Dropping models

If we no longer need a trained model and want to free up memory, we can drop the model from the catalog.

Syntax

Drop a model from the catalog:

CALL gds.model.drop(modelName: String, failIfMissing: Boolean)
YIELD
    modelName: String,
    modelType: String,
    modelInfo: Map,
    creationTime: DateTime,
    trainConfig: Map,
    graphSchema: Map,
    loaded: Boolean,
    stored: Boolean,
    published: Boolean

Table 1. Parameters
Name	Type	Default	Optional	Description
modelName	String	`n/a`	no	Name of a model stored in the catalog.
failIfMissing	Boolean	`true`	yes	Whether an error should be raised when the model does not exist. When set to `false`, the procedure returns an empty result if the model does not exist.

Table 2. Results
Name	Type	Description
modelName	String	Name of the model.
modelType	String	Type of the model. Indicates what training algorithm was used to train the model.
modelInfo	Map	Detailed type-specific information about the trained model.
creationTime	Datetime	Time when the model was created.
trainConfig	Map	Train configuration used for training the model.
graphSchema	Map	Schema of the graph on which the model was trained.
loaded	Boolean	True, if the model is loaded in the in-memory model catalog.
stored	Boolean	True, if the model is stored on disk.
published	Boolean	True, if the model has been published.

Examples

In this section we are going to demonstrate the usage of gds.model.drop. For simplicity, we will assume that an example model named my-model1 has already been trained and exists in the model catalog.

Dropping a model

To drop a model, we only need to specify its name.

Drop a model from the catalog:

CALL gds.model.drop('my-model1')
YIELD modelName, modelType, modelInfo, loaded, stored, published

Table 3. Results
modelName	modelType	modelInfo	loaded	stored	published
`"my-model1"`	`"example-model-type"`	`{exampleModelInfo="exampleValue"}`	`true`	`false`	`false`

When dropping a model, we get a result that represents its state in the catalog just prior to being dropped. The dropped model is now no longer available in the catalog, which we can verify by running gds.model.list.

Listing a dropped model:

CALL gds.model.list('my-model1')
YIELD modelName, modelType, modelInfo, loaded, stored, published

Table 4. Results
modelName	modelType	modelInfo	loaded	stored	published

Dropping a model that does not exist

If we try to drop a model that does not exist, an error is raised by default. To avoid this, we can set failIfMissing to false.

Dropping model from the catalog without failing:

CALL gds.model.drop('my-model1', false)

Table 5. Results
modelName	modelType	modelInfo	creationTime	trainConfig	graphSchema	loaded	stored	published

As we can see, the procedure returns an empty result instead of raising an error.