As of January 1, 2020 this library no longer supports Python 2 on the latest released version. Library versions released prior to that date will continue to be available. For more information please visit Python 2 support on Google Cloud.

ModelMonitoringService

class google.cloud.aiplatform_v1beta1.services.model_monitoring_service.ModelMonitoringServiceAsyncClient(*, credentials: ~typing.Optional[~google.auth.credentials.Credentials] = None, transport: ~typing.Optional[~typing.Union[str, ~google.cloud.aiplatform_v1beta1.services.model_monitoring_service.transports.base.ModelMonitoringServiceTransport, ~typing.Callable[[...], ~google.cloud.aiplatform_v1beta1.services.model_monitoring_service.transports.base.ModelMonitoringServiceTransport]]] = 'grpc_asyncio', client_options: ~typing.Optional[~google.api_core.client_options.ClientOptions] = None, client_info: ~google.api_core.gapic_v1.client_info.ClientInfo = <google.api_core.gapic_v1.client_info.ClientInfo object>)

A service for creating and managing Vertex AI Model moitoring. This includes ModelMonitor resources, ModelMonitoringJob resources.

Instantiates the model monitoring service async client.

Parameters:

• credentials (Optional[google.auth.credentials.Credentials]) – The authorization credentials to attach to requests. These credentials identify the application to the service; if none are specified, the client will attempt to ascertain the credentials from the environment.

• transport (Optional[Union[str,ModelMonitoringServiceTransport,Callable[..., ModelMonitoringServiceTransport]]]) – The transport to use, or a Callable that constructs and returns a new transport to use. If a Callable is given, it will be called with the same set of initialization arguments as used in the ModelMonitoringServiceTransport constructor. If set to None, a transport is chosen automatically.

• client_options (Optional[Union[google.api_core.client_options.ClientOptions, dict]]) –

  Custom options for the client.

  1. The api_endpoint property can be used to override the default endpoint provided by the client when transport is not explicitly provided. Only if this property is not set and transport was not explicitly provided, the endpoint is determined by the GOOGLE_API_USE_MTLS_ENDPOINT environment variable, which have one of the following values: “always” (always use the default mTLS endpoint), “never” (always use the default regular endpoint) and “auto” (auto-switch to the default mTLS endpoint if client certificate is present; this is the default value).

  2. If the GOOGLE_API_USE_CLIENT_CERTIFICATE environment variable is “true”, then the client_cert_source property can be used to provide a client certificate for mTLS transport. If not provided, the default SSL client certificate will be used if present. If GOOGLE_API_USE_CLIENT_CERTIFICATE is “false” or not set, no client certificate will be used.

  3. The universe_domain property can be used to override the default “googleapis.com” universe. Note that api_endpoint property still takes precedence; and universe_domain is currently not supported for mTLS.

• client_info (google.api_core.gapic_v1.client_info.ClientInfo) – The client info used to send a user-agent string along with API requests. If None, then default info will be used. Generally, you only need to set this if you’re developing your own client library.

Raises:

google.auth.exceptions.MutualTlsChannelError – If mutual TLS transport creation failed for any reason.

property api_endpoint

Return the API endpoint used by the client instance.

Returns:

The API endpoint used by the client instance.

Return type:

str

static batch_prediction_job_path(project: str, location: str, batch_prediction_job: str) → str

Returns a fully-qualified batch_prediction_job string.

async cancel_operation(request: Optional[CancelOperationRequest] = None, *, retry: Optional[Union[AsyncRetry, _MethodDefault]] = _MethodDefault._DEFAULT_VALUE, timeout: Union[float, object] = _MethodDefault._DEFAULT_VALUE, metadata: Sequence[Tuple[str, str]] = ()) → None

Starts asynchronous cancellation on a long-running operation.

The server makes a best effort to cancel the operation, but success is not guaranteed. If the server doesn’t support this method, it returns google.rpc.Code.UNIMPLEMENTED.

Parameters:

• request (CancelOperationRequest) – The request object. Request message for CancelOperation method.

• retry (google.api_core.retry_async.AsyncRetry) – Designation of what errors, if any, should be retried.

• timeout (float) – The timeout for this request.

• metadata (Sequence[Tuple[str, str]]) – Strings which should be sent along with the request as metadata.

Returns:

None

static common_billing_account_path(billing_account: str) → str

Returns a fully-qualified billing_account string.

static common_folder_path(folder: str) → str

Returns a fully-qualified folder string.

static common_location_path(project: str, location: str) → str

Returns a fully-qualified location string.

static common_organization_path(organization: str) → str

Returns a fully-qualified organization string.

static common_project_path(project: str) → str

Returns a fully-qualified project string.

async create_model_monitor(request: Optional[Union[CreateModelMonitorRequest, dict]] = None, *, parent: Optional[str] = None, model_monitor: Optional[ModelMonitor] = None, retry: Optional[Union[AsyncRetry, _MethodDefault]] = _MethodDefault._DEFAULT_VALUE, timeout: Union[float, object] = _MethodDefault._DEFAULT_VALUE, metadata: Sequence[Tuple[str, str]] = ()) → AsyncOperation

Creates a ModelMonitor.

# This snippet has been automatically generated and should be regarded as a
# code template only.
# It will require modifications to work:
# - It may require correct/in-range values for request initialization.
# - It may require specifying regional endpoints when creating the service
#   client as shown in:
#   https://googleapis.dev/python/google-api-core/latest/client_options.html
from google.cloud import aiplatform_v1beta1

async def sample_create_model_monitor():
    # Create a client
    client = aiplatform_v1beta1.ModelMonitoringServiceAsyncClient()

    # Initialize request argument(s)
    request = aiplatform_v1beta1.CreateModelMonitorRequest(
        parent="parent_value",
    )

    # Make the request
    operation = client.create_model_monitor(request=request)

    print("Waiting for operation to complete...")

    response = (await operation).result()

    # Handle the response
    print(response)

Parameters:

• request (Optional[Union[google.cloud.aiplatform_v1beta1.types.CreateModelMonitorRequest, dict]]) – The request object. Request message for [ModelMonitoringService.CreateModelMonitor][google.cloud.aiplatform.v1beta1.ModelMonitoringService.CreateModelMonitor].

• parent (str) –

  Required. The resource name of the Location to create the ModelMonitor in. Format: projects/{project}/locations/{location}

  This corresponds to the parent field on the request instance; if request is provided, this should not be set.

• model_monitor (google.cloud.aiplatform_v1beta1.types.ModelMonitor) – Required. The ModelMonitor to create. This corresponds to the model_monitor field on the request instance; if request is provided, this should not be set.

• retry (google.api_core.retry_async.AsyncRetry) – Designation of what errors, if any, should be retried.

• timeout (float) – The timeout for this request.

• metadata (Sequence[Tuple[str, str]]) – Strings which should be sent along with the request as metadata.

Returns:

An object representing a long-running operation.

The result type for the operation will be google.cloud.aiplatform_v1beta1.types.ModelMonitor Vertex AI Model Monitoring Service serves as a central hub for the analysis

and visualization of data quality and performance related to models. ModelMonitor stands as a top level resource for overseeing your model monitoring tasks.

Return type:

google.api_core.operation_async.AsyncOperation

async create_model_monitoring_job(request: Optional[Union[CreateModelMonitoringJobRequest, dict]] = None, *, parent: Optional[str] = None, model_monitoring_job: Optional[ModelMonitoringJob] = None, retry: Optional[Union[AsyncRetry, _MethodDefault]] = _MethodDefault._DEFAULT_VALUE, timeout: Union[float, object] = _MethodDefault._DEFAULT_VALUE, metadata: Sequence[Tuple[str, str]] = ()) → ModelMonitoringJob

Creates a ModelMonitoringJob.

# This snippet has been automatically generated and should be regarded as a
# code template only.
# It will require modifications to work:
# - It may require correct/in-range values for request initialization.
# - It may require specifying regional endpoints when creating the service
#   client as shown in:
#   https://googleapis.dev/python/google-api-core/latest/client_options.html
from google.cloud import aiplatform_v1beta1

async def sample_create_model_monitoring_job():
    # Create a client
    client = aiplatform_v1beta1.ModelMonitoringServiceAsyncClient()

    # Initialize request argument(s)
    request = aiplatform_v1beta1.CreateModelMonitoringJobRequest(
        parent="parent_value",
    )

    # Make the request
    response = await client.create_model_monitoring_job(request=request)

    # Handle the response
    print(response)

Parameters:

• request (Optional[Union[google.cloud.aiplatform_v1beta1.types.CreateModelMonitoringJobRequest, dict]]) – The request object. Request message for [ModelMonitoringService.CreateModelMonitoringJob][google.cloud.aiplatform.v1beta1.ModelMonitoringService.CreateModelMonitoringJob].

• parent (str) –

  Required. The parent of the ModelMonitoringJob. Format: projects/{project}/locations/{location}/modelMoniitors/{model_monitor}

  This corresponds to the parent field on the request instance; if request is provided, this should not be set.

• model_monitoring_job (google.cloud.aiplatform_v1beta1.types.ModelMonitoringJob) –

  Required. The ModelMonitoringJob to create

  This corresponds to the model_monitoring_job field on the request instance; if request is provided, this should not be set.

• retry (google.api_core.retry_async.AsyncRetry) – Designation of what errors, if any, should be retried.

• timeout (float) – The timeout for this request.

• metadata (Sequence[Tuple[str, str]]) – Strings which should be sent along with the request as metadata.

Returns:

Represents a model monitoring job that analyze dataset using different monitoring algorithm.

Return type:

google.cloud.aiplatform_v1beta1.types.ModelMonitoringJob

static dataset_path(project: str, location: str, dataset: str) → str

Returns a fully-qualified dataset string.

async delete_model_monitor(request: Optional[Union[DeleteModelMonitorRequest, dict]] = None, *, name: Optional[str] = None, retry: Optional[Union[AsyncRetry, _MethodDefault]] = _MethodDefault._DEFAULT_VALUE, timeout: Union[float, object] = _MethodDefault._DEFAULT_VALUE, metadata: Sequence[Tuple[str, str]] = ()) → AsyncOperation

Deletes a ModelMonitor.

# This snippet has been automatically generated and should be regarded as a
# code template only.
# It will require modifications to work:
# - It may require correct/in-range values for request initialization.
# - It may require specifying regional endpoints when creating the service
#   client as shown in:
#   https://googleapis.dev/python/google-api-core/latest/client_options.html
from google.cloud import aiplatform_v1beta1

async def sample_delete_model_monitor():
    # Create a client
    client = aiplatform_v1beta1.ModelMonitoringServiceAsyncClient()

    # Initialize request argument(s)
    request = aiplatform_v1beta1.DeleteModelMonitorRequest(
        name="name_value",
    )

    # Make the request
    operation = client.delete_model_monitor(request=request)

    print("Waiting for operation to complete...")

    response = (await operation).result()

    # Handle the response
    print(response)

Parameters:

• request (Optional[Union[google.cloud.aiplatform_v1beta1.types.DeleteModelMonitorRequest, dict]]) – The request object. Request message for [ModelMonitoringService.DeleteModelMonitor][google.cloud.aiplatform.v1beta1.ModelMonitoringService.DeleteModelMonitor].

• name (str) –

  Required. The name of the ModelMonitor resource to be deleted. Format: projects/{project}/locations/{location}/modelMonitords/{model_monitor}

  This corresponds to the name field on the request instance; if request is provided, this should not be set.

• retry (google.api_core.retry_async.AsyncRetry) – Designation of what errors, if any, should be retried.

• timeout (float) – The timeout for this request.

• metadata (Sequence[Tuple[str, str]]) – Strings which should be sent along with the request as metadata.

Returns:

An object representing a long-running operation.

The result type for the operation will be google.protobuf.empty_pb2.Empty A generic empty message that you can re-use to avoid defining duplicated

empty messages in your APIs. A typical example is to use it as the request or the response type of an API method. For instance:

service Foo {

rpc Bar(google.protobuf.Empty) returns (google.protobuf.Empty);

}

Return type:

google.api_core.operation_async.AsyncOperation

async delete_model_monitoring_job(request: Optional[Union[DeleteModelMonitoringJobRequest, dict]] = None, *, name: Optional[str] = None, retry: Optional[Union[AsyncRetry, _MethodDefault]] = _MethodDefault._DEFAULT_VALUE, timeout: Union[float, object] = _MethodDefault._DEFAULT_VALUE, metadata: Sequence[Tuple[str, str]] = ()) → AsyncOperation

Deletes a ModelMonitoringJob.

# This snippet has been automatically generated and should be regarded as a
# code template only.
# It will require modifications to work:
# - It may require correct/in-range values for request initialization.
# - It may require specifying regional endpoints when creating the service
#   client as shown in:
#   https://googleapis.dev/python/google-api-core/latest/client_options.html
from google.cloud import aiplatform_v1beta1

async def sample_delete_model_monitoring_job():
    # Create a client
    client = aiplatform_v1beta1.ModelMonitoringServiceAsyncClient()

    # Initialize request argument(s)
    request = aiplatform_v1beta1.DeleteModelMonitoringJobRequest(
        name="name_value",
    )

    # Make the request
    operation = client.delete_model_monitoring_job(request=request)

    print("Waiting for operation to complete...")

    response = (await operation).result()

    # Handle the response
    print(response)

Parameters:

• request (Optional[Union[google.cloud.aiplatform_v1beta1.types.DeleteModelMonitoringJobRequest, dict]]) – The request object. Request message for [ModelMonitoringService.DeleteModelMonitoringJob][google.cloud.aiplatform.v1beta1.ModelMonitoringService.DeleteModelMonitoringJob].

