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TFX-BSL Public Beam

tfx_bsl.public.beam

Module level imports for tfx_bsl.beam.

Functions

RunInference 

RunInference(
    examples: PCollection,
    inference_spec_type: InferenceSpecType,
    load_override_fn: Optional[LoadOverrideFnType] = None,
) -> PCollection

Run inference with a model.

There are two types of inference you can perform using this PTransform
  1. In-process inference from a SavedModel instance. Used when saved_model_spec field is set in inference_spec_type.
  2. Remote inference by using a service endpoint. Used when ai_platform_prediction_model_spec field is set in inference_spec_type.
PARAMETER DESCRIPTION
examples

A PCollection containing examples of the following possible kinds, each with their corresponding return type. - PCollection[Example] -> PCollection[PredictionLog] * Works with Classify, Regress, MultiInference, Predict and RemotePredict.

  • PCollection[SequenceExample] -> PCollection[PredictionLog]

    • Works with Predict and (serialized) RemotePredict.

  • PCollection[bytes] -> PCollection[PredictionLog]

    • For serialized Example: Works with Classify, Regress, MultiInference, Predict and RemotePredict.
    • For everything else: Works with Predict and RemotePredict.

  • PCollection[Tuple[K, Example]] -> PCollection[ Tuple[K, PredictionLog]]

    • Works with Classify, Regress, MultiInference, Predict and RemotePredict.

  • PCollection[Tuple[K, SequenceExample]] -> PCollection[ Tuple[K, PredictionLog]]

    • Works with Predict and (serialized) RemotePredict.

  • PCollection[Tuple[K, bytes]] -> PCollection[ Tuple[K, PredictionLog]]

    • For serialized Example: Works with Classify, Regress, MultiInference, Predict and RemotePredict.
    • For everything else: Works with Predict and RemotePredict.

TYPE: PCollection

inference_spec_type

Model inference endpoint.

TYPE: InferenceSpecType

load_override_fn

Optional function taking a model path and sequence of tags, and returning a tf SavedModel. The loaded model must be equivalent in interface to the model that would otherwise be loaded. It is up to the caller to ensure compatibility. This argument is experimental and subject to change.

TYPE: Optional[LoadOverrideFnType] DEFAULT: None

RETURNS DESCRIPTION
PCollection

A PCollection (possibly keyed) containing prediction logs.
Source code in tfx_bsl/public/beam/run_inference.py 
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@beam.ptransform_fn
@beam.typehints.with_input_types(_MaybeKeyedInput)
@beam.typehints.with_output_types(Union[_OUTPUT_TYPE, Tuple[_K, _OUTPUT_TYPE]])
def RunInference(
    examples: beam.pvalue.PCollection,
    inference_spec_type: model_spec_pb2.InferenceSpecType,
    load_override_fn: Optional[run_inference.LoadOverrideFnType] = None
) -> beam.pvalue.PCollection:
  """Run inference with a model.

  There are two types of inference you can perform using this PTransform:
    1. In-process inference from a SavedModel instance. Used when
      `saved_model_spec` field is set in `inference_spec_type`.
    2. Remote inference by using a service endpoint. Used when
      `ai_platform_prediction_model_spec` field is set in
      `inference_spec_type`.

  Args:
    examples: A PCollection containing examples of the following possible kinds,
      each with their corresponding return type.
        - PCollection[Example]                   -> PCollection[PredictionLog]
            * Works with Classify, Regress, MultiInference, Predict and
              RemotePredict.

        - PCollection[SequenceExample]           -> PCollection[PredictionLog]
            * Works with Predict and (serialized) RemotePredict.

        - PCollection[bytes]                     -> PCollection[PredictionLog]
            * For serialized Example: Works with Classify, Regress,
              MultiInference, Predict and RemotePredict.
            * For everything else: Works with Predict and RemotePredict.

        - PCollection[Tuple[K, Example]]         -> PCollection[
                                                        Tuple[K, PredictionLog]]
            * Works with Classify, Regress, MultiInference, Predict and
              RemotePredict.

