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Endpoint(
endpoint_name: str,
project: Optional[str] = None,
location: Optional[str] = None,
credentials: Optional[google.auth.credentials.Credentials] = None,
)
Retrieves an endpoint resource.
Parameters
Name | Description |
endpoint_name |
str
Required. A fully-qualified endpoint resource name or endpoint ID. Example: "projects/123/locations/us-central1/endpoints/456" or "456" when project and location are initialized or passed. |
project |
str
Optional. Project to retrieve endpoint from. If not set, project set in aiplatform.init will be used. |
location |
str
Optional. Location to retrieve endpoint from. If not set, location set in aiplatform.init will be used. |
credentials |
auth_credentials.Credentials
Optional. Custom credentials to use to upload this model. Overrides credentials set in aiplatform.init. |
Inheritance
builtins.object > google.cloud.aiplatform.base.VertexAiResourceNoun > builtins.object > google.cloud.aiplatform.base.FutureManager > google.cloud.aiplatform.base.VertexAiResourceNounWithFutureManager > EndpointProperties
network
The full name of the Google Compute Engine network to which this Endpoint should be peered.
Takes the format projects/{project}/global/networks/{network}
. Where
{project} is a project number, as in 12345
, and {network} is a network name.
Private services access must already be configured for the network. If left unspecified, the Endpoint is not peered with any network.
traffic_split
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.
Methods
create
create(
display_name: Optional[str] = None,
description: Optional[str] = None,
labels: Optional[Dict[str, str]] = None,
metadata: Optional[Sequence[Tuple[str, str]]] = (),
project: Optional[str] = None,
location: Optional[str] = None,
credentials: Optional[google.auth.credentials.Credentials] = None,
encryption_spec_key_name: Optional[str] = None,
sync=True,
create_request_timeout: Optional[float] = None,
endpoint_id: Optional[str] = None,
)
Creates a new endpoint.
Name | Description |
display_name |
str
Optional. The user-defined name of the Endpoint. The name can be up to 128 characters long and can be consist of any UTF-8 characters. |
description |
str
Optional. The description of the Endpoint. |
labels |
Dict[str, str]
Optional. 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. |
metadata |
Sequence[Tuple[str, str]]
Optional. Strings which should be sent along with the request as metadata. |
project |
str
Required. Project to retrieve endpoint from. If not set, project set in aiplatform.init will be used. |
location |
str
Required. Location to retrieve endpoint from. If not set, location set in aiplatform.init will be used. |
credentials |
auth_credentials.Credentials
Optional. Custom credentials to use to upload this model. Overrides credentials set in aiplatform.init. |
encryption_spec_key_name |
str
Optional. The Cloud KMS resource identifier of the customer managed encryption key used to protect the model. Has the form: |
create_request_timeout |
float
Optional. The timeout for the create request in seconds. |
endpoint_id |
str
Optional. 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. This value should be 1-10 characters, and valid characters are /[0-9]/. When using HTTP/JSON, this field is populated based on a query string argument, such as |
sync |
bool
Whether to execute this method synchronously. If False, this method will be executed in concurrent Future and any downstream object will be immediately returned and synced when the Future has completed. |
Type | Description |
endpoint (aiplatform.Endpoint) | Created endpoint. |
delete
delete(force: bool = False, sync: bool = True)
Deletes this Vertex AI Endpoint resource. If force is set to True, all models on this Endpoint will be undeployed prior to deletion.
Name | Description |
force |
bool
Required. If force is set to True, all deployed models on this Endpoint will be undeployed first. Default is False. |
sync |
bool
Whether to execute this method synchronously. If False, this method will be executed in concurrent Future and any downstream object will be immediately returned and synced when the Future has completed. |
Type | Description |
FailedPrecondition | If models are deployed on this Endpoint and force = False. |
deploy
deploy(
model: google.cloud.aiplatform.models.Model,
deployed_model_display_name: Optional[str] = None,
traffic_percentage: int = 0,
traffic_split: Optional[Dict[str, int]] = None,
machine_type: Optional[str] = None,
min_replica_count: int = 1,
max_replica_count: int = 1,
accelerator_type: Optional[str] = None,
accelerator_count: Optional[int] = None,
service_account: Optional[str] = None,
explanation_metadata: Optional[
google.cloud.aiplatform_v1.types.explanation_metadata.ExplanationMetadata
] = None,
explanation_parameters: Optional[
google.cloud.aiplatform_v1.types.explanation.ExplanationParameters
] = None,
metadata: Optional[Sequence[Tuple[str, str]]] = (),
sync=True,
deploy_request_timeout: Optional[float] = None,
autoscaling_target_cpu_utilization: Optional[int] = None,
autoscaling_target_accelerator_duty_cycle: Optional[int] = None,
)
Deploys a Model to the Endpoint.