• name (str) –

  Required. The resource name of the model monitoring job to delete. Format: projects/{project}/locations/{location}/modelMonitors/{model_monitor}/modelMonitoringJobs/{model_monitoring_job}

  This corresponds to the name field on the request instance; if request is provided, this should not be set.

• retry (google.api_core.retry_async.AsyncRetry) – Designation of what errors, if any, should be retried.

• timeout (float) – The timeout for this request.

• metadata (Sequence[Tuple[str, str]]) – Strings which should be sent along with the request as metadata.

Returns:

An object representing a long-running operation.

The result type for the operation will be google.protobuf.empty_pb2.Empty A generic empty message that you can re-use to avoid defining duplicated

empty messages in your APIs. A typical example is to use it as the request or the response type of an API method. For instance:

service Foo {

rpc Bar(google.protobuf.Empty) returns (google.protobuf.Empty);

}

Return type:

google.api_core.operation_async.AsyncOperation

async delete_operation(request: Optional[DeleteOperationRequest] = None, *, retry: Optional[Union[AsyncRetry, _MethodDefault]] = _MethodDefault._DEFAULT_VALUE, timeout: Union[float, object] = _MethodDefault._DEFAULT_VALUE, metadata: Sequence[Tuple[str, str]] = ()) → None

Deletes a long-running operation.

This method indicates that the client is no longer interested in the operation result. It does not cancel the operation. If the server doesn’t support this method, it returns google.rpc.Code.UNIMPLEMENTED.

Parameters:

• request (DeleteOperationRequest) – The request object. Request message for DeleteOperation method.

• retry (google.api_core.retry_async.AsyncRetry) – Designation of what errors, if any, should be retried.

• timeout (float) – The timeout for this request.

• metadata (Sequence[Tuple[str, str]]) – Strings which should be sent along with the request as metadata.

Returns:

None

static endpoint_path(project: str, location: str, endpoint: str) → str

Returns a fully-qualified endpoint string.

classmethod from_service_account_file(filename: str, *args, **kwargs)

Creates an instance of this client using the provided credentials

file.

Parameters:

• filename (str) – The path to the service account private key json file.

• args – Additional arguments to pass to the constructor.

• kwargs – Additional arguments to pass to the constructor.

Returns:

The constructed client.

Return type:

ModelMonitoringServiceAsyncClient

classmethod from_service_account_info(info: dict, *args, **kwargs)

Creates an instance of this client using the provided credentials

info.

Parameters:

• info (dict) – The service account private key info.

• args – Additional arguments to pass to the constructor.

• kwargs – Additional arguments to pass to the constructor.

Returns:

The constructed client.

Return type:

ModelMonitoringServiceAsyncClient

classmethod from_service_account_json(filename: str, *args, **kwargs)

Creates an instance of this client using the provided credentials

file.

Parameters:

• filename (str) – The path to the service account private key json file.

• args – Additional arguments to pass to the constructor.

• kwargs – Additional arguments to pass to the constructor.

Returns:

The constructed client.

Return type:

ModelMonitoringServiceAsyncClient

async get_iam_policy(request: Optional[GetIamPolicyRequest] = None, *, retry: Optional[Union[AsyncRetry, _MethodDefault]] = _MethodDefault._DEFAULT_VALUE, timeout: Union[float, object] = _MethodDefault._DEFAULT_VALUE, metadata: Sequence[Tuple[str, str]] = ()) → Policy

Gets the IAM access control policy for a function.

Returns an empty policy if the function exists and does not have a policy set.

Parameters:

• request (GetIamPolicyRequest) – The request object. Request message for GetIamPolicy method.

• retry (google.api_core.retry_async.AsyncRetry) – Designation of what errors, if any, should be retried.

• timeout (float) – The timeout for this request.

• metadata (Sequence[Tuple[str, str]]) – Strings which should be sent along with the request as metadata.

Returns:

Defines an Identity and Access Management (IAM) policy. It is used to specify access control policies for Cloud Platform resources. A Policy is a collection of bindings. A binding binds one or more members to a single role. Members can be user accounts, service accounts, Google groups, and domains (such as G Suite). A role is a named list of permissions (defined by IAM or configured by users). A binding can optionally specify a condition, which is a logic expression that further constrains the role binding based on attributes about the request and/or target resource.

JSON Example

{
  "bindings": [
    {
      "role": "roles/resourcemanager.organizationAdmin",
      "members": [
        "user:mike@example.com",
        "group:admins@example.com",
        "domain:google.com",
        "serviceAccount:my-project-id@appspot.gserviceaccount.com"
      ]
    },
    {
      "role": "roles/resourcemanager.organizationViewer",
      "members": ["user:eve@example.com"],
      "condition": {
        "title": "expirable access",
        "description": "Does not grant access after Sep 2020",
        "expression": "request.time <
        timestamp('2020-10-01T00:00:00.000Z')",
      }
    }
  ]
}

YAML Example

bindings:
- members:
  - user:mike@example.com
  - group:admins@example.com
  - domain:google.com
  - serviceAccount:my-project-id@appspot.gserviceaccount.com
  role: roles/resourcemanager.organizationAdmin
- members:
  - user:eve@example.com
  role: roles/resourcemanager.organizationViewer
  condition:
    title: expirable access
    description: Does not grant access after Sep 2020
    expression: request.time < timestamp('2020-10-01T00:00:00.000Z')

For a description of IAM and its features, see the IAM developer’s guide.

Return type:

Policy

async get_location(request: Optional[GetLocationRequest] = None, *, retry: Optional[Union[AsyncRetry, _MethodDefault]] = _MethodDefault._DEFAULT_VALUE, timeout: Union[float, object] = _MethodDefault._DEFAULT_VALUE, metadata: Sequence[Tuple[str, str]] = ()) → Location

Gets information about a location.

Parameters:

• request (GetLocationRequest) – The request object. Request message for GetLocation method.

• retry (google.api_core.retry_async.AsyncRetry) – Designation of what errors, if any, should be retried.

• timeout (float) – The timeout for this request.

• metadata (Sequence[Tuple[str, str]]) – Strings which should be sent along with the request as metadata.

Returns:

Location object.

Return type:

Location

async get_model_monitor(request: Optional[Union[GetModelMonitorRequest, dict]] = None, *, name: Optional[str] = None, retry: Optional[Union[AsyncRetry, _MethodDefault]] = _MethodDefault._DEFAULT_VALUE, timeout: Union[float, object] = _MethodDefault._DEFAULT_VALUE, metadata: Sequence[Tuple[str, str]] = ()) → ModelMonitor

Gets a ModelMonitor.

# This snippet has been automatically generated and should be regarded as a
# code template only.
# It will require modifications to work:
# - It may require correct/in-range values for request initialization.
# - It may require specifying regional endpoints when creating the service
#   client as shown in:
#   https://googleapis.dev/python/google-api-core/latest/client_options.html
from google.cloud import aiplatform_v1beta1

async def sample_get_model_monitor():
    # Create a client
    client = aiplatform_v1beta1.ModelMonitoringServiceAsyncClient()

    # Initialize request argument(s)
    request = aiplatform_v1beta1.GetModelMonitorRequest(
        name="name_value",
    )

    # Make the request
    response = await client.get_model_monitor(request=request)

    # Handle the response
    print(response)

Parameters:

• request (Optional[Union[google.cloud.aiplatform_v1beta1.types.GetModelMonitorRequest, dict]]) – The request object. Request message for [ModelMonitoringService.GetModelMonitor][google.cloud.aiplatform.v1beta1.ModelMonitoringService.GetModelMonitor].

• name (str) –

  Required. The name of the ModelMonitor resource. Format: projects/{project}/locations/{location}/modelMonitors/{model_monitor}

  This corresponds to the name field on the request instance; if request is provided, this should not be set.

• retry (google.api_core.retry_async.AsyncRetry) – Designation of what errors, if any, should be retried.

• timeout (float) – The timeout for this request.

• metadata (Sequence[Tuple[str, str]]) – Strings which should be sent along with the request as metadata.

Returns:

Vertex AI Model Monitoring Service serves as a central hub for the analysis and visualization of data quality and performance related to models. ModelMonitor stands as a top level resource for overseeing your model monitoring tasks.

Return type:

google.cloud.aiplatform_v1beta1.types.ModelMonitor

async get_model_monitoring_job(request: Optional[Union[GetModelMonitoringJobRequest, dict]] = None, *, name: Optional[str] = None, retry: Optional[Union[AsyncRetry, _MethodDefault]] = _MethodDefault._DEFAULT_VALUE, timeout: Union[float, object] = _MethodDefault._DEFAULT_VALUE, metadata: Sequence[Tuple[str, str]] = ()) → ModelMonitoringJob

Gets a ModelMonitoringJob.

# This snippet has been automatically generated and should be regarded as a
# code template only.
# It will require modifications to work:
# - It may require correct/in-range values for request initialization.
# - It may require specifying regional endpoints when creating the service
#   client as shown in:
#   https://googleapis.dev/python/google-api-core/latest/client_options.html
from google.cloud import aiplatform_v1beta1

async def sample_get_model_monitoring_job():
    # Create a client
    client = aiplatform_v1beta1.ModelMonitoringServiceAsyncClient()

    # Initialize request argument(s)
    request = aiplatform_v1beta1.GetModelMonitoringJobRequest(
        name="name_value",
    )

    # Make the request
    response = await client.get_model_monitoring_job(request=request)

    # Handle the response
    print(response)

Parameters:

• request (Optional[Union[google.cloud.aiplatform_v1beta1.types.GetModelMonitoringJobRequest, dict]]) – The request object. Request message for [ModelMonitoringService.GetModelMonitoringJob][google.cloud.aiplatform.v1beta1.ModelMonitoringService.GetModelMonitoringJob].

• name (str) –

  Required. The resource name of the ModelMonitoringJob. Format: projects/{project}/locations/{location}/modelMonitors/{model_monitor}/modelMonitoringJobs/{model_monitoring_job}

  This corresponds to the name field on the request instance; if request is provided, this should not be set.

• retry (google.api_core.retry_async.AsyncRetry) – Designation of what errors, if any, should be retried.

• timeout (float) – The timeout for this request.

• metadata (Sequence[Tuple[str, str]]) – Strings which should be sent along with the request as metadata.

Returns:

Represents a model monitoring job that analyze dataset using different monitoring algorithm.

Return type:

google.cloud.aiplatform_v1beta1.types.ModelMonitoringJob

classmethod get_mtls_endpoint_and_cert_source(client_options: Optional[ClientOptions] = None)

Return the API endpoint and client cert source for mutual TLS.

The client cert source is determined in the following order: (1) if GOOGLE_API_USE_CLIENT_CERTIFICATE environment variable is not “true”, the client cert source is None. (2) if client_options.client_cert_source is provided, use the provided one; if the default client cert source exists, use the default one; otherwise the client cert source is None.

The API endpoint is determined in the following order: (1) if client_options.api_endpoint if provided, use the provided one. (2) if GOOGLE_API_USE_CLIENT_CERTIFICATE environment variable is “always”, use the default mTLS endpoint; if the environment variable is “never”, use the default API endpoint; otherwise if client cert source exists, use the default mTLS endpoint, otherwise use the default API endpoint.

More details can be found at https://google.aip.dev/auth/4114.

Parameters:

client_options (google.api_core.client_options.ClientOptions) – Custom options for the client. Only the api_endpoint and client_cert_source properties may be used in this method.

Returns:

returns the API endpoint and the

client cert source to use.

Return type:

Tuple[str, Callable[[], Tuple[bytes, bytes]]]

Raises:

google.auth.exceptions.MutualTLSChannelError – If any errors happen.

async get_operation(request: Optional[GetOperationRequest] = None, *, retry: Optional[Union[AsyncRetry, _MethodDefault]] = _MethodDefault._DEFAULT_VALUE, timeout: Union[float, object] = _MethodDefault._DEFAULT_VALUE, metadata: Sequence[Tuple[str, str]] = ()) → Operation

Gets the latest state of a long-running operation.

Parameters:

• request (GetOperationRequest) – The request object. Request message for GetOperation method.

• retry (google.api_core.retry_async.AsyncRetry) – Designation of what errors, if any, should be retried.

• timeout (float) – The timeout for this request.

• metadata (Sequence[Tuple[str, str]]) – Strings which should be sent along with the request as metadata.

Returns:

An Operation object.

Return type:

Operation

classmethod get_transport_class(label: Optional[str] = None) → Type[ModelMonitoringServiceTransport]

Returns an appropriate transport class.

Parameters:

label – The name of the desired transport. If none is provided, then the first transport in the registry is used.

Returns:

The transport class to use.

async list_locations(request: Optional[ListLocationsRequest] = None, *, retry: Optional[Union[AsyncRetry, _MethodDefault]] = _MethodDefault._DEFAULT_VALUE, timeout: Union[float, object] = _MethodDefault._DEFAULT_VALUE, metadata: Sequence[Tuple[str, str]] = ()) → ListLocationsResponse

Lists information about the supported locations for this service.

