oracle.oci.oci_ai_document_model – Manage a Model resource in Oracle Cloud Infrastructure

Note

This plugin is part of the oracle.oci collection (version 5.5.0).

You might already have this collection installed if you are using the ansible package. It is not included in ansible-core. To check whether it is installed, run ansible-galaxy collection list.

To install it, use: ansible-galaxy collection install oracle.oci.

To use it in a playbook, specify: oracle.oci.oci_ai_document_model.

New in version 2.9.0: of oracle.oci

Synopsis

  • This module allows the user to create, update, patch and delete a Model resource in Oracle Cloud Infrastructure

  • For state=present, create a new model.

  • This resource has the following action operations in the oracle.oci.oci_ai_document_model_actions module: change_compartment.

Requirements

The below requirements are needed on the host that executes this module.

Parameters

Parameter Choices/Defaults Comments
alias_name
string
the alias name of the model.
api_user
string
The OCID of the user, on whose behalf, OCI APIs are invoked. If not set, then the value of the OCI_USER_ID environment variable, if any, is used. This option is required if the user is not specified through a configuration file (See config_file_location). To get the user's OCID, please refer https://docs.us-phoenix-1.oraclecloud.com/Content/API/Concepts/apisigningkey.htm.
api_user_fingerprint
string
Fingerprint for the key pair being used. If not set, then the value of the OCI_USER_FINGERPRINT environment variable, if any, is used. This option is required if the key fingerprint is not specified through a configuration file (See config_file_location). To get the key pair's fingerprint value please refer https://docs.us-phoenix-1.oraclecloud.com/Content/API/Concepts/apisigningkey.htm.
api_user_key_file
string
Full path and filename of the private key (in PEM format). If not set, then the value of the OCI_USER_KEY_FILE variable, if any, is used. This option is required if the private key is not specified through a configuration file (See config_file_location). If the key is encrypted with a pass-phrase, the api_user_key_pass_phrase option must also be provided.
api_user_key_pass_phrase
string
Passphrase used by the key referenced in api_user_key_file, if it is encrypted. If not set, then the value of the OCI_USER_KEY_PASS_PHRASE variable, if any, is used. This option is required if the key passphrase is not specified through a configuration file (See config_file_location).
auth_purpose
string
    Choices:
  • service_principal
The auth purpose which can be used in conjunction with 'auth_type=instance_principal'. The default auth_purpose for instance_principal is None.
auth_type
string
    Choices:
  • api_key ←
  • instance_principal
  • instance_obo_user
  • resource_principal
  • security_token
The type of authentication to use for making API requests. By default auth_type="api_key" based authentication is performed and the API key (see api_user_key_file) in your config file will be used. If this 'auth_type' module option is not specified, the value of the OCI_ANSIBLE_AUTH_TYPE, if any, is used. Use auth_type="instance_principal" to use instance principal based authentication when running ansible playbooks within an OCI compute instance.
cert_bundle
string
The full path to a CA certificate bundle to be used for SSL verification. This will override the default CA certificate bundle. If not set, then the value of the OCI_ANSIBLE_CERT_BUNDLE variable, if any, is used.
compartment_id
string
The compartment identifier.
Required for create using state=present.
component_models
list / elements=dictionary
The OCID list of active custom Key Value models that need to be composed.
model_id
string
The OCID of active custom Key Value model that need to be composed.
config_file_location
string
Path to configuration file. If not set then the value of the OCI_CONFIG_FILE environment variable, if any, is used. Otherwise, defaults to ~/.oci/config.
config_profile_name
string
The profile to load from the config file referenced by config_file_location. If not set, then the value of the OCI_CONFIG_PROFILE environment variable, if any, is used. Otherwise, defaults to the "DEFAULT" profile in config_file_location.
defined_tags
dictionary
Defined tags for this resource. Each key is predefined and scoped to a namespace. For example: `{"foo-namespace": {"bar-key": "value"}}`
This parameter is updatable.
description
string
An optional description of the model.
This parameter is updatable.
display_name
string
A human-friendly name for the model, which can be changed.
Required for create, update, delete when environment variable OCI_USE_NAME_AS_IDENTIFIER is set.
This parameter is updatable when OCI_USE_NAME_AS_IDENTIFIER is not set.

