Class Endpoint (1.73.0)

Endpoint(
    endpoint_name: str,
    project: typing.Optional[str] = None,
    location: typing.Optional[str] = None,
    credentials: typing.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.

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.

private_service_connect_config

The Private Service Connect configuration for this Endpoint.

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: typing.Optional[str] = None,
    description: typing.Optional[str] = None,
    labels: typing.Optional[typing.Dict[str, str]] = None,
    metadata: typing.Optional[typing.Sequence[typing.Tuple[str, str]]] = (),
    project: typing.Optional[str] = None,
    location: typing.Optional[str] = None,
    credentials: typing.Optional[google.auth.credentials.Credentials] = None,
    encryption_spec_key_name: typing.Optional[str] = None,
    sync=True,
    create_request_timeout: typing.Optional[float] = None,
    endpoint_id: typing.Optional[str] = None,
    enable_request_response_logging=False,
    request_response_logging_sampling_rate: typing.Optional[float] = None,
    request_response_logging_bq_destination_table: typing.Optional[str] = None,
    dedicated_endpoint_enabled=False,
) -> google.cloud.aiplatform.models.Endpoint

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` 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.
`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.
`dedicated_endpoint_enabled`	`bool` Optional. If enabled, a dedicated dns will be created and your traffic will be fully isolated from other customers' traffic and latency will be reduced.

Returns
Type	Description
`endpoint (aiplatform.Endpoint)`	Created endpoint.

delete

delete(force: bool = False, sync: bool = True) -> None

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` 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.

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: typing.Optional[str] = None,
    traffic_percentage: int = 0,
    traffic_split: typing.Optional[typing.Dict[str, int]] = None,
    machine_type: typing.Optional[str] = None,
    min_replica_count: int = 1,
    max_replica_count: int = 1,
    accelerator_type: typing.Optional[str] = None,
    accelerator_count: typing.Optional[int] = None,
    tpu_topology: typing.Optional[str] = None,
    service_account: typing.Optional[str] = None,
    explanation_metadata: typing.Optional[
        google.cloud.aiplatform_v1.types.explanation_metadata.ExplanationMetadata
    ] = None,
    explanation_parameters: typing.Optional[
        google.cloud.aiplatform_v1.types.explanation.ExplanationParameters
    ] = None,
    metadata: typing.Optional[typing.Sequence[typing.Tuple[str, str]]] = (),
    sync=True,
    deploy_request_timeout: typing.Optional[float] = None,
    autoscaling_target_cpu_utilization: typing.Optional[int] = None,
    autoscaling_target_accelerator_duty_cycle: typing.Optional[int] = None,
    enable_access_logging=False,
    disable_container_logging: bool = False,
    deployment_resource_pool: typing.Optional[
        google.cloud.aiplatform.models.DeploymentResourcePool
    ] = None,
    reservation_affinity_type: typing.Optional[str] = None,
    reservation_affinity_key: typing.Optional[str] = None,
    reservation_affinity_values: typing.Optional[typing.List[str]] = None,
    spot: bool = False,
    fast_tryout_enabled: bool = False,
    system_labels: typing.Optional[typing.Dict[str, str]] = None,
) -> None

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.
`tpu_topology`	`str` Optional. The TPU topology to use for the DeployedModel. Required for CloudTPU multihost deployments.
`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.
`disable_container_logging`	`bool` If True, container logs from the deployed model will not be written to Cloud Logging. Defaults to False.
`deployment_resource_pool`	`DeploymentResourcePool` Resource pool where the model will be deployed. All models that are deployed to the same DeploymentResourcePool will be hosted in a shared model server. If provided, will override replica count arguments.
`reservation_affinity_type`	`str` Optional. The type of reservation affinity. One of NO_RESERVATION, ANY_RESERVATION, SPECIFIC_RESERVATION, SPECIFIC_THEN_ANY_RESERVATION, SPECIFIC_THEN_NO_RESERVATION
`reservation_affinity_key`	`str` Optional. Corresponds to the label key of a reservation resource. To target a SPECIFIC_RESERVATION by name, use `compute.googleapis.com/reservation-name` as the key and specify the name of your reservation as its value.
`reservation_affinity_values`	`List[str]` Optional. Corresponds to the label values of a reservation resource. This must be the full resource name of the reservation. Format: 'projects/{project_id_or_number}/zones/{zone}/reservations/{reservation_name}'
`spot`	`bool` Optional. Whether to schedule the deployment workload on spot VMs.
`fast_tryout_enabled`	`bool` Optional. Defaults to False. If True, model will be deployed using faster deployment path. Useful for quick experiments. Not for production workloads. Only available for most popular models with certain machine types.
`system_labels`	`Dict[str, str]` Optional. System labels to apply to Model Garden deployments. System labels are managed by Google for internal use only.
`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.
`enable_access_logging`	`bool` Whether to enable endpoint access logging. Defaults to False.

direct_predict

direct_predict(
    inputs: typing.List,
    parameters: typing.Optional[typing.Dict] = None,
    timeout: typing.Optional[float] = None,
) -> google.cloud.aiplatform.models.Prediction

Makes a direct (gRPC) prediction against this Endpoint for a pre-built image.