Parameters:

• request (ListLocationsRequest) – The request object. Request message for ListLocations method.

• retry (google.api_core.retry_async.AsyncRetry) – Designation of what errors, if any, should be retried.

• timeout (float) – The timeout for this request.

• metadata (Sequence[Tuple[str, str]]) – Strings which should be sent along with the request as metadata.

Returns:

Response message for ListLocations method.

Return type:

ListLocationsResponse

async list_model_monitoring_jobs(request: Optional[Union[ListModelMonitoringJobsRequest, dict]] = None, *, parent: Optional[str] = None, retry: Optional[Union[AsyncRetry, _MethodDefault]] = _MethodDefault._DEFAULT_VALUE, timeout: Union[float, object] = _MethodDefault._DEFAULT_VALUE, metadata: Sequence[Tuple[str, str]] = ()) → ListModelMonitoringJobsAsyncPager

Lists ModelMonitoringJobs. Callers may choose to read across multiple Monitors as per AIP-159 by using ‘-’ (the hyphen or dash character) as a wildcard character instead of modelMonitor id in the parent. Format projects/{project_id}/locations/{location}/moodelMonitors/-/modelMonitoringJobs

# This snippet has been automatically generated and should be regarded as a
# code template only.
# It will require modifications to work:
# - It may require correct/in-range values for request initialization.
# - It may require specifying regional endpoints when creating the service
#   client as shown in:
#   https://googleapis.dev/python/google-api-core/latest/client_options.html
from google.cloud import aiplatform_v1beta1

async def sample_list_model_monitoring_jobs():
    # Create a client
    client = aiplatform_v1beta1.ModelMonitoringServiceAsyncClient()

    # Initialize request argument(s)
    request = aiplatform_v1beta1.ListModelMonitoringJobsRequest(
        parent="parent_value",
    )

    # Make the request
    page_result = client.list_model_monitoring_jobs(request=request)

    # Handle the response
    async for response in page_result:
        print(response)

Parameters:

• request (Optional[Union[google.cloud.aiplatform_v1beta1.types.ListModelMonitoringJobsRequest, dict]]) – The request object. Request message for [ModelMonitoringService.ListModelMonitoringJobs][google.cloud.aiplatform.v1beta1.ModelMonitoringService.ListModelMonitoringJobs].

• parent (str) –

  Required. The parent of the ModelMonitoringJob. Format: projects/{project}/locations/{location}/modelMonitors/{model_monitor}

  This corresponds to the parent field on the request instance; if request is provided, this should not be set.

• retry (google.api_core.retry_async.AsyncRetry) – Designation of what errors, if any, should be retried.

• timeout (float) – The timeout for this request.

• metadata (Sequence[Tuple[str, str]]) – Strings which should be sent along with the request as metadata.

Returns:

Response message for

[ModelMonitoringService.ListModelMonitoringJobs][google.cloud.aiplatform.v1beta1.ModelMonitoringService.ListModelMonitoringJobs].

Iterating over this object will yield results and resolve additional pages automatically.

Return type:

google.cloud.aiplatform_v1beta1.services.model_monitoring_service.pagers.ListModelMonitoringJobsAsyncPager

async list_model_monitors(request: Optional[Union[ListModelMonitorsRequest, dict]] = None, *, parent: Optional[str] = None, retry: Optional[Union[AsyncRetry, _MethodDefault]] = _MethodDefault._DEFAULT_VALUE, timeout: Union[float, object] = _MethodDefault._DEFAULT_VALUE, metadata: Sequence[Tuple[str, str]] = ()) → ListModelMonitorsAsyncPager

Lists ModelMonitors in a Location.

# This snippet has been automatically generated and should be regarded as a
# code template only.
# It will require modifications to work:
# - It may require correct/in-range values for request initialization.
# - It may require specifying regional endpoints when creating the service
#   client as shown in:
#   https://googleapis.dev/python/google-api-core/latest/client_options.html
from google.cloud import aiplatform_v1beta1

async def sample_list_model_monitors():
    # Create a client
    client = aiplatform_v1beta1.ModelMonitoringServiceAsyncClient()

    # Initialize request argument(s)
    request = aiplatform_v1beta1.ListModelMonitorsRequest(
        parent="parent_value",
    )

    # Make the request
    page_result = client.list_model_monitors(request=request)

    # Handle the response
    async for response in page_result:
        print(response)

Parameters:

• request (Optional[Union[google.cloud.aiplatform_v1beta1.types.ListModelMonitorsRequest, dict]]) – The request object. Request message for [ModelMonitoringService.ListModelMonitors][google.cloud.aiplatform.v1beta1.ModelMonitoringService.ListModelMonitors].

• parent (str) –

  Required. The resource name of the Location to list the ModelMonitors from. Format: projects/{project}/locations/{location}

  This corresponds to the parent field on the request instance; if request is provided, this should not be set.

• retry (google.api_core.retry_async.AsyncRetry) – Designation of what errors, if any, should be retried.

• timeout (float) – The timeout for this request.

• metadata (Sequence[Tuple[str, str]]) – Strings which should be sent along with the request as metadata.

Returns:

Response message for

[ModelMonitoringService.ListModelMonitors][google.cloud.aiplatform.v1beta1.ModelMonitoringService.ListModelMonitors]

Iterating over this object will yield results and resolve additional pages automatically.

Return type:

google.cloud.aiplatform_v1beta1.services.model_monitoring_service.pagers.ListModelMonitorsAsyncPager

async list_operations(request: Optional[ListOperationsRequest] = None, *, retry: Optional[Union[AsyncRetry, _MethodDefault]] = _MethodDefault._DEFAULT_VALUE, timeout: Union[float, object] = _MethodDefault._DEFAULT_VALUE, metadata: Sequence[Tuple[str, str]] = ()) → ListOperationsResponse

Lists operations that match the specified filter in the request.

Parameters:

• request (ListOperationsRequest) – The request object. Request message for ListOperations method.

• retry (google.api_core.retry_async.AsyncRetry) – Designation of what errors, if any, should be retried.

• timeout (float) – The timeout for this request.

• metadata (Sequence[Tuple[str, str]]) – Strings which should be sent along with the request as metadata.

Returns:

Response message for ListOperations method.

Return type:

ListOperationsResponse

static model_monitor_path(project: str, location: str, model_monitor: str) → str

Returns a fully-qualified model_monitor string.

static model_monitoring_job_path(project: str, location: str, model_monitor: str, model_monitoring_job: str) → str

Returns a fully-qualified model_monitoring_job string.

static model_path(project: str, location: str, model: str) → str

Returns a fully-qualified model string.

static parse_batch_prediction_job_path(path: str) → Dict[str, str]

Parses a batch_prediction_job path into its component segments.

static parse_common_billing_account_path(path: str) → Dict[str, str]

Parse a billing_account path into its component segments.

static parse_common_folder_path(path: str) → Dict[str, str]

Parse a folder path into its component segments.

static parse_common_location_path(path: str) → Dict[str, str]

Parse a location path into its component segments.

static parse_common_organization_path(path: str) → Dict[str, str]

Parse a organization path into its component segments.

static parse_common_project_path(path: str) → Dict[str, str]

Parse a project path into its component segments.

static parse_dataset_path(path: str) → Dict[str, str]

Parses a dataset path into its component segments.

static parse_endpoint_path(path: str) → Dict[str, str]

Parses a endpoint path into its component segments.

static parse_model_monitor_path(path: str) → Dict[str, str]

Parses a model_monitor path into its component segments.

static parse_model_monitoring_job_path(path: str) → Dict[str, str]

Parses a model_monitoring_job path into its component segments.

static parse_model_path(path: str) → Dict[str, str]

Parses a model path into its component segments.

static parse_reservation_path(path: str) → Dict[str, str]

Parses a reservation path into its component segments.

static parse_schedule_path(path: str) → Dict[str, str]

Parses a schedule path into its component segments.

static reservation_path(project_id_or_number: str, zone: str, reservation_name: str) → str

Returns a fully-qualified reservation string.

static schedule_path(project: str, location: str, schedule: str) → str

Returns a fully-qualified schedule string.

async search_model_monitoring_alerts(request: Optional[Union[SearchModelMonitoringAlertsRequest, dict]] = None, *, model_monitor: Optional[str] = None, retry: Optional[Union[AsyncRetry, _MethodDefault]] = _MethodDefault._DEFAULT_VALUE, timeout: Union[float, object] = _MethodDefault._DEFAULT_VALUE, metadata: Sequence[Tuple[str, str]] = ()) → SearchModelMonitoringAlertsAsyncPager

Returns the Model Monitoring alerts.

# This snippet has been automatically generated and should be regarded as a
# code template only.
# It will require modifications to work:
# - It may require correct/in-range values for request initialization.
# - It may require specifying regional endpoints when creating the service
#   client as shown in:
#   https://googleapis.dev/python/google-api-core/latest/client_options.html
from google.cloud import aiplatform_v1beta1

async def sample_search_model_monitoring_alerts():
    # Create a client
    client = aiplatform_v1beta1.ModelMonitoringServiceAsyncClient()

    # Initialize request argument(s)
    request = aiplatform_v1beta1.SearchModelMonitoringAlertsRequest(
        model_monitor="model_monitor_value",
    )

    # Make the request
    page_result = client.search_model_monitoring_alerts(request=request)

    # Handle the response
    async for response in page_result:
        print(response)

Parameters:

• request (Optional[Union[google.cloud.aiplatform_v1beta1.types.SearchModelMonitoringAlertsRequest, dict]]) – The request object. Request message for [ModelMonitoringService.SearchModelMonitoringAlerts][google.cloud.aiplatform.v1beta1.ModelMonitoringService.SearchModelMonitoringAlerts].

• model_monitor (str) –

  Required. ModelMonitor resource name. Format: projects/{project}/locations/{location}/modelMonitors/{model_monitor}

  This corresponds to the model_monitor field on the request instance; if request is provided, this should not be set.

• retry (google.api_core.retry_async.AsyncRetry) – Designation of what errors, if any, should be retried.

• timeout (float) – The timeout for this request.

• metadata (Sequence[Tuple[str, str]]) – Strings which should be sent along with the request as metadata.

Returns:

Response message for

[ModelMonitoringService.SearchModelMonitoringAlerts][google.cloud.aiplatform.v1beta1.ModelMonitoringService.SearchModelMonitoringAlerts].

Iterating over this object will yield results and resolve additional pages automatically.

Return type:

google.cloud.aiplatform_v1beta1.services.model_monitoring_service.pagers.SearchModelMonitoringAlertsAsyncPager

async search_model_monitoring_stats(request: Optional[Union[SearchModelMonitoringStatsRequest, dict]] = None, *, model_monitor: Optional[str] = None, retry: Optional[Union[AsyncRetry, _MethodDefault]] = _MethodDefault._DEFAULT_VALUE, timeout: Union[float, object] = _MethodDefault._DEFAULT_VALUE, metadata: Sequence[Tuple[str, str]] = ()) → SearchModelMonitoringStatsAsyncPager

Searches Model Monitoring Stats generated within a given time window.

# This snippet has been automatically generated and should be regarded as a
# code template only.
# It will require modifications to work:
# - It may require correct/in-range values for request initialization.
# - It may require specifying regional endpoints when creating the service
#   client as shown in:
#   https://googleapis.dev/python/google-api-core/latest/client_options.html
from google.cloud import aiplatform_v1beta1

async def sample_search_model_monitoring_stats():
    # Create a client
    client = aiplatform_v1beta1.ModelMonitoringServiceAsyncClient()

    # Initialize request argument(s)
    request = aiplatform_v1beta1.SearchModelMonitoringStatsRequest(
        model_monitor="model_monitor_value",
    )

    # Make the request
    page_result = client.search_model_monitoring_stats(request=request)

    # Handle the response
    async for response in page_result:
        print(response)

Parameters:

• request (Optional[Union[google.cloud.aiplatform_v1beta1.types.SearchModelMonitoringStatsRequest, dict]]) – The request object. Request message for [ModelMonitoringService.SearchModelMonitoringStats][google.cloud.aiplatform.v1beta1.ModelMonitoringService.SearchModelMonitoringStats].

• model_monitor (str) –

  Required. ModelMonitor resource name. Format: projects/{project}/locations/{location}/modelMonitors/{model_monitor}

  This corresponds to the model_monitor field on the request instance; if request is provided, this should not be set.

• retry (google.api_core.retry_async.AsyncRetry) – Designation of what errors, if any, should be retried.

• timeout (float) – The timeout for this request.

• metadata (Sequence[Tuple[str, str]]) – Strings which should be sent along with the request as metadata.

Returns:

Response message for

[ModelMonitoringService.SearchModelMonitoringStats][google.cloud.aiplatform.v1beta1.ModelMonitoringService.SearchModelMonitoringStats].

Iterating over this object will yield results and resolve additional pages automatically.