aliases: name
force_create
boolean
    Choices:
  • no ←
  • yes
Whether to attempt non-idempotent creation of a resource. By default, create resource is an idempotent operation, and doesn't create the resource if it already exists. Setting this option to true, forcefully creates a copy of the resource, even if it already exists.This option is mutually exclusive with key_by.
freeform_tags
dictionary
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"}`
This parameter is updatable.
is_quick_mode
boolean
    Choices:
  • no
  • yes
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.
key_by
list / elements=string
The list of attributes of this resource which should be used to uniquely identify an instance of the resource. By default, all the attributes of a resource are used to uniquely identify a resource.
max_training_time_in_hours
float
The maximum model training time in hours, expressed as a decimal fraction.
model_id
string
A unique model identifier.
Required for update using state=present when environment variable OCI_USE_NAME_AS_IDENTIFIER is not set.
Required for delete using state=absent when environment variable OCI_USE_NAME_AS_IDENTIFIER is not set.

aliases: id
model_type
string
The type of the Document model.
Required for create using state=present.
model_version
string
The model version
operations
list / elements=dictionary
A list of patch operations for model.
operation
string
    Choices:
  • DELETE
  • ADD
  • REPLACE
The value of the parameter to be updated.
path
string
The parameter of the resource to be changed.
value
string
The value of the parameter to be updated.
project_id
string
The OCID of the project that contains the model.
Required for create using state=present.
realm_specific_endpoint_template_enabled
boolean
    Choices:
  • no
  • yes
Enable/Disable realm specific endpoint template for service client. By Default, realm specific endpoint template is disabled. If not set, then the value of the OCI_REALM_SPECIFIC_SERVICE_ENDPOINT_TEMPLATE_ENABLED variable, if any, is used.
region
string
The Oracle Cloud Infrastructure region to use for all OCI API requests. If not set, then the value of the OCI_REGION variable, if any, is used. This option is required if the region is not specified through a configuration file (See config_file_location). Please refer to https://docs.us-phoenix-1.oraclecloud.com/Content/General/Concepts/regions.htm for more information on OCI regions.
state
string
    Choices:
  • present ←
  • absent
The state of the Model.
Use state=present to create or update a Model.
Use state=absent to delete a Model.
tenancy
string
OCID of your tenancy. If not set, then the value of the OCI_TENANCY variable, if any, is used. This option is required if the tenancy OCID is not specified through a configuration file (See config_file_location). To get the tenancy OCID, please refer https://docs.us-phoenix-1.oraclecloud.com/Content/API/Concepts/apisigningkey.htm
testing_dataset
dictionary
bucket_name
string
The name of the Object Storage bucket that contains the input data file.
Required when dataset_type is 'OBJECT_STORAGE'
dataset_id
string
OCID of the Data Labeling dataset.
Required when dataset_type is 'DATA_SCIENCE_LABELING'
dataset_type
string / required
    Choices:
  • DATA_SCIENCE_LABELING
  • OBJECT_STORAGE
The dataset type, based on where it is stored.
namespace_name
string
The namespace name of the Object Storage bucket that contains the input data file.
Required when dataset_type is 'OBJECT_STORAGE'
object_name
string
The object name of the input data file.
Required when dataset_type is 'OBJECT_STORAGE'
training_dataset
dictionary
bucket_name
string
The name of the Object Storage bucket that contains the input data file.
Required when dataset_type is 'OBJECT_STORAGE'
dataset_id
string
OCID of the Data Labeling dataset.
Required when dataset_type is 'DATA_SCIENCE_LABELING'
dataset_type
string / required
    Choices:
  • DATA_SCIENCE_LABELING
  • OBJECT_STORAGE
The dataset type, based on where it is stored.
namespace_name
string
The namespace name of the Object Storage bucket that contains the input data file.
Required when dataset_type is 'OBJECT_STORAGE'
object_name
string
The object name of the input data file.
Required when dataset_type is 'OBJECT_STORAGE'
validation_dataset
dictionary
bucket_name
string
The name of the Object Storage bucket that contains the input data file.
Required when dataset_type is 'OBJECT_STORAGE'
dataset_id
string
OCID of the Data Labeling dataset.
Required when dataset_type is 'DATA_SCIENCE_LABELING'
dataset_type
string / required
    Choices:
  • DATA_SCIENCE_LABELING
  • OBJECT_STORAGE
The dataset type, based on where it is stored.
namespace_name
string
The namespace name of the Object Storage bucket that contains the input data file.
Required when dataset_type is 'OBJECT_STORAGE'
object_name
string
The object name of the input data file.
Required when dataset_type is 'OBJECT_STORAGE'
wait
boolean
    Choices:
  • no
  • yes ←
Whether to wait for create or delete operation to complete.
wait_timeout
integer
Time, in seconds, to wait when wait=yes. Defaults to 1200 for most of the services but some services might have a longer wait timeout.

