Skip to main content
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

<registry name>

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

<registry name>

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

<registry name>

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

<model name>

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>

​
Parameters

<model name>

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

<model name>

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

<model name>

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 ColumnDescription
MODELName of the ML model
ENTITY_IDInternal unique ID for the ML model
INPUT_SCHEMAList of columns types that valid input data is expected to match
OUTPUT_SCHEMAList 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 *