        - PCollection[Tuple[K, SequenceExample]] -> PCollection[
                                                        Tuple[K, PredictionLog]]
            * Works with Predict and (serialized) RemotePredict.

        - PCollection[Tuple[K, bytes]]           -> PCollection[
                                                        Tuple[K, PredictionLog]]
            * For serialized Example: Works with Classify, Regress,
              MultiInference, Predict and RemotePredict.
            * For everything else: Works with Predict and RemotePredict.

    inference_spec_type: Model inference endpoint.
    load_override_fn: Optional function taking a model path and sequence of
      tags, and returning a tf SavedModel. The loaded model must be equivalent
      in interface to the model that would otherwise be loaded. It is up to the
      caller to ensure compatibility. This argument is experimental and subject
      to change.

  Returns:
    A PCollection (possibly keyed) containing prediction logs.
  """
  return (examples
          | 'RunInferenceImpl' >> run_inference.RunInferenceImpl(
              inference_spec_type, load_override_fn))

Modules

run_inference

Public API of batch inference.

Functions
CreateModelHandler 
CreateModelHandler(
    inference_spec_type: InferenceSpecType,
) -> ModelHandler

Creates a Beam ModelHandler based on the inference spec type.

There are two model handlers
  1. In-process inference from a SavedModel instance. Used when saved_model_spec field is set in inference_spec_type.
  2. Remote inference by using a service endpoint. Used when ai_platform_prediction_model_spec field is set in inference_spec_type.

Example Usage:

from apache_beam.ml.inference import base

tf_handler = CreateModelHandler(inference_spec_type)
# unkeyed
base.RunInference(tf_handler)

# keyed
base.RunInference(base.KeyedModelHandler(tf_handler))
PARAMETER DESCRIPTION
inference_spec_type

Model inference endpoint.

TYPE: InferenceSpecType

RETURNS DESCRIPTION
ModelHandler

A Beam RunInference ModelHandler for TensorFlow
Source code in tfx_bsl/public/beam/run_inference.py 
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def CreateModelHandler(
    inference_spec_type: model_spec_pb2.InferenceSpecType) -> ModelHandler:
  """Creates a Beam ModelHandler based on the inference spec type.

  There are two model handlers:
    1. In-process inference from a SavedModel instance. Used when
      `saved_model_spec` field is set in `inference_spec_type`.
    2. Remote inference by using a service endpoint. Used when
      `ai_platform_prediction_model_spec` field is set in
      `inference_spec_type`.

  Example Usage:

    ```
    from apache_beam.ml.inference import base

    tf_handler = CreateModelHandler(inference_spec_type)
    # unkeyed
    base.RunInference(tf_handler)

    # keyed
    base.RunInference(base.KeyedModelHandler(tf_handler))
    ```

  Args:
    inference_spec_type: Model inference endpoint.

  Returns:
    A Beam RunInference ModelHandler for TensorFlow
  """
  return run_inference.create_model_handler(inference_spec_type, None, None)
RunInference 
RunInference(
    examples: PCollection,
    inference_spec_type: InferenceSpecType,
    load_override_fn: Optional[LoadOverrideFnType] = None,
) -> PCollection

Run inference with a model.

There are two types of inference you can perform using this PTransform
  1. In-process inference from a SavedModel instance. Used when saved_model_spec field is set in inference_spec_type.
  2. Remote inference by using a service endpoint. Used when ai_platform_prediction_model_spec field is set in inference_spec_type.
PARAMETER DESCRIPTION
examples

A PCollection containing examples of the following possible kinds, each with their corresponding return type. - PCollection[Example] -> PCollection[PredictionLog] * Works with Classify, Regress, MultiInference, Predict and RemotePredict.

  • PCollection[SequenceExample] -> PCollection[PredictionLog]

    • Works with Predict and (serialized) RemotePredict.

  • PCollection[bytes] -> PCollection[PredictionLog]

    • For serialized Example: Works with Classify, Regress, MultiInference, Predict and RemotePredict.
    • For everything else: Works with Predict and RemotePredict.

