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.

EndpointService

class google.cloud.aiplatform_v1.services.endpoint_service.EndpointServiceAsyncClient(*, credentials: ~typing.Optional[~google.auth.credentials.Credentials] = None, transport: ~typing.Optional[~typing.Union[str, ~google.cloud.aiplatform_v1.services.endpoint_service.transports.base.EndpointServiceTransport, ~typing.Callable[[...], ~google.cloud.aiplatform_v1.services.endpoint_service.transports.base.EndpointServiceTransport]]] = '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>)[source]

A service for managing Vertex AI’s Endpoints.

Instantiates the endpoint 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,EndpointServiceTransport,Callable[..., EndpointServiceTransport]]]) – 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 EndpointServiceTransport 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

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[source]

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_endpoint(request: Optional[Union[CreateEndpointRequest, dict]] = None, *, parent: Optional[str] = None, endpoint: Optional[Endpoint] = None, endpoint_id: Optional[str] = None, retry: Optional[Union[AsyncRetry, _MethodDefault]] = _MethodDefault._DEFAULT_VALUE, timeout: Union[float, object] = _MethodDefault._DEFAULT_VALUE, metadata: Sequence[Tuple[str, str]] = ()) → AsyncOperation[source]

Creates an Endpoint.

# 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_v1

async def sample_create_endpoint():
    # Create a client
    client = aiplatform_v1.EndpointServiceAsyncClient()

    # Initialize request argument(s)
    endpoint = aiplatform_v1.Endpoint()
    endpoint.display_name = "display_name_value"

    request = aiplatform_v1.CreateEndpointRequest(
        parent="parent_value",
        endpoint=endpoint,
    )

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

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

    response = (await operation).result()

    # Handle the response
    print(response)
Parameters:

  • request (Optional[Union[google.cloud.aiplatform_v1.types.CreateEndpointRequest, dict]]) – The request object. Request message for [EndpointService.CreateEndpoint][google.cloud.aiplatform.v1.EndpointService.CreateEndpoint].

  • parent (str) –

    Required. The resource name of the Location to create the Endpoint 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.

  • endpoint (google.cloud.aiplatform_v1.types.Endpoint) – Required. The Endpoint to create. This corresponds to the endpoint field on the request instance; if request is provided, this should not be set.

  • endpoint_id (str) –

    Immutable. The ID to use for endpoint, which will become the final component of the endpoint resource name. If not provided, Vertex AI will generate a value for this ID.

    If the first character is a letter, this value may be up to 63 characters, and valid characters are [a-z0-9-]. The last character must be a letter or number.

    If the first character is a number, this value may be up to 9 characters, and valid characters are [0-9] with no leading zeros.

    When using HTTP/JSON, this field is populated based on a query string argument, such as ?endpoint_id=12345. This is the fallback for fields that are not included in either the URI or the body.

    This corresponds to the endpoint_id 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_v1.types.Endpoint Models are deployed into it, and afterwards Endpoint is called to obtain

predictions and explanations.

Return type:

google.api_core.operation_async.AsyncOperation

async delete_endpoint(request: Optional[Union[DeleteEndpointRequest, 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[source]

Deletes an Endpoint.

# 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_v1

async def sample_delete_endpoint():
    # Create a client
    client = aiplatform_v1.EndpointServiceAsyncClient()

    # Initialize request argument(s)
    request = aiplatform_v1.DeleteEndpointRequest(
        name="name_value",
    )

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

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

    response = (await operation).result()

    # Handle the response
    print(response)
Parameters:

  • request (Optional[Union[google.cloud.aiplatform_v1.types.DeleteEndpointRequest, dict]]) – The request object. Request message for [EndpointService.DeleteEndpoint][google.cloud.aiplatform.v1.EndpointService.DeleteEndpoint].

  • name (str) –

    Required. The name of the Endpoint resource to be deleted. Format: projects/{project}/locations/{location}/endpoints/{endpoint}

    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[source]

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

async deploy_model(request: Optional[Union[DeployModelRequest, dict]] = None, *, endpoint: Optional[str] = None, deployed_model: Optional[DeployedModel] = None, traffic_split: Optional[MutableMapping[str, int]] = None, retry: Optional[Union[AsyncRetry, _MethodDefault]] = _MethodDefault._DEFAULT_VALUE, timeout: Union[float, object] = _MethodDefault._DEFAULT_VALUE, metadata: Sequence[Tuple[str, str]] = ()) → AsyncOperation[source]

Deploys a Model into this Endpoint, creating a DeployedModel within it.