Return type:

google.cloud.aiplatform_v1beta1.services.model_monitoring_service.pagers.SearchModelMonitoringStatsAsyncPager

async set_iam_policy(request: Optional[SetIamPolicyRequest] = None, *, retry: Optional[Union[AsyncRetry, _MethodDefault]] = _MethodDefault._DEFAULT_VALUE, timeout: Union[float, object] = _MethodDefault._DEFAULT_VALUE, metadata: Sequence[Tuple[str, str]] = ()) → Policy

Sets the IAM access control policy on the specified function.

Replaces any existing policy.

Parameters:

• request (SetIamPolicyRequest) – The request object. Request message for SetIamPolicy method.

• retry (google.api_core.retry_async.AsyncRetry) – Designation of what errors, if any, should be retried.

• timeout (float) – The timeout for this request.

• metadata (Sequence[Tuple[str, str]]) – Strings which should be sent along with the request as metadata.

Returns:

Defines an Identity and Access Management (IAM) policy. It is used to specify access control policies for Cloud Platform resources. A Policy is a collection of bindings. A binding binds one or more members to a single role. Members can be user accounts, service accounts, Google groups, and domains (such as G Suite). A role is a named list of permissions (defined by IAM or configured by users). A binding can optionally specify a condition, which is a logic expression that further constrains the role binding based on attributes about the request and/or target resource.

JSON Example

{
  "bindings": [
    {
      "role": "roles/resourcemanager.organizationAdmin",
      "members": [
        "user:mike@example.com",
        "group:admins@example.com",
        "domain:google.com",
        "serviceAccount:my-project-id@appspot.gserviceaccount.com"
      ]
    },
    {
      "role": "roles/resourcemanager.organizationViewer",
      "members": ["user:eve@example.com"],
      "condition": {
        "title": "expirable access",
        "description": "Does not grant access after Sep 2020",
        "expression": "request.time <
        timestamp('2020-10-01T00:00:00.000Z')",
      }
    }
  ]
}

YAML Example

bindings:
- members:
  - user:mike@example.com
  - group:admins@example.com
  - domain:google.com
  - serviceAccount:my-project-id@appspot.gserviceaccount.com
  role: roles/resourcemanager.organizationAdmin
- members:
  - user:eve@example.com
  role: roles/resourcemanager.organizationViewer
  condition:
    title: expirable access
    description: Does not grant access after Sep 2020
    expression: request.time < timestamp('2020-10-01T00:00:00.000Z')

For a description of IAM and its features, see the IAM developer’s guide.

Return type:

Policy

async test_iam_permissions(request: Optional[TestIamPermissionsRequest] = None, *, retry: Optional[Union[AsyncRetry, _MethodDefault]] = _MethodDefault._DEFAULT_VALUE, timeout: Union[float, object] = _MethodDefault._DEFAULT_VALUE, metadata: Sequence[Tuple[str, str]] = ()) → TestIamPermissionsResponse

Tests the specified IAM permissions against the IAM access control

policy for a function.

If the function does not exist, this will return an empty set of permissions, not a NOT_FOUND error.

Parameters:

• request (TestIamPermissionsRequest) – The request object. Request message for TestIamPermissions method.

• retry (google.api_core.retry_async.AsyncRetry) – Designation of what errors, if any, should be retried.

• timeout (float) – The timeout for this request.

• metadata (Sequence[Tuple[str, str]]) – Strings which should be sent along with the request as metadata.

Returns:

Response message for TestIamPermissions method.

Return type:

TestIamPermissionsResponse

property transport: ModelMonitoringServiceTransport

Returns the transport used by the client instance.

Returns:

The transport used by the client instance.

Return type:

ModelMonitoringServiceTransport

property universe_domain: str

Return the universe domain used by the client instance.

Returns:

The universe domain used

by the client instance.

Return type:

str

async update_model_monitor(request: Optional[Union[UpdateModelMonitorRequest, dict]] = None, *, model_monitor: Optional[ModelMonitor] = None, update_mask: Optional[FieldMask] = None, retry: Optional[Union[AsyncRetry, _MethodDefault]] = _MethodDefault._DEFAULT_VALUE, timeout: Union[float, object] = _MethodDefault._DEFAULT_VALUE, metadata: Sequence[Tuple[str, str]] = ()) → AsyncOperation

Updates a ModelMonitor.

# This snippet has been automatically generated and should be regarded as a
# code template only.
# It will require modifications to work:
# - It may require correct/in-range values for request initialization.
# - It may require specifying regional endpoints when creating the service
#   client as shown in:
#   https://googleapis.dev/python/google-api-core/latest/client_options.html
from google.cloud import aiplatform_v1beta1

async def sample_update_model_monitor():
    # Create a client
    client = aiplatform_v1beta1.ModelMonitoringServiceAsyncClient()

    # Initialize request argument(s)
    request = aiplatform_v1beta1.UpdateModelMonitorRequest(
    )

    # Make the request
    operation = client.update_model_monitor(request=request)

    print("Waiting for operation to complete...")

    response = (await operation).result()

    # Handle the response
    print(response)

Parameters:

• request (Optional[Union[google.cloud.aiplatform_v1beta1.types.UpdateModelMonitorRequest, dict]]) – The request object. Request message for [ModelMonitoringService.UpdateModelMonitor][google.cloud.aiplatform.v1beta1.ModelMonitoringService.UpdateModelMonitor].

• model_monitor (google.cloud.aiplatform_v1beta1.types.ModelMonitor) –

  Required. The model monitoring configuration which replaces the resource on the server.

  This corresponds to the model_monitor field on the request instance; if request is provided, this should not be set.

• update_mask (google.protobuf.field_mask_pb2.FieldMask) –

  Required. Mask specifying which fields to update.

  This corresponds to the update_mask field on the request instance; if request is provided, this should not be set.

• retry (google.api_core.retry_async.AsyncRetry) – Designation of what errors, if any, should be retried.

• timeout (float) – The timeout for this request.

• metadata (Sequence[Tuple[str, str]]) – Strings which should be sent along with the request as metadata.

Returns:

An object representing a long-running operation.

The result type for the operation will be google.cloud.aiplatform_v1beta1.types.ModelMonitor Vertex AI Model Monitoring Service serves as a central hub for the analysis

and visualization of data quality and performance related to models. ModelMonitor stands as a top level resource for overseeing your model monitoring tasks.

Return type:

google.api_core.operation_async.AsyncOperation

async wait_operation(request: Optional[WaitOperationRequest] = None, *, retry: Optional[Union[AsyncRetry, _MethodDefault]] = _MethodDefault._DEFAULT_VALUE, timeout: Union[float, object] = _MethodDefault._DEFAULT_VALUE, metadata: Sequence[Tuple[str, str]] = ()) → Operation

Waits until the specified long-running operation is done or reaches at most a specified timeout, returning the latest state.

If the operation is already done, the latest state is immediately returned. If the timeout specified is greater than the default HTTP/RPC timeout, the HTTP/RPC timeout is used. If the server does not support this method, it returns google.rpc.Code.UNIMPLEMENTED.

Parameters:

• request (WaitOperationRequest) – The request object. Request message for WaitOperation method.

• retry (google.api_core.retry_async.AsyncRetry) – Designation of what errors, if any, should be retried.

• timeout (float) – The timeout for this request.

• metadata (Sequence[Tuple[str, str]]) – Strings which should be sent along with the request as metadata.

Returns:

An Operation object.

Return type:

Operation

class google.cloud.aiplatform_v1beta1.services.model_monitoring_service.ModelMonitoringServiceClient(*, credentials: ~typing.Optional[~google.auth.credentials.Credentials] = None, transport: ~typing.Optional[~typing.Union[str, ~google.cloud.aiplatform_v1beta1.services.model_monitoring_service.transports.base.ModelMonitoringServiceTransport, ~typing.Callable[[...], ~google.cloud.aiplatform_v1beta1.services.model_monitoring_service.transports.base.ModelMonitoringServiceTransport]]] = None, client_options: ~typing.Optional[~typing.Union[~google.api_core.client_options.ClientOptions, dict]] = None, client_info: ~google.api_core.gapic_v1.client_info.ClientInfo = <google.api_core.gapic_v1.client_info.ClientInfo object>)

A service for creating and managing Vertex AI Model moitoring. This includes ModelMonitor resources, ModelMonitoringJob resources.

Instantiates the model monitoring service client.

Parameters:

• credentials (Optional[google.auth.credentials.Credentials]) – The authorization credentials to attach to requests. These credentials identify the application to the service; if none are specified, the client will attempt to ascertain the credentials from the environment.

• transport (Optional[Union[str,ModelMonitoringServiceTransport,Callable[..., ModelMonitoringServiceTransport]]]) – The transport to use, or a Callable that constructs and returns a new transport. If a Callable is given, it will be called with the same set of initialization arguments as used in the ModelMonitoringServiceTransport constructor. If set to None, a transport is chosen automatically.

• client_options (Optional[Union[google.api_core.client_options.ClientOptions, dict]]) –

  Custom options for the client.

  1. The api_endpoint property can be used to override the default endpoint provided by the client when transport is not explicitly provided. Only if this property is not set and transport was not explicitly provided, the endpoint is determined by the GOOGLE_API_USE_MTLS_ENDPOINT environment variable, which have one of the following values: “always” (always use the default mTLS endpoint), “never” (always use the default regular endpoint) and “auto” (auto-switch to the default mTLS endpoint if client certificate is present; this is the default value).

  2. If the GOOGLE_API_USE_CLIENT_CERTIFICATE environment variable is “true”, then the client_cert_source property can be used to provide a client certificate for mTLS transport. If not provided, the default SSL client certificate will be used if present. If GOOGLE_API_USE_CLIENT_CERTIFICATE is “false” or not set, no client certificate will be used.

  3. The universe_domain property can be used to override the default “googleapis.com” universe. Note that the api_endpoint property still takes precedence; and universe_domain is currently not supported for mTLS.

• client_info (google.api_core.gapic_v1.client_info.ClientInfo) – The client info used to send a user-agent string along with API requests. If None, then default info will be used. Generally, you only need to set this if you’re developing your own client library.

Raises:

google.auth.exceptions.MutualTLSChannelError – If mutual TLS transport creation failed for any reason.

__exit__(type, value, traceback)

Releases underlying transport’s resources.

Warning

ONLY use as a context manager if the transport is NOT shared with other clients! Exiting the with block will CLOSE the transport and may cause errors in other clients!

property api_endpoint

Return the API endpoint used by the client instance.

Returns:

The API endpoint used by the client instance.

Return type:

str

static batch_prediction_job_path(project: str, location: str, batch_prediction_job: str) → str

Returns a fully-qualified batch_prediction_job string.

cancel_operation(request: Optional[CancelOperationRequest] = None, *, retry: Optional[Union[Retry, _MethodDefault]] = _MethodDefault._DEFAULT_VALUE, timeout: Union[float, object] = _MethodDefault._DEFAULT_VALUE, metadata: Sequence[Tuple[str, str]] = ()) → None

Starts asynchronous cancellation on a long-running operation.

The server makes a best effort to cancel the operation, but success is not guaranteed. If the server doesn’t support this method, it returns google.rpc.Code.UNIMPLEMENTED.

Parameters:

• request (CancelOperationRequest) – The request object. Request message for CancelOperation method.

• retry (google.api_core.retry.Retry) – Designation of what errors, if any, should be retried.

• timeout (float) – The timeout for this request.

• metadata (Sequence[Tuple[str, str]]) – Strings which should be sent along with the request as metadata.

Returns:

None

static common_billing_account_path(billing_account: str) → str

Returns a fully-qualified billing_account string.

static common_folder_path(folder: str) → str

Returns a fully-qualified folder string.

static common_location_path(project: str, location: str) → str

Returns a fully-qualified location string.

static common_organization_path(organization: str) → str

Returns a fully-qualified organization string.

static common_project_path(project: str) → str

Returns a fully-qualified project string.

create_model_monitor(request: Optional[Union[CreateModelMonitorRequest, dict]] = None, *, parent: Optional[str] = None, model_monitor: Optional[ModelMonitor] = None, retry: Optional[Union[Retry, _MethodDefault]] = _MethodDefault._DEFAULT_VALUE, timeout: Union[float, object] = _MethodDefault._DEFAULT_VALUE, metadata: Sequence[Tuple[str, str]] = ()) → Operation

Creates a ModelMonitor.

# This snippet has been automatically generated and should be regarded as a
# code template only.
# It will require modifications to work:
# - It may require correct/in-range values for request initialization.
# - It may require specifying regional endpoints when creating the service
#   client as shown in:
#   https://googleapis.dev/python/google-api-core/latest/client_options.html
from google.cloud import aiplatform_v1beta1

def sample_create_model_monitor():
    # Create a client
    client = aiplatform_v1beta1.ModelMonitoringServiceClient()

    # Initialize request argument(s)
    request = aiplatform_v1beta1.CreateModelMonitorRequest(
        parent="parent_value",
    )

    # Make the request
    operation = client.create_model_monitor(request=request)

    print("Waiting for operation to complete...")

    response = operation.result()

    # Handle the response
    print(response)

Parameters:

• request (Union[google.cloud.aiplatform_v1beta1.types.CreateModelMonitorRequest, dict]) – The request object. Request message for [ModelMonitoringService.CreateModelMonitor][google.cloud.aiplatform.v1beta1.ModelMonitoringService.CreateModelMonitor].