  • PCollection[Tuple[K, Example]] -> PCollection[ Tuple[K, PredictionLog]]

    • Works with Classify, Regress, MultiInference, Predict and RemotePredict.

  • PCollection[Tuple[K, SequenceExample]] -> PCollection[ Tuple[K, PredictionLog]]

    • Works with Predict and (serialized) RemotePredict.

  • PCollection[Tuple[K, bytes]] -> PCollection[ Tuple[K, PredictionLog]]

    • For serialized Example: Works with Classify, Regress, MultiInference, Predict and RemotePredict.
    • For everything else: Works with Predict and RemotePredict.

TYPE: PCollection

inference_spec_type

Model inference endpoint.

TYPE: InferenceSpecType

load_override_fn

Optional function taking a model path and sequence of tags, and returning a tf SavedModel. The loaded model must be equivalent in interface to the model that would otherwise be loaded. It is up to the caller to ensure compatibility. This argument is experimental and subject to change.

TYPE: Optional[LoadOverrideFnType] DEFAULT: None

RETURNS DESCRIPTION
PCollection

A PCollection (possibly keyed) containing prediction logs.
Source code in tfx_bsl/public/beam/run_inference.py 
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@beam.ptransform_fn
@beam.typehints.with_input_types(_MaybeKeyedInput)
@beam.typehints.with_output_types(Union[_OUTPUT_TYPE, Tuple[_K, _OUTPUT_TYPE]])
def RunInference(
    examples: beam.pvalue.PCollection,
    inference_spec_type: model_spec_pb2.InferenceSpecType,
    load_override_fn: Optional[run_inference.LoadOverrideFnType] = None
) -> beam.pvalue.PCollection:
  """Run inference with a model.

  There are two types of inference you can perform using this PTransform:
    1. In-process inference from a SavedModel instance. Used when
      `saved_model_spec` field is set in `inference_spec_type`.
    2. Remote inference by using a service endpoint. Used when
      `ai_platform_prediction_model_spec` field is set in
      `inference_spec_type`.

  Args:
    examples: A PCollection containing examples of the following possible kinds,
      each with their corresponding return type.
        - PCollection[Example]                   -> PCollection[PredictionLog]
            * Works with Classify, Regress, MultiInference, Predict and
              RemotePredict.

        - PCollection[SequenceExample]           -> PCollection[PredictionLog]
            * Works with Predict and (serialized) RemotePredict.

        - PCollection[bytes]                     -> PCollection[PredictionLog]
            * For serialized Example: Works with Classify, Regress,
              MultiInference, Predict and RemotePredict.
            * For everything else: Works with Predict and RemotePredict.

        - PCollection[Tuple[K, Example]]         -> PCollection[
                                                        Tuple[K, PredictionLog]]
            * Works with Classify, Regress, MultiInference, Predict and
              RemotePredict.

        - PCollection[Tuple[K, SequenceExample]] -> PCollection[
                                                        Tuple[K, PredictionLog]]
            * Works with Predict and (serialized) RemotePredict.

        - PCollection[Tuple[K, bytes]]           -> PCollection[
                                                        Tuple[K, PredictionLog]]
            * For serialized Example: Works with Classify, Regress,
              MultiInference, Predict and RemotePredict.
            * For everything else: Works with Predict and RemotePredict.

    inference_spec_type: Model inference endpoint.
    load_override_fn: Optional function taking a model path and sequence of
      tags, and returning a tf SavedModel. The loaded model must be equivalent
      in interface to the model that would otherwise be loaded. It is up to the
      caller to ensure compatibility. This argument is experimental and subject
      to change.

  Returns:
    A PCollection (possibly keyed) containing prediction logs.
  """
  return (examples
          | 'RunInferenceImpl' >> run_inference.RunInferenceImpl(
              inference_spec_type, load_override_fn))
RunInferenceOnKeyedBatches 
RunInferenceOnKeyedBatches(
    examples: PCollection,
    inference_spec_type: InferenceSpecType,
    load_override_fn: Optional[LoadOverrideFnType] = None,
) -> PCollection

Run inference over pre-batched keyed inputs.

This API is experimental and may change in the future.

