Removing pipelines

If we no longer need a training pipeline, we can remove it from the catalog.

Syntax

Remove a pipeline from the catalog:

CALL gds.pipeline.drop(pipelineName: String, failIfMissing: Boolean)
YIELD
    pipelineName: String,
    pipelineType: String,
    creationTime: DateTime,
    pipelineInfo: Map

Table 1. Parameters
Name	Type	Default	Optional	Description
pipelineName	String	`n/a`	yes	The name of a pipeline. If not specified, all pipelines in the catalog are listed.
failIfMissing	Boolean	`true`	yes	By default, the library will raise an error when trying to remove a non-existing pipeline. When set to `false`, the procedure returns an empty result.

Table 2. Results
Name	Type	Description
pipelineName	String	The name of the pipeline.
pipelineType	String	The type of the pipeline.
creationTime	Datetime	Time when the pipeline was created.
pipelineInfo	Map	Detailed information about this particular training pipeline, such as about intermediate steps in the pipeline.

Example

In this section we are going to demonstrate the usage of gds.pipeline.drop. To exemplify this, we first create a link prediction pipeline.

Creating a link prediction training pipelines:

CALL gds.beta.pipeline.linkPrediction.create('pipe')

Remove a pipeline from the catalog:

CALL gds.pipeline.drop('pipe')
YIELD pipelineName, pipelineType

Table 3. Results
pipelineName	pipelineType
`"pipe"`	`"Link prediction training pipeline"`

Since the failIfMissing flag defaults to true, if the pipeline name does not exist, an error will be raised.