• parent (str) –

  Required. The resource name of the Location to create the ModelMonitor in. Format: projects/{project}/locations/{location}

  This corresponds to the parent field on the request instance; if request is provided, this should not be set.

• model_monitor (google.cloud.aiplatform_v1beta1.types.ModelMonitor) – Required. The ModelMonitor to create. This corresponds to the model_monitor field on the request instance; if request is provided, this should not be set.

• retry (google.api_core.retry.Retry) – Designation of what errors, if any, should be retried.

• timeout (float) – The timeout for this request.

• metadata (Sequence[Tuple[str, str]]) – Strings which should be sent along with the request as metadata.

Returns:

An object representing a long-running operation.

The result type for the operation will be google.cloud.aiplatform_v1beta1.types.ModelMonitor Vertex AI Model Monitoring Service serves as a central hub for the analysis

and visualization of data quality and performance related to models. ModelMonitor stands as a top level resource for overseeing your model monitoring tasks.

Return type:

google.api_core.operation.Operation

create_model_monitoring_job(request: Optional[Union[CreateModelMonitoringJobRequest, dict]] = None, *, parent: Optional[str] = None, model_monitoring_job: Optional[ModelMonitoringJob] = None, retry: Optional[Union[Retry, _MethodDefault]] = _MethodDefault._DEFAULT_VALUE, timeout: Union[float, object] = _MethodDefault._DEFAULT_VALUE, metadata: Sequence[Tuple[str, str]] = ()) → ModelMonitoringJob

Creates a ModelMonitoringJob.

# This snippet has been automatically generated and should be regarded as a
# code template only.
# It will require modifications to work:
# - It may require correct/in-range values for request initialization.
# - It may require specifying regional endpoints when creating the service
#   client as shown in:
#   https://googleapis.dev/python/google-api-core/latest/client_options.html
from google.cloud import aiplatform_v1beta1

def sample_create_model_monitoring_job():
    # Create a client
    client = aiplatform_v1beta1.ModelMonitoringServiceClient()

    # Initialize request argument(s)
    request = aiplatform_v1beta1.CreateModelMonitoringJobRequest(
        parent="parent_value",
    )

    # Make the request
    response = client.create_model_monitoring_job(request=request)

    # Handle the response
    print(response)

Parameters:

• request (Union[google.cloud.aiplatform_v1beta1.types.CreateModelMonitoringJobRequest, dict]) – The request object. Request message for [ModelMonitoringService.CreateModelMonitoringJob][google.cloud.aiplatform.v1beta1.ModelMonitoringService.CreateModelMonitoringJob].

• parent (str) –

  Required. The parent of the ModelMonitoringJob. Format: projects/{project}/locations/{location}/modelMoniitors/{model_monitor}

  This corresponds to the parent field on the request instance; if request is provided, this should not be set.

• model_monitoring_job (google.cloud.aiplatform_v1beta1.types.ModelMonitoringJob) –

  Required. The ModelMonitoringJob to create

  This corresponds to the model_monitoring_job field on the request instance; if request is provided, this should not be set.

• retry (google.api_core.retry.Retry) – Designation of what errors, if any, should be retried.

• timeout (float) – The timeout for this request.

• metadata (Sequence[Tuple[str, str]]) – Strings which should be sent along with the request as metadata.

Returns:

Represents a model monitoring job that analyze dataset using different monitoring algorithm.

Return type:

google.cloud.aiplatform_v1beta1.types.ModelMonitoringJob

static dataset_path(project: str, location: str, dataset: str) → str

Returns a fully-qualified dataset string.

delete_model_monitor(request: Optional[Union[DeleteModelMonitorRequest, dict]] = None, *, name: Optional[str] = None, retry: Optional[Union[Retry, _MethodDefault]] = _MethodDefault._DEFAULT_VALUE, timeout: Union[float, object] = _MethodDefault._DEFAULT_VALUE, metadata: Sequence[Tuple[str, str]] = ()) → Operation

Deletes a ModelMonitor.

# This snippet has been automatically generated and should be regarded as a
# code template only.
# It will require modifications to work:
# - It may require correct/in-range values for request initialization.
# - It may require specifying regional endpoints when creating the service
#   client as shown in:
#   https://googleapis.dev/python/google-api-core/latest/client_options.html
from google.cloud import aiplatform_v1beta1

def sample_delete_model_monitor():
    # Create a client
    client = aiplatform_v1beta1.ModelMonitoringServiceClient()

    # Initialize request argument(s)
    request = aiplatform_v1beta1.DeleteModelMonitorRequest(
        name="name_value",
    )

    # Make the request
    operation = client.delete_model_monitor(request=request)

    print("Waiting for operation to complete...")

    response = operation.result()

    # Handle the response
    print(response)

Parameters:

• request (Union[google.cloud.aiplatform_v1beta1.types.DeleteModelMonitorRequest, dict]) – The request object. Request message for [ModelMonitoringService.DeleteModelMonitor][google.cloud.aiplatform.v1beta1.ModelMonitoringService.DeleteModelMonitor].

• name (str) –

  Required. The name of the ModelMonitor resource to be deleted. Format: projects/{project}/locations/{location}/modelMonitords/{model_monitor}

  This corresponds to the name field on the request instance; if request is provided, this should not be set.

• retry (google.api_core.retry.Retry) – Designation of what errors, if any, should be retried.

• timeout (float) – The timeout for this request.

• metadata (Sequence[Tuple[str, str]]) – Strings which should be sent along with the request as metadata.

Returns:

An object representing a long-running operation.

The result type for the operation will be google.protobuf.empty_pb2.Empty A generic empty message that you can re-use to avoid defining duplicated

empty messages in your APIs. A typical example is to use it as the request or the response type of an API method. For instance:

service Foo {

rpc Bar(google.protobuf.Empty) returns (google.protobuf.Empty);

}

Return type:

google.api_core.operation.Operation

delete_model_monitoring_job(request: Optional[Union[DeleteModelMonitoringJobRequest, dict]] = None, *, name: Optional[str] = None, retry: Optional[Union[Retry, _MethodDefault]] = _MethodDefault._DEFAULT_VALUE, timeout: Union[float, object] = _MethodDefault._DEFAULT_VALUE, metadata: Sequence[Tuple[str, str]] = ()) → Operation

Deletes a ModelMonitoringJob.

# This snippet has been automatically generated and should be regarded as a
# code template only.
# It will require modifications to work:
# - It may require correct/in-range values for request initialization.
# - It may require specifying regional endpoints when creating the service
#   client as shown in:
#   https://googleapis.dev/python/google-api-core/latest/client_options.html
from google.cloud import aiplatform_v1beta1

def sample_delete_model_monitoring_job():
    # Create a client
    client = aiplatform_v1beta1.ModelMonitoringServiceClient()

    # Initialize request argument(s)
    request = aiplatform_v1beta1.DeleteModelMonitoringJobRequest(
        name="name_value",
    )

    # Make the request
    operation = client.delete_model_monitoring_job(request=request)

    print("Waiting for operation to complete...")

    response = operation.result()

    # Handle the response
    print(response)

Parameters:

• request (Union[google.cloud.aiplatform_v1beta1.types.DeleteModelMonitoringJobRequest, dict]) – The request object. Request message for [ModelMonitoringService.DeleteModelMonitoringJob][google.cloud.aiplatform.v1beta1.ModelMonitoringService.DeleteModelMonitoringJob].

• name (str) –

  Required. The resource name of the model monitoring job to delete. Format: projects/{project}/locations/{location}/modelMonitors/{model_monitor}/modelMonitoringJobs/{model_monitoring_job}

  This corresponds to the name field on the request instance; if request is provided, this should not be set.

• retry (google.api_core.retry.Retry) – Designation of what errors, if any, should be retried.

• timeout (float) – The timeout for this request.

• metadata (Sequence[Tuple[str, str]]) – Strings which should be sent along with the request as metadata.

Returns:

An object representing a long-running operation.

The result type for the operation will be google.protobuf.empty_pb2.Empty A generic empty message that you can re-use to avoid defining duplicated

empty messages in your APIs. A typical example is to use it as the request or the response type of an API method. For instance:

service Foo {

rpc Bar(google.protobuf.Empty) returns (google.protobuf.Empty);

}

Return type:

google.api_core.operation.Operation

delete_operation(request: Optional[DeleteOperationRequest] = None, *, retry: Optional[Union[Retry, _MethodDefault]] = _MethodDefault._DEFAULT_VALUE, timeout: Union[float, object] = _MethodDefault._DEFAULT_VALUE, metadata: Sequence[Tuple[str, str]] = ()) → None

Deletes a long-running operation.

This method indicates that the client is no longer interested in the operation result. It does not cancel the operation. If the server doesn’t support this method, it returns google.rpc.Code.UNIMPLEMENTED.

Parameters:

• request (DeleteOperationRequest) – The request object. Request message for DeleteOperation method.

• retry (google.api_core.retry.Retry) – Designation of what errors, if any, should be retried.

• timeout (float) – The timeout for this request.

• metadata (Sequence[Tuple[str, str]]) – Strings which should be sent along with the request as metadata.

Returns:

None

static endpoint_path(project: str, location: str, endpoint: str) → str

Returns a fully-qualified endpoint string.

classmethod from_service_account_file(filename: str, *args, **kwargs)

Creates an instance of this client using the provided credentials

file.

Parameters:

• filename (str) – The path to the service account private key json file.

• args – Additional arguments to pass to the constructor.

• kwargs – Additional arguments to pass to the constructor.

Returns:

The constructed client.

Return type:

ModelMonitoringServiceClient

classmethod from_service_account_info(info: dict, *args, **kwargs)

Creates an instance of this client using the provided credentials

info.

Parameters:

• info (dict) – The service account private key info.

• args – Additional arguments to pass to the constructor.

• kwargs – Additional arguments to pass to the constructor.

Returns:

The constructed client.

Return type:

ModelMonitoringServiceClient

classmethod from_service_account_json(filename: str, *args, **kwargs)

Creates an instance of this client using the provided credentials

file.

Parameters:

• filename (str) – The path to the service account private key json file.

• args – Additional arguments to pass to the constructor.

• kwargs – Additional arguments to pass to the constructor.

Returns:

The constructed client.

Return type:

ModelMonitoringServiceClient

get_iam_policy(request: Optional[GetIamPolicyRequest] = None, *, retry: Optional[Union[Retry, _MethodDefault]] = _MethodDefault._DEFAULT_VALUE, timeout: Union[float, object] = _MethodDefault._DEFAULT_VALUE, metadata: Sequence[Tuple[str, str]] = ()) → Policy

Gets the IAM access control policy for a function.

Returns an empty policy if the function exists and does not have a policy set.

Parameters:

• request (GetIamPolicyRequest) – The request object. Request message for GetIamPolicy method.

• retry (google.api_core.retry.Retry) – Designation of what errors, if any, should be retried.

• timeout (float) – The timeout for this request.

• metadata (Sequence[Tuple[str, str]]) – Strings which should be sent along with the request as metadata.

Returns:

Defines an Identity and Access Management (IAM) policy. It is used to specify access control policies for Cloud Platform resources. A Policy is a collection of bindings. A binding binds one or more members to a single role. Members can be user accounts, service accounts, Google groups, and domains (such as G Suite). A role is a named list of permissions (defined by IAM or configured by users). A binding can optionally specify a condition, which is a logic expression that further constrains the role binding based on attributes about the request and/or target resource.

JSON Example

{
  "bindings": [
    {
      "role": "roles/resourcemanager.organizationAdmin",
      "members": [
        "user:mike@example.com",
        "group:admins@example.com",
        "domain:google.com",
        "serviceAccount:my-project-id@appspot.gserviceaccount.com"
      ]
    },
    {
      "role": "roles/resourcemanager.organizationViewer",
      "members": ["user:eve@example.com"],
      "condition": {
        "title": "expirable access",
        "description": "Does not grant access after Sep 2020",
        "expression": "request.time <
        timestamp('2020-10-01T00:00:00.000Z')",
      }
    }
  ]
}

YAML Example

bindings:
- members:
  - user:mike@example.com
  - group:admins@example.com
  - domain:google.com
  - serviceAccount:my-project-id@appspot.gserviceaccount.com
  role: roles/resourcemanager.organizationAdmin
- members:
  - user:eve@example.com
  role: roles/resourcemanager.organizationViewer
  condition:
    title: expirable access
    description: Does not grant access after Sep 2020
    expression: request.time < timestamp('2020-10-01T00:00:00.000Z')

For a description of IAM and its features, see the IAM developer’s guide.

Return type:

Policy

get_location(request: Optional[GetLocationRequest] = None, *, retry: Optional[Union[Retry, _MethodDefault]] = _MethodDefault._DEFAULT_VALUE, timeout: Union[float, object] = _MethodDefault._DEFAULT_VALUE, metadata: Sequence[Tuple[str, str]] = ()) → Location

Gets information about a location.

Parameters:

• request (GetLocationRequest) – The request object. Request message for GetLocation method.

• retry (google.api_core.retry.Retry) – Designation of what errors, if any, should be retried.

• timeout (float) – The timeout for this request.

• metadata (Sequence[Tuple[str, str]]) – Strings which should be sent along with the request as metadata.

Returns:

Location object.