Supports the same inference specs as RunInference. Inputs must consist of a keyed list of examples, and outputs consist of keyed list of prediction logs corresponding by index.

PARAMETER DESCRIPTION
examples

A PCollection of keyed, batched inputs of type Example, SequenceExample, or bytes. Each type support inference specs corresponding to the unbatched cases described in RunInference. Supports - PCollection[Tuple[K, List[Example]]] - PCollection[Tuple[K, List[SequenceExample]]] - PCollection[Tuple[K, List[Bytes]]]

TYPE: PCollection

inference_spec_type

Model inference endpoint.

TYPE: InferenceSpecType

load_override_fn

Optional function taking a model path and sequence of tags, and returning a tf SavedModel. The loaded model must be equivalent in interface to the model that would otherwise be loaded. It is up to the caller to ensure compatibility. This argument is experimental and subject to change.

TYPE: Optional[LoadOverrideFnType] DEFAULT: None

RETURNS DESCRIPTION
PCollection

A PCollection of Tuple[K, List[PredictionLog]].
Source code in tfx_bsl/public/beam/run_inference.py 
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@beam.ptransform_fn
@beam.typehints.with_input_types(_KeyedBatchesInput)
@beam.typehints.with_output_types(Tuple[_K, List[_OUTPUT_TYPE]])
def RunInferenceOnKeyedBatches(
    examples: beam.pvalue.PCollection,
    inference_spec_type: model_spec_pb2.InferenceSpecType,
    load_override_fn: Optional[run_inference.LoadOverrideFnType] = None
) -> beam.pvalue.PCollection:
  """Run inference over pre-batched keyed inputs.

  This API is experimental and may change in the future.

  Supports the same inference specs as RunInference. Inputs must consist of a
  keyed list of examples, and outputs consist of keyed list of prediction logs
  corresponding by index.

  Args:
    examples: A PCollection of keyed, batched inputs of type Example,
      SequenceExample, or bytes. Each type support inference specs corresponding
      to the unbatched cases described in RunInference. Supports
        - PCollection[Tuple[K, List[Example]]]
        - PCollection[Tuple[K, List[SequenceExample]]]
        - PCollection[Tuple[K, List[Bytes]]]
    inference_spec_type: Model inference endpoint.
    load_override_fn: Optional function taking a model path and sequence of
      tags, and returning a tf SavedModel. The loaded model must be equivalent
      in interface to the model that would otherwise be loaded. It is up to the
      caller to ensure compatibility. This argument is experimental and subject
      to change.

  Returns:
    A PCollection of Tuple[K, List[PredictionLog]].
  """
  return (examples
          | 'RunInferenceOnKeyedBatchesImpl' >> run_inference.RunInferenceImpl(
              inference_spec_type, load_override_fn))
RunInferencePerModel 
RunInferencePerModel(
    examples: PCollection,
    inference_spec_types: Iterable[InferenceSpecType],
    load_override_fn: Optional[LoadOverrideFnType] = None,
) -> PCollection

Vectorized variant of RunInference (useful for ensembles).

PARAMETER DESCRIPTION
examples

A PCollection containing examples of the following possible kinds, each with their corresponding return type. - PCollection[Example] -> PCollection[ Tuple[PredictionLog, ...]] * Works with Classify, Regress, MultiInference, Predict and RemotePredict.

  • PCollection[SequenceExample] -> PCollection[ Tuple[PredictionLog, ...]]

    • Works with Predict and (serialized) RemotePredict.

  • PCollection[bytes] -> PCollection[ Tuple[PredictionLog, ...]]

    • For serialized Example: Works with Classify, Regress, MultiInference, Predict and RemotePredict.
    • For everything else: Works with Predict and RemotePredict.

  • PCollection[Tuple[K, Example]] -> PCollection[ Tuple[K, Tuple[PredictionLog, ...]]]

    • Works with Classify, Regress, MultiInference, Predict and RemotePredict.

  • PCollection[Tuple[K, SequenceExample]] -> PCollection[ Tuple[K, Tuple[PredictionLog, ...]]]

