npu.pipeline.pipeline
Classes
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Exceptions
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- exception npu.pipeline.pipeline.PipelineException(message='')
- Author:
Alberto M. Esmoris Pena
Class for exceptions related to pipeline components. See
NAIPSException.- __init__(message='')
- class npu.pipeline.pipeline.Pipeline(**kwargs)
- Author:
Alberto M. Esmoris Pena
Abstract class providing the interface for any pipeline and a common baseline implementation.
- Variables:
pipeline_input (object) – The object representing the pipeline’s input.
pipeline_output (object) – The object representing the pipeline’s output.
- __init__(**kwargs)
Handles the root-level (most basic) initialization of any pipeline.
- Parameters:
kwargs – The attributes for the Pipeline.
- build_pipeline_input(**kwargs)
Build the pipeline input object.
- Returns:
The object representing the pipeline input.
- build_pipeline_output(**kwargs)
- Parameters:
kwargs
- Returns:
- abstractmethod run()
Run the pipeline.
- Returns:
Nothing.
- to_predictive_pipeline(**kwargs)
Transforms the current pipeline to a predictive pipeline, if possible. See
PredictivePipeline.- Returns:
A predictive pipeline wrapping this pipeline and providing a predictive strategy.
- Return type:
- is_using_deep_learning()
Check whether the pipeline uses deep learning or not.
By default, pipelines do not support deep learning. Any pipeline that supports deep learning models must explicitly overload this method to return True.
- Returns:
True if the pipeline uses deep learning, false otherwise.
- Return type:
bool
- write_deep_learning_model(path)
Write the deep learning model used in the pipeline to disk.
- Parameters:
path (str) – Path where the deep learning model must be written.
- validate_attributes()
Logic to validate the attributes (mostly the input and output of the pipeline).
- Returns:
Nothing at all, but throws an exception if the attributes are not valid.