Parameters
Name	Description
`inputs`	`List` Required. The inputs 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` Optional. 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`.
`timeout`	`Optional[float]` Optional. The timeout for this request in seconds.

Returns
Type	Description
`prediction (aiplatform.Prediction)`	The resulting prediction.

direct_predict_async

direct_predict_async(
    inputs: typing.List,
    *,
    parameters: typing.Optional[typing.Dict] = None,
    timeout: typing.Optional[float] = None
) -> google.cloud.aiplatform.models.Prediction

Makes an asynchronous direct (gRPC) prediction against this Endpoint for a pre-built image.

Example usage:

response = await my_endpoint.direct_predict_async(inputs=[...])
my_predictions = response.predictions
```

Parameters
Name	Description
`inputs`	`List` Required. The inputs 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` Optional. 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`.
`timeout`	`Optional[float]` Optional. The timeout for this request in seconds.

Returns
Type	Description
`prediction (aiplatform.Prediction)`	The resulting prediction.

direct_raw_predict

direct_raw_predict(
    method_name: str, request: bytes, timeout: typing.Optional[float] = None
) -> google.cloud.aiplatform.models.Prediction

Makes a direct (gRPC) prediction request using arbitrary headers for a custom container.

Example usage:

my_endpoint = aiplatform.Endpoint(ENDPOINT_ID)
response = my_endpoint.direct_raw_predict(request=b'...')
```

Parameters
Name	Description
`method_name`	`str` Fully qualified name of the API method being invoked to perform prediction.
`request`	`bytes` The body of the prediction request in bytes.
`timeout`	`Optional[float]` Optional. The timeout for this request in seconds.

Returns
Type	Description
`prediction (aiplatform.Prediction)`	The resulting prediction.

direct_raw_predict_async

direct_raw_predict_async(
    method_name: str, request: bytes, timeout: typing.Optional[float] = None
) -> google.cloud.aiplatform.models.Prediction

Makes a direct (gRPC) prediction request for a custom container.

Example usage:

my_endpoint = aiplatform.Endpoint(ENDPOINT_ID)
response = await my_endpoint.direct_raw_predict(request=b'...')
```

Parameters
Name	Description
`method_name`	`str` Fully qualified name of the API method being invoked to perform prediction.
`request`	`bytes` The body of the prediction request in bytes.
`timeout`	`Optional[float]` Optional. The timeout for this request in seconds.

Returns
Type	Description
`prediction (aiplatform.Prediction)`	The resulting prediction.

explain

explain(
    instances: typing.List[typing.Dict],
    parameters: typing.Optional[typing.Dict] = None,
    deployed_model_id: typing.Optional[str] = None,
    timeout: typing.Optional[float] = None,
) -> google.cloud.aiplatform.models.Prediction

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.

explain_async

explain_async(
    instances: typing.List[typing.Dict],
    *,
    parameters: typing.Optional[typing.Dict] = None,
    deployed_model_id: typing.Optional[str] = None,
    timeout: typing.Optional[float] = None
) -> google.cloud.aiplatform.models.Prediction

Make a prediction with explanations against this Endpoint.

Example usage:

response = await my_endpoint.explain_async(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: typing.Optional[str] = None,
    order_by: typing.Optional[str] = None,
    project: typing.Optional[str] = None,
    location: typing.Optional[str] = None,
    credentials: typing.Optional[google.auth.credentials.Credentials] = None,
) -> typing.List[google.cloud.aiplatform.models.Endpoint]

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() -> (
    typing.List[google.cloud.aiplatform_v1.types.endpoint.DeployedModel]
)

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: typing.List,
    parameters: typing.Optional[typing.Dict] = None,
    timeout: typing.Optional[float] = None,
    use_raw_predict: typing.Optional[bool] = False,
    *,
    use_dedicated_endpoint: typing.Optional[bool] = False
) -> google.cloud.aiplatform.models.Prediction

Make a prediction against this Endpoint.

For dedicated endpoint, set use_dedicated_endpoint = True:

response = my_endpoint.predict(instances=[...],
    use_dedicated_endpoint=True)
my_predictions = response.predictions
```

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`.
`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().
`use_dedicated_endpoint`	`bool` Optional. Default value is False. If set to True, the underlying prediction call will be made using the dedicated endpoint dns.