# 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_v1

async def sample_deploy_model():
    # Create a client
    client = aiplatform_v1.EndpointServiceAsyncClient()

    # Initialize request argument(s)
    deployed_model = aiplatform_v1.DeployedModel()
    deployed_model.dedicated_resources.min_replica_count = 1803
    deployed_model.model = "model_value"

    request = aiplatform_v1.DeployModelRequest(
        endpoint="endpoint_value",
        deployed_model=deployed_model,
    )

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

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

    response = (await operation).result()

    # Handle the response
    print(response)
Parameters:

  • request (Optional[Union[google.cloud.aiplatform_v1.types.DeployModelRequest, dict]]) – The request object. Request message for [EndpointService.DeployModel][google.cloud.aiplatform.v1.EndpointService.DeployModel].

  • endpoint (str) –

    Required. The name of the Endpoint resource into which to deploy a Model. Format: projects/{project}/locations/{location}/endpoints/{endpoint}

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

  • deployed_model (google.cloud.aiplatform_v1.types.DeployedModel) –

    Required. The DeployedModel to be created within the Endpoint. Note that [Endpoint.traffic_split][google.cloud.aiplatform.v1.Endpoint.traffic_split] must be updated for the DeployedModel to start receiving traffic, either as part of this call, or via [EndpointService.UpdateEndpoint][google.cloud.aiplatform.v1.EndpointService.UpdateEndpoint].

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

  • traffic_split (MutableMapping[str, int]) –

    A map from a DeployedModel’s ID to the percentage of this Endpoint’s traffic that should be forwarded to that DeployedModel.

    If this field is non-empty, then the Endpoint’s [traffic_split][google.cloud.aiplatform.v1.Endpoint.traffic_split] will be overwritten with it. To refer to the ID of the just being deployed Model, a “0” should be used, and the actual ID of the new DeployedModel will be filled in its place by this method. The traffic percentage values must add up to 100.

    If this field is empty, then the Endpoint’s [traffic_split][google.cloud.aiplatform.v1.Endpoint.traffic_split] is not updated.

    This corresponds to the traffic_split 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_v1.types.DeployModelResponse Response message for

[EndpointService.DeployModel][google.cloud.aiplatform.v1.EndpointService.DeployModel].

Return type:

google.api_core.operation_async.AsyncOperation

static deployment_resource_pool_path(project: str, location: str, deployment_resource_pool: str) → str

Returns a fully-qualified deployment_resource_pool string.

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)[source]
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:

EndpointServiceAsyncClient

classmethod from_service_account_info(info: dict, *args, **kwargs)[source]
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:

EndpointServiceAsyncClient

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:

EndpointServiceAsyncClient

async get_endpoint(request: Optional[Union[GetEndpointRequest, 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]] = ()) → Endpoint[source]

Gets an Endpoint.

# 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_v1

async def sample_get_endpoint():
    # Create a client
    client = aiplatform_v1.EndpointServiceAsyncClient()

    # Initialize request argument(s)
    request = aiplatform_v1.GetEndpointRequest(
        name="name_value",
    )

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

    # Handle the response
    print(response)
Parameters:

  • request (Optional[Union[google.cloud.aiplatform_v1.types.GetEndpointRequest, dict]]) – The request object. Request message for [EndpointService.GetEndpoint][google.cloud.aiplatform.v1.EndpointService.GetEndpoint]

  • name (str) –

    Required. The name of the Endpoint resource. Format: projects/{project}/locations/{location}/endpoints/{endpoint}

    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:

Models are deployed into it, and afterwards Endpoint is called to obtain predictions and explanations.

Return type:

google.cloud.aiplatform_v1.types.Endpoint

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[source]

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[source]

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

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

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[source]

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[EndpointServiceTransport]

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_endpoints(request: Optional[Union[ListEndpointsRequest, 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]] = ()) → ListEndpointsAsyncPager[source]

Lists Endpoints 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_v1

async def sample_list_endpoints():
    # Create a client
    client = aiplatform_v1.EndpointServiceAsyncClient()

    # Initialize request argument(s)
    request = aiplatform_v1.ListEndpointsRequest(
        parent="parent_value",
    )

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

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

  • request (Optional[Union[google.cloud.aiplatform_v1.types.ListEndpointsRequest, dict]]) – The request object. Request message for [EndpointService.ListEndpoints][google.cloud.aiplatform.v1.EndpointService.ListEndpoints].

  • parent (str) –

    Required. The resource name of the Location from which to list the Endpoints. 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

[EndpointService.ListEndpoints][google.cloud.aiplatform.v1.EndpointService.ListEndpoints].

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

Return type:

google.cloud.aiplatform_v1.services.endpoint_service.pagers.ListEndpointsAsyncPager

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[source]

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_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[source]

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_deployment_monitoring_job_path(project: str, location: str, model_deployment_monitoring_job: str) → str

Returns a fully-qualified model_deployment_monitoring_job string.

