Google Cloud Ai Platform V1 Client - Class DeployedModel (0.23.0)

Version 0.23.0

• 1.13.1 (latest)
• 1.13.0
• 1.12.0
• 1.11.0
• 1.10.0
• 1.9.0
• 1.8.0
• 1.7.0
• 1.6.0
• 1.5.0
• 1.4.0
• 1.3.0
• 1.2.0
• 1.1.0
• 1.0.0
• 0.39.0
• 0.38.0
• 0.37.1
• 0.32.0
• 0.31.0
• 0.30.0
• 0.29.0
• 0.28.0
• 0.27.0
• 0.26.2
• 0.25.0
• 0.24.0
• 0.23.0
• 0.22.0
• 0.21.0
• 0.20.0
• 0.19.0
• 0.18.0
• 0.17.0
• 0.16.0
• 0.15.0
• 0.13.0
• 0.12.0
• 0.11.1
• 0.10.0

Reference documentation and code samples for the Google Cloud Ai Platform V1 Client class DeployedModel.

A deployment of a Model. Endpoints contain one or more DeployedModels.

Generated from protobuf message google.cloud.aiplatform.v1.DeployedModel

Namespace

Google \ Cloud \ AIPlatform \ V1

Methods

__construct

Constructor.

Parameters
Name	Description
data	array Optional. Data for populating the Message object.
↳ dedicated_resources	Google\Cloud\AIPlatform\V1\DedicatedResources A description of resources that are dedicated to the DeployedModel, and that need a higher degree of manual configuration.
↳ automatic_resources	Google\Cloud\AIPlatform\V1\AutomaticResources A description of resources that to large degree are decided by Vertex AI, and require only a modest additional configuration.
↳ id	string Immutable. The ID of the DeployedModel. If not provided upon deployment, Vertex AI will generate a value for this ID. This value should be 1-10 characters, and valid characters are /[0-9]/.
↳ model	string Required. The resource name of the Model that this is the deployment of. Note that the Model may be in a different location than the DeployedModel's Endpoint. The resource name may contain version id or version alias to specify the version. Example: projects/{project}/locations/{location}/models/{model}@2 or projects/{project}/locations/{location}/models/{model}@golden if no version is specified, the default version will be deployed.
↳ model_version_id	string Output only. The version ID of the model that is deployed.
↳ display_name	string The display name of the DeployedModel. If not provided upon creation, the Model's display_name is used.
↳ create_time	Google\Protobuf\Timestamp Output only. Timestamp when the DeployedModel was created.
↳ explanation_spec	Google\Cloud\AIPlatform\V1\ExplanationSpec Explanation configuration for this DeployedModel. When deploying a Model using EndpointService.DeployModel, this value overrides the value of Model.explanation_spec. All fields of explanation_spec are optional in the request. If a field of explanation_spec is not populated, the value of the same field of Model.explanation_spec is inherited. If the corresponding Model.explanation_spec is not populated, all fields of the explanation_spec will be used for the explanation configuration.
↳ service_account	string 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.
↳ disable_container_logging	bool For custom-trained Models and AutoML Tabular Models, the container of the DeployedModel instances will send stderr and stdout streams to Cloud Logging by default. Please note that the logs incur cost, which are subject to Cloud Logging pricing. User can disable container logging by setting this flag to true.
↳ enable_access_logging	bool If true, online prediction access logs are sent to Cloud Logging. These logs are like standard server access logs, containing information like timestamp and latency for each prediction request. Note that logs may incur a cost, especially if your project receives prediction requests at a high queries per second rate (QPS). Estimate your costs before enabling this option.
↳ private_endpoints	Google\Cloud\AIPlatform\V1\PrivateEndpoints Output only. Provide paths for users to send predict/explain/health requests directly to the deployed model services running on Cloud via private services access. This field is populated if network is configured.

getDedicatedResources

A description of resources that are dedicated to the DeployedModel, and that need a higher degree of manual configuration.

Returns
Type	Description
Google\Cloud\AIPlatform\V1\DedicatedResources|null

hasDedicatedResources

setDedicatedResources

A description of resources that are dedicated to the DeployedModel, and that need a higher degree of manual configuration.

Parameter
Name	Description
var	Google\Cloud\AIPlatform\V1\DedicatedResources

Returns
Type	Description
$this

getAutomaticResources

A description of resources that to large degree are decided by Vertex AI, and require only a modest additional configuration.

Returns
Type	Description
Google\Cloud\AIPlatform\V1\AutomaticResources|null

hasAutomaticResources

setAutomaticResources

A description of resources that to large degree are decided by Vertex AI, and require only a modest additional configuration.

Parameter
Name	Description
var	Google\Cloud\AIPlatform\V1\AutomaticResources

Returns
Type	Description
$this

getId

Immutable. The ID of the DeployedModel. If not provided upon deployment, Vertex AI will generate a value for this ID.

This value should be 1-10 characters, and valid characters are /[0-9]/.

Returns
Type	Description
string

setId

Immutable. The ID of the DeployedModel. If not provided upon deployment, Vertex AI will generate a value for this ID.