    • Works with Predict and (serialized) RemotePredict.

  • PCollection[Tuple[K, bytes]] -> PCollection[ Tuple[K, Tuple[PredictionLog, ...]]]

    • For serialized Example: Works with Classify, Regress, MultiInference, Predict and RemotePredict.
    • For everything else: Works with Predict and RemotePredict.

TYPE: PCollection

inference_spec_types

A flat iterable of Model inference endpoints. Inference will happen in a fused fashion (ie without data materialization), sequentially across Models within a Beam thread (but in parallel across threads and workers).

TYPE: Iterable[InferenceSpecType]

load_override_fn

Optional function taking a model path and sequence of tags, and returning a tf SavedModel. The loaded model must be equivalent in interface to the model that would otherwise be loaded. It is up to the caller to ensure compatibility. This argument is experimental and subject to change.

TYPE: Optional[LoadOverrideFnType] DEFAULT: None

RETURNS DESCRIPTION
PCollection

A PCollection (possibly keyed) containing a Tuple of prediction logs. The
PCollection

Tuple of prediction logs is 1-1 aligned with inference_spec_types.
Source code in tfx_bsl/public/beam/run_inference.py 
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@beam.ptransform_fn
@beam.typehints.with_input_types(_MaybeKeyedInput)
@beam.typehints.with_output_types(Union[Tuple[_OUTPUT_TYPE, ...],
                                        Tuple[_K, Tuple[_OUTPUT_TYPE, ...]]])
def RunInferencePerModel(
    examples: beam.pvalue.PCollection,
    inference_spec_types: Iterable[model_spec_pb2.InferenceSpecType],
    load_override_fn: Optional[run_inference.LoadOverrideFnType] = None
) -> beam.pvalue.PCollection:
  """Vectorized variant of RunInference (useful for ensembles).

  Args:
    examples: A PCollection containing examples of the following possible kinds,
      each with their corresponding return type.
        - PCollection[Example]                  -> PCollection[
                                                     Tuple[PredictionLog, ...]]
            * Works with Classify, Regress, MultiInference, Predict and
              RemotePredict.

        - PCollection[SequenceExample]          -> PCollection[
                                                     Tuple[PredictionLog, ...]]
            * Works with Predict and (serialized) RemotePredict.

        - PCollection[bytes]                    -> PCollection[
                                                     Tuple[PredictionLog, ...]]
            * For serialized Example: Works with Classify, Regress,
              MultiInference, Predict and RemotePredict.
            * For everything else: Works with Predict and RemotePredict.

        - PCollection[Tuple[K, Example]]        -> PCollection[
                                                     Tuple[K,
                                                           Tuple[PredictionLog,
                                                                 ...]]]
            * Works with Classify, Regress, MultiInference, Predict and
              RemotePredict.

        - PCollection[Tuple[K, SequenceExample]] -> PCollection[
                                                     Tuple[K,
                                                           Tuple[PredictionLog,
                                                                 ...]]]
            * Works with Predict and (serialized) RemotePredict.

        - PCollection[Tuple[K, bytes]]           -> PCollection[
                                                     Tuple[K,
                                                           Tuple[PredictionLog,
                                                                 ...]]]
            * For serialized Example: Works with Classify, Regress,
              MultiInference, Predict and RemotePredict.
            * For everything else: Works with Predict and RemotePredict.

    inference_spec_types: A flat iterable of Model inference endpoints.
      Inference will happen in a fused fashion (ie without data
      materialization), sequentially across Models within a Beam thread (but
      in parallel across threads and workers).
    load_override_fn: Optional function taking a model path and sequence of
      tags, and returning a tf SavedModel. The loaded model must be equivalent
      in interface to the model that would otherwise be loaded. It is up to the
      caller to ensure compatibility. This argument is experimental and subject
      to change.