Examples

- name: Create model
  oci_ai_document_model:
    # required
    model_type: model_type_example
    compartment_id: "ocid1.compartment.oc1..xxxxxxEXAMPLExxxxxx"
    project_id: "ocid1.project.oc1..xxxxxxEXAMPLExxxxxx"

    # optional
    model_version: model_version_example
    is_quick_mode: true
    max_training_time_in_hours: 3.4
    training_dataset:
      # required
      dataset_id: "ocid1.dataset.oc1..xxxxxxEXAMPLExxxxxx"
      dataset_type: DATA_SCIENCE_LABELING
    testing_dataset:
      # required
      dataset_id: "ocid1.dataset.oc1..xxxxxxEXAMPLExxxxxx"
      dataset_type: DATA_SCIENCE_LABELING
    validation_dataset:
      # required
      dataset_id: "ocid1.dataset.oc1..xxxxxxEXAMPLExxxxxx"
      dataset_type: DATA_SCIENCE_LABELING
    component_models:
    - # optional
      model_id: "ocid1.model.oc1..xxxxxxEXAMPLExxxxxx"
    alias_name: alias_name_example
    display_name: display_name_example
    description: description_example
    freeform_tags: {'Department': 'Finance'}
    defined_tags: {'Operations': {'CostCenter': 'US'}}

- name: Update model
  oci_ai_document_model:
    # required
    model_id: "ocid1.model.oc1..xxxxxxEXAMPLExxxxxx"

    # optional
    display_name: display_name_example
    description: description_example
    freeform_tags: {'Department': 'Finance'}
    defined_tags: {'Operations': {'CostCenter': 'US'}}

- name: Update model using name (when environment variable OCI_USE_NAME_AS_IDENTIFIER is set)
  oci_ai_document_model:
    # required
    display_name: display_name_example

    # optional
    description: description_example
    freeform_tags: {'Department': 'Finance'}
    defined_tags: {'Operations': {'CostCenter': 'US'}}

- name: Delete model
  oci_ai_document_model:
    # required
    model_id: "ocid1.model.oc1..xxxxxxEXAMPLExxxxxx"
    state: absent

- name: Delete model using name (when environment variable OCI_USE_NAME_AS_IDENTIFIER is set)
  oci_ai_document_model:
    # required
    display_name: display_name_example
    state: absent

Return Values

Common return values are documented here, the following are the fields unique to this module:

Key Returned Description
model
complex
on success
Details of the Model resource acted upon by the current operation

