oci_datascience_model
This resource provides the Model resource in Oracle Cloud Infrastructure Data Science service. Api doc link for the resource: https://docs.oracle.com/iaas/api/#/en/data-science/latest/Model
Example terraform configs related to the resource : https://github.com/oracle/terraform-provider-oci/tree/master/examples/datascience
Creates a new model.
Example Usage
resource "oci_datascience_model" "test_model" {
	#Required
	compartment_id = var.compartment_id
	project_id = oci_datascience_project.test_project.id
	#Optional
	backup_setting {
		#Required
		backup_region = var.model_backup_setting_backup_region
		is_backup_enabled = var.model_backup_setting_is_backup_enabled
		#Optional
		customer_notification_type = var.model_backup_setting_customer_notification_type
	}
	custom_metadata_list {
		#Optional
		category = var.model_custom_metadata_list_category
		description = var.model_custom_metadata_list_description
		has_artifact = var.model_custom_metadata_list_has_artifact
		key = var.model_custom_metadata_list_key
		keywords = var.model_custom_metadata_list_keywords
		value = var.model_custom_metadata_list_value
	}
	defined_metadata_list {
		#Optional
		category = var.model_defined_metadata_list_category
		description = var.model_defined_metadata_list_description
		has_artifact = var.model_defined_metadata_list_has_artifact
		key = var.model_defined_metadata_list_key
		keywords = var.model_defined_metadata_list_keywords
		value = var.model_defined_metadata_list_value
	}
	defined_tags = {"Operations.CostCenter"= "42"}
	description = var.model_description
	display_name = var.model_display_name
	freeform_tags = {"Department"= "Finance"}
	input_schema = var.model_input_schema
	output_schema = var.model_output_schema
	retention_setting {
		#Required
		archive_after_days = var.model_retention_setting_archive_after_days
		#Optional
		customer_notification_type = var.model_retention_setting_customer_notification_type
		delete_after_days = var.model_retention_setting_delete_after_days
	}
	version_label = var.model_version_label
}
Argument Reference
The following arguments are supported:
- backup_setting- (Optional) (Updatable) Back up setting details of the model.- backup_region- (Required) (Updatable) Oracle Cloud Infrastructure backup region for the model.
- customer_notification_type- (Optional) (Updatable) Customer notification on backup success/failure events.
- is_backup_enabled- (Required) (Updatable) Boolean flag representing whether backup needs to be enabled/disabled for the model.
 
- compartment_id- (Required) (Updatable) The OCID of the compartment to create the model in.
- custom_metadata_list- (Optional) (Updatable) An array of custom metadata details for the model.- category- (Optional) (Updatable) 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,Reports,Readme,other”.
- description- (Optional) (Updatable) Description of model metadata
- has_artifact- (Optional) (Updatable) Is there any artifact present for the metadata.
- key- (Optional) (Updatable) 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
- fineTuningConfiguration
- deploymentConfiguration
- readme
- license
- evaluationConfiguration
 
- keywords- (Optional) (Updatable) list of keywords for searching
- value- (Optional) (Updatable) 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- (Optional) (Updatable) An array of defined metadata details for the model.- category- (Optional) (Updatable) 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,Reports,Readme,other”.
- description- (Optional) (Updatable) Description of model metadata
- has_artifact- (Optional) (Updatable) Is there any artifact present for the metadata.
- key- (Optional) (Updatable) 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
- fineTuningConfiguration
- deploymentConfiguration
- readme
- license
- evaluationConfiguration
 
- keywords- (Optional) (Updatable) list of keywords for searching
- value- (Optional) (Updatable) 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- (Optional) (Updatable) Defined tags for this resource. Each key is predefined and scoped to a namespace. See Resource Tags. Example:- {"Operations.CostCenter": "42"}
- description- (Optional) (Updatable) A short description of the model.
- display_name- (Optional) (Updatable) A user-friendly display name for the resource. It does not have to be unique and can be modified. Avoid entering confidential information. Example:- My Model
- freeform_tags- (Optional) (Updatable) 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"}
- input_schema- (Optional) Input schema file content in String format
- output_schema- (Optional) Output schema file content in String format
- project_id- (Required) The OCID of the project to associate with the model.
- retention_setting- (Optional) (Updatable) Retention setting details of the model.- archive_after_days- (Required) (Updatable) Number of days after which the model will be archived.
- customer_notification_type- (Optional) (Updatable) Customer notification options on success/failure of archival, deletion events.
- delete_after_days- (Optional) (Updatable) Number of days after which the archived model will be deleted.
 
- version_label- (Optional) (Updatable) The version label can add an additional description of the lifecycle state of the model or the application using/training the model.
- model_artifact- (Optional) The model artifact to upload. It is a ZIP archive of the files necessary to run the model. This can be done in a separate step or using cli/sdk. The Model will remain in “Creating” state until its artifact is uploaded.
- artifact_content_disposition- (Optional) This allows to specify a filename during upload. This file name is used to dispose of the file contents while downloading the file. Example:- attachment; filename=model-artifact.zip
- artifact_content_length- (Optional, Required if- model_artifactis set) The content length of the model_artifact.
** IMPORTANT ** Any change to a property that does not support update will force the destruction and recreation of the resource with the new property values
Attributes 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.
 
- category- The category of 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,Reports,Readme,other”.
- description- Description of model metadata
- has_artifact- Is there any artifact present for the 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
- fineTuningConfiguration
- deploymentConfiguration
- readme
- license
- evaluationConfiguration
 
- keywords- list of keywords for searching
- 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,Reports,Readme,other”.
- description- Description of model metadata
- has_artifact- Is there any artifact present for the 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
- fineTuningConfiguration
- deploymentConfiguration
- readme
- license
- evaluationConfiguration
 
- keywords- list of keywords for searching
- 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
- is_model_by_reference- Identifier to indicate whether a model artifact resides in the Service Tenancy or Customer Tenancy.
- 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
Timeouts
The timeouts block allows you to specify timeouts for certain operations:
	* create - (Defaults to 20 minutes), when creating the Model
	* update - (Defaults to 20 minutes), when updating the Model
	* delete - (Defaults to 20 minutes), when destroying the Model
Import
Models can be imported using the id, e.g.
$ terraform import oci_datascience_model.test_model "id"