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

Returns a fully-qualified model string.

async mutate_deployed_model(request: Optional[Union[MutateDeployedModelRequest, dict]] = None, *, endpoint: Optional[str] = None, deployed_model: Optional[DeployedModel] = 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[source]

Updates an existing deployed model. Updatable fields include min_replica_count, max_replica_count, autoscaling_metric_specs, disable_container_logging (v1 only), and enable_container_logging (v1beta1 only).

# 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_v1

async def sample_mutate_deployed_model():
    # Create a client
    client = aiplatform_v1.EndpointServiceAsyncClient()

    # Initialize request argument(s)
    deployed_model = aiplatform_v1.DeployedModel()
    deployed_model.dedicated_resources.min_replica_count = 1803
    deployed_model.model = "model_value"

    request = aiplatform_v1.MutateDeployedModelRequest(
        endpoint="endpoint_value",
        deployed_model=deployed_model,
    )

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

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

    response = (await operation).result()

    # Handle the response
    print(response)
Parameters:

  • request (Optional[Union[google.cloud.aiplatform_v1.types.MutateDeployedModelRequest, dict]]) – The request object. Request message for [EndpointService.MutateDeployedModel][google.cloud.aiplatform.v1.EndpointService.MutateDeployedModel].

  • endpoint (str) –

    Required. The name of the Endpoint resource into which to mutate a DeployedModel. Format: projects/{project}/locations/{location}/endpoints/{endpoint}

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

  • deployed_model (google.cloud.aiplatform_v1.types.DeployedModel) –

    Required. The DeployedModel to be mutated within the Endpoint. Only the following fields can be mutated:

    • min_replica_count in either [DedicatedResources][google.cloud.aiplatform.v1.DedicatedResources] or [AutomaticResources][google.cloud.aiplatform.v1.AutomaticResources]

    • max_replica_count in either [DedicatedResources][google.cloud.aiplatform.v1.DedicatedResources] or [AutomaticResources][google.cloud.aiplatform.v1.AutomaticResources]

    • [autoscaling_metric_specs][google.cloud.aiplatform.v1.DedicatedResources.autoscaling_metric_specs]

    • disable_container_logging (v1 only)

    • enable_container_logging (v1beta1 only)

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

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

    Required. The update mask applies to the resource. See [google.protobuf.FieldMask][google.protobuf.FieldMask].

    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_v1.types.MutateDeployedModelResponse Response message for

[EndpointService.MutateDeployedModel][google.cloud.aiplatform.v1.EndpointService.MutateDeployedModel].

Return type:

google.api_core.operation_async.AsyncOperation

static network_path(project: str, network: str) → str

Returns a fully-qualified network string.

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_deployment_resource_pool_path(path: str) → Dict[str, str]

Parses a deployment_resource_pool 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_deployment_monitoring_job_path(path: str) → Dict[str, str]

Parses a model_deployment_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_network_path(path: str) → Dict[str, str]

Parses a network path into its component segments.

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

Parses a reservation 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.

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[source]

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[source]
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: EndpointServiceTransport

Returns the transport used by the client instance.

Returns:

The transport used by the client instance.

Return type:

EndpointServiceTransport

async undeploy_model(request: Optional[Union[UndeployModelRequest, dict]] = None, *, endpoint: Optional[str] = None, deployed_model_id: Optional[str] = None, traffic_split: Optional[MutableMapping[str, int]] = None, retry: Optional[Union[AsyncRetry, _MethodDefault]] = _MethodDefault._DEFAULT_VALUE, timeout: Union[float, object] = _MethodDefault._DEFAULT_VALUE, metadata: Sequence[Tuple[str, str]] = ()) → AsyncOperation[source]

Undeploys a Model from an Endpoint, removing a DeployedModel from it, and freeing all resources it’s using.

# 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_v1

async def sample_undeploy_model():
    # Create a client
    client = aiplatform_v1.EndpointServiceAsyncClient()

    # Initialize request argument(s)
    request = aiplatform_v1.UndeployModelRequest(
        endpoint="endpoint_value",
        deployed_model_id="deployed_model_id_value",
    )

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

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

    response = (await operation).result()

    # Handle the response
    print(response)
Parameters:

  • request (Optional[Union[google.cloud.aiplatform_v1.types.UndeployModelRequest, dict]]) – The request object. Request message for [EndpointService.UndeployModel][google.cloud.aiplatform.v1.EndpointService.UndeployModel].

  • endpoint (str) –

    Required. The name of the Endpoint resource from which to undeploy a Model. Format: projects/{project}/locations/{location}/endpoints/{endpoint}

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

  • deployed_model_id (str) –

    Required. The ID of the DeployedModel to be undeployed from the Endpoint.

