Pipeline: Workflow Pipeline class
In DyfanJones/sagemaker-r-workflow: sagemaker pipeline and workflows
Pipeline R Documentation
Workflow Pipeline class
Description
Pipeline for workflow.
Super class
sagemaker.workflow::Entity -> Pipeline
Methods
Public methods
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Inherited methods
Method new()
Initialize Pipeline Class
Usage
Pipeline$new(
name,
parameters = list(),
pipeline_experiment_config = PipelineExperimentConfig$new(ExecutionVariables$PIPELINE_NAME,
ExecutionVariables$PIPELINE_EXECUTION_ID),
steps = list(),
sagemaker_session = NULL
)
Arguments
name(str): The name of the pipeline.
parameters(Sequence[Parameter]): The list of the parameters.
pipeline_experiment_config(Optional[PipelineExperimentConfig]): If set,
the workflow will attempt to create an experiment and trial before
executing the steps. Creation will be skipped if an experiment or a trial with
the same name already exists. By default, pipeline name is used as
experiment name and execution id is used as the trial name.
If set to None, no experiment or trial will be created automatically.
steps(Sequence[Union[Step, StepCollection]]): The list of the non-conditional steps
associated with the pipeline. Any steps that are within the
'if_steps' or 'else_steps' of a 'ConditionStep' cannot be listed in the steps of a
pipeline. Of particular note, the workflow service rejects any pipeline definitions that
specify a step in the list of steps of a pipeline and that step in the 'if_steps' or
'else_steps' of any 'ConditionStep'.
sagemaker_session(Session): Session object that manages interactions
with Amazon SageMaker APIs and any other AWS services needed. If not specified, the
pipeline creates one using the default AWS configuration chain.
Method to_request()
Gets the request structure for workflow service calls.
Usage
Pipeline$to_request()
Method create()
Creates a Pipeline in the Pipelines service.
Usage
Pipeline$create(
role_arn,
description = NULL,
tags = NULL,
parallelism_config = NULL
)
Arguments
role_arn(str): The role arn that is assumed by the pipeline to create step artifacts.
description(str): A description of the pipeline.
tags(List[Dict[str, str]]): A list of "Key": "string", "Value": "string" dicts as
tags.
parallelism_config(Optional[ParallelismConfiguration]): Parallelism configuration
that is applied to each of the executions of the pipeline. It takes precedence
over the parallelism configuration of the parent pipeline.
Returns
A response dict from the service.
Method describe()
Describes a Pipeline in the Workflow service.
Usage
Pipeline$describe()
Returns
Response dict from the service. See 'boto3 client documentation
https://boto3.amazonaws.com/v1/documentation/api/latest/reference/services/sagemaker.html#SageMaker.Client.describe_pipeline
Method update()
Updates a Pipeline in the Workflow service.
Usage
Pipeline$update(role_arn, description = NULL, parallelism_config = NULL)
Arguments
role_arn(str): The role arn that is assumed by pipelines to create step artifacts.
description(str): A description of the pipeline.
parallelism_config(Optional[ParallelismConfiguration]): Parallelism configuration
that is applied to each of the executions of the pipeline. It takes precedence
over the parallelism configuration of the parent pipeline.
Returns
A response dict from the service.
Method upsert()
Creates a pipeline or updates it, if it already exists.
Usage
Pipeline$upsert(
role_arn,
description = NULL,
tags = NULL,
parallelism_config = NULL
)
Arguments
role_arn(str): The role arn that is assumed by workflow to create step artifacts.
description(str): A description of the pipeline.
tags(List[Dict[str, str]]): A list of "Key": "string", "Value": "string" dicts as
tags.
parallelism_config(Optional[Config for parallel steps, Parallelism configuration that
is applied to each of. the executions
Returns
response dict from service
Method delete()
Deletes a Pipeline in the Workflow service.
Usage
Pipeline$delete()
Returns
A response dict from the service.
Method start()
Starts a Pipeline execution in the Workflow service.
Usage
Pipeline$start(
parameters = NULL,
execution_display_name = NULL,
execution_description = NULL,
parallelism_config = NULL
)
Arguments
parameters(Dict[str, Union[str, bool, int, float]]): values to override
pipeline parameters.
execution_display_name(str): The display name of the pipeline execution.
execution_description(str): A description of the execution.
parallelism_config(Optional[ParallelismConfiguration]): Parallelism configuration
that is applied to each of the executions of the pipeline. It takes precedence
over the parallelism configuration of the parent pipeline.
Returns
A '.PipelineExecution' instance, if successful.
Method definition()
Converts a request structure to string representation for workflow service calls.
Usage
Pipeline$definition()
Method clone()
The objects of this class are cloneable with this method.