This value should be 1-10 characters, and valid characters are /[0-9]/.

Parameter
Name	Description
var	string

Returns
Type	Description
$this

getModel

Required. The resource name of the Model that this is the deployment of.

Note that the Model may be in a different location than the DeployedModel's Endpoint. The resource name may contain version id or version alias to specify the version. Example: projects/{project}/locations/{location}/models/{model}@2 or projects/{project}/locations/{location}/models/{model}@golden if no version is specified, the default version will be deployed.

Returns
Type	Description
string

setModel

Required. The resource name of the Model that this is the deployment of.

Note that the Model may be in a different location than the DeployedModel's Endpoint. The resource name may contain version id or version alias to specify the version. Example: projects/{project}/locations/{location}/models/{model}@2 or projects/{project}/locations/{location}/models/{model}@golden if no version is specified, the default version will be deployed.

Parameter
Name	Description
var	string

Returns
Type	Description
$this

getModelVersionId

Output only. The version ID of the model that is deployed.

Returns
Type	Description
string

setModelVersionId

Output only. The version ID of the model that is deployed.

Parameter
Name	Description
var	string

Returns
Type	Description
$this

getDisplayName

The display name of the DeployedModel. If not provided upon creation, the Model's display_name is used.

Returns
Type	Description
string

setDisplayName

The display name of the DeployedModel. If not provided upon creation, the Model's display_name is used.

Parameter
Name	Description
var	string

Returns
Type	Description
$this

getCreateTime

Output only. Timestamp when the DeployedModel was created.

Returns
Type	Description
Google\Protobuf\Timestamp|null

hasCreateTime

clearCreateTime

setCreateTime

Output only. Timestamp when the DeployedModel was created.

Parameter
Name	Description
var	Google\Protobuf\Timestamp

Returns
Type	Description
$this

getExplanationSpec

Explanation configuration for this DeployedModel.

When deploying a Model using EndpointService.DeployModel, this value overrides the value of Model.explanation_spec. All fields of explanation_spec are optional in the request. If a field of explanation_spec is not populated, the value of the same field of Model.explanation_spec is inherited. If the corresponding Model.explanation_spec is not populated, all fields of the explanation_spec will be used for the explanation configuration.

Returns
Type	Description
Google\Cloud\AIPlatform\V1\ExplanationSpec|null

hasExplanationSpec

clearExplanationSpec

setExplanationSpec

Explanation configuration for this DeployedModel.

When deploying a Model using EndpointService.DeployModel, this value overrides the value of Model.explanation_spec. All fields of explanation_spec are optional in the request. If a field of explanation_spec is not populated, the value of the same field of Model.explanation_spec is inherited. If the corresponding Model.explanation_spec is not populated, all fields of the explanation_spec will be used for the explanation configuration.

Parameter
Name	Description
var	Google\Cloud\AIPlatform\V1\ExplanationSpec

Returns
Type	Description
$this

getServiceAccount

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.

Returns
Type	Description
string

setServiceAccount

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.

Parameter
Name	Description
var	string

Returns
Type	Description
$this

getDisableContainerLogging

For custom-trained Models and AutoML Tabular Models, the container of the DeployedModel instances will send stderr and stdout streams to Cloud Logging by default. Please note that the logs incur cost, which are subject to Cloud Logging pricing.

User can disable container logging by setting this flag to true.

Returns
Type	Description
bool

setDisableContainerLogging

For custom-trained Models and AutoML Tabular Models, the container of the DeployedModel instances will send stderr and stdout streams to Cloud Logging by default. Please note that the logs incur cost, which are subject to Cloud Logging pricing.

User can disable container logging by setting this flag to true.

Parameter
Name	Description
var	bool

Returns
Type	Description
$this

getEnableAccessLogging

If true, online prediction access logs are sent to Cloud Logging.

These logs are like standard server access logs, containing information like timestamp and latency for each prediction request. Note that logs may incur a cost, especially if your project receives prediction requests at a high queries per second rate (QPS). Estimate your costs before enabling this option.

Returns
Type	Description
bool

setEnableAccessLogging

If true, online prediction access logs are sent to Cloud Logging.

These logs are like standard server access logs, containing information like timestamp and latency for each prediction request. Note that logs may incur a cost, especially if your project receives prediction requests at a high queries per second rate (QPS). Estimate your costs before enabling this option.

Parameter
Name	Description
var	bool

Returns
Type	Description
$this

getPrivateEndpoints

Output only. Provide paths for users to send predict/explain/health requests directly to the deployed model services running on Cloud via private services access. This field is populated if network is configured.

Returns
Type	Description
Google\Cloud\AIPlatform\V1\PrivateEndpoints|null

hasPrivateEndpoints

clearPrivateEndpoints

setPrivateEndpoints

Output only. Provide paths for users to send predict/explain/health requests directly to the deployed model services running on Cloud via private services access. This field is populated if network is configured.

Parameter
Name	Description
var	Google\Cloud\AIPlatform\V1\PrivateEndpoints

Returns
Type	Description
$this

getPredictionResources

Returns
Type	Description
string