API Reference¶

Complete API reference for all Kedro AzureML Pipeline classes and functions. Use the search box to filter, or click any name to see full documentation.

Name	Type	Module	Description
`AzureMLAssetDataset`	Class	kedro_azureml_pipeline.datasets	Kedro dataset backed by an Azure ML Data Asset.
`AzureMLLocalRunHook`	Class	kedro_azureml_pipeline.hooks	Hook that configures Azure ML asset datasets for local and remote runs.
`AzureMLPipelineDataset`	Class	kedro_azureml_pipeline.datasets	Dataset for passing data between Azure ML pipeline nodes.
`AzureMLPipelineGenerator`	Class	kedro_azureml_pipeline.generator	Translate a Kedro pipeline into an Azure ML pipeline job.
`AzureMLPipelinesClient`	Class	kedro_azureml_pipeline.client	Client wrapper for submitting Azure ML pipeline jobs.
`AzureMLScheduleClient`	Class	kedro_azureml_pipeline.scheduler	Client for creating and updating Azure ML schedules.
`AzurePipelinesRunner`	Class	kedro_azureml_pipeline.runner	Sequential runner that rewires dataset paths for Azure ML.
`BatchClients`	Class	kedro_azureml_pipeline.client	One credential and one ``MLClient`` per workspace, shared across a batch.
`build_job_schedule`	Function	kedro_azureml_pipeline.scheduler	Wrap an Azure ML pipeline job into a ``JobSchedule``.
`build_trigger`	Function	kedro_azureml_pipeline.scheduler	Convert a ``ScheduleConfig`` into an Azure ML trigger object.
`CliContext`	Class	kedro_azureml_pipeline.utils	Runtime context passed to CLI command handlers.
`ClusterConfig`	Class	kedro_azureml_pipeline.config	Single compute cluster reference.
`ComputeConfig`	Class	kedro_azureml_pipeline.config	Named compute clusters with a mandatory ``__default__`` entry.
`ConfigException`	Class	kedro_azureml_pipeline.generator	Raised when pipeline generator configuration is invalid.
`CronScheduleConfig`	Class	kedro_azureml_pipeline.config	Cron schedule configuration mapping to ``azure.ai.ml.entities.CronTrigger``.
`distributed_job`	Function	kedro_azureml_pipeline.distributed	Mark a Kedro node function for distributed execution.
`DistributedNodeConfig`	Class	kedro_azureml_pipeline.distributed	Configuration for a distributed training node.
`enumerate_jobs`	Function	kedro_azureml_pipeline.factory	Render every derived binding into a ``name -> JobConfig`` map, overlaying literals.
`ExecutionConfig`	Class	kedro_azureml_pipeline.config	Code packaging and execution settings for Azure ML.
`Framework`	Class	kedro_azureml_pipeline.distributed	Supported distributed training frameworks.
`get_azureml_credentials`	Function	kedro_azureml_pipeline.client	Obtain Azure credentials for Azure ML access.
`is_distributed_environment`	Function	kedro_azureml_pipeline.distributed	Check whether the process is running in a distributed context.
`is_distributed_master_node`	Function	kedro_azureml_pipeline.distributed	Check whether this process is the master node.
`is_factory`	Function	kedro_azureml_pipeline.factory	True if a ``jobs`` key is a factory (contains ``{placeholder}`` markers).
`JobConfig`	Class	kedro_azureml_pipeline.config	A single named job configuration.
`KedroAzureMLConfig`	Class	kedro_azureml_pipeline.config	Top-level plugin configuration loaded from ``azureml.yml``.
`KedroContextManager`	Class	kedro_azureml_pipeline.manager	Context manager that wraps a ``KedroSession`` and exposes plugin config.
`LimitsConfig`	Class	kedro_azureml_pipeline.config	Run-duration limits for Azure ML pipeline steps.
`MlflowAzureMLHook`	Class	kedro_azureml_pipeline.hooks	Coordinates kedro-mlflow inside Azure ML pipeline component jobs.
`mpi_rank`	Function	kedro_azureml_pipeline.distributed	Return the 0-based rank of this process in an MPI or PyTorch step, else 0.
`NotificationConfig`	Class	kedro_azureml_pipeline.config	Notification for the runs of a job, posted to a webhook or the Slack API.
`NotificationEvent`	Class	kedro_azureml_pipeline.hooks	What a payload builder receives.
`NotificationHook`	Class	kedro_azureml_pipeline.hooks	Post a job's ``start``, ``success``, and ``failure`` events to a webhook.
`NotificationRoles`	Class	kedro_azureml_pipeline.generator	Which steps of a job speak for it.
`PipelineFilterOptions`	Class	kedro_azureml_pipeline.config	Kedro pipeline filter options for selecting nodes.
`RecurrencePatternConfig`	Class	kedro_azureml_pipeline.config	Recurrence pattern mapping to ``azure.ai.ml.entities.RecurrencePattern``.
`RecurrenceScheduleConfig`	Class	kedro_azureml_pipeline.config	Recurrence schedule mapping to ``azure.ai.ml.entities.RecurrenceTrigger``.
`register_code_snapshot`	Function	kedro_azureml_pipeline.client	Register staged_dir as one anonymous code asset and return its id.
`resolve_jobs`	Function	kedro_azureml_pipeline.factory	Resolve requested job names to ``JobConfig`` via literals + rendered bindings.
`resolve_schedule`	Function	kedro_azureml_pipeline.scheduler	Resolve a schedule reference to a ``ScheduleConfig``.
`resolve_target`	Function	kedro_azureml_pipeline.factory	Render a single target into its ``(name, JobConfig)``, or ``None``.
`ScheduleConfig`	Class	kedro_azureml_pipeline.config	Schedule trigger configuration requiring exactly one of ``cron`` or ``recurrence``.
`select_snapshot_files`	Function	kedro_azureml_pipeline.snapshot	Return the files the Azure ML SDK would upload from code_directory.
`SiblingOutcome`	Class	kedro_azureml_pipeline.hooks	Result of waiting for the poster's sibling leaves.
`stage_code_snapshot`	Function	kedro_azureml_pipeline.snapshot	Copy the snapshot files of code_directory into a temporary directory.
`update_dict`	Function	kedro_azureml_pipeline.utils	Return a deep copy of dictionary with nested keys updated.
`WorkspaceConfig`	Class	kedro_azureml_pipeline.config	Azure ML workspace identity.
`WorkspacesConfig`	Class	kedro_azureml_pipeline.config	Named workspaces with a mandatory ``__default__`` entry.