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|
Abstract class for DoFns that need the shared models.
Inherits From: DoFnWithModels
tfma.model_util.BatchReducibleBatchedDoFnWithModels(
model_loaders: Dict[Text, tfma.types.ModelLoader]
)
This DoFn operates on batched Arrow RecordBatch as input. This DoFn will try to use a large batch size at first. If a functional failure is caught, an attempt will be made to process the elements serially at batch size 1.
Child Classes
Methods
default_label
default_label()
default_type_hints
default_type_hints()
display_data
display_data()
Returns the display data associated to a pipeline component.
It should be reimplemented in pipeline components that wish to have static display data.
| Returns | |
|---|---|
Dict[str, Any]: A dictionary containing key:value pairs.
The value might be an integer, float or string value; a
:class:DisplayDataItem for values that have more data
(e.g. short value, label, url); or a :class:HasDisplayData instance
that has more display data that should be picked up. For example::
{ 'key1': 'string_value', 'key2': 1234, 'key3': 3.14159265, 'key4': DisplayDataItem('apache.org', url='http://apache.org'), 'key5': subComponent } |
finish_bundle
finish_bundle()
Called after a bundle of elements is processed on a worker.
from_callable
@staticmethodfrom_callable( fn )
from_runner_api
@classmethodfrom_runner_api( fn_proto, context )
Converts from an FunctionSpec to a Fn object.
Prefer registering a urn with its parameter type and constructor.
get_function_arguments
get_function_arguments(
func
)
get_type_hints
get_type_hints()
Gets and/or initializes type hints for this object.
If type hints have not been set, attempts to initialize type hints in this order:
- Using self.default_type_hints().
- Using self.class type hints.
infer_output_type
infer_output_type(
input_type
)
process
process(
element: tfma.types.Extracts
) -> Sequence[tfma.types.Extracts]
Method to use for processing elements.
This is invoked by DoFnRunner for each element of a input
PCollection.
If specified, following default arguments are used by the DoFnRunner to
be able to pass the parameters correctly.
DoFn.ElementParam: element to be processed, should not be mutated.
DoFn.SideInputParam: a side input that may be used when processing.
DoFn.TimestampParam: timestamp of the input element.
DoFn.WindowParam: Window the input element belongs to.
DoFn.TimerParam: a userstate.RuntimeTimer object defined by the spec
of the parameter.
DoFn.StateParam: a userstate.RuntimeState object defined by the spec
of the parameter.
DoFn.KeyParam: key associated with the element.
DoFn.RestrictionParam: an iobase.RestrictionTracker will be
provided here to allow treatment as a Splittable DoFn. The restriction
tracker will be derived from the restriction provider in the parameter.
DoFn.WatermarkEstimatorParam: a function that can be used to track
output watermark of Splittable DoFn implementations.
| Args | |
|---|---|
element
|
The element to be processed |
*args
|
side inputs |
**kwargs
|
other keyword arguments. |
| Returns | |
|---|---|
| An Iterable of output elements or None. |
register_pickle_urn
@classmethodregister_pickle_urn( pickle_urn )
Registers and implements the given urn via pickling.
register_urn
@classmethodregister_urn( urn, parameter_type, fn=None )
Registers a urn with a constructor.
For example, if 'beam:fn:foo' had parameter type FooPayload, one could
write RunnerApiFn.register_urn('bean:fn:foo', FooPayload, foo_from_proto)
where foo_from_proto took as arguments a FooPayload and a PipelineContext.
This function can also be used as a decorator rather than passing the
callable in as the final parameter.
A corresponding to_runner_api_parameter method would be expected that returns the tuple ('beam:fn:foo', FooPayload)
setup
setup()
Called to prepare an instance for processing bundles of elements.
This is a good place to initialize transient in-memory resources, such as
network connections. The resources can then be disposed in
DoFn.teardown.
start_bundle
start_bundle()
Called before a bundle of elements is processed on a worker.
Elements to be processed are split into bundles and distributed to workers. Before a worker calls process() on the first element of its bundle, it calls this method.
teardown
teardown()
Called to use to clean up this instance before it is discarded.
A runner will do its best to call this method on any given instance to prevent leaks of transient resources, however, there may be situations where this is impossible (e.g. process crash, hardware failure, etc.) or unnecessary (e.g. the pipeline is shutting down and the process is about to be killed anyway, so all transient resources will be released automatically by the OS). In these cases, the call may not happen. It will also not be retried, because in such situations the DoFn instance no longer exists, so there's no instance to retry it on.
Thus, all work that depends on input elements, and all externally important
side effects, must be performed in DoFn.process or
DoFn.finish_bundle.
to_runner_api
to_runner_api(
context
)
Returns an FunctionSpec encoding this Fn.
Prefer overriding self.to_runner_api_parameter.
to_runner_api_parameter
to_runner_api_parameter(
context
)
with_input_types
with_input_types(
*arg_hints, **kwarg_hints
)
with_output_types
with_output_types(
*arg_hints, **kwarg_hints
)