Machine Learning (ML) - Kinetica Docs

Kinetica provides support for Machine Learning (ML) container registry & model creation and management in SQL. A Docker registry serves as a container for ML models, while an ML model represents a function from which an inference can be computed.

For statistical analysis functions that don’t require a model, see ML Functions.

Container registry features accessible via SQL include:

ML model features accessible via SQL include:

CREATE CONTAINER REGISTRY

Creates a new reference to a Docker container registry for accessing ML models.

CREATE CONTAINER REGISTRY Syntax

CREATE [OR REPLACE] CONTAINER REGISTRY <registry name>
URI = '<registry URI>'
CREDENTIAL = <credential name>

Parameters

OR REPLACE

Any existing container registry reference with the same name will be dropped before creating this one

Name of the reference to the container registry, which can be referenced in subsequent commands

URI

The URI to the container registry itself

CREDENTIAL

Name of the credential to use to authenticate to the container registry

Examples

CREATE CONTAINER REGISTRY Example

CREATE CONTAINER REGISTRY model_registry_docker
URI = 'https://index.docker.io'
CREDENTIAL = reg_cred;

DROP CONTAINER REGISTRY

Removes an existing Docker container registry reference.

DROP CONTAINER REGISTRY Syntax

DROP CONTAINER REGISTRY <registry name>

Parameters

Name of the reference to the container registry to remove; has no effect on the container registry itself

Examples

DROP CONTAINER REGISTRY Example

DROP CONTAINER REGISTRY model_registry_docker

SHOW CONTAINER REGISTRY

Outputs the DDL statement required to reconstruct the given Docker container registry reference.

SHOW CONTAINER REGISTRY Syntax

SHOW CONTAINER REGISTRY < <registry name> | * >

The response to SHOW CONTAINER REGISTRY is a single-column result set with the DDL statement as the value in the DDL column.

Parameters

Name of the existing reference to the container registry for which the DDL will be output. Use * instead to output the DDL of all references to container registries.

Examples

SHOW CONTAINER REGISTRY Example

SHOW CONTAINER REGISTRY model_registry_docker

SHOW CONTAINER REGISTRY Example (All Registries)

SHOW CONTAINER REGISTRY *

IMPORT MODEL

Imports an ML model from a Docker container registry into the database.

IMPORT MODEL Syntax

IMPORT MODEL <model name>
REGISTRY = <registry name>
CONTAINER = '<path to container>'
RUN_FUNCTION = '<model run function>'

Parameters

Name of the ML model to import via the given container registry reference

REGISTRY

Name of the reference to the container registry from which to import the ML model

CONTAINER

The full path to the ML model container within the container registry

RUN_FUNCTION

Name of the function within the ML model to execute when running inferences against it

Examples

IMPORT MODEL Example

IMPORT MODEL temp_2c
REGISTRY = model_registry_docker
CONTAINER = 'kinetica/kinetica-blackbox-sdk:r7.2.0'
RUN_FUNCTION = 'module_temperature.convert_to_celsius'

EVALUATE_MODEL

Runs an inference against an ML model. There are two methods to evaluate a model:

By Query - An ML model can be queried, where the inference is returned as the result set.
By Execution - An ML model can be run with the EXECUTE FUNCTION command, where the inference is persisted to a specified table.

EVALUATE_MODEL Table Function Syntax

SELECT * FROM TABLE(
    EVALUATE_MODEL
    (
        MODEL => '<model name>',
        DEPLOYMENT_MODE => 'BATCH',
        REPLICATIONS => <number of replications>,
        SOURCE_TABLE => INPUT_TABLE(<source data set>)
    )
)

EVALUATE_MODEL EXECUTE FUNCTION Syntax

EXECUTE FUNCTION EVALUATE_MODEL(
    MODEL => '<model name>',
    DEPLOYMENT_MODE => '<BATCH | CONTINUOUS>',
    REPLICATIONS => <number of replications>,
    SOURCE_TABLE => INPUT_TABLE(<source data set>),
    DESTINATION_TABLE => '<destination table name>'
)

Parameters

MODEL

Name of the ML model on which to run an inference

DEPLOYMENT_MODE

Scheme used in making inferences on the ML model:

BATCH - inference tests are run against a batch of data in an existing table all at once
CONTINUOUS - inference tests are run automatically against records being streamed into an input table; inference results are inserted into an output table, which will be updated upon each subsequent inference
Continuous deployment mode is only available when using the EXECUTE FUNCTION syntax, as it creates a destination table with the continuously updated results.

