oci_ai_vision_model

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

Create 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 = var.model_defined_tags
	description = var.model_description
	display_name = var.model_display_name
	freeform_tags = var.model_freeform_tags
	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) The compartment identifier.
• defined_tags - (Optional) (Updatable) Defined tags for this resource. Each key is predefined and scoped to a namespace. For example: {"foo-namespace": {"bar-key": "value"}}
• description - (Optional) (Updatable) An optional description of the model.
• display_name - (Optional) (Updatable) A human-friendly name for the model, which can be changed.
• freeform_tags - (Optional) (Updatable) A simple key-value pair that is applied without any predefined name, type, or scope. It exists for cross-compatibility only. For example: {"bar-key": "value"}
• is_quick_mode - (Optional) Set to true when experimenting with a new model type or dataset, so the model training is quick, with a predefined low number of passes through the training data.
• max_training_duration_in_hours - (Optional) The maximum model training duration in hours, expressed as a decimal fraction.
• model_type - (Required) Which type of Vision model this is.
• model_version - (Optional) The model version
• project_id - (Required) The OCID of the project that contains the model.
• testing_dataset - (Optional) The base entity which is the input for creating and training a model.
  • bucket - (Required when dataset_type=OBJECT_STORAGE) The name of the Object Storage bucket that contains the input data file.
  • dataset_id - (Required when dataset_type=DATA_SCIENCE_LABELING) OCID of the Data Labeling dataset.
  • dataset_type - (Required) The dataset type, based on where it is stored.
  • namespace - (Required when dataset_type=OBJECT_STORAGE) The namespace name of the Object Storage bucket that contains the input data file.
  • object - (Required when dataset_type=OBJECT_STORAGE) The object name of the input data file.
• training_dataset - (Required) The base entity which is the input for creating and training a model.
  • bucket - (Required when dataset_type=OBJECT_STORAGE) The name of the Object Storage bucket that contains the input data file.
  • dataset_id - (Required when dataset_type=DATA_SCIENCE_LABELING) OCID of the Data Labeling dataset.
  • dataset_type - (Required) The dataset type, based on where it is stored.
  • namespace - (Required when dataset_type=OBJECT_STORAGE) The namespace name of the Object Storage bucket that contains the input data file.
  • object - (Required when dataset_type=OBJECT_STORAGE) The object name of the input data file.
• validation_dataset - (Optional) The base entity which is the input for creating and training a model.
  • bucket - (Required when dataset_type=OBJECT_STORAGE) The name of the Object Storage bucket that contains the input data file.
  • dataset_id - (Required when dataset_type=DATA_SCIENCE_LABELING) OCID of the Data Labeling dataset.
  • dataset_type - (Required) The dataset type, based on where it is stored.
  • namespace - (Required when dataset_type=OBJECT_STORAGE) The namespace name of the Object Storage bucket that contains the input data file.
  • object - (Required 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 - The mean average precision of the trained model.
• compartment_id - The compartment identifier.
• confidence_threshold - The intersection over the union threshold used for calculating precision and recall.
• defined_tags - Defined tags for this resource. Each key is predefined and scoped to a namespace. For example: {"foo-namespace": {"bar-key": "value"}}
• description - An optional description of the model.
• display_name - A human-friendly name for the model, which can be changed.
• freeform_tags - A simple key-value pair that is applied without any predefined name, type, or scope. It exists for cross-compatibility only. For example: {"bar-key": "value"}
• id - A unique identifier that is immutable after creation.
• is_quick_mode - Set to true when experimenting with a new model type or dataset, so model training is quick, with a predefined low number of passes through the training data.
• lifecycle_details - A message describing the current state in more detail, that can provide actionable information if training failed.
• max_training_duration_in_hours - The maximum model training duration in hours, expressed as a decimal fraction.
• metrics - The complete set of per-label metrics for successfully trained models.
• model_type - What type of Vision model this is.
• model_version - The version of the model.
• precision - The precision of the trained model.
• project_id - The OCID of the project that contains 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. For example: {"orcl-cloud": {"free-tier-retained": "true"}}
• test_image_count - The number of images set aside for evaluating model performance metrics after training.
• testing_dataset - The base entity which is the input for creating and training a model.
  • bucket - The name of the Object Storage bucket that contains the input data file.
  • dataset_id - OCID of the Data Labeling dataset.
  • dataset_type - The dataset type, based on where it is stored.
  • namespace - The namespace name of the Object Storage bucket that contains the input data file.
  • object - The object name of the input data file.
• time_created - When the model was created, as an RFC3339 datetime string.
• time_updated - When the model was updated, as an RFC3339 datetime string.
• total_image_count - The number of images in the dataset used to train, validate, and test the model.
• trained_duration_in_hours - The total hours actually used for model training.
• training_dataset - The base entity which is the input for creating and training a model.
  • bucket - The name of the Object Storage bucket that contains the input data file.
  • dataset_id - OCID of the Data Labeling dataset.
  • dataset_type - The dataset type, based on where it is stored.
  • namespace - The namespace name of the Object Storage bucket that contains the input data file.
  • object - The object name of the input data file.
• validation_dataset - The base entity which is the input for creating and training a model.
  • bucket - The name of the Object Storage bucket that contains the input data file.
  • dataset_id - OCID of the Data Labeling dataset.
  • dataset_type - The dataset type, based on where it is stored.
  • namespace - The namespace name of the Object Storage 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"