Usage
Pipeline$clone(deep = FALSE)
Arguments
deepWhether to make a deep clone.
DyfanJones/sagemaker-r-workflow documentation built on April 3, 2022, 11:28 p.m.
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| Pipeline | R Documentation |
Workflow Pipeline class
Description
Pipeline for workflow.
Super class
sagemaker.workflow::Entity -> Pipeline
Methods
Public methods
Inherited methods
Method new()
Initialize Pipeline Class
Usage
Pipeline$new(
name,
parameters = list(),
pipeline_experiment_config = PipelineExperimentConfig$new(ExecutionVariables$PIPELINE_NAME,
ExecutionVariables$PIPELINE_EXECUTION_ID),
steps = list(),
sagemaker_session = NULL
)Arguments
name(str): The name of the pipeline.
parameters(Sequence[Parameter]): The list of the parameters.
pipeline_experiment_config(Optional[PipelineExperimentConfig]): If set, the workflow will attempt to create an experiment and trial before executing the steps. Creation will be skipped if an experiment or a trial with the same name already exists. By default, pipeline name is used as experiment name and execution id is used as the trial name. If set to None, no experiment or trial will be created automatically.
steps(Sequence[Union[Step, StepCollection]]): The list of the non-conditional steps associated with the pipeline. Any steps that are within the 'if_steps' or 'else_steps' of a 'ConditionStep' cannot be listed in the steps of a pipeline. Of particular note, the workflow service rejects any pipeline definitions that specify a step in the list of steps of a pipeline and that step in the 'if_steps' or 'else_steps' of any 'ConditionStep'.
sagemaker_session(Session): Session object that manages interactions with Amazon SageMaker APIs and any other AWS services needed. If not specified, the pipeline creates one using the default AWS configuration chain.
Method to_request()
Gets the request structure for workflow service calls.
Usage
Pipeline$to_request()
Method create()
Creates a Pipeline in the Pipelines service.
Usage
Pipeline$create( role_arn, description = NULL, tags = NULL, parallelism_config = NULL )
Arguments
role_arn(str): The role arn that is assumed by the pipeline to create step artifacts.
description(str): A description of the pipeline.
tags(List[Dict[str, str]]): A list of "Key": "string", "Value": "string" dicts as tags.
parallelism_config(Optional[ParallelismConfiguration]): Parallelism configuration that is applied to each of the executions of the pipeline. It takes precedence over the parallelism configuration of the parent pipeline.
Returns
A response dict from the service.
Method describe()
Describes a Pipeline in the Workflow service.
Usage
Pipeline$describe()
Returns
Response dict from the service. See 'boto3 client documentation https://boto3.amazonaws.com/v1/documentation/api/latest/reference/services/sagemaker.html#SageMaker.Client.describe_pipeline
Method update()
Updates a Pipeline in the Workflow service.
Usage
Pipeline$update(role_arn, description = NULL, parallelism_config = NULL)
Arguments
role_arn(str): The role arn that is assumed by pipelines to create step artifacts.
description(str): A description of the pipeline.
parallelism_config(Optional[ParallelismConfiguration]): Parallelism configuration that is applied to each of the executions of the pipeline. It takes precedence over the parallelism configuration of the parent pipeline.
Returns
A response dict from the service.
Method upsert()
Creates a pipeline or updates it, if it already exists.
Usage
Pipeline$upsert( role_arn, description = NULL, tags = NULL, parallelism_config = NULL )
Arguments
role_arn(str): The role arn that is assumed by workflow to create step artifacts.
description(str): A description of the pipeline.
tags(List[Dict[str, str]]): A list of "Key": "string", "Value": "string" dicts as tags.
parallelism_config(Optional[Config for parallel steps, Parallelism configuration that is applied to each of. the executions
Returns
response dict from service
Method delete()
Deletes a Pipeline in the Workflow service.
Usage
Pipeline$delete()
Returns
A response dict from the service.
Method start()
Starts a Pipeline execution in the Workflow service.
Usage
Pipeline$start( parameters = NULL, execution_display_name = NULL, execution_description = NULL, parallelism_config = NULL )
Arguments
parameters(Dict[str, Union[str, bool, int, float]]): values to override pipeline parameters.
execution_display_name(str): The display name of the pipeline execution.
execution_description(str): A description of the execution.
parallelism_config(Optional[ParallelismConfiguration]): Parallelism configuration that is applied to each of the executions of the pipeline. It takes precedence over the parallelism configuration of the parent pipeline.
Returns
A '.PipelineExecution' instance, if successful.
Method definition()
Converts a request structure to string representation for workflow service calls.
Usage
Pipeline$definition()
Method clone()
The objects of this class are cloneable with this method.
Usage
Pipeline$clone(deep = FALSE)
Arguments
deepWhether to make a deep clone.
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