builder Module
Defines classes for building a Azure Machine Learning pipeline.
A pipeline graph is composed of pipeline steps (PipelineStep), optional pipeline data (PipelineData) produced or consumed in each step, and an optional step execution sequence (StepSequence).
Classes
PipelineData |
Represents intermediate data in an Azure Machine Learning pipeline. Data used in pipeline can be produced by one step and consumed in another step by providing a PipelineData object as an output of one step and an input of one or more subsequent steps. Note if you are using the pipeline data, please make sure the directory used existed. A python example to ensure the directory existed, suppose you have a output port named output_folder in one pipeline step, you want to write some data to relative path in this folder.
PipelineData use DataReference underlying which is no longer the recommended approach for data access and delivery, please use OutputFileDatasetConfig instead, you can find sample here: Pipeline using OutputFileDatasetConfig. Initialize PipelineData. |
PipelineStep |
Represents an execution step in an Azure Machine Learning pipeline. Pipelines are constructed from multiple pipeline steps, which are distinct computational units in the pipeline. Each step can run independently and use isolated compute resources. Each step typically has its own named inputs, outputs, and parameters. The PipelineStep class is the base class from which other built-in step classes designed for common scenarios inherit, such as PythonScriptStep, DataTransferStep, and HyperDriveStep. For an overview of how Pipelines and PipelineSteps relate, see What are ML Pipelines. Initialize PipelineStep. |
StepSequence |
Represents a list of steps in a Pipeline and the order in which to execute them. Use a StepSequence when initializing a pipeline to create a workflow that contains steps to run in a specific order. Initialize StepSequence. |