The BulkInferrer TFX Pipeline Component
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The BulkInferrer TFX component performs batch inference on unlabeled data. The
generated
InferenceResult(tensorflow_serving.apis.prediction_log_pb2.PredictionLog)
contains the original features and the prediction results.
BulkInferrer consumes:
- A trained model in
SavedModel format.
- Unlabelled tf.Examples that contain features.
- (Optional) Validation result from
Evaluator component.
BulkInferrer emits:
Using the BulkInferrer Component
A BulkInferrer TFX component is used to perform batch inference on unlabeled
tf.Examples. It is typically deployed after an
Evaluator component to
perform inference with a validated model, or after a
Trainer component to directly
perform inference on exported model.
It currently performs in-memory model inference and remote inference.
Remote inference requires the model to be hosted on Cloud AI Platform.
Typical code looks like this:
bulk_inferrer = BulkInferrer(
examples=examples_gen.outputs['examples'],
model=trainer.outputs['model'],
model_blessing=evaluator.outputs['blessing'],
data_spec=bulk_inferrer_pb2.DataSpec(),
model_spec=bulk_inferrer_pb2.ModelSpec()
)
More details are available in the
BulkInferrer API reference.
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Last updated 2024-09-06 UTC.
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