entities in TFX Pipeline.
class RegressOutput: One type of output_type under proto.OutputColumnsSpec.
class RequestSpec: Optional configuration about making requests from examples input in components.InfraValidator.
class RollingRange: Describes a rolling range.
class SecretKeySelector: SecretKeySelector selects a key of a Secret.
class ServingSpec: Defines an environment of the validating infrastructure in components.InfraValidator.
class SingleSlicingSpec: Specifies a single directive for choosing features for slicing.
class SplitConfig: A config to partition examples into split in proto.Output of ExampleGen.
class SplitsConfig: Defines the splits config in components.Transform.
class StaticRange: Describes a static window within the specified span numbers [start_span_number, end_span_number].
class TensorFlowServing: TensorFlow Serving docker image (tensorflow/serving) for serving binary.
class TensorFlowServingRequestSpec: Request spec for building TF Serving requests.
class TrainArgs: Args specific to training in components.Trainer.
class TuneArgs: Args specific to tuning in components.Tuner.
class ValidationSpec: Specification for validation criteria and thresholds in components.InfraValidator.