Name | Description |
deployed_model_display_name |
str
Optional. The display name of the DeployedModel. If not provided upon creation, the Model's display_name is used. |
traffic_percentage |
int
Optional. Desired traffic to newly deployed model. Defaults to 0 if there are pre-existing deployed models. Defaults to 100 if there are no pre-existing deployed models. Negative values should not be provided. Traffic of previously deployed models at the endpoint will be scaled down to accommodate new deployed model's traffic. Should not be provided if traffic_split is provided. |
traffic_split |
Dict[str, int]
Optional. 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 the moment. Key for model being deployed is "0". Should not be provided if traffic_percentage is provided. |
machine_type |
str
Optional. The type of machine. Not specifying machine type will result in model to be deployed with automatic resources. |
min_replica_count |
int
Optional. The minimum number of machine replicas this deployed model will be always deployed on. If traffic against it increases, it may dynamically be deployed onto more replicas, and as traffic decreases, some of these extra replicas may be freed. |
max_replica_count |
int
Optional. The maximum number of replicas this deployed model may be deployed on when the traffic against it increases. If requested value is too large, the deployment will error, but if deployment succeeds then the ability to scale the model to that many replicas is guaranteed (barring service outages). If traffic against the deployed model increases beyond what its replicas at maximum may handle, a portion of the traffic will be dropped. If this value is not provided, the larger value of min_replica_count or 1 will be used. If value provided is smaller than min_replica_count, it will automatically be increased to be min_replica_count. |
accelerator_type |
str
Optional. Hardware accelerator type. Must also set accelerator_count if used. One of ACCELERATOR_TYPE_UNSPECIFIED, NVIDIA_TESLA_K80, NVIDIA_TESLA_P100, NVIDIA_TESLA_V100, NVIDIA_TESLA_P4, NVIDIA_TESLA_T4 |
accelerator_count |
int
Optional. The number of accelerators to attach to a worker replica. |
service_account |
str
The service account that the DeployedModel's container runs as. Specify the email address of the service account. If this service account is not specified, the container runs as a service account that doesn't have access to the resource project. Users deploying the Model must have the |
explanation_metadata |
aiplatform.explain.ExplanationMetadata
Optional. Metadata describing the Model's input and output for explanation. |
explanation_parameters |
aiplatform.explain.ExplanationParameters
Optional. Parameters to configure explaining for Model's predictions. For more details, see |
metadata |
Sequence[Tuple[str, str]]
Optional. Strings which should be sent along with the request as metadata. |
deploy_request_timeout |
float
Optional. The timeout for the deploy request in seconds. |
autoscaling_target_cpu_utilization |
int
Target CPU Utilization to use for Autoscaling Replicas. A default value of 60 will be used if not specified. |
autoscaling_target_accelerator_duty_cycle |
int
Target Accelerator Duty Cycle. Must also set accelerator_type and accelerator_count if specified. A default value of 60 will be used if not specified. |
model |
aiplatform.Model
Required. Model to be deployed. |
sync |
bool
Whether to execute this method synchronously. If False, this method will be executed in concurrent Future and any downstream object will be immediately returned and synced when the Future has completed. |
explain
explain(
instances: List[Dict],
parameters: Optional[Dict] = None,
deployed_model_id: Optional[str] = None,
timeout: Optional[float] = None,
)
Make a prediction with explanations against this Endpoint.
Example usage: response = my_endpoint.explain(instances=[...]) my_explanations = response.explanations
Name | Description |
instances |
List
Required. The instances that are the input to the prediction call. A DeployedModel may have an upper limit on the number of instances it supports per request, and when it is exceeded the prediction call errors in case of AutoML Models, or, in case of customer created Models, the behaviour is as documented by that Model. The schema of any single instance may be specified via Endpoint's DeployedModels' [Model's][google.cloud.aiplatform.v1beta1.DeployedModel.model] [PredictSchemata's][google.cloud.aiplatform.v1beta1.Model.predict_schemata] |
parameters |
Dict
The parameters that govern the prediction. The schema of the parameters may be specified via Endpoint's DeployedModels' [Model's ][google.cloud.aiplatform.v1beta1.DeployedModel.model] [PredictSchemata's][google.cloud.aiplatform.v1beta1.Model.predict_schemata] |
deployed_model_id |
str
Optional. If specified, this ExplainRequest will be served by the chosen DeployedModel, overriding this Endpoint's traffic split. |
timeout |
float
Optional. The timeout for this request in seconds. |
Type | Description |
prediction (aiplatform.Prediction) | Prediction with returned predictions, explanations, and Model ID. |
list
list(
filter: Optional[str] = None,
order_by: Optional[str] = None,
project: Optional[str] = None,
location: Optional[str] = None,
credentials: Optional[google.auth.credentials.Credentials] = None,
)
List all Endpoint resource instances.
Example Usage: aiplatform.Endpoint.list( filter='labels.my_label="my_label_value" OR display_name=!"old_endpoint"', )
Name | Description |
filter |
str
Optional. An expression for filtering the results of the request. For field names both snake_case and camelCase are supported. |
order_by |
str
Optional. A comma-separated list of fields to order by, sorted in ascending order. Use "desc" after a field name for descending. Supported fields: |
project |
str
Optional. Project to retrieve list from. If not set, project set in aiplatform.init will be used. |
location |
str
Optional. Location to retrieve list from. If not set, location set in aiplatform.init will be used. |
credentials |
auth_credentials.Credentials
Optional. Custom credentials to use to retrieve list. Overrides credentials set in aiplatform.init. |
Type | Description |
List[models.Endpoint] | A list of Endpoint resource objects |
list_models
list_models()
Returns a list of the models deployed to this Endpoint.
