Data Source: oci_datascience_models

This data source provides the list of Models in Oracle Cloud Infrastructure Data Science service.

Lists models in the specified compartment.

Example Usage

data "oci_datascience_models" "test_models" {
	#Required
	compartment_id = var.compartment_id

	#Optional
	created_by = var.model_created_by
	display_name = var.model_display_name
	id = var.model_id
	model_version_set_name = oci_datascience_model_version_set.test_model_version_set.name
	project_id = oci_datascience_project.test_project.id
	state = var.model_state
}

Argument Reference

The following arguments are supported:

• compartment_id - (Required) Filter results by the OCID of the compartment.
• created_by - (Optional) Filter results by the OCID of the user who created the resource.
• display_name - (Optional) Filter results by its user-friendly name.
• id - (Optional) Filter results by OCID. Must be an OCID of the correct type for the resource type.
• project_id - (Optional) Filter results by the OCID of the project.
• state - (Optional) Filter results by the specified lifecycle state. Must be a valid state for the resource type.

Attributes Reference

The following attributes are exported:

• models - The list of models.

Model Reference

The following attributes are exported:

• backup_operation_details - Backup operation details of the model.

  • backup_state - The backup status of the model.
  • backup_state_details - The backup execution status details of the model.
  • time_last_backup - The last backup execution time of the model.

• backup_setting - Back up setting details of the model.

  • backup_region - Oracle Cloud Infrastructure backup region for the model.
  • customer_notification_type - Customer notification on backup success/failure events.
  • is_backup_enabled - Boolean flag representing whether backup needs to be enabled/disabled for the model.

• compartment_id - The OCID of the model’s compartment.

• created_by - The OCID of the user who created the model.

• custom_metadata_list - An array of custom metadata details for the model.

  • category - Category of model metadata which should be null for defined metadata.For custom metadata is should be one of the following values “Performance,Training Profile,Training and Validation Datasets,Training Environment,other”.

  • description - Description of model metadata

  • key - Key of the model Metadata. The key can either be user defined or Oracle Cloud Infrastructure defined. List of Oracle Cloud Infrastructure defined keys:

    • useCaseType
    • libraryName
    • libraryVersion
    • estimatorClass
    • hyperParameters
    • testartifactresults

  • value - Allowed values for useCaseType: binary_classification, regression, multinomial_classification, clustering, recommender, dimensionality_reduction/representation, time_series_forecasting, anomaly_detection, topic_modeling, ner, sentiment_analysis, image_classification, object_localization, other

    Allowed values for libraryName: scikit-learn, xgboost, tensorflow, pytorch, mxnet, keras, lightGBM, pymc3, pyOD, spacy, prophet, sktime, statsmodels, cuml, oracle_automl, h2o, transformers, nltk, emcee, pystan, bert, gensim, flair, word2vec, ensemble, other

• defined_metadata_list - An array of defined metadata details for the model.

  • category - Category of model metadata which should be null for defined metadata.For custom metadata is should be one of the following values “Performance,Training Profile,Training and Validation Datasets,Training Environment,other”.

  • description - Description of model metadata

  • key - Key of the model Metadata. The key can either be user defined or Oracle Cloud Infrastructure defined. List of Oracle Cloud Infrastructure defined keys:

    • useCaseType
    • libraryName
    • libraryVersion
    • estimatorClass
    • hyperParameters
    • testartifactresults

  • value - Allowed values for useCaseType: binary_classification, regression, multinomial_classification, clustering, recommender, dimensionality_reduction/representation, time_series_forecasting, anomaly_detection, topic_modeling, ner, sentiment_analysis, image_classification, object_localization, other

    Allowed values for libraryName: scikit-learn, xgboost, tensorflow, pytorch, mxnet, keras, lightGBM, pymc3, pyOD, spacy, prophet, sktime, statsmodels, cuml, oracle_automl, h2o, transformers, nltk, emcee, pystan, bert, gensim, flair, word2vec, ensemble, other

• defined_tags - Defined tags for this resource. Each key is predefined and scoped to a namespace. See Resource Tags. Example: {"Operations.CostCenter": "42"}

• description - A short description of the model.

• display_name - A user-friendly display name for the resource. It does not have to be unique and can be modified. Avoid entering confidential information.

• freeform_tags - Free-form tags for this resource. Each tag is a simple key-value pair with no predefined name, type, or namespace. See Resource Tags. Example: {"Department": "Finance"}

• id - The OCID of the model.

• input_schema - Input schema file content in String format

• lifecycle_details - Details about the lifecycle state of the model.

• model_version_set_id - The OCID of the model version set that the model is associated to.

• model_version_set_name - The name of the model version set that the model is associated to.

• output_schema - Output schema file content in String format

• project_id - The OCID of the project associated with the model.

• retention_operation_details - Retention operation details for the model.

  • archive_state - The archival status of model.
  • archive_state_details - The archival state details of the model.
  • delete_state - The deletion status of the archived model.
  • delete_state_details - The deletion status details of the archived model.
  • time_archival_scheduled - The estimated archival time of the model based on the provided retention setting.
  • time_deletion_scheduled - The estimated deletion time of the model based on the provided retention setting.

• retention_setting - Retention setting details of the model.

  • archive_after_days - Number of days after which the model will be archived.
  • customer_notification_type - Customer notification options on success/failure of archival, deletion events.
  • delete_after_days - Number of days after which the archived model will be deleted.

• state - The state of the model.

• time_created - The date and time the resource was created in the timestamp format defined by RFC3339. Example: 2019-08-25T21:10:29.41Z