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Endpoint(mapping=None, *, ignore_unknown_fields=False, **kwargs)
Models are deployed into it, and afterwards Endpoint is called to obtain predictions and explanations.
Attributes |
|
---|---|
Name | Description |
name |
str
Output only. The resource name of the Endpoint. |
display_name |
str
Required. The display name of the Endpoint. The name can be up to 128 characters long and can consist of any UTF-8 characters. |
description |
str
The description of the Endpoint. |
deployed_models |
MutableSequence[google.cloud.aiplatform_v1beta1.types.DeployedModel]
Output only. The models deployed in this Endpoint. To add or remove DeployedModels use EndpointService.DeployModel and EndpointService.UndeployModel respectively. |
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 a DeployedModel's ID is not listed in this map, then it receives no traffic. The traffic percentage values must add up to 100, or map must be empty if the Endpoint is to not accept any traffic at a moment. |
etag |
str
Used to perform consistent read-modify-write updates. If not set, a blind "overwrite" update happens. |
labels |
MutableMapping[str, str]
The labels with user-defined metadata to organize your Endpoints. Label keys and values can be no longer than 64 characters (Unicode codepoints), can only contain lowercase letters, numeric characters, underscores and dashes. International characters are allowed. See https://goo.gl/xmQnxf for more information and examples of labels. |
create_time |
google.protobuf.timestamp_pb2.Timestamp
Output only. Timestamp when this Endpoint was created. |
update_time |
google.protobuf.timestamp_pb2.Timestamp
Output only. Timestamp when this Endpoint was last updated. |
encryption_spec |
google.cloud.aiplatform_v1beta1.types.EncryptionSpec
Customer-managed encryption key spec for an Endpoint. If set, this Endpoint and all sub-resources of this Endpoint will be secured by this key. |
network |
str
Optional. The full name of the Google Compute Engine network __
to which the Endpoint should be peered.
Private services access must already be configured for the
network. If left unspecified, the Endpoint is not peered
with any network.
Only one of the fields,
network
or
enable_private_service_connect,
can be set.
Format __:
projects/{project}/global/networks/{network} . Where
{project} is a project number, as in 12345 , and
{network} is network name.
|
enable_private_service_connect |
bool
Deprecated: If true, expose the Endpoint via private service connect. Only one of the fields, network or enable_private_service_connect, can be set. |
model_deployment_monitoring_job |
str
Output only. Resource name of the Model Monitoring job associated with this Endpoint if monitoring is enabled by JobService.CreateModelDeploymentMonitoringJob. Format: projects/{project}/locations/{location}/modelDeploymentMonitoringJobs/{model_deployment_monitoring_job}
|
predict_request_response_logging_config |
google.cloud.aiplatform_v1beta1.types.PredictRequestResponseLoggingConfig
Configures the request-response logging for online prediction. |
Classes
LabelsEntry
LabelsEntry(mapping=None, *, ignore_unknown_fields=False, **kwargs)
The abstract base class for a message.
Parameters | |
---|---|
Name | Description |
kwargs |
dict
Keys and values corresponding to the fields of the message. |
mapping |
Union[dict,
A dictionary or message to be used to determine the values for this message. |
ignore_unknown_fields |
Optional(bool)
If True, do not raise errors for unknown fields. Only applied if |
TrafficSplitEntry
TrafficSplitEntry(mapping=None, *, ignore_unknown_fields=False, **kwargs)
The abstract base class for a message.
Parameters | |
---|---|
Name | Description |
kwargs |
dict
Keys and values corresponding to the fields of the message. |
mapping |
Union[dict,
A dictionary or message to be used to determine the values for this message. |
ignore_unknown_fields |
Optional(bool)
If True, do not raise errors for unknown fields. Only applied if |
Methods
Endpoint
Endpoint(mapping=None, *, ignore_unknown_fields=False, **kwargs)
Models are deployed into it, and afterwards Endpoint is called to obtain predictions and explanations.