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

  • traffic_split (MutableMapping[str, int]) –

    If this field is provided, then the Endpoint’s [traffic_split][google.cloud.aiplatform.v1.Endpoint.traffic_split] will be overwritten with it. If last DeployedModel is being undeployed from the Endpoint, the [Endpoint.traffic_split] will always end up empty when this call returns. A DeployedModel will be successfully undeployed only if it doesn’t have any traffic assigned to it when this method executes, or if this field unassigns any traffic to it.

    This corresponds to the traffic_split 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_v1.types.UndeployModelResponse Response message for

[EndpointService.UndeployModel][google.cloud.aiplatform.v1.EndpointService.UndeployModel].

Return type:

google.api_core.operation_async.AsyncOperation

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_endpoint(request: Optional[Union[UpdateEndpointRequest, dict]] = None, *, endpoint: Optional[Endpoint] = 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]] = ()) → Endpoint[source]

Updates an Endpoint.

# 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_v1

async def sample_update_endpoint():
    # Create a client
    client = aiplatform_v1.EndpointServiceAsyncClient()

    # Initialize request argument(s)
    endpoint = aiplatform_v1.Endpoint()
    endpoint.display_name = "display_name_value"

    request = aiplatform_v1.UpdateEndpointRequest(
        endpoint=endpoint,
    )

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

    # Handle the response
    print(response)
Parameters:

  • request (Optional[Union[google.cloud.aiplatform_v1.types.UpdateEndpointRequest, dict]]) – The request object. Request message for [EndpointService.UpdateEndpoint][google.cloud.aiplatform.v1.EndpointService.UpdateEndpoint].

  • endpoint (google.cloud.aiplatform_v1.types.Endpoint) –

    Required. The Endpoint which replaces the resource on the server.

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

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

    Required. The update mask applies to the resource. See [google.protobuf.FieldMask][google.protobuf.FieldMask].

    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:

Models are deployed into it, and afterwards Endpoint is called to obtain predictions and explanations.

Return type:

google.cloud.aiplatform_v1.types.Endpoint

async update_endpoint_long_running(request: Optional[Union[UpdateEndpointLongRunningRequest, dict]] = None, *, endpoint: Optional[Endpoint] = None, retry: Optional[Union[AsyncRetry, _MethodDefault]] = _MethodDefault._DEFAULT_VALUE, timeout: Union[float, object] = _MethodDefault._DEFAULT_VALUE, metadata: Sequence[Tuple[str, str]] = ()) → AsyncOperation[source]

Updates an Endpoint with a long running operation.

# 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_v1

async def sample_update_endpoint_long_running():
    # Create a client
    client = aiplatform_v1.EndpointServiceAsyncClient()

    # Initialize request argument(s)
    endpoint = aiplatform_v1.Endpoint()
    endpoint.display_name = "display_name_value"

    request = aiplatform_v1.UpdateEndpointLongRunningRequest(
        endpoint=endpoint,
    )

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

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

    response = (await operation).result()

    # Handle the response
    print(response)
Parameters:

  • request (Optional[Union[google.cloud.aiplatform_v1.types.UpdateEndpointLongRunningRequest, dict]]) – The request object. Request message for [EndpointService.UpdateEndpointLongRunning][google.cloud.aiplatform.v1.EndpointService.UpdateEndpointLongRunning].

  • endpoint (google.cloud.aiplatform_v1.types.Endpoint) –

    Required. The Endpoint which replaces the resource on the server. Currently we only support updating the client_connection_config field, all the other fields’ update will be blocked.

    This corresponds to the endpoint 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_v1.types.Endpoint Models are deployed into it, and afterwards Endpoint is called to obtain

predictions and explanations.

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[source]

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_v1.services.endpoint_service.EndpointServiceClient(*, credentials: ~typing.Optional[~google.auth.credentials.Credentials] = None, transport: ~typing.Optional[~typing.Union[str, ~google.cloud.aiplatform_v1.services.endpoint_service.transports.base.EndpointServiceTransport, ~typing.Callable[[...], ~google.cloud.aiplatform_v1.services.endpoint_service.transports.base.EndpointServiceTransport]]] = 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>)[source]

A service for managing Vertex AI’s Endpoints.

Instantiates the endpoint 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,EndpointServiceTransport,Callable[..., EndpointServiceTransport]]]) – 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 EndpointServiceTransport 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)[source]

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

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[source]

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[source]

Returns a fully-qualified billing_account string.

static common_folder_path(folder: str) → str[source]

Returns a fully-qualified folder string.

static common_location_path(project: str, location: str) → str[source]

Returns a fully-qualified location string.

static common_organization_path(organization: str) → str[source]

Returns a fully-qualified organization string.

static common_project_path(project: str) → str[source]

Returns a fully-qualified project string.

create_endpoint(request: Optional[Union[CreateEndpointRequest, dict]] = None, *, parent: Optional[str] = None, endpoint: Optional[Endpoint] = None, endpoint_id: Optional[str] = None, retry: Optional[Union[Retry, _MethodDefault]] = _MethodDefault._DEFAULT_VALUE, timeout: Union[float, object] = _MethodDefault._DEFAULT_VALUE, metadata: Sequence[Tuple[str, str]] = ()) → Operation[source]

Creates an Endpoint.