Sample:
{'alias_name': 'alias_name_example', 'compartment_id': 'ocid1.compartment.oc1..xxxxxxEXAMPLExxxxxx', 'component_models': [{'model_id': 'ocid1.model.oc1..xxxxxxEXAMPLExxxxxx'}], 'defined_tags': {'Operations': {'CostCenter': 'US'}}, 'description': 'description_example', 'display_name': 'display_name_example', 'freeform_tags': {'Department': 'Finance'}, 'id': 'ocid1.resource.oc1..xxxxxxEXAMPLExxxxxx', 'is_composed_model': True, 'is_quick_mode': True, 'labels': [], 'lifecycle_details': 'lifecycle_details_example', 'lifecycle_state': 'CREATING', 'max_training_time_in_hours': 1.2, 'metrics': {'dataset_summary': {'test_sample_count': 56, 'training_sample_count': 56, 'validation_sample_count': 56}, 'label_metrics_report': [{'confidence_entries': [{'accuracy': 3.4, 'f1_score': 3.4, 'precision': 3.4, 'recall': 3.4, 'threshold': 3.4}], 'document_count': 56, 'label': 'label_example', 'mean_average_precision': 3.4}], 'model_type': 'KEY_VALUE_EXTRACTION', 'overall_metrics_report': {'confidence_entries': [{'accuracy': 3.4, 'f1_score': 3.4, 'precision': 3.4, 'recall': 3.4, 'threshold': 3.4}], 'document_count': 56, 'mean_average_precision': 3.4}}, 'model_type': 'KEY_VALUE_EXTRACTION', 'model_version': 'model_version_example', 'project_id': 'ocid1.project.oc1..xxxxxxEXAMPLExxxxxx', 'system_tags': {}, 'tenancy_id': 'ocid1.tenancy.oc1..xxxxxxEXAMPLExxxxxx', 'testing_dataset': {'bucket_name': 'bucket_name_example', 'dataset_id': 'ocid1.dataset.oc1..xxxxxxEXAMPLExxxxxx', 'dataset_type': 'DATA_SCIENCE_LABELING', 'namespace_name': 'namespace_name_example', 'object_name': 'object_name_example'}, 'time_created': '2013-10-20T19:20:30+01:00', 'time_updated': '2013-10-20T19:20:30+01:00', 'trained_time_in_hours': 1.2, 'training_dataset': {'bucket_name': 'bucket_name_example', 'dataset_id': 'ocid1.dataset.oc1..xxxxxxEXAMPLExxxxxx', 'dataset_type': 'DATA_SCIENCE_LABELING', 'namespace_name': 'namespace_name_example', 'object_name': 'object_name_example'}, 'validation_dataset': {'bucket_name': 'bucket_name_example', 'dataset_id': 'ocid1.dataset.oc1..xxxxxxEXAMPLExxxxxx', 'dataset_type': 'DATA_SCIENCE_LABELING', 'namespace_name': 'namespace_name_example', 'object_name': 'object_name_example'}}
 
alias_name
string
on success
the alias name of the model.

Sample:
alias_name_example
 
compartment_id
string
on success
The compartment identifier.

Sample:
ocid1.compartment.oc1..xxxxxxEXAMPLExxxxxx
 
component_models
complex
on success
The OCID collection of active custom Key Value models that need to be composed.
   
model_id
string
on success
The OCID of active custom Key Value model that need to be composed.

Sample:
ocid1.model.oc1..xxxxxxEXAMPLExxxxxx
 
defined_tags
dictionary
on success
Defined tags for this resource. Each key is predefined and scoped to a namespace. For example: `{"foo-namespace": {"bar-key": "value"}}`

Sample:
{'Operations': {'CostCenter': 'US'}}
 
description
string
on success
An optional description of the model.

Sample:
description_example
 
display_name
string
on success
A human-friendly name for the model, which can be changed.

Sample:
display_name_example
 
freeform_tags
dictionary
on success
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"}`

Sample:
{'Department': 'Finance'}
 
id
string
on success
A unique identifier that is immutable after creation.

Sample:
ocid1.resource.oc1..xxxxxxEXAMPLExxxxxx
 
is_composed_model
boolean
on success
Set to true when the model is created by using multiple key value extraction models.

Sample:
True
 
is_quick_mode
boolean
on success
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.

Sample:
True
 
labels
list / elements=string
on success
The collection of labels used to train the custom model.
 
lifecycle_details
string
on success
A message describing the current state in more detail, that can provide actionable information if training failed.

Sample:
lifecycle_details_example
 
lifecycle_state
string
on success
The current state of the model.

Sample:
CREATING
 
max_training_time_in_hours
float
on success
The maximum model training time in hours, expressed as a decimal fraction.

Sample:
1.2
 
metrics
complex
on success

   
dataset_summary
complex
on success

     
test_sample_count
integer
on success
Number of samples used for testing the model.

Sample:
56
     
training_sample_count
integer
on success
Number of samples used for training the model.

Sample:
56
     
validation_sample_count
integer
on success
Number of samples used for validating the model.

Sample:
56
   
label_metrics_report
complex
on success
List of metrics entries per label.
     
confidence_entries
complex
on success
List of document classification confidence report.
       
accuracy
float
on success
accuracy under the threshold

Sample:
3.4
       
f1_score
float
on success
f1Score under the threshold

Sample:
3.4
       
precision
float
on success
Precision under the threshold

Sample:
3.4
       
recall
float
on success
Recall under the threshold

Sample:
3.4
       
threshold
float
on success
Threshold used to calculate precision and recall.

