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 5.3.0).
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.
python >= 3.6
Python SDK for Oracle Cloud Infrastructure https://oracle-cloud-infrastructure-python-sdk.readthedocs.io
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
|
|
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
|
|
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
|
|
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
|
|
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
|
|
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
|
|
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
|
|
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
|
|
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
|
|
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
|
|
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
|
|
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.
|
||
Notes¶
Note
For OCI python sdk configuration, please refer to https://oracle-cloud-infrastructure-python-sdk.readthedocs.io/en/latest/configuration.html
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)