Return type:

Location

get_model_monitor(request: Optional[Union[GetModelMonitorRequest, dict]] = None, *, name: Optional[str] = None, retry: Optional[Union[Retry, _MethodDefault]] = _MethodDefault._DEFAULT_VALUE, timeout: Union[float, object] = _MethodDefault._DEFAULT_VALUE, metadata: Sequence[Tuple[str, str]] = ()) → ModelMonitor

Gets a ModelMonitor.

# This snippet has been automatically generated and should be regarded as a
# code template only.
# It will require modifications to work:
# - It may require correct/in-range values for request initialization.
# - It may require specifying regional endpoints when creating the service
#   client as shown in:
#   https://googleapis.dev/python/google-api-core/latest/client_options.html
from google.cloud import aiplatform_v1beta1

def sample_get_model_monitor():
    # Create a client
    client = aiplatform_v1beta1.ModelMonitoringServiceClient()

    # Initialize request argument(s)
    request = aiplatform_v1beta1.GetModelMonitorRequest(
        name="name_value",
    )

    # Make the request
    response = client.get_model_monitor(request=request)

    # Handle the response
    print(response)

Parameters:

• request (Union[google.cloud.aiplatform_v1beta1.types.GetModelMonitorRequest, dict]) – The request object. Request message for [ModelMonitoringService.GetModelMonitor][google.cloud.aiplatform.v1beta1.ModelMonitoringService.GetModelMonitor].

• name (str) –

  Required. The name of the ModelMonitor resource. Format: projects/{project}/locations/{location}/modelMonitors/{model_monitor}

  This corresponds to the name field on the request instance; if request is provided, this should not be set.

• retry (google.api_core.retry.Retry) – Designation of what errors, if any, should be retried.

• timeout (float) – The timeout for this request.

• metadata (Sequence[Tuple[str, str]]) – Strings which should be sent along with the request as metadata.

Returns:

Vertex AI Model Monitoring Service serves as a central hub for the analysis and visualization of data quality and performance related to models. ModelMonitor stands as a top level resource for overseeing your model monitoring tasks.

Return type:

google.cloud.aiplatform_v1beta1.types.ModelMonitor

get_model_monitoring_job(request: Optional[Union[GetModelMonitoringJobRequest, dict]] = None, *, name: Optional[str] = None, retry: Optional[Union[Retry, _MethodDefault]] = _MethodDefault._DEFAULT_VALUE, timeout: Union[float, object] = _MethodDefault._DEFAULT_VALUE, metadata: Sequence[Tuple[str, str]] = ()) → ModelMonitoringJob

Gets a ModelMonitoringJob.

# This snippet has been automatically generated and should be regarded as a
# code template only.
# It will require modifications to work:
# - It may require correct/in-range values for request initialization.
# - It may require specifying regional endpoints when creating the service
#   client as shown in:
#   https://googleapis.dev/python/google-api-core/latest/client_options.html
from google.cloud import aiplatform_v1beta1

def sample_get_model_monitoring_job():
    # Create a client
    client = aiplatform_v1beta1.ModelMonitoringServiceClient()

    # Initialize request argument(s)
    request = aiplatform_v1beta1.GetModelMonitoringJobRequest(
        name="name_value",
    )

    # Make the request
    response = client.get_model_monitoring_job(request=request)

    # Handle the response
    print(response)

Parameters:

• request (Union[google.cloud.aiplatform_v1beta1.types.GetModelMonitoringJobRequest, dict]) – The request object. Request message for [ModelMonitoringService.GetModelMonitoringJob][google.cloud.aiplatform.v1beta1.ModelMonitoringService.GetModelMonitoringJob].

• name (str) –

  Required. The resource name of the ModelMonitoringJob. Format: projects/{project}/locations/{location}/modelMonitors/{model_monitor}/modelMonitoringJobs/{model_monitoring_job}

  This corresponds to the name field on the request instance; if request is provided, this should not be set.

• retry (google.api_core.retry.Retry) – Designation of what errors, if any, should be retried.

• timeout (float) – The timeout for this request.

• metadata (Sequence[Tuple[str, str]]) – Strings which should be sent along with the request as metadata.

Returns:

Represents a model monitoring job that analyze dataset using different monitoring algorithm.

Return type:

google.cloud.aiplatform_v1beta1.types.ModelMonitoringJob

classmethod get_mtls_endpoint_and_cert_source(client_options: Optional[ClientOptions] = None)

Deprecated. Return the API endpoint and client cert source for mutual TLS.

The client cert source is determined in the following order: (1) if GOOGLE_API_USE_CLIENT_CERTIFICATE environment variable is not “true”, the client cert source is None. (2) if client_options.client_cert_source is provided, use the provided one; if the default client cert source exists, use the default one; otherwise the client cert source is None.

The API endpoint is determined in the following order: (1) if client_options.api_endpoint if provided, use the provided one. (2) if GOOGLE_API_USE_CLIENT_CERTIFICATE environment variable is “always”, use the default mTLS endpoint; if the environment variable is “never”, use the default API endpoint; otherwise if client cert source exists, use the default mTLS endpoint, otherwise use the default API endpoint.

More details can be found at https://google.aip.dev/auth/4114.

Parameters:

client_options (google.api_core.client_options.ClientOptions) – Custom options for the client. Only the api_endpoint and client_cert_source properties may be used in this method.

Returns:

returns the API endpoint and the

client cert source to use.

Return type:

Tuple[str, Callable[[], Tuple[bytes, bytes]]]

Raises:

google.auth.exceptions.MutualTLSChannelError – If any errors happen.

get_operation(request: Optional[GetOperationRequest] = None, *, retry: Optional[Union[Retry, _MethodDefault]] = _MethodDefault._DEFAULT_VALUE, timeout: Union[float, object] = _MethodDefault._DEFAULT_VALUE, metadata: Sequence[Tuple[str, str]] = ()) → Operation

Gets the latest state of a long-running operation.

Parameters:

• request (GetOperationRequest) – The request object. Request message for GetOperation method.

• retry (google.api_core.retry.Retry) – Designation of what errors, if any, should be retried.

• timeout (float) – The timeout for this request.

• metadata (Sequence[Tuple[str, str]]) – Strings which should be sent along with the request as metadata.

Returns:

An Operation object.

Return type:

Operation

list_locations(request: Optional[ListLocationsRequest] = None, *, retry: Optional[Union[Retry, _MethodDefault]] = _MethodDefault._DEFAULT_VALUE, timeout: Union[float, object] = _MethodDefault._DEFAULT_VALUE, metadata: Sequence[Tuple[str, str]] = ()) → ListLocationsResponse

Lists information about the supported locations for this service.

Parameters:

• request (ListLocationsRequest) – The request object. Request message for ListLocations method.

• retry (google.api_core.retry.Retry) – Designation of what errors, if any, should be retried.

• timeout (float) – The timeout for this request.

• metadata (Sequence[Tuple[str, str]]) – Strings which should be sent along with the request as metadata.

Returns:

Response message for ListLocations method.

Return type:

ListLocationsResponse

list_model_monitoring_jobs(request: Optional[Union[ListModelMonitoringJobsRequest, dict]] = None, *, parent: Optional[str] = None, retry: Optional[Union[Retry, _MethodDefault]] = _MethodDefault._DEFAULT_VALUE, timeout: Union[float, object] = _MethodDefault._DEFAULT_VALUE, metadata: Sequence[Tuple[str, str]] = ()) → ListModelMonitoringJobsPager

Lists ModelMonitoringJobs. Callers may choose to read across multiple Monitors as per AIP-159 by using ‘-’ (the hyphen or dash character) as a wildcard character instead of modelMonitor id in the parent. Format projects/{project_id}/locations/{location}/moodelMonitors/-/modelMonitoringJobs

# This snippet has been automatically generated and should be regarded as a
# code template only.
# It will require modifications to work:
# - It may require correct/in-range values for request initialization.
# - It may require specifying regional endpoints when creating the service
#   client as shown in:
#   https://googleapis.dev/python/google-api-core/latest/client_options.html
from google.cloud import aiplatform_v1beta1

def sample_list_model_monitoring_jobs():
    # Create a client
    client = aiplatform_v1beta1.ModelMonitoringServiceClient()

    # Initialize request argument(s)
    request = aiplatform_v1beta1.ListModelMonitoringJobsRequest(
        parent="parent_value",
    )

    # Make the request
    page_result = client.list_model_monitoring_jobs(request=request)

    # Handle the response
    for response in page_result:
        print(response)

Parameters:

• request (Union[google.cloud.aiplatform_v1beta1.types.ListModelMonitoringJobsRequest, dict]) – The request object. Request message for [ModelMonitoringService.ListModelMonitoringJobs][google.cloud.aiplatform.v1beta1.ModelMonitoringService.ListModelMonitoringJobs].

• parent (str) –

  Required. The parent of the ModelMonitoringJob. Format: projects/{project}/locations/{location}/modelMonitors/{model_monitor}

  This corresponds to the parent field on the request instance; if request is provided, this should not be set.

• retry (google.api_core.retry.Retry) – Designation of what errors, if any, should be retried.

• timeout (float) – The timeout for this request.

• metadata (Sequence[Tuple[str, str]]) – Strings which should be sent along with the request as metadata.

Returns:

Response message for

[ModelMonitoringService.ListModelMonitoringJobs][google.cloud.aiplatform.v1beta1.ModelMonitoringService.ListModelMonitoringJobs].

Iterating over this object will yield results and resolve additional pages automatically.

Return type:

google.cloud.aiplatform_v1beta1.services.model_monitoring_service.pagers.ListModelMonitoringJobsPager

list_model_monitors(request: Optional[Union[ListModelMonitorsRequest, dict]] = None, *, parent: Optional[str] = None, retry: Optional[Union[Retry, _MethodDefault]] = _MethodDefault._DEFAULT_VALUE, timeout: Union[float, object] = _MethodDefault._DEFAULT_VALUE, metadata: Sequence[Tuple[str, str]] = ()) → ListModelMonitorsPager

Lists ModelMonitors in a Location.

# This snippet has been automatically generated and should be regarded as a
# code template only.
# It will require modifications to work:
# - It may require correct/in-range values for request initialization.
# - It may require specifying regional endpoints when creating the service
#   client as shown in:
#   https://googleapis.dev/python/google-api-core/latest/client_options.html
from google.cloud import aiplatform_v1beta1

def sample_list_model_monitors():
    # Create a client
    client = aiplatform_v1beta1.ModelMonitoringServiceClient()

    # Initialize request argument(s)
    request = aiplatform_v1beta1.ListModelMonitorsRequest(
        parent="parent_value",
    )

    # Make the request
    page_result = client.list_model_monitors(request=request)

    # Handle the response
    for response in page_result:
        print(response)

Parameters:

• request (Union[google.cloud.aiplatform_v1beta1.types.ListModelMonitorsRequest, dict]) – The request object. Request message for [ModelMonitoringService.ListModelMonitors][google.cloud.aiplatform.v1beta1.ModelMonitoringService.ListModelMonitors].

• parent (str) –

  Required. The resource name of the Location to list the ModelMonitors from. Format: projects/{project}/locations/{location}

  This corresponds to the parent field on the request instance; if request is provided, this should not be set.

• retry (google.api_core.retry.Retry) – Designation of what errors, if any, should be retried.

• timeout (float) – The timeout for this request.

• metadata (Sequence[Tuple[str, str]]) – Strings which should be sent along with the request as metadata.

Returns:

Response message for

[ModelMonitoringService.ListModelMonitors][google.cloud.aiplatform.v1beta1.ModelMonitoringService.ListModelMonitors]

Iterating over this object will yield results and resolve additional pages automatically.

Return type:

google.cloud.aiplatform_v1beta1.services.model_monitoring_service.pagers.ListModelMonitorsPager

list_operations(request: Optional[ListOperationsRequest] = None, *, retry: Optional[Union[Retry, _MethodDefault]] = _MethodDefault._DEFAULT_VALUE, timeout: Union[float, object] = _MethodDefault._DEFAULT_VALUE, metadata: Sequence[Tuple[str, str]] = ()) → ListOperationsResponse

Lists operations that match the specified filter in the request.

Parameters:

• request (ListOperationsRequest) – The request object. Request message for ListOperations method.

• retry (google.api_core.retry.Retry) – Designation of what errors, if any, should be retried.

• timeout (float) – The timeout for this request.

• metadata (Sequence[Tuple[str, str]]) – Strings which should be sent along with the request as metadata.

Returns:

Response message for ListOperations method.