Sample:
3.4
     
document_count
integer
on success
Total test documents in the label.

Sample:
56
     
label
string
on success
Label name

Sample:
label_example
     
mean_average_precision
float
on success
Mean average precision under different thresholds

Sample:
3.4
   
model_type
string
on success
The type of custom model trained.

Sample:
KEY_VALUE_EXTRACTION
   
overall_metrics_report
complex
on success

     
confidence_entries
complex
on success
List of document classification confidence report.
       
accuracy
float
on success
accuracy under the threshold

Sample:
3.4
       
f1_score
float
on success
f1Score under the threshold

Sample:
3.4
       
precision
float
on success
Precision under the threshold

Sample:
3.4
       
recall
float
on success
Recall under the threshold

Sample:
3.4
       
threshold
float
on success
Threshold used to calculate precision and recall.

Sample:
3.4
     
document_count
integer
on success
Total test documents in the label.

Sample:
56
     
mean_average_precision
float
on success
Mean average precision under different thresholds

Sample:
3.4
 
model_type
string
on success
The type of the Document model.

Sample:
KEY_VALUE_EXTRACTION
 
model_version
string
on success
The version of the model.

Sample:
model_version_example
 
project_id
string
on success
The OCID of the project that contains the model.

Sample:
ocid1.project.oc1..xxxxxxEXAMPLExxxxxx
 
system_tags
dictionary
on success
Usage of system tag keys. These predefined keys are scoped to namespaces. For example: `{"orcl-cloud": {"free-tier-retained": "true"}}`
 
tenancy_id
string
on success
The tenancy id of the model.

Sample:
ocid1.tenancy.oc1..xxxxxxEXAMPLExxxxxx
 
testing_dataset
complex
on success

   
bucket_name
string
on success
The name of the Object Storage bucket that contains the input data file.

Sample:
bucket_name_example
   
dataset_id
string
on success
OCID of the Data Labeling dataset.

Sample:
ocid1.dataset.oc1..xxxxxxEXAMPLExxxxxx
   
dataset_type
string
on success
The dataset type, based on where it is stored.

Sample:
DATA_SCIENCE_LABELING
   
namespace_name
string
on success
The namespace name of the Object Storage bucket that contains the input data file.

Sample:
namespace_name_example
   
object_name
string
on success
The object name of the input data file.

Sample:
object_name_example
 
time_created
string
on success
When the model was created, as an RFC3339 datetime string.

Sample:
2013-10-20T19:20:30+01:00
 
time_updated
string
on success
When the model was updated, as an RFC3339 datetime string.

Sample:
2013-10-20T19:20:30+01:00
 
trained_time_in_hours
float
on success
The total hours actually used for model training.

Sample:
1.2
 
training_dataset
complex
on success

   
bucket_name
string
on success
The name of the Object Storage bucket that contains the input data file.

Sample:
bucket_name_example
   
dataset_id
string
on success
OCID of the Data Labeling dataset.

Sample:
ocid1.dataset.oc1..xxxxxxEXAMPLExxxxxx
   
dataset_type
string
on success
The dataset type, based on where it is stored.

Sample:
DATA_SCIENCE_LABELING
   
namespace_name
string
on success
The namespace name of the Object Storage bucket that contains the input data file.

Sample:
namespace_name_example
   
object_name
string
on success
The object name of the input data file.

Sample:
object_name_example
 
validation_dataset
complex
on success

   
bucket_name
string
on success
The name of the Object Storage bucket that contains the input data file.

Sample:
bucket_name_example
   
dataset_id
string
on success
OCID of the Data Labeling dataset.

Sample:
ocid1.dataset.oc1..xxxxxxEXAMPLExxxxxx
   
dataset_type
string
on success
The dataset type, based on where it is stored.

Sample:
DATA_SCIENCE_LABELING
   
namespace_name
string
on success
The namespace name of the Object Storage bucket that contains the input data file.

Sample:
namespace_name_example
   
object_name
string
on success
The object name of the input data file.

Sample:
object_name_example


Authors

  • Oracle (@oracle)