# 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_v1

def sample_create_endpoint():
    # Create a client
    client = aiplatform_v1.EndpointServiceClient()

    # Initialize request argument(s)
    endpoint = aiplatform_v1.Endpoint()
    endpoint.display_name = "display_name_value"

    request = aiplatform_v1.CreateEndpointRequest(
        parent="parent_value",
        endpoint=endpoint,
    )

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

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

    response = operation.result()

    # Handle the response
    print(response)
Parameters:

  • request (Union[google.cloud.aiplatform_v1.types.CreateEndpointRequest, dict]) – The request object. Request message for [EndpointService.CreateEndpoint][google.cloud.aiplatform.v1.EndpointService.CreateEndpoint].

  • parent (str) –

    Required. The resource name of the Location to create the Endpoint 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.

  • endpoint (google.cloud.aiplatform_v1.types.Endpoint) – Required. The Endpoint to create. This corresponds to the endpoint field on the request instance; if request is provided, this should not be set.

  • endpoint_id (str) –

    Immutable. The ID to use for endpoint, which will become the final component of the endpoint resource name. If not provided, Vertex AI will generate a value for this ID.

    If the first character is a letter, this value may be up to 63 characters, and valid characters are [a-z0-9-]. The last character must be a letter or number.

    If the first character is a number, this value may be up to 9 characters, and valid characters are [0-9] with no leading zeros.

    When using HTTP/JSON, this field is populated based on a query string argument, such as ?endpoint_id=12345. This is the fallback for fields that are not included in either the URI or the body.

    This corresponds to the endpoint_id 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_v1.types.Endpoint Models are deployed into it, and afterwards Endpoint is called to obtain

predictions and explanations.

Return type:

google.api_core.operation.Operation

delete_endpoint(request: Optional[Union[DeleteEndpointRequest, 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[source]

Deletes an Endpoint.

# 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_v1

def sample_delete_endpoint():
    # Create a client
    client = aiplatform_v1.EndpointServiceClient()

    # Initialize request argument(s)
    request = aiplatform_v1.DeleteEndpointRequest(
        name="name_value",
    )

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

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

    response = operation.result()

    # Handle the response
    print(response)
Parameters:

  • request (Union[google.cloud.aiplatform_v1.types.DeleteEndpointRequest, dict]) – The request object. Request message for [EndpointService.DeleteEndpoint][google.cloud.aiplatform.v1.EndpointService.DeleteEndpoint].

  • name (str) –

    Required. The name of the Endpoint resource to be deleted. Format: projects/{project}/locations/{location}/endpoints/{endpoint}

    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[source]

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

deploy_model(request: Optional[Union[DeployModelRequest, dict]] = None, *, endpoint: Optional[str] = None, deployed_model: Optional[DeployedModel] = None, traffic_split: Optional[MutableMapping[str, int]] = None, retry: Optional[Union[Retry, _MethodDefault]] = _MethodDefault._DEFAULT_VALUE, timeout: Union[float, object] = _MethodDefault._DEFAULT_VALUE, metadata: Sequence[Tuple[str, str]] = ()) → Operation[source]

Deploys a Model into this Endpoint, creating a DeployedModel within it.

# 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_v1

def sample_deploy_model():
    # Create a client
    client = aiplatform_v1.EndpointServiceClient()

    # Initialize request argument(s)
    deployed_model = aiplatform_v1.DeployedModel()
    deployed_model.dedicated_resources.min_replica_count = 1803
    deployed_model.model = "model_value"

    request = aiplatform_v1.DeployModelRequest(
        endpoint="endpoint_value",
        deployed_model=deployed_model,
    )

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

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

    response = operation.result()

    # Handle the response
    print(response)
Parameters:

  • request (Union[google.cloud.aiplatform_v1.types.DeployModelRequest, dict]) – The request object. Request message for [EndpointService.DeployModel][google.cloud.aiplatform.v1.EndpointService.DeployModel].

  • endpoint (str) –

    Required. The name of the Endpoint resource into which to deploy a Model. Format: projects/{project}/locations/{location}/endpoints/{endpoint}

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

  • deployed_model (google.cloud.aiplatform_v1.types.DeployedModel) –

    Required. The DeployedModel to be created within the Endpoint. Note that [Endpoint.traffic_split][google.cloud.aiplatform.v1.Endpoint.traffic_split] must be updated for the DeployedModel to start receiving traffic, either as part of this call, or via [EndpointService.UpdateEndpoint][google.cloud.aiplatform.v1.EndpointService.UpdateEndpoint].