Return type:

ListOperationsResponse

static model_monitor_path(project: str, location: str, model_monitor: str) → str

Returns a fully-qualified model_monitor string.

static model_monitoring_job_path(project: str, location: str, model_monitor: str, model_monitoring_job: str) → str

Returns a fully-qualified model_monitoring_job string.

static model_path(project: str, location: str, model: str) → str

Returns a fully-qualified model string.

static parse_batch_prediction_job_path(path: str) → Dict[str, str]

Parses a batch_prediction_job path into its component segments.

static parse_common_billing_account_path(path: str) → Dict[str, str]

Parse a billing_account path into its component segments.

static parse_common_folder_path(path: str) → Dict[str, str]

Parse a folder path into its component segments.

static parse_common_location_path(path: str) → Dict[str, str]

Parse a location path into its component segments.

static parse_common_organization_path(path: str) → Dict[str, str]

Parse a organization path into its component segments.

static parse_common_project_path(path: str) → Dict[str, str]

Parse a project path into its component segments.

static parse_dataset_path(path: str) → Dict[str, str]

Parses a dataset path into its component segments.

static parse_endpoint_path(path: str) → Dict[str, str]

Parses a endpoint path into its component segments.

static parse_model_monitor_path(path: str) → Dict[str, str]

Parses a model_monitor path into its component segments.

static parse_model_monitoring_job_path(path: str) → Dict[str, str]

Parses a model_monitoring_job path into its component segments.

static parse_model_path(path: str) → Dict[str, str]

Parses a model path into its component segments.

static parse_reservation_path(path: str) → Dict[str, str]

Parses a reservation path into its component segments.

static parse_schedule_path(path: str) → Dict[str, str]

Parses a schedule path into its component segments.

static reservation_path(project_id_or_number: str, zone: str, reservation_name: str) → str

Returns a fully-qualified reservation string.

static schedule_path(project: str, location: str, schedule: str) → str

Returns a fully-qualified schedule string.

search_model_monitoring_alerts(request: Optional[Union[SearchModelMonitoringAlertsRequest, dict]] = None, *, model_monitor: Optional[str] = None, retry: Optional[Union[Retry, _MethodDefault]] = _MethodDefault._DEFAULT_VALUE, timeout: Union[float, object] = _MethodDefault._DEFAULT_VALUE, metadata: Sequence[Tuple[str, str]] = ()) → SearchModelMonitoringAlertsPager

Returns the Model Monitoring alerts.

# This snippet has been automatically generated and should be regarded as a
# code template only.
# It will require modifications to work:
# - It may require correct/in-range values for request initialization.
# - It may require specifying regional endpoints when creating the service
#   client as shown in:
#   https://googleapis.dev/python/google-api-core/latest/client_options.html
from google.cloud import aiplatform_v1beta1

def sample_search_model_monitoring_alerts():
    # Create a client
    client = aiplatform_v1beta1.ModelMonitoringServiceClient()

    # Initialize request argument(s)
    request = aiplatform_v1beta1.SearchModelMonitoringAlertsRequest(
        model_monitor="model_monitor_value",
    )

    # Make the request
    page_result = client.search_model_monitoring_alerts(request=request)

    # Handle the response
    for response in page_result:
        print(response)

Parameters:

• request (Union[google.cloud.aiplatform_v1beta1.types.SearchModelMonitoringAlertsRequest, dict]) – The request object. Request message for [ModelMonitoringService.SearchModelMonitoringAlerts][google.cloud.aiplatform.v1beta1.ModelMonitoringService.SearchModelMonitoringAlerts].

• model_monitor (str) –

  Required. ModelMonitor resource name. Format: projects/{project}/locations/{location}/modelMonitors/{model_monitor}

  This corresponds to the model_monitor field on the request instance; if request is provided, this should not be set.

• retry (google.api_core.retry.Retry) – Designation of what errors, if any, should be retried.

• timeout (float) – The timeout for this request.

• metadata (Sequence[Tuple[str, str]]) – Strings which should be sent along with the request as metadata.

Returns:

Response message for

[ModelMonitoringService.SearchModelMonitoringAlerts][google.cloud.aiplatform.v1beta1.ModelMonitoringService.SearchModelMonitoringAlerts].

Iterating over this object will yield results and resolve additional pages automatically.

Return type:

google.cloud.aiplatform_v1beta1.services.model_monitoring_service.pagers.SearchModelMonitoringAlertsPager

search_model_monitoring_stats(request: Optional[Union[SearchModelMonitoringStatsRequest, dict]] = None, *, model_monitor: Optional[str] = None, retry: Optional[Union[Retry, _MethodDefault]] = _MethodDefault._DEFAULT_VALUE, timeout: Union[float, object] = _MethodDefault._DEFAULT_VALUE, metadata: Sequence[Tuple[str, str]] = ()) → SearchModelMonitoringStatsPager

Searches Model Monitoring Stats generated within a given time window.

# This snippet has been automatically generated and should be regarded as a
# code template only.
# It will require modifications to work:
# - It may require correct/in-range values for request initialization.
# - It may require specifying regional endpoints when creating the service
#   client as shown in:
#   https://googleapis.dev/python/google-api-core/latest/client_options.html
from google.cloud import aiplatform_v1beta1

def sample_search_model_monitoring_stats():
    # Create a client
    client = aiplatform_v1beta1.ModelMonitoringServiceClient()

    # Initialize request argument(s)
    request = aiplatform_v1beta1.SearchModelMonitoringStatsRequest(
        model_monitor="model_monitor_value",
    )

    # Make the request
    page_result = client.search_model_monitoring_stats(request=request)

    # Handle the response
    for response in page_result:
        print(response)

Parameters:

• request (Union[google.cloud.aiplatform_v1beta1.types.SearchModelMonitoringStatsRequest, dict]) – The request object. Request message for [ModelMonitoringService.SearchModelMonitoringStats][google.cloud.aiplatform.v1beta1.ModelMonitoringService.SearchModelMonitoringStats].

• model_monitor (str) –

  Required. ModelMonitor resource name. Format: projects/{project}/locations/{location}/modelMonitors/{model_monitor}

  This corresponds to the model_monitor field on the request instance; if request is provided, this should not be set.

• retry (google.api_core.retry.Retry) – Designation of what errors, if any, should be retried.

• timeout (float) – The timeout for this request.

• metadata (Sequence[Tuple[str, str]]) – Strings which should be sent along with the request as metadata.

Returns:

Response message for

[ModelMonitoringService.SearchModelMonitoringStats][google.cloud.aiplatform.v1beta1.ModelMonitoringService.SearchModelMonitoringStats].

Iterating over this object will yield results and resolve additional pages automatically.

Return type:

google.cloud.aiplatform_v1beta1.services.model_monitoring_service.pagers.SearchModelMonitoringStatsPager

set_iam_policy(request: Optional[SetIamPolicyRequest] = None, *, retry: Optional[Union[Retry, _MethodDefault]] = _MethodDefault._DEFAULT_VALUE, timeout: Union[float, object] = _MethodDefault._DEFAULT_VALUE, metadata: Sequence[Tuple[str, str]] = ()) → Policy

Sets the IAM access control policy on the specified function.

Replaces any existing policy.

Parameters:

• request (SetIamPolicyRequest) – The request object. Request message for SetIamPolicy method.

• retry (google.api_core.retry.Retry) – Designation of what errors, if any, should be retried.

• timeout (float) – The timeout for this request.

• metadata (Sequence[Tuple[str, str]]) – Strings which should be sent along with the request as metadata.

Returns:

Defines an Identity and Access Management (IAM) policy. It is used to specify access control policies for Cloud Platform resources. A Policy is a collection of bindings. A binding binds one or more members to a single role. Members can be user accounts, service accounts, Google groups, and domains (such as G Suite). A role is a named list of permissions (defined by IAM or configured by users). A binding can optionally specify a condition, which is a logic expression that further constrains the role binding based on attributes about the request and/or target resource.

JSON Example

{
  "bindings": [
    {
      "role": "roles/resourcemanager.organizationAdmin",
      "members": [
        "user:mike@example.com",
        "group:admins@example.com",
        "domain:google.com",
        "serviceAccount:my-project-id@appspot.gserviceaccount.com"
      ]
    },
    {
      "role": "roles/resourcemanager.organizationViewer",
      "members": ["user:eve@example.com"],
      "condition": {
        "title": "expirable access",
        "description": "Does not grant access after Sep 2020",
        "expression": "request.time <
        timestamp('2020-10-01T00:00:00.000Z')",
      }
    }
  ]
}

YAML Example

bindings:
- members:
  - user:mike@example.com
  - group:admins@example.com
  - domain:google.com
  - serviceAccount:my-project-id@appspot.gserviceaccount.com
  role: roles/resourcemanager.organizationAdmin
- members:
  - user:eve@example.com
  role: roles/resourcemanager.organizationViewer
  condition:
    title: expirable access
    description: Does not grant access after Sep 2020
    expression: request.time < timestamp('2020-10-01T00:00:00.000Z')

For a description of IAM and its features, see the IAM developer’s guide.

Return type:

Policy

test_iam_permissions(request: Optional[TestIamPermissionsRequest] = None, *, retry: Optional[Union[Retry, _MethodDefault]] = _MethodDefault._DEFAULT_VALUE, timeout: Union[float, object] = _MethodDefault._DEFAULT_VALUE, metadata: Sequence[Tuple[str, str]] = ()) → TestIamPermissionsResponse

Tests the specified IAM permissions against the IAM access control

policy for a function.

If the function does not exist, this will return an empty set of permissions, not a NOT_FOUND error.

Parameters:

• request (TestIamPermissionsRequest) – The request object. Request message for TestIamPermissions method.

• retry (google.api_core.retry.Retry) – Designation of what errors, if any, should be retried.

• timeout (float) – The timeout for this request.

• metadata (Sequence[Tuple[str, str]]) – Strings which should be sent along with the request as metadata.

Returns:

Response message for TestIamPermissions method.

Return type:

TestIamPermissionsResponse

property transport: ModelMonitoringServiceTransport

Returns the transport used by the client instance.

Returns:

The transport used by the client

instance.

Return type:

ModelMonitoringServiceTransport

property universe_domain: str

Return the universe domain used by the client instance.

Returns:

The universe domain used by the client instance.

Return type:

str

update_model_monitor(request: Optional[Union[UpdateModelMonitorRequest, dict]] = None, *, model_monitor: Optional[ModelMonitor] = None, update_mask: Optional[FieldMask] = None, retry: Optional[Union[Retry, _MethodDefault]] = _MethodDefault._DEFAULT_VALUE, timeout: Union[float, object] = _MethodDefault._DEFAULT_VALUE, metadata: Sequence[Tuple[str, str]] = ()) → Operation

Updates a ModelMonitor.

# This snippet has been automatically generated and should be regarded as a
# code template only.
# It will require modifications to work:
# - It may require correct/in-range values for request initialization.
# - It may require specifying regional endpoints when creating the service
#   client as shown in:
#   https://googleapis.dev/python/google-api-core/latest/client_options.html
from google.cloud import aiplatform_v1beta1

def sample_update_model_monitor():
    # Create a client
    client = aiplatform_v1beta1.ModelMonitoringServiceClient()

    # Initialize request argument(s)
    request = aiplatform_v1beta1.UpdateModelMonitorRequest(
    )

    # Make the request
    operation = client.update_model_monitor(request=request)

    print("Waiting for operation to complete...")

    response = operation.result()

    # Handle the response
    print(response)

Parameters:

• request (Union[google.cloud.aiplatform_v1beta1.types.UpdateModelMonitorRequest, dict]) – The request object. Request message for [ModelMonitoringService.UpdateModelMonitor][google.cloud.aiplatform.v1beta1.ModelMonitoringService.UpdateModelMonitor].

• model_monitor (google.cloud.aiplatform_v1beta1.types.ModelMonitor) –

  Required. The model monitoring configuration which replaces the resource on the server.

  This corresponds to the model_monitor field on the request instance; if request is provided, this should not be set.

• update_mask (google.protobuf.field_mask_pb2.FieldMask) –

  Required. Mask specifying which fields to update.

  This corresponds to the update_mask field on the request instance; if request is provided, this should not be set.

• retry (google.api_core.retry.Retry) – Designation of what errors, if any, should be retried.

• timeout (float) – The timeout for this request.

• metadata (Sequence[Tuple[str, str]]) – Strings which should be sent along with the request as metadata.

Returns:

An object representing a long-running operation.

The result type for the operation will be google.cloud.aiplatform_v1beta1.types.ModelMonitor Vertex AI Model Monitoring Service serves as a central hub for the analysis

and visualization of data quality and performance related to models. ModelMonitor stands as a top level resource for overseeing your model monitoring tasks.

Return type:

google.api_core.operation.Operation

wait_operation(request: Optional[WaitOperationRequest] = None, *, retry: Optional[Union[Retry, _MethodDefault]] = _MethodDefault._DEFAULT_VALUE, timeout: Union[float, object] = _MethodDefault._DEFAULT_VALUE, metadata: Sequence[Tuple[str, str]] = ()) → Operation

Waits until the specified long-running operation is done or reaches at most a specified timeout, returning the latest state.

If the operation is already done, the latest state is immediately returned. If the timeout specified is greater than the default HTTP/RPC timeout, the HTTP/RPC timeout is used. If the server does not support this method, it returns google.rpc.Code.UNIMPLEMENTED.

Parameters:

• request (WaitOperationRequest) – The request object. Request message for WaitOperation method.

• retry (google.api_core.retry.Retry) – Designation of what errors, if any, should be retried.

• timeout (float) – The timeout for this request.

• metadata (Sequence[Tuple[str, str]]) – Strings which should be sent along with the request as metadata.

Returns:

An Operation object.

Return type:

Operation

class google.cloud.aiplatform_v1beta1.services.model_monitoring_service.pagers.ListModelMonitoringJobsAsyncPager(method: Callable[[...], Awaitable[ListModelMonitoringJobsResponse]], request: ListModelMonitoringJobsRequest, response: ListModelMonitoringJobsResponse, *, retry: Optional[Union[AsyncRetry, _MethodDefault]] = _MethodDefault._DEFAULT_VALUE, timeout: Union[float, object] = _MethodDefault._DEFAULT_VALUE, metadata: Sequence[Tuple[str, str]] = ())

A pager for iterating through list_model_monitoring_jobs requests.

