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ModelDeploymentMonitoringJob(
model_deployment_monitoring_job_name: str,
project: typing.Optional[str] = None,
location: typing.Optional[str] = None,
credentials: typing.Optional[google.auth.credentials.Credentials] = None,
)
Vertex AI Model Deployment Monitoring Job.
This class should be used in conjunction with the Endpoint class in order to configure model monitoring for deployed models.
Properties
create_time
Time this resource was created.
display_name
Display name of this resource.
encryption_spec
Customer-managed encryption key options for this Vertex AI resource.
If this is set, then all resources created by this Vertex AI resource will be encrypted with the provided encryption key.
end_time
Time when the Job resource entered the JOB_STATE_SUCCEEDED
,
JOB_STATE_FAILED
, or JOB_STATE_CANCELLED
state.
error
Detailed error info for this Job resource. Only populated when the
Job's state is JOB_STATE_FAILED
or JOB_STATE_CANCELLED
.
gca_resource
The underlying resource proto representation.
labels
User-defined labels containing metadata about this resource.
Read more about labels at https://goo.gl/xmQnxf
name
Name of this resource.
resource_name
Full qualified resource name.
start_time
Time when the Job resource entered the JOB_STATE_RUNNING
for the
first time.
state
Fetch Job again and return the current JobState.
Returns | |
---|---|
Type | Description |
state (job_state.JobState) |
Enum that describes the state of a Vertex AI job. |
update_time
Time this resource was last updated.
Methods
ModelDeploymentMonitoringJob
ModelDeploymentMonitoringJob(
model_deployment_monitoring_job_name: str,
project: typing.Optional[str] = None,
location: typing.Optional[str] = None,
credentials: typing.Optional[google.auth.credentials.Credentials] = None,
)
Initializer for ModelDeploymentMonitoringJob.
Parameter | |
---|---|
Name | Description |
model_deployment_monitoring_job_name |
str
Required. A fully-qualified ModelDeploymentMonitoringJob resource name or ID. Example: "projects/.../locations/.../modelDeploymentMonitoringJobs/456" or "456" when project and location are initialized or passed. |
cancel
cancel()
Cancels this Job.
Success of cancellation is not guaranteed. Use Job.state
property to verify if cancellation was successful.
create
create(
endpoint: typing.Union[str, google.cloud.aiplatform.models.Endpoint],
objective_configs: typing.Optional[
typing.Union[
google.cloud.aiplatform.model_monitoring.objective.ObjectiveConfig,
typing.Dict[
str, google.cloud.aiplatform.model_monitoring.objective.ObjectiveConfig
],
]
] = None,
logging_sampling_strategy: typing.Optional[
google.cloud.aiplatform.model_monitoring.sampling.RandomSampleConfig
] = None,
schedule_config: typing.Optional[
google.cloud.aiplatform.model_monitoring.schedule.ScheduleConfig
] = None,
display_name: typing.Optional[str] = None,
deployed_model_ids: typing.Optional[typing.List[str]] = None,
alert_config: typing.Optional[
google.cloud.aiplatform.model_monitoring.alert.EmailAlertConfig
] = None,
predict_instance_schema_uri: typing.Optional[str] = None,
sample_predict_instance: typing.Optional[str] = None,
analysis_instance_schema_uri: typing.Optional[str] = None,
bigquery_tables_log_ttl: typing.Optional[int] = None,
stats_anomalies_base_directory: typing.Optional[str] = None,
enable_monitoring_pipeline_logs: typing.Optional[bool] = None,
labels: typing.Optional[typing.Dict[str, str]] = None,
encryption_spec_key_name: typing.Optional[str] = None,
project: typing.Optional[str] = None,
location: typing.Optional[str] = None,
credentials: typing.Optional[google.auth.credentials.Credentials] = None,
create_request_timeout: typing.Optional[float] = None,
) -> google.cloud.aiplatform.jobs.ModelDeploymentMonitoringJob
Creates and launches a model monitoring job.