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

  • traffic_split (MutableMapping[str, int]) –

    A map from a DeployedModel’s ID to the percentage of this Endpoint’s traffic that should be forwarded to that DeployedModel.

    If this field is non-empty, then the Endpoint’s [traffic_split][google.cloud.aiplatform.v1.Endpoint.traffic_split] will be overwritten with it. To refer to the ID of the just being deployed Model, a “0” should be used, and the actual ID of the new DeployedModel will be filled in its place by this method. The traffic percentage values must add up to 100.

    If this field is empty, then the Endpoint’s [traffic_split][google.cloud.aiplatform.v1.Endpoint.traffic_split] is not updated.

    This corresponds to the traffic_split 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_v1.types.DeployModelResponse Response message for

[EndpointService.DeployModel][google.cloud.aiplatform.v1.EndpointService.DeployModel].

Return type:

google.api_core.operation.Operation

static deployment_resource_pool_path(project: str, location: str, deployment_resource_pool: str) → str[source]

Returns a fully-qualified deployment_resource_pool string.

static endpoint_path(project: str, location: str, endpoint: str) → str[source]

Returns a fully-qualified endpoint string.

classmethod from_service_account_file(filename: str, *args, **kwargs)[source]
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:

EndpointServiceClient

classmethod from_service_account_info(info: dict, *args, **kwargs)[source]
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:

EndpointServiceClient

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:

EndpointServiceClient

get_endpoint(request: Optional[Union[GetEndpointRequest, 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]] = ()) → Endpoint[source]

Gets an Endpoint.

# 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_v1

def sample_get_endpoint():
    # Create a client
    client = aiplatform_v1.EndpointServiceClient()

    # Initialize request argument(s)
    request = aiplatform_v1.GetEndpointRequest(
        name="name_value",
    )

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

    # Handle the response
    print(response)
Parameters:

  • request (Union[google.cloud.aiplatform_v1.types.GetEndpointRequest, dict]) – The request object. Request message for [EndpointService.GetEndpoint][google.cloud.aiplatform.v1.EndpointService.GetEndpoint]

  • name (str) –

    Required. The name of the Endpoint resource. Format: projects/{project}/locations/{location}/endpoints/{endpoint}

    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:

Models are deployed into it, and afterwards Endpoint is called to obtain predictions and explanations.

Return type:

google.cloud.aiplatform_v1.types.Endpoint

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[source]

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[source]

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

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

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[source]

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_endpoints(request: Optional[Union[ListEndpointsRequest, 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]] = ()) → ListEndpointsPager[source]

Lists Endpoints 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_v1

def sample_list_endpoints():
    # Create a client
    client = aiplatform_v1.EndpointServiceClient()

    # Initialize request argument(s)
    request = aiplatform_v1.ListEndpointsRequest(
        parent="parent_value",
    )

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

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

  • request (Union[google.cloud.aiplatform_v1.types.ListEndpointsRequest, dict]) – The request object. Request message for [EndpointService.ListEndpoints][google.cloud.aiplatform.v1.EndpointService.ListEndpoints].

  • parent (str) –

    Required. The resource name of the Location from which to list the Endpoints. 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

[EndpointService.ListEndpoints][google.cloud.aiplatform.v1.EndpointService.ListEndpoints].

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

Return type:

google.cloud.aiplatform_v1.services.endpoint_service.pagers.ListEndpointsPager

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[source]

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_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[source]

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_deployment_monitoring_job_path(project: str, location: str, model_deployment_monitoring_job: str) → str[source]

Returns a fully-qualified model_deployment_monitoring_job string.

static model_path(project: str, location: str, model: str) → str[source]

Returns a fully-qualified model string.

mutate_deployed_model(request: Optional[Union[MutateDeployedModelRequest, dict]] = None, *, endpoint: Optional[str] = None, deployed_model: Optional[DeployedModel] = 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[source]

Updates an existing deployed model. Updatable fields include min_replica_count, max_replica_count, autoscaling_metric_specs, disable_container_logging (v1 only), and enable_container_logging (v1beta1 only).

