Removing pipelines

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

1. Syntax

Remove a pipeline from the catalog:
CALL gds.beta.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.

2. Example

In this section we are going to demonstrate the usage of gds.beta.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.beta.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.