azureml-pipeline-steps Package

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Packages

steps

Contains pre-built steps that can be executed in an Azure Machine Learning Pipeline.

Azure ML Pipeline steps can be configured together to construct a Pipeline, which represents a shareable and reusable Azure Machine Learning workflow. Each step of a pipeline can be configured to allow reuse of its previous run results if the step contents (scripts and dependencies) as well as inputs and parameters remain unchanged.

The classes in this package are typically used together with the classes in the core package. The core package contains classes for configuring data (PipelineData), scheduling (Schedule), and managing the output of steps (StepRun).

The pre-built steps in this package cover many common scenarios encountered in machine learning workflows. To get started with pre-built pipeline steps, see:

Modules

adla_step

Contains functionality to create an Azure ML Pipeline step to run a U-SQL script with Azure Data Lake Analytics.
automl_step

Contains functionality for adding and managing an automated ML pipeline step in Azure Machine Learning.
azurebatch_step

Contains functionality to create an Azure ML Pipeline step that runs a Windows executable in Azure Batch.
command_step

Contains functionality to create an Azure ML Pipeline step that runs commands.
data_transfer_step

Contains functionality to create an Azure ML Pipeline step that transfers data between storage options.
databricks_step

Contains functionality to create an Azure ML pipeline step to run a Databricks notebook or Python script on DBFS.
estimator_step

Contains functionality to create a pipeline step that runs an Estimator for Machine Learning model training.
hyper_drive_step

Contains funtionality for creating and managing Azure ML Pipeline steps that run hyperparameter tuning.
kusto_step

Contains functionality to create an Azure ML pipeline step to run a Kusto notebook.
module_step

Contains functionality to add an Azure Machine Learning Pipeline step using an existing version of a Module.
mpi_step

Contains functionality to add a Azure ML Pipeline step to run an MPI job for Machine Learning model training.
parallel_run_config

Contains functionality for configuring a ParallelRunStep.
parallel_run_step

Contains functionality to add a step to run user script in parallel mode on multiple AmlCompute targets.
python_script_step

Contains functionality to create an Azure ML Pipeline step that runs Python script.
r_script_step

Contains functionality to create an Azure ML Pipeline step that runs R script.
synapse_spark_step

Contains functionality to create an Azure ML Synapse step that runs Python script.