Class Endpoint (1.26.1)

  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 > builtins.object > abc.ABC > google.cloud.aiplatform.base.PreviewMixin > Endpoint

Properties

create_time

Time this resource was created.

display_name

Display name of this resource.

encryption_spec

Customer-managed encryption key options for this Vertex AI resource.

If this is set, then all resources created by this Vertex AI resource will be encrypted with the provided encryption key.

gca_resource

The underlying resource proto representation.

labels

User-defined labels containing metadata about this resource.

Read more about labels at https://goo.gl/xmQnxf

name

Name of this resource.

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.

preview

Return an Endpoint instance with preview features enabled.

resource_name

Full qualified resource name.

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.

update_time

Time this resource was last updated.

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 
 , 
 enable_request_response_logging 
 = 
 False 
 , 
 request_response_logging_sampling_rate 
 : 
 Optional 
 [ 
 float 
 ] 
 = 
 None 
 , 
 request_response_logging_bq_destination_table 
 : 
 Optional 
 [ 
 str 
 ] 
 = 
 None 
 , 
 )

Creates a new endpoint.

Parameters

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: projects/my-project/locations/my-region/keyRings/my-kr/cryptoKeys/my-key . The key needs to be in the same region as where the compute resource is created. If set, this Endpoint and all sub-resources of this Endpoint will be secured by this key. Overrides encryption_spec_key_name set in aiplatform.init.

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 ?endpoint_id=12345 . This is the fallback for fields that are not included in either the URI or the body.

request_response_logging_sampling_rate

float

Optional. The request response logging sampling rate. If not set, default is 0.0.

request_response_logging_bq_destination_table

str

Optional. The request response logging bigquery destination. If not set, will create a table with name: bq://{project_id}.logging_{endpoint_display_name}_{endpoint_id}.request_response_logging .

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.

enable_request_response_logging

bool

Optional. Whether to enable request & response logging for this endpoint.

Returns

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.

Parameters

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

Exceptions

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 
 , 
 enable_access_logging 
 = 
 False 
 , 
 )

Deploys a Model to the Endpoint.

Parameters

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 iam.serviceAccounts.actAs permission on this service account.

explanation_metadata

aiplatform.explain.ExplanationMetadata

Optional. Metadata describing the Model's input and output for explanation. explanation_metadata is optional while explanation_parameters must be specified when used. For more details, see Ref docs http://tinyurl.com/1igh60kt

explanation_parameters

aiplatform.explain.ExplanationParameters

Optional. Parameters to configure explaining for Model's predictions. For more details, see Ref docs http://tinyurl.com/1an4zake

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

enable_access_logging

bool

Whether to enable endpoint access logging. Defaults to False.

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

Parameters

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] instance_schema_uri .

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] parameters_schema_uri .

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.

Returns

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"', )

Parameters

Name

Description

filter

str

Optional. An expression for filtering the results of the request. For field names both snake_case and camelCase are supported.

order_by

str

Optional. A comma-separated list of fields to order by, sorted in ascending order. Use "desc" after a field name for descending. Supported fields: display_name , create_time , update_time

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.

Returns

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.

Returns

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.

Parameters

Name

Description

instances

List

parameters

Dict

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().

Returns

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)

Parameters

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.

to_dict

  to_dict 
 ()

Returns the resource proto as a dictionary.

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.

Parameters

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.

Parameter

Name

Description

sync

bool

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, }, )

Parameters

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]

traffic_split

Dict[str, int]

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.

Exceptions

Type

Description

ValueError

If labels is not the correct format.

Returns

Type

Description

Endpoint (aiplatform.Prediction)

Updated endpoint resource.

wait

  wait 
 ()

Helper method that blocks until all futures are complete.

Class Endpoint (1.26.1) Stay organized with collections Save and categorize content based on your preferences.

Parameters

Inheritance

Properties

create_time

display_name

encryption_spec

gca_resource

labels

name

network

preview

resource_name

traffic_split

update_time

Methods

create

delete

deploy

explain

list

list_models

predict

raw_predict

to_dict

undeploy

undeploy_all

update

wait

Class Endpoint (1.26.1)