azureml-pipeline-core Package

Packages

core

Contains core functionality for Azure Machine Learning pipelines, which are configurable machine learning workflows.

Azure Machine Learning pipelines allow you to create resusable machine learning workflows that can be used as a template for your machine learning scenarios. This package contains the core functionality for working with Azure ML pipelines and is typically used along with the classes in the steps package.

A machine learning pipeline is represented by a collection of PipelineStep objects that can sequenced and parallelized, or be created with explicit dependencies between steps. Pipeline steps are used to define a Pipeline object which represents the workflow to execute. You can create and work with pipelines in a Jupyter Notebook or any other IDE with the Azure ML SDK installed.

Azure ML pipelines enable you to focus on machine learning rather than infrastructure. To get started building a pipeline, see https://aka.ms/pl-first-pipeline.

For more information about the benefits of the Machine Learning Pipeline and how it is related to other pipelines offered by Azure, see What are ML pipelines in Azure Machine Learning service?

Modules

builder

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).

graph

Defines classes for constructing Azure Machine Learning pipeline graphs.

Azure ML pipeline graphs are created for Pipeline objects, when you use PipelineStep (and derived classes), PipelineData, and PipelineData objects. In typical use cases, you will not need to directly use the classes in this module.

A pipeline run graph consists of module nodes which represent basic units such as a datasource or step. Nodes can have input ports and output ports, and associated parameters. Edges define relationships between two node ports in a graph.

module

Contains classes for creating and managing resusable computational units of an Azure Machine Learning pipeline.

Modules allow you to create computational units in a Pipeline, which can have inputs, outputs, and rely on parameters and an environment configuration to operate. A module can be versioned and be used in different Azure Machine Learning pipelines unlike PipelineStep (and derived classes) which are used in one Pipeline.

Modules are designed to be reused in several pipelines and can evolve to adapt a specific computation logic for different use cases. A step in a pipeline can be used in fast iterations to improve an algorithm, and once the goal is achieved, the algorithm is usually published as a module to enable reuse.

module_step_base

Contains functionality to add a step to a pipeline using a version of a Module.

pipeline

Defines the class for creating reusable Azure Machine Learning workflows.

pipeline_draft

Defines classes for managing mutable pipelines.

pipeline_endpoint

Defines classes for managing pipelines including versioning and endpoints.

pipeline_output_dataset

Contains functionality for promoting an intermediate output to an Azure Machine Learning Dataset.

Intermediate data (output) in a pipeline by default will not become an Azure Machine Learning Dataset. To promote intermediate data to an Azure Machine Learning Dataset, call the as_dataset method on the PipelineData class to return a PipelineOutputFileDataset object. From a PipelineOutputFileDataset object, you can then create an PipelineOutputTabularDataset object.

run

Defines classes for submitted pipelines, including classes for checking status and retrieving run details.

schedule

Defines classes for scheduling submissions of Azure Machine Learning Pipelines.