ml model create
Create a model with the defined payload. A model represents a machine learning model asset.
Syntax
cpd-cli ml model create \
[--async] \
[--content-location=<attachment-details>] \
[--context=<catalog-project-or-space-id>] \
[--cpd-config=<cpd-config-location>] \
[--cpd-scope=<cpd-config-location>] \
[--custom=<map<key,value>>] \
[--description=<resource-description>] \
[--domain=<model-domain-name>] \
[--hyper-parameters=<model-training-hyper-parameters>] \
[--jmes-query=<jmespath-query>] \
[--label-column=<label-column>] \
[--metrics=<operation-metrics>] \
[--model-definition=<model-definition>] \
--name=<resource-name> \
[--output=json|yaml|table] \
[--output-file=<output-file-location>] \
[--pipeline=<resource-reference>] \
--profile=<cpd-configuration-profile-name> \
[--project-id=<cpd-project-id>] \
[--quiet] \
[--raw-output=true|false] \
[--schemas=<expected-data-schemas>] \
[--size=<model-size>] \
--software-spec=<software-specification> \
[--space-id=<space-identifier>] \
[--tags=<tag1,tag2,...>] \
[--test-data-references=<holdout-or-test-data-set>] \
[--training-data-references=<model-training-data>] \
[--transformed-label-column=<transformed-label-column-name>] \
--type=<model-type> \
[--user-defined-objects=<objects-defined-by-model>] \
[--verbose]
Arguments
The ml model create
command has no
arguments.
Options
Option | Description |
---|---|
--async |
Run the command asynchronously. By default, processing finishes before the command runs.
|
--content-location |
Specify details about the attachments that are uploaded with the model.
|
--context |
Specify the configuration context
name.
|
--cpd-config |
The Cloud Pak for Data
configuration location (for example, $HOME/.cpd-cli/config).
|
--cpd-scope |
The Cloud Pak for Data space, project, or catalog scope (for example, cpd://default-context/spaces/7bccdda4-9752-4f37-868e-891de6c48135).
|
--custom |
Specify user-defined properties as key-value pairs.
|
--description |
Specify a resource
description.
|
--domain |
Provide a domain name for the model (for example, sentiment, entity, visual-recognition, finance, retail, real estate).
|
|
Display command
help.
|
--hyper-parameters |
Specify the hyper parameters that are used for training the model.
|
--jmes-query |
Provide a JMESPath query to customize the output.
|
--label-column |
Specify the label column.
|
--metrics |
Specify metrics that can be returned by an operation.
|
--model-definition |
Specify the model definition. The 'software_spec' is a reference to a software specification. The 'hardware_spec' is a reference to a hardware specification.
|
--name |
Specify a resource name.
|
--output |
Specify an output format.
Valid formats include json, yaml, or text (the default
format).
|
--output-file |
Specify a file path where all output is redirected.
|
--pipeline |
Specify a reference to a resource. The 'hardware_spec' is a reference to the hardware specification. The 'hybrid_pipeline_hardware_specs' reference is used only when training a hybrid pipeline. A hybrid pipeline is used to specify the compute requirements for each pipeline node.
|
--profile |
The profile-name from the Cloud
Pak for Data configuration.
|
--project-id |
Specify a Cloud Pak for Data project instance that contains the resource.
|
--quiet |
Suppress verbose messages.
|
--raw-output |
When set to true, single values in
JSON output mode are not surrounded by quotes.
|
--schemas |
Specify the expected data schemas. Schemas that are defined here take precedent over any schemas that are provided in the data references.
|
--size |
Specify the model size. This setting is used by scoring to record the model size.
|
--software-spec |
Specify a software specification (SoftwareSpecRel object).
|
--space-id |
Specify a space identifier.
|
--tags |
Specify tags that can be used when searching for resources.
|
--test-data-references |
Specify the holdout or test data sets.
|
--training-data-references |
Specify the training data that was used to create the model.
|
--transformed-label-column |
Specify the name of the label column (as seen by the estimator), which might be transformed by the previous pipeline transformers. The name is not necessarily the same column as the 'label_column' in the initial data set.
|
--type |
Specify the model type. For information on supported
model types, see Supported machine learning tools, libraries, frameworks, and software
specifications.
|
--user-defined-objects |
Specify defined objects that are referenced by the model. For any defined model class or function, the name (as referenced in the model) must be specified as the 'key' and a fully qualified class or function name must be specified as the 'value'. This is applicable for 'Tensorflow 2.X' models serialized in 'H5' format that uses the 'tf.keras' API.
|
--verbose |
Logs include more detailed
messages.
|