Parameters | |
---|---|
Name | Description |
endpoint |
Union[str, "aiplatform.Endpoint"]
Required. Endpoint resource name or an instance of |
logging_sampling_strategy |
model_monitoring.sampling.RandomSampleConfig
Optional. Sample Strategy for logging. |
schedule_config |
model_monitoring.schedule.ScheduleConfig
Optional. Configures model monitoring job scheduling interval in hours. This defines how often the monitoring jobs are triggered. |
display_name |
str
Optional. The user-defined name of the ModelDeploymentMonitoringJob. The name can be up to 128 characters long and can be consist of any UTF-8 characters. Display name of a ModelDeploymentMonitoringJob. |
deployed_model_ids |
List[str]
Optional. Use this argument to specify which deployed models to apply the objective config to. If left unspecified, the same config will be applied to all deployed models. |
alert_config |
model_monitoring.alert.EmailAlertConfig
Optional. Configures how alerts are sent to the user. Right now only email alert is supported. |
predict_instance_schema_uri |
str
Optional. YAML schema file uri describing the format of a single instance, which are given to format the Endpoint's prediction (and explanation). If not set, the schema will be generated from collected predict requests. |
sample_predict_instance |
str
Optional. Sample Predict instance, same format as PredictionRequest.instances, this can be set as a replacement of predict_instance_schema_uri If not set, the schema will be generated from collected predict requests. |
analysis_instance_schema_uri |
str
Optional. YAML schema file uri describing the format of a single instance that you want Tensorflow Data Validation (TFDV) to analyze. If this field is empty, all the feature data types are inferred from predict_instance_schema_uri, meaning that TFDV will use the data in the exact format as prediction request/response. If there are any data type differences between predict instance and TFDV instance, this field can be used to override the schema. For models trained with Vertex AI, this field must be set as all the fields in predict instance formatted as string. |
bigquery_tables_log_ttl |
int
Optional. The TTL(time to live) of BigQuery tables in user projects which stores logs. A day is the basic unit of the TTL and we take the ceil of TTL/86400(a day). e.g. { second: 3600} indicates ttl = 1 day. |
stats_anomalies_base_directory |
str
Optional. Stats anomalies base folder path. |
enable_monitoring_pipeline_logs |
bool
Optional. If true, the scheduled monitoring pipeline logs are sent to Google Cloud Logging, including pipeline status and anomalies detected. Please note the logs incur cost, which are subject to |
labels |
Dict[str, str]
Optional. The labels with user-defined metadata to organize the ModelDeploymentMonitoringJob. Label keys and values can be no longer than 64 characters (Unicode codepoints), can only contain lowercase letters, numeric characters, underscores and dashes. International characters are allowed. See https://goo.gl/xmQnxf for more information and examples of labels. |
encryption_spec_key_name |
str
Optional. Customer-managed encryption key spec for a ModelDeploymentMonitoringJob. If set, this ModelDeploymentMonitoringJob and all sub-resources of this ModelDeploymentMonitoringJob will be secured by this key. |
create_request_timeout |
int
Optional. Timeout in seconds for the model monitoring job creation request. |
delete
delete() -> None
Deletes an MDM job.
done
done() -> bool
Method indicating whether a job has completed.
list
list(
filter: typing.Optional[str] = None,
order_by: typing.Optional[str] = None,
project: typing.Optional[str] = None,
location: typing.Optional[str] = None,
credentials: typing.Optional[google.auth.credentials.Credentials] = None,
) -> typing.List[google.cloud.aiplatform.base.VertexAiResourceNoun]
List all instances of this Job Resource.