# 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_v1

def sample_mutate_deployed_model():
    # Create a client
    client = aiplatform_v1.EndpointServiceClient()

    # Initialize request argument(s)
    deployed_model = aiplatform_v1.DeployedModel()
    deployed_model.dedicated_resources.min_replica_count = 1803
    deployed_model.model = "model_value"

    request = aiplatform_v1.MutateDeployedModelRequest(
        endpoint="endpoint_value",
        deployed_model=deployed_model,
    )

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

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

    response = operation.result()

    # Handle the response
    print(response)
Parameters:

  • request (Union[google.cloud.aiplatform_v1.types.MutateDeployedModelRequest, dict]) – The request object. Request message for [EndpointService.MutateDeployedModel][google.cloud.aiplatform.v1.EndpointService.MutateDeployedModel].

  • endpoint (str) –

    Required. The name of the Endpoint resource into which to mutate a DeployedModel. Format: projects/{project}/locations/{location}/endpoints/{endpoint}

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

  • deployed_model (google.cloud.aiplatform_v1.types.DeployedModel) –

    Required. The DeployedModel to be mutated within the Endpoint. Only the following fields can be mutated:

    • min_replica_count in either [DedicatedResources][google.cloud.aiplatform.v1.DedicatedResources] or [AutomaticResources][google.cloud.aiplatform.v1.AutomaticResources]

    • max_replica_count in either [DedicatedResources][google.cloud.aiplatform.v1.DedicatedResources] or [AutomaticResources][google.cloud.aiplatform.v1.AutomaticResources]

    • [autoscaling_metric_specs][google.cloud.aiplatform.v1.DedicatedResources.autoscaling_metric_specs]

    • disable_container_logging (v1 only)

    • enable_container_logging (v1beta1 only)

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

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

    Required. The update mask applies to the resource. See [google.protobuf.FieldMask][google.protobuf.FieldMask].

    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_v1.types.MutateDeployedModelResponse Response message for

[EndpointService.MutateDeployedModel][google.cloud.aiplatform.v1.EndpointService.MutateDeployedModel].

Return type:

google.api_core.operation.Operation

static network_path(project: str, network: str) → str[source]

Returns a fully-qualified network string.

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

Parse a billing_account path into its component segments.

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

Parse a folder path into its component segments.

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

Parse a location path into its component segments.

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

Parse a organization path into its component segments.

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

Parse a project path into its component segments.

static parse_deployment_resource_pool_path(path: str) → Dict[str, str][source]

Parses a deployment_resource_pool path into its component segments.

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

Parses a endpoint path into its component segments.

static parse_model_deployment_monitoring_job_path(path: str) → Dict[str, str][source]

Parses a model_deployment_monitoring_job path into its component segments.

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

Parses a model path into its component segments.

static parse_network_path(path: str) → Dict[str, str][source]

Parses a network path into its component segments.

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

Parses a reservation path into its component segments.

static reservation_path(project_id_or_number: str, zone: str, reservation_name: str) → str[source]

Returns a fully-qualified reservation string.

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[source]

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[source]
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: EndpointServiceTransport

Returns the transport used by the client instance.

Returns:

The transport used by the client

instance.

Return type:

EndpointServiceTransport

undeploy_model(request: Optional[Union[UndeployModelRequest, dict]] = None, *, endpoint: Optional[str] = None, deployed_model_id: Optional[str] = None, traffic_split: Optional[MutableMapping[str, int]] = None, retry: Optional[Union[Retry, _MethodDefault]] = _MethodDefault._DEFAULT_VALUE, timeout: Union[float, object] = _MethodDefault._DEFAULT_VALUE, metadata: Sequence[Tuple[str, str]] = ()) → Operation[source]

Undeploys a Model from an Endpoint, removing a DeployedModel from it, and freeing all resources it’s using.

# 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_v1

def sample_undeploy_model():
    # Create a client
    client = aiplatform_v1.EndpointServiceClient()

    # Initialize request argument(s)
    request = aiplatform_v1.UndeployModelRequest(
        endpoint="endpoint_value",
        deployed_model_id="deployed_model_id_value",
    )

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

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

    response = operation.result()

    # Handle the response
    print(response)
Parameters:

  • request (Union[google.cloud.aiplatform_v1.types.UndeployModelRequest, dict]) – The request object. Request message for [EndpointService.UndeployModel][google.cloud.aiplatform.v1.EndpointService.UndeployModel].

  • endpoint (str) –

    Required. The name of the Endpoint resource from which to undeploy a Model. Format: projects/{project}/locations/{location}/endpoints/{endpoint}

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

  • deployed_model_id (str) –

    Required. The ID of the DeployedModel to be undeployed from the Endpoint.

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

  • traffic_split (MutableMapping[str, int]) –

    If this field is provided, then the Endpoint’s [traffic_split][google.cloud.aiplatform.v1.Endpoint.traffic_split] will be overwritten with it. If last DeployedModel is being undeployed from the Endpoint, the [Endpoint.traffic_split] will always end up empty when this call returns. A DeployedModel will be successfully undeployed only if it doesn’t have any traffic assigned to it when this method executes, or if this field unassigns any traffic to it.