REPLICATIONS

The number of replicas to launch on Kubernetes

SOURCE_TABLE

Table or query to use as input to the ML model, specified with either of these two forms:

INPUT_TABLE(<table name>) - Use the given table or view as input; e.g.:
INPUT_TABLE(price_history)
INPUT_TABLE(<query>) - Use the given query as input; e.g.:
INPUT_TABLE(SELECT ts, item, price FROM price_history)

DESTINATION_TABLE

Only applicable when using EXECUTE FUNCTION syntax. Name of the table in which to store the results of the inference, in [schema_name.]table_name format, using standard name resolution rules and meeting table naming criteria

Examples

EVALUATE_MODEL Table Function Example

SELECT * FROM TABLE(
    EVALUATE_MODEL
    (
        MODEL => 'raster_model',
        DEPLOYMENT_MODE => 'batch',
        REPLICATIONS =>1,
        SOURCE_TABLE => INPUT_TABLE(select raster_uri from ki_home.raster_input)
    )
)

EVALUATE_MODEL EXECUTE FUNCTION Example

EXECUTE FUNCTION EVALUATE_MODEL(
    MODEL => 'raster_model',
    DEPLOYMENT_MODE => 'batch',
    REPLICATIONS =>1,
    SOURCE_TABLE => INPUT_TABLE(select raster_uri from ki_home.raster_input),
    DESTINATION_TABLE => 'raster_output'
)

ALTER MODEL

Modifies the source of an ML model and re-imports it from that new location. Either the container registry or the container path can be modified.

ALTER MODEL <model name>
SET REGISTRY = <registry name>

ALTER MODEL <model name>
SET CONTAINER = '<path to container>'

Parameters

Name of the ML model to modify

REGISTRY

Name of the reference to the container registry from which to import the ML model

CONTAINER

The full path to the ML model container within the container registry

Examples

ALTER MODEL Registry Example

ALTER MODEL temp_2c
SET REGISTRY = my_registry

ALTER MODEL Container Path Example

ALTER MODEL temp_2c
SET CONTAINER = 'kinetica/kinetica-blackbox-sdk:r7.2.0'

REFRESH MODEL

Refreshes an ML model from its source.

REFRESH MODEL Syntax

REFRESH MODEL <model name>

Examples

REFRESH MODEL Example

REFRESH MODEL temp_2c

DROP MODEL

Removes an ML model.

DROP MODEL Syntax

DROP MODEL <model name>

Examples

DROP MODEL Example

DROP MODEL temp_2c

SHOW MODEL

Displays the statement used to create an ML model.

SHOW MODEL Syntax

SHOW MODEL < <model name> | * >

Parameters

Name of the existing reference to the model for which the DDL will be output. Use * instead to output the DDL of all references to models.

Examples

SHOW MODEL Example

SHOW MODEL temp_2c

SHOW MODEL Example (All Models)

SHOW MODEL *

DESCRIBE MODEL

Displays the configuration of an ML model.

DESCRIBE MODEL Syntax

DESCRIBE MODEL < <model name> | * >

Parameters

Name of the existing model for which the configuration will be output. Use * instead to output the configuration of all models.

Response

The response to DESCRIBE MODEL is a four-column result set:

Output Column	Description
`MODEL`	Name of the ML model
`ENTITY_ID`	Internal unique ID for the ML model
`INPUT_SCHEMA`	List of columns types that valid input data is expected to match
`OUTPUT_SCHEMA`	List of column types that will either be returned by the model as a result set or used in creating a result table, depending on how the model’s evaluation is invoked

Examples

DESCRIBE MODEL Example

DESCRIBE MODEL temp_2c

DESCRIBE MODEL Example (All Models)

DESCRIBE MODEL *

​CREATE CONTAINER REGISTRY

​Parameters

​Examples

​DROP CONTAINER REGISTRY

​Parameters

​Examples

​SHOW CONTAINER REGISTRY

​Parameters

​Examples

​IMPORT MODEL

​Parameters

​Examples

​EVALUATE_MODEL

​Parameters

​Examples

​ALTER MODEL

​Parameters

​Examples

​REFRESH MODEL

​Examples

​DROP MODEL

​Examples

​SHOW MODEL

​Parameters

​Examples

​DESCRIBE MODEL

​Parameters

​Response

​Examples

CREATE CONTAINER REGISTRY

Parameters

Examples

DROP CONTAINER REGISTRY

Parameters

Examples

SHOW CONTAINER REGISTRY

Parameters

Examples

IMPORT MODEL

Parameters

Examples

EVALUATE_MODEL

Parameters

Examples

ALTER MODEL

Parameters

Examples

REFRESH MODEL

Examples

DROP MODEL

Examples

SHOW MODEL

Parameters

Examples

DESCRIBE MODEL

Parameters

Response

Examples