This class thinly wraps an initial google.cloud.aiplatform_v1beta1.types.ListModelMonitoringJobsResponse object, and provides an __aiter__ method to iterate through its model_monitoring_jobs field.

If there are more pages, the __aiter__ method will make additional ListModelMonitoringJobs requests and continue to iterate through the model_monitoring_jobs field on the corresponding responses.

All the usual google.cloud.aiplatform_v1beta1.types.ListModelMonitoringJobsResponse attributes are available on the pager. If multiple requests are made, only the most recent response is retained, and thus used for attribute lookup.

Instantiates the pager.

Parameters:

• method (Callable) – The method that was originally called, and which instantiated this pager.

• request (google.cloud.aiplatform_v1beta1.types.ListModelMonitoringJobsRequest) – The initial request object.

• response (google.cloud.aiplatform_v1beta1.types.ListModelMonitoringJobsResponse) – The initial response object.

• retry (google.api_core.retry.AsyncRetry) – Designation of what errors, if any, should be retried.

• timeout (float) – The timeout for this request.

• metadata (Sequence[Tuple[str, str]]) – Strings which should be sent along with the request as metadata.

class google.cloud.aiplatform_v1beta1.services.model_monitoring_service.pagers.ListModelMonitoringJobsPager(method: Callable[[...], ListModelMonitoringJobsResponse], request: ListModelMonitoringJobsRequest, response: ListModelMonitoringJobsResponse, *, retry: Optional[Union[Retry, _MethodDefault]] = _MethodDefault._DEFAULT_VALUE, timeout: Union[float, object] = _MethodDefault._DEFAULT_VALUE, metadata: Sequence[Tuple[str, str]] = ())

A pager for iterating through list_model_monitoring_jobs requests.

This class thinly wraps an initial google.cloud.aiplatform_v1beta1.types.ListModelMonitoringJobsResponse object, and provides an __iter__ method to iterate through its model_monitoring_jobs field.

If there are more pages, the __iter__ method will make additional ListModelMonitoringJobs requests and continue to iterate through the model_monitoring_jobs field on the corresponding responses.

All the usual google.cloud.aiplatform_v1beta1.types.ListModelMonitoringJobsResponse attributes are available on the pager. If multiple requests are made, only the most recent response is retained, and thus used for attribute lookup.

Instantiate the pager.

Parameters:

• method (Callable) – The method that was originally called, and which instantiated this pager.

• request (google.cloud.aiplatform_v1beta1.types.ListModelMonitoringJobsRequest) – The initial request object.

• response (google.cloud.aiplatform_v1beta1.types.ListModelMonitoringJobsResponse) – The initial response object.

• retry (google.api_core.retry.Retry) – Designation of what errors, if any, should be retried.

• timeout (float) – The timeout for this request.

• metadata (Sequence[Tuple[str, str]]) – Strings which should be sent along with the request as metadata.

class google.cloud.aiplatform_v1beta1.services.model_monitoring_service.pagers.ListModelMonitorsAsyncPager(method: Callable[[...], Awaitable[ListModelMonitorsResponse]], request: ListModelMonitorsRequest, response: ListModelMonitorsResponse, *, retry: Optional[Union[AsyncRetry, _MethodDefault]] = _MethodDefault._DEFAULT_VALUE, timeout: Union[float, object] = _MethodDefault._DEFAULT_VALUE, metadata: Sequence[Tuple[str, str]] = ())

A pager for iterating through list_model_monitors requests.

This class thinly wraps an initial google.cloud.aiplatform_v1beta1.types.ListModelMonitorsResponse object, and provides an __aiter__ method to iterate through its model_monitors field.

If there are more pages, the __aiter__ method will make additional ListModelMonitors requests and continue to iterate through the model_monitors field on the corresponding responses.

All the usual google.cloud.aiplatform_v1beta1.types.ListModelMonitorsResponse attributes are available on the pager. If multiple requests are made, only the most recent response is retained, and thus used for attribute lookup.

Instantiates the pager.

Parameters:

• method (Callable) – The method that was originally called, and which instantiated this pager.

• request (google.cloud.aiplatform_v1beta1.types.ListModelMonitorsRequest) – The initial request object.

• response (google.cloud.aiplatform_v1beta1.types.ListModelMonitorsResponse) – The initial response object.

• retry (google.api_core.retry.AsyncRetry) – Designation of what errors, if any, should be retried.

• timeout (float) – The timeout for this request.

• metadata (Sequence[Tuple[str, str]]) – Strings which should be sent along with the request as metadata.

class google.cloud.aiplatform_v1beta1.services.model_monitoring_service.pagers.ListModelMonitorsPager(method: Callable[[...], ListModelMonitorsResponse], request: ListModelMonitorsRequest, response: ListModelMonitorsResponse, *, retry: Optional[Union[Retry, _MethodDefault]] = _MethodDefault._DEFAULT_VALUE, timeout: Union[float, object] = _MethodDefault._DEFAULT_VALUE, metadata: Sequence[Tuple[str, str]] = ())

A pager for iterating through list_model_monitors requests.

This class thinly wraps an initial google.cloud.aiplatform_v1beta1.types.ListModelMonitorsResponse object, and provides an __iter__ method to iterate through its model_monitors field.

If there are more pages, the __iter__ method will make additional ListModelMonitors requests and continue to iterate through the model_monitors field on the corresponding responses.

All the usual google.cloud.aiplatform_v1beta1.types.ListModelMonitorsResponse attributes are available on the pager. If multiple requests are made, only the most recent response is retained, and thus used for attribute lookup.

Instantiate the pager.

Parameters:

• method (Callable) – The method that was originally called, and which instantiated this pager.

• request (google.cloud.aiplatform_v1beta1.types.ListModelMonitorsRequest) – The initial request object.

• response (google.cloud.aiplatform_v1beta1.types.ListModelMonitorsResponse) – The initial response object.

• retry (google.api_core.retry.Retry) – Designation of what errors, if any, should be retried.

• timeout (float) – The timeout for this request.

• metadata (Sequence[Tuple[str, str]]) – Strings which should be sent along with the request as metadata.

class google.cloud.aiplatform_v1beta1.services.model_monitoring_service.pagers.SearchModelMonitoringAlertsAsyncPager(method: Callable[[...], Awaitable[SearchModelMonitoringAlertsResponse]], request: SearchModelMonitoringAlertsRequest, response: SearchModelMonitoringAlertsResponse, *, retry: Optional[Union[AsyncRetry, _MethodDefault]] = _MethodDefault._DEFAULT_VALUE, timeout: Union[float, object] = _MethodDefault._DEFAULT_VALUE, metadata: Sequence[Tuple[str, str]] = ())

A pager for iterating through search_model_monitoring_alerts requests.

This class thinly wraps an initial google.cloud.aiplatform_v1beta1.types.SearchModelMonitoringAlertsResponse object, and provides an __aiter__ method to iterate through its model_monitoring_alerts field.

If there are more pages, the __aiter__ method will make additional SearchModelMonitoringAlerts requests and continue to iterate through the model_monitoring_alerts field on the corresponding responses.

All the usual google.cloud.aiplatform_v1beta1.types.SearchModelMonitoringAlertsResponse attributes are available on the pager. If multiple requests are made, only the most recent response is retained, and thus used for attribute lookup.

Instantiates the pager.

Parameters:

• method (Callable) – The method that was originally called, and which instantiated this pager.

• request (google.cloud.aiplatform_v1beta1.types.SearchModelMonitoringAlertsRequest) – The initial request object.

• response (google.cloud.aiplatform_v1beta1.types.SearchModelMonitoringAlertsResponse) – The initial response object.

• retry (google.api_core.retry.AsyncRetry) – Designation of what errors, if any, should be retried.

• timeout (float) – The timeout for this request.

• metadata (Sequence[Tuple[str, str]]) – Strings which should be sent along with the request as metadata.

class google.cloud.aiplatform_v1beta1.services.model_monitoring_service.pagers.SearchModelMonitoringAlertsPager(method: Callable[[...], SearchModelMonitoringAlertsResponse], request: SearchModelMonitoringAlertsRequest, response: SearchModelMonitoringAlertsResponse, *, retry: Optional[Union[Retry, _MethodDefault]] = _MethodDefault._DEFAULT_VALUE, timeout: Union[float, object] = _MethodDefault._DEFAULT_VALUE, metadata: Sequence[Tuple[str, str]] = ())

A pager for iterating through search_model_monitoring_alerts requests.

This class thinly wraps an initial google.cloud.aiplatform_v1beta1.types.SearchModelMonitoringAlertsResponse object, and provides an __iter__ method to iterate through its model_monitoring_alerts field.

If there are more pages, the __iter__ method will make additional SearchModelMonitoringAlerts requests and continue to iterate through the model_monitoring_alerts field on the corresponding responses.

All the usual google.cloud.aiplatform_v1beta1.types.SearchModelMonitoringAlertsResponse attributes are available on the pager. If multiple requests are made, only the most recent response is retained, and thus used for attribute lookup.

Instantiate the pager.

Parameters:

• method (Callable) – The method that was originally called, and which instantiated this pager.

• request (google.cloud.aiplatform_v1beta1.types.SearchModelMonitoringAlertsRequest) – The initial request object.

• response (google.cloud.aiplatform_v1beta1.types.SearchModelMonitoringAlertsResponse) – The initial response object.

• retry (google.api_core.retry.Retry) – Designation of what errors, if any, should be retried.

• timeout (float) – The timeout for this request.

• metadata (Sequence[Tuple[str, str]]) – Strings which should be sent along with the request as metadata.

class google.cloud.aiplatform_v1beta1.services.model_monitoring_service.pagers.SearchModelMonitoringStatsAsyncPager(method: Callable[[...], Awaitable[SearchModelMonitoringStatsResponse]], request: SearchModelMonitoringStatsRequest, response: SearchModelMonitoringStatsResponse, *, retry: Optional[Union[AsyncRetry, _MethodDefault]] = _MethodDefault._DEFAULT_VALUE, timeout: Union[float, object] = _MethodDefault._DEFAULT_VALUE, metadata: Sequence[Tuple[str, str]] = ())

A pager for iterating through search_model_monitoring_stats requests.

This class thinly wraps an initial google.cloud.aiplatform_v1beta1.types.SearchModelMonitoringStatsResponse object, and provides an __aiter__ method to iterate through its monitoring_stats field.

If there are more pages, the __aiter__ method will make additional SearchModelMonitoringStats requests and continue to iterate through the monitoring_stats field on the corresponding responses.

All the usual google.cloud.aiplatform_v1beta1.types.SearchModelMonitoringStatsResponse attributes are available on the pager. If multiple requests are made, only the most recent response is retained, and thus used for attribute lookup.

Instantiates the pager.

Parameters:

• method (Callable) – The method that was originally called, and which instantiated this pager.

• request (google.cloud.aiplatform_v1beta1.types.SearchModelMonitoringStatsRequest) – The initial request object.

• response (google.cloud.aiplatform_v1beta1.types.SearchModelMonitoringStatsResponse) – The initial response object.

• retry (google.api_core.retry.AsyncRetry) – Designation of what errors, if any, should be retried.

• timeout (float) – The timeout for this request.

• metadata (Sequence[Tuple[str, str]]) – Strings which should be sent along with the request as metadata.

class google.cloud.aiplatform_v1beta1.services.model_monitoring_service.pagers.SearchModelMonitoringStatsPager(method: Callable[[...], SearchModelMonitoringStatsResponse], request: SearchModelMonitoringStatsRequest, response: SearchModelMonitoringStatsResponse, *, retry: Optional[Union[Retry, _MethodDefault]] = _MethodDefault._DEFAULT_VALUE, timeout: Union[float, object] = _MethodDefault._DEFAULT_VALUE, metadata: Sequence[Tuple[str, str]] = ())

A pager for iterating through search_model_monitoring_stats requests.

This class thinly wraps an initial google.cloud.aiplatform_v1beta1.types.SearchModelMonitoringStatsResponse object, and provides an __iter__ method to iterate through its monitoring_stats field.

If there are more pages, the __iter__ method will make additional SearchModelMonitoringStats requests and continue to iterate through the monitoring_stats field on the corresponding responses.

All the usual google.cloud.aiplatform_v1beta1.types.SearchModelMonitoringStatsResponse attributes are available on the pager. If multiple requests are made, only the most recent response is retained, and thus used for attribute lookup.

Instantiate the pager.

Parameters:

• method (Callable) – The method that was originally called, and which instantiated this pager.

• request (google.cloud.aiplatform_v1beta1.types.SearchModelMonitoringStatsRequest) – The initial request object.

• response (google.cloud.aiplatform_v1beta1.types.SearchModelMonitoringStatsResponse) – The initial response object.

• retry (google.api_core.retry.Retry) – Designation of what errors, if any, should be retried.

• timeout (float) – The timeout for this request.

• metadata (Sequence[Tuple[str, str]]) – Strings which should be sent along with the request as metadata.