Type | Description |
deployed_models (List[aiplatform.gapic.DeployedModel]) | A list of the models deployed in this Endpoint. |
predict
predict(
instances: List,
parameters: Optional[Dict] = None,
timeout: Optional[float] = None,
use_raw_predict: Optional[bool] = False,
)
Make a prediction against this Endpoint.
Name | Description |
instances |
List
Required. The instances that are the input to the prediction call. A DeployedModel may have an upper limit on the number of instances it supports per request, and when it is exceeded the prediction call errors in case of AutoML Models, or, in case of customer created Models, the behaviour is as documented by that Model. The schema of any single instance may be specified via Endpoint's DeployedModels' [Model's][google.cloud.aiplatform.v1beta1.DeployedModel.model] [PredictSchemata's][google.cloud.aiplatform.v1beta1.Model.predict_schemata] |
parameters |
Dict
The parameters that govern the prediction. The schema of the parameters may be specified via Endpoint's DeployedModels' [Model's ][google.cloud.aiplatform.v1beta1.DeployedModel.model] [PredictSchemata's][google.cloud.aiplatform.v1beta1.Model.predict_schemata] |
timeout |
float
Optional. The timeout for this request in seconds. |
use_raw_predict |
bool
Optional. Default value is False. If set to True, the underlying prediction call will be made against Endpoint.raw_predict(). Note that model version information will not be available in the prediciton response using raw_predict. |
Type | Description |
prediction (aiplatform.Prediction) | Prediction with returned predictions and Model ID. |
raw_predict
raw_predict(body: bytes, headers: Dict[str, str])
Makes a prediction request using arbitrary headers.
Example usage: my_endpoint = aiplatform.Endpoint(ENDPOINT_ID) response = my_endpoint.raw_predict( body = b'{"instances":[{"feat_1":val_1, "feat_2":val_2}]}' headers = {'Content-Type':'application/json'} ) status_code = response.status_code results = json.dumps(response.text)
Name | Description |
body |
bytes
The body of the prediction request in bytes. This must not exceed 1.5 mb per request. |
headers |
Dict[str, str]
The header of the request as a dictionary. There are no restrictions on the header. |
undeploy
undeploy(
deployed_model_id: str,
traffic_split: Optional[Dict[str, int]] = None,
metadata: Optional[Sequence[Tuple[str, str]]] = (),
sync=True,
)
Undeploys a deployed model.
The model to be undeployed should have no traffic or user must provide
a new traffic_split with the remaining deployed models. Refer
to Endpoint.traffic_split
for the current traffic split mapping.
Name | Description |
deployed_model_id |
str
Required. The ID of the DeployedModel to be undeployed from the Endpoint. |
traffic_split |
Dict[str, int]
Optional. A map of DeployedModel IDs to the percentage of this Endpoint's traffic that should be forwarded to that DeployedModel. Required if undeploying a model with non-zero traffic from an Endpoint with multiple deployed models. The traffic percentage values must add up to 100, or map must be empty if the Endpoint is to not accept any traffic at the moment. If a DeployedModel's ID is not listed in this map, then it receives no traffic. |
metadata |
Sequence[Tuple[str, str]]
Optional. Strings which should be sent along with the request as metadata. |
undeploy_all
undeploy_all(sync: bool = True)
Undeploys every model deployed to this Endpoint.
Name | Description |
sync |
bool
Whether to execute this method synchronously. If False, this method will be executed in concurrent Future and any downstream object will be immediately returned and synced when the Future has completed. |
update
update(
display_name: Optional[str] = None,
description: Optional[str] = None,
labels: Optional[Dict[str, str]] = None,
traffic_split: Optional[Dict[str, int]] = None,
request_metadata: Optional[Sequence[Tuple[str, str]]] = (),
update_request_timeout: Optional[float] = None,
)
Updates an endpoint.
Example usage: my_endpoint = my_endpoint.update( display_name='my-updated-endpoint', description='my updated description', labels={'key': 'value'}, traffic_split={ '123456': 20, '234567': 80, }, )
Name | Description |
display_name |
str
Optional. The display name of the Endpoint. The name can be up to 128 characters long and can be consist of any UTF-8 characters. |
description |
str
Optional. The description of the Endpoint. |
labels |
Dict[str, str]
Optional. 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. |
traffic_split |
Dict[str, int]
Optional. 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. |
request_metadata |
Sequence[Tuple[str, str]]
Optional. Strings which should be sent along with the request as metadata. |
update_request_timeout |
float
Optional. The timeout for the update request in seconds. |
Type | Description |
ValueError | If `labels` is not the correct format. |
Type | Description |
Endpoint (aiplatform.Prediction) | Updated endpoint resource. |