oci_ai_vision_model

This resource provides the Model resource in Oracle Cloud Infrastructure Ai Vision service.

Creates a new Model.

Example Usage

resource "oci_ai_vision_model" "test_model" {
	#Required
	compartment_id = var.compartment_id
	model_type = var.model_model_type
	project_id = oci_ai_vision_project.test_project.id
	training_dataset {
		#Required
		dataset_type = var.model_training_dataset_dataset_type

		#Optional
		bucket = var.model_training_dataset_bucket
		dataset_id = oci_data_labeling_service_dataset.test_dataset.id
		namespace_name = var.model_training_dataset_namespace
		object = var.model_training_dataset_object
	}

	#Optional
	defined_tags = {"foo-namespace.bar-key"= "value"}
	description = var.model_description
	display_name = var.model_display_name
	freeform_tags = {"bar-key"= "value"}
	is_quick_mode = var.model_is_quick_mode
	max_training_duration_in_hours = var.model_max_training_duration_in_hours
	model_version = var.model_model_version
	testing_dataset {
		#Required
		dataset_type = var.model_testing_dataset_dataset_type

		#Optional
		bucket = var.model_testing_dataset_bucket
		dataset_id = oci_data_labeling_service_dataset.test_dataset.id
		namespace_name = var.model_testing_dataset_namespace
		object = var.model_testing_dataset_object
	}
	validation_dataset {
		#Required
		dataset_type = var.model_validation_dataset_dataset_type

		#Optional
		bucket = var.model_validation_dataset_bucket
		dataset_id = oci_data_labeling_service_dataset.test_dataset.id
		namespace_name = var.model_validation_dataset_namespace
		object = var.model_validation_dataset_object
	}
}

Argument Reference

The following arguments are supported:

• compartment_id - (Required) (Updatable) Compartment Identifier
• defined_tags - (Optional) (Updatable) Defined tags for this resource. Each key is predefined and scoped to a namespace. Example: {"foo-namespace.bar-key": "value"}
• description - (Optional) (Updatable) A short description of the Model.
• display_name - (Optional) (Updatable) Model Identifier
• freeform_tags - (Optional) (Updatable) Simple key-value pair that is applied without any predefined name, type or scope. Exists for cross-compatibility only. Example: {"bar-key": "value"}
• is_quick_mode - (Optional) If It’s true, Training is set for recommended epochs needed for quick training.
• max_training_duration_in_hours - (Optional) The maximum duration in hours for which the training will run.
• model_type - (Required) The type of the model.
• model_version - (Optional) Model version.
• project_id - (Required) The OCID of the project to associate with the model.
• testing_dataset - (Optional) The base entity for a Dataset, which is the input for Model creation.
  • bucket - (Applicable when dataset_type=OBJECT_STORAGE) The name of the ObjectStorage bucket that contains the input data file.
  • dataset_id - (Applicable when dataset_type=DATA_SCIENCE_LABELING) The OCID of the Data Science Labeling Dataset.
  • dataset_type - (Required) Type of the Dataset.
  • namespace_name - (Applicable when dataset_type=OBJECT_STORAGE) The namespace name of the ObjectStorage bucket that contains the input data file.
  • object - (Applicable when dataset_type=OBJECT_STORAGE) The object name of the input data file.
• training_dataset - (Required) The base entity for a Dataset, which is the input for Model creation.
  • bucket - (Applicable when dataset_type=OBJECT_STORAGE) The name of the ObjectStorage bucket that contains the input data file.
  • dataset_id - (Applicable when dataset_type=DATA_SCIENCE_LABELING) The OCID of the Data Science Labeling Dataset.
  • dataset_type - (Required) Type of the Dataset.
  • namespace_name - (Applicable when dataset_type=OBJECT_STORAGE) The namespace name of the ObjectStorage bucket that contains the input data file.
  • object - (Applicable when dataset_type=OBJECT_STORAGE) The object name of the input data file.
• validation_dataset - (Optional) The base entity for a Dataset, which is the input for Model creation.
  • bucket - (Applicable when dataset_type=OBJECT_STORAGE) The name of the ObjectStorage bucket that contains the input data file.
  • dataset_id - (Applicable when dataset_type=DATA_SCIENCE_LABELING) The OCID of the Data Science Labeling Dataset.
  • dataset_type - (Required) Type of the Dataset.
  • namespace_name - (Applicable when dataset_type=OBJECT_STORAGE) The namespace name of the ObjectStorage bucket that contains the input data file.
  • object - (Applicable when dataset_type=OBJECT_STORAGE) The object name of the input data file.

** 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:

• average_precision - Average precision of the trained model
• compartment_id - Compartment Identifier
• confidence_threshold - Confidence ratio of the calculation
• defined_tags - Defined tags for this resource. Each key is predefined and scoped to a namespace. Example: {"foo-namespace.bar-key": "value"}
• description - A short description of the model.
• display_name - Model Identifier, can be renamed
• freeform_tags - Simple key-value pair that is applied without any predefined name, type or scope. Exists for cross-compatibility only. Example: {"bar-key": "value"}
• id - Unique identifier that is immutable on creation
• is_quick_mode - If It’s true, Training is set for recommended epochs needed for quick training.
• lifecycle_details - A message describing the current state in more detail. For example, can be used to provide actionable information for a resource in Failed state.
• max_training_duration_in_hours - The maximum duration in hours for which the training will run.
• metrics - Complete Training Metrics for successful trained model
• model_type - Type of the Model.
• model_version - The version of the model
• precision - Precision of the trained model
• project_id - The OCID of the project to associate with the model.
• recall - Recall of the trained model
• state - The current state of the Model.
• system_tags - Usage of system tag keys. These predefined keys are scoped to namespaces. Example: {"orcl-cloud.free-tier-retained": "true"}
• test_image_count - Total number of testing Images
• testing_dataset - The base entity for a Dataset, which is the input for Model creation.
  • bucket - The name of the ObjectStorage bucket that contains the input data file.
  • dataset_id - The OCID of the Data Science Labeling Dataset.
  • dataset_type - Type of the Dataset.
  • namespace_name - The namespace name of the ObjectStorage bucket that contains the input data file.
  • object - The object name of the input data file.
• time_created - The time the Model was created. An RFC3339 formatted datetime string
• time_updated - The time the Model was updated. An RFC3339 formatted datetime string
• total_image_count - Total number of training Images
• trained_duration_in_hours - Total hours actually used for training
• training_dataset - The base entity for a Dataset, which is the input for Model creation.
  • bucket - The name of the ObjectStorage bucket that contains the input data file.
  • dataset_id - The OCID of the Data Science Labeling Dataset.
  • dataset_type - Type of the Dataset.
  • namespace_name - The namespace name of the ObjectStorage bucket that contains the input data file.
  • object - The object name of the input data file.
• validation_dataset - The base entity for a Dataset, which is the input for Model creation.
  • bucket - The name of the ObjectStorage bucket that contains the input data file.
  • dataset_id - The OCID of the Data Science Labeling Dataset.
  • dataset_type - Type of the Dataset.
  • namespace_name - The namespace name of the ObjectStorage bucket that contains the input data file.
  • object - The object name of the input data file.

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_ai_vision_model.test_model "id"