Example Usage:
aiplatform.BatchPredictionJobs.list( filter='state="JOB_STATE_SUCCEEDED" AND display_name="my_job"', )
Parameters | |
---|---|
Name | Description |
filter |
str
Optional. An expression for filtering the results of the request. For field names both snake_case and camelCase are supported. |
order_by |
str
Optional. A comma-separated list of fields to order by, sorted in ascending order. Use "desc" after a field name for descending. Supported fields: |
project |
str
Optional. Project to retrieve list from. If not set, project set in aiplatform.init will be used. |
location |
str
Optional. Location to retrieve list from. If not set, location set in aiplatform.init will be used. |
credentials |
auth_credentials.Credentials
Optional. Custom credentials to use to retrieve list. Overrides credentials set in aiplatform.init. |
pause
pause() -> google.cloud.aiplatform.jobs.ModelDeploymentMonitoringJob
Pause a running MDM job.
resume
resume() -> google.cloud.aiplatform.jobs.ModelDeploymentMonitoringJob
Resumes a paused MDM job.
to_dict
to_dict() -> typing.Dict[str, typing.Any]
Returns the resource proto as a dictionary.
update
update(
*,
display_name: typing.Optional[str] = None,
schedule_config: typing.Optional[
google.cloud.aiplatform.model_monitoring.schedule.ScheduleConfig
] = None,
alert_config: typing.Optional[
google.cloud.aiplatform.model_monitoring.alert.EmailAlertConfig
] = None,
logging_sampling_strategy: typing.Optional[
google.cloud.aiplatform.model_monitoring.sampling.RandomSampleConfig
] = None,
labels: typing.Optional[typing.Dict[str, str]] = None,
bigquery_tables_log_ttl: typing.Optional[int] = None,
enable_monitoring_pipeline_logs: typing.Optional[bool] = None,
objective_configs: typing.Optional[
typing.Union[
google.cloud.aiplatform.model_monitoring.objective.ObjectiveConfig,
typing.Dict[
str, google.cloud.aiplatform.model_monitoring.objective.ObjectiveConfig
],
]
] = None,
deployed_model_ids: typing.Optional[typing.List[str]] = None,
update_request_timeout: typing.Optional[float] = None
) -> google.cloud.aiplatform.jobs.ModelDeploymentMonitoringJob
Updates an existing ModelDeploymentMonitoringJob.
Parameters | |
---|---|
Name | Description |
display_name |
str
Optional. The user-defined name of the ModelDeploymentMonitoringJob. The name can be up to 128 characters long and can be consist of any UTF-8 characters. Display name of a ModelDeploymentMonitoringJob. |
schedule_config |
model_monitoring.schedule.ScheduleConfig
Required. Configures model monitoring job scheduling interval in hours. This defines how often the monitoring jobs are triggered. |
alert_config |
model_monitoring.alert.EmailAlertConfig
Optional. Configures how alerts are sent to the user. Right now only email alert is supported. |
logging_sampling_strategy |
model_monitoring.sampling.RandomSampleConfig
Required. Sample Strategy for logging. |
labels |
Dict[str, str]
Optional. The labels with user-defined metadata to organize the ModelDeploymentMonitoringJob. Label keys and values can be no longer than 64 characters (Unicode codepoints), can only contain lowercase letters, numeric characters, underscores and dashes. International characters are allowed. See https://goo.gl/xmQnxf for more information and examples of labels. |
bigquery_tables_log_ttl |
int
Optional. The number of days for which the logs are stored. The TTL(time to live) of BigQuery tables in user projects which stores logs. A day is the basic unit of the TTL and we take the ceil of TTL/86400(a day). e.g. { second: 3600} indicates ttl = 1 day. |
enable_monitoring_pipeline_logs |
bool
Optional. If true, the scheduled monitoring pipeline logs are sent to Google Cloud Logging, including pipeline status and anomalies detected. Please note the logs incur cost, which are subject to |
deployed_model_ids |
List[str]
Optional. Use this argument to specify which deployed models to apply the updated objective config to. If left unspecified, the same config will be applied to all deployed models. |
upate_request_timeout |
float
Optional. Timeout in seconds for the model monitoring job update request. |
wait
wait()
Helper method that blocks until all futures are complete.
wait_for_completion
wait_for_completion() -> None
Waits for job to complete.
Exceptions | |
---|---|
Type | Description |
RuntimeError |
If job failed or cancelled. |