    This corresponds to the traffic_split 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_v1.types.UndeployModelResponse Response message for

[EndpointService.UndeployModel][google.cloud.aiplatform.v1.EndpointService.UndeployModel].

Return type:

google.api_core.operation.Operation

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_endpoint(request: Optional[Union[UpdateEndpointRequest, dict]] = None, *, endpoint: Optional[Endpoint] = 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]] = ()) → Endpoint[source]

Updates an Endpoint.

# 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_v1

def sample_update_endpoint():
    # Create a client
    client = aiplatform_v1.EndpointServiceClient()

    # Initialize request argument(s)
    endpoint = aiplatform_v1.Endpoint()
    endpoint.display_name = "display_name_value"

    request = aiplatform_v1.UpdateEndpointRequest(
        endpoint=endpoint,
    )

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

    # Handle the response
    print(response)
Parameters:

  • request (Union[google.cloud.aiplatform_v1.types.UpdateEndpointRequest, dict]) – The request object. Request message for [EndpointService.UpdateEndpoint][google.cloud.aiplatform.v1.EndpointService.UpdateEndpoint].

  • endpoint (google.cloud.aiplatform_v1.types.Endpoint) –

    Required. The Endpoint which replaces the resource on the server.

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

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

    Required. The update mask applies to the resource. See [google.protobuf.FieldMask][google.protobuf.FieldMask].

    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:

Models are deployed into it, and afterwards Endpoint is called to obtain predictions and explanations.

Return type:

google.cloud.aiplatform_v1.types.Endpoint

update_endpoint_long_running(request: Optional[Union[UpdateEndpointLongRunningRequest, dict]] = None, *, endpoint: Optional[Endpoint] = None, retry: Optional[Union[Retry, _MethodDefault]] = _MethodDefault._DEFAULT_VALUE, timeout: Union[float, object] = _MethodDefault._DEFAULT_VALUE, metadata: Sequence[Tuple[str, str]] = ()) → Operation[source]

Updates an Endpoint with a long running operation.

# 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_v1

def sample_update_endpoint_long_running():
    # Create a client
    client = aiplatform_v1.EndpointServiceClient()

    # Initialize request argument(s)
    endpoint = aiplatform_v1.Endpoint()
    endpoint.display_name = "display_name_value"

    request = aiplatform_v1.UpdateEndpointLongRunningRequest(
        endpoint=endpoint,
    )

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

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

    response = operation.result()

    # Handle the response
    print(response)
Parameters:

  • request (Union[google.cloud.aiplatform_v1.types.UpdateEndpointLongRunningRequest, dict]) – The request object. Request message for [EndpointService.UpdateEndpointLongRunning][google.cloud.aiplatform.v1.EndpointService.UpdateEndpointLongRunning].

  • endpoint (google.cloud.aiplatform_v1.types.Endpoint) –

    Required. The Endpoint which replaces the resource on the server. Currently we only support updating the client_connection_config field, all the other fields’ update will be blocked.

    This corresponds to the endpoint 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_v1.types.Endpoint Models are deployed into it, and afterwards Endpoint is called to obtain

predictions and explanations.

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[source]

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_v1.services.endpoint_service.pagers.ListEndpointsAsyncPager(method: Callable[[...], Awaitable[ListEndpointsResponse]], request: ListEndpointsRequest, response: ListEndpointsResponse, *, retry: Optional[Union[AsyncRetry, _MethodDefault]] = _MethodDefault._DEFAULT_VALUE, timeout: Union[float, object] = _MethodDefault._DEFAULT_VALUE, metadata: Sequence[Tuple[str, str]] = ())[source]

A pager for iterating through list_endpoints requests.

This class thinly wraps an initial google.cloud.aiplatform_v1.types.ListEndpointsResponse object, and provides an __aiter__ method to iterate through its endpoints field.

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

All the usual google.cloud.aiplatform_v1.types.ListEndpointsResponse 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:
class google.cloud.aiplatform_v1.services.endpoint_service.pagers.ListEndpointsPager(method: Callable[[...], ListEndpointsResponse], request: ListEndpointsRequest, response: ListEndpointsResponse, *, retry: Optional[Union[Retry, _MethodDefault]] = _MethodDefault._DEFAULT_VALUE, timeout: Union[float, object] = _MethodDefault._DEFAULT_VALUE, metadata: Sequence[Tuple[str, str]] = ())[source]

A pager for iterating through list_endpoints requests.

This class thinly wraps an initial google.cloud.aiplatform_v1.types.ListEndpointsResponse object, and provides an __iter__ method to iterate through its endpoints field.

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

All the usual google.cloud.aiplatform_v1.types.ListEndpointsResponse 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: