oci_datascience_model
This resource provides the Model resource in Oracle Cloud Infrastructure Data Science service.
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
key = var.model_custom_metadata_list_key
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
key = var.model_defined_metadata_list_key
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,other”.description
- (Optional) (Updatable) Description of model metadatakey
- (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
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, otherAllowed 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,other”.description
- (Optional) (Updatable) Description of model metadatakey
- (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
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, otherAllowed 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 formatoutput_schema
- (Optional) Output schema file content in String formatproject_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 ifmodel_artifact
is 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.
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 metadatakey
- 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, otherAllowed 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 metadatakey
- 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, otherAllowed 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 formatlifecycle_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 formatproject_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"