Oracle Cloud Infrastructure v2.29.0, Apr 9 25

Oracle Cloud Infrastructure v2.29.0 published on Wednesday, Apr 9, 2025 by Pulumi

oci.DataScience.getModelDeployments

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Oracle Cloud Infrastructure v2.29.0 published on Wednesday, Apr 9, 2025 by Pulumi

Example Usage

TypeScript
Python
Go
C#
Java
YAML

import * as pulumi from "@pulumi/pulumi";
import * as oci from "@pulumi/oci";

const testModelDeployments = oci.DataScience.getModelDeployments({
    compartmentId: compartmentId,
    createdBy: modelDeploymentCreatedBy,
    displayName: modelDeploymentDisplayName,
    id: modelDeploymentId,
    projectId: testProject.id,
    state: modelDeploymentState,
});

import pulumi
import pulumi_oci as oci

test_model_deployments