Returns
Type	Description
`prediction (aiplatform.Prediction)`	Prediction with returned predictions and Model ID.

predict_async

predict_async(
    instances: typing.List,
    *,
    parameters: typing.Optional[typing.Dict] = None,
    timeout: typing.Optional[float] = None
) -> google.cloud.aiplatform.models.Prediction

Make an asynchronous prediction against this Endpoint. Example usage:

response = await my_endpoint.predict_async(instances=[...])
my_predictions = response.predictions
```

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` Optional. 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`.
`timeout`	`float` Optional. The timeout for this request in seconds.

Returns
Type	Description
`prediction (aiplatform.Prediction)`	Prediction with returned predictions and Model ID.

raw_predict

raw_predict(
    body: bytes,
    headers: typing.Dict[str, str],
    *,
    use_dedicated_endpoint: typing.Optional[bool] = False,
    timeout: typing.Optional[float] = None
) -> requests.models.Response

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'} )

For dedicated endpoint:

response = my_endpoint.raw_predict(
    body = b'{"instances":[{"feat_1":val_1, "feat_2":val_2}]}',
    headers = {'Content-Type':'application/json'},
    dedicated_endpoint=True,
)
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.
`use_dedicated_endpoint`	`bool` Optional. Default value is False. If set to True, the underlying prediction call will be made using the dedicated endpoint dns.
`timeout`	`float` Optional. The timeout for this request in seconds.

stream_direct_predict

stream_direct_predict(
    inputs_iterator: typing.Iterator[typing.List],
    parameters: typing.Optional[typing.Dict] = None,
    timeout: typing.Optional[float] = None,
) -> typing.Iterator[google.cloud.aiplatform.models.Prediction]

Makes a streaming direct (gRPC) prediction against this Endpoint for a pre-built image.

Parameters
Name	Description
`inputs_iterator`	`Iterator[List]` Required. An iterator of the inputs 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` Optional. 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`.
`timeout`	`Optional[float] :Yields: predictions (Iterator[aiplatform.Prediction]) -- The resulting streamed predictions.` Optional. The timeout for this request in seconds.

stream_direct_raw_predict

stream_direct_raw_predict(
    method_name: str,
    requests: typing.Iterator[bytes],
    timeout: typing.Optional[float] = None,
) -> typing.Iterator[google.cloud.aiplatform.models.Prediction]

Makes a direct (gRPC) streaming prediction request for a custom container.

Example usage:

my_endpoint = aiplatform.Endpoint(ENDPOINT_ID)
for stream_response in my_endpoint.stream_direct_raw_predict(
    request=b'...'
):
    yield stream_response
```

Parameters
Name	Description
`method_name`	`str` Fully qualified name of the API method being invoked to perform prediction.
`requests`	`Iterator[bytes]` The body of the prediction requests in bytes.
`timeout`	`Optional[float] :Yields: predictions (Iterator[aiplatform.Prediction]) -- The resulting streamed predictions.` Optional. The timeout for this request in seconds.

stream_raw_predict

stream_raw_predict(
    body: bytes,
    headers: typing.Dict[str, str],
    *,
    use_dedicated_endpoint: typing.Optional[bool] = False,
    timeout: typing.Optional[float] = None
) -> typing.Iterator[requests.models.Response]

Makes a streaming prediction request using arbitrary headers.

Example usage:

my_endpoint = aiplatform.Endpoint(ENDPOINT_ID)
for stream_response in my_endpoint.stream_raw_predict(
    body = b'{"instances":[{"feat_1":val_1, "feat_2":val_2}]}'
    headers = {'Content-Type':'application/json'}
):
    status_code = response.status_code
    stream_result = json.dumps(response.text)
```

For dedicated endpoint:
```
my_endpoint = aiplatform.Endpoint(ENDPOINT_ID)
for stream_response in my_endpoint.stream_raw_predict(
    body = b'{"instances":[{"feat_1":val_1, "feat_2":val_2}]}',
    headers = {'Content-Type':'application/json'},
    use_dedicated_endpoint=True,
):
    status_code = response.status_code
    stream_result = json.dumps(response.text)
```

Parameters
Name	Description
`body`	`bytes` The body of the prediction request in bytes. This must not exceed 10 mb per request.
`headers`	`Dict[str, str]` The header of the request as a dictionary. There are no restrictions on the header.
`use_dedicated_endpoint`	`bool` Optional. Default value is False. If set to True, the underlying prediction call will be made using the dedicated endpoint dns.
`timeout`	`float :Yields: predictions (Iterator[requests.models.Response]) -- The streaming prediction results.` Optional. The timeout for this request in seconds.

to_dict

to_dict() -> typing.Dict[str, typing.Any]

Returns the resource proto as a dictionary.

undeploy

undeploy(
    deployed_model_id: str,
    traffic_split: typing.Optional[typing.Dict[str, int]] = None,
    metadata: typing.Optional[typing.Sequence[typing.Tuple[str, str]]] = (),
    sync=True,
) -> None

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) -> google.cloud.aiplatform.models.Endpoint

Undeploys every model deployed to this Endpoint.

Parameter
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: typing.Optional[str] = None,
    description: typing.Optional[str] = None,
    labels: typing.Optional[typing.Dict[str, str]] = None,
    traffic_split: typing.Optional[typing.Dict[str, int]] = None,
    request_metadata: typing.Optional[typing.Sequence[typing.Tuple[str, str]]] = (),
    update_request_timeout: typing.Optional[float] = None,
) -> google.cloud.aiplatform.models.Endpoint

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]` 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.

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.