  Returns:
    A PCollection (possibly keyed) containing a Tuple of prediction logs. The
    Tuple of prediction logs is 1-1 aligned with inference_spec_types.
  """
  return (examples
          |
          'RunInferencePerModelImpl' >> run_inference.RunInferencePerModelImpl(
              inference_spec_types, load_override_fn))
RunInferencePerModelOnKeyedBatches 
RunInferencePerModelOnKeyedBatches(
    examples: PCollection,
    inference_spec_types: Iterable[InferenceSpecType],
    load_override_fn: Optional[LoadOverrideFnType] = None,
) -> PCollection

Run inference over pre-batched keyed inputs on multiple models.

This API is experimental and may change in the future.

Supports the same inference specs as RunInferencePerModel. Inputs must consist of a keyed list of examples, and outputs consist of keyed list of prediction logs corresponding by index.

PARAMETER DESCRIPTION
examples

A PCollection of keyed, batched inputs of type Example, SequenceExample, or bytes. Each type support inference specs corresponding to the unbatched cases described in RunInferencePerModel. Supports - PCollection[Tuple[K, List[Example]]] - PCollection[Tuple[K, List[SequenceExample]]] - PCollection[Tuple[K, List[Bytes]]]

TYPE: PCollection

inference_spec_types

A flat iterable of Model inference endpoints. Inference will happen in a fused fashion (ie without data materialization), sequentially across Models within a Beam thread (but in parallel across threads and workers).

TYPE: Iterable[InferenceSpecType]

load_override_fn

Optional function taking a model path and sequence of tags, and returning a tf SavedModel. The loaded model must be equivalent in interface to the model that would otherwise be loaded. It is up to the caller to ensure compatibility. This argument is experimental and subject to change.

TYPE: Optional[LoadOverrideFnType] DEFAULT: None

RETURNS DESCRIPTION
PCollection

A PCollection containing Tuples of a key and lists of batched prediction
PCollection

logs from each model provided in inference_spec_types. The Tuple of batched
PCollection

prediction logs is 1-1 aligned with inference_spec_types. The individual
PCollection

prediction logs in the batch are 1-1 aligned with the rows of data in the
PCollection

batch key.
Source code in tfx_bsl/public/beam/run_inference.py 
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@beam.ptransform_fn
@beam.typehints.with_input_types(_KeyedBatchesInput)
@beam.typehints.with_output_types(Tuple[_K, Tuple[List[_OUTPUT_TYPE]]])
def RunInferencePerModelOnKeyedBatches(
    examples: beam.pvalue.PCollection,
    inference_spec_types: Iterable[model_spec_pb2.InferenceSpecType],
    load_override_fn: Optional[run_inference.LoadOverrideFnType] = None
) -> beam.pvalue.PCollection:
  """Run inference over pre-batched keyed inputs on multiple models.

  This API is experimental and may change in the future.

  Supports the same inference specs as RunInferencePerModel. Inputs must consist
  of a keyed list of examples, and outputs consist of keyed list of prediction
  logs corresponding by index.

  Args:
    examples: A PCollection of keyed, batched inputs of type Example,
      SequenceExample, or bytes. Each type support inference specs corresponding
      to the unbatched cases described in RunInferencePerModel. Supports -
      PCollection[Tuple[K, List[Example]]] - PCollection[Tuple[K,
      List[SequenceExample]]] - PCollection[Tuple[K, List[Bytes]]]
    inference_spec_types: A flat iterable of Model inference endpoints.
      Inference will happen in a fused fashion (ie without data
      materialization), sequentially across Models within a Beam thread (but in
      parallel across threads and workers).
    load_override_fn: Optional function taking a model path and sequence of
      tags, and returning a tf SavedModel. The loaded model must be equivalent
      in interface to the model that would otherwise be loaded. It is up to the
      caller to ensure compatibility. This argument is experimental and subject
      to change.

  Returns:
    A PCollection containing Tuples of a key and lists of batched prediction
    logs from each model provided in inference_spec_types. The Tuple of batched
    prediction logs is 1-1 aligned with inference_spec_types. The individual
    prediction logs in the batch are 1-1 aligned with the rows of data in the
    batch key.
  """
  return (examples
          | 'RunInferencePerModelOnKeyedBatchesImpl' >>
          run_inference.RunInferencePerModelImpl(inference_spec_types,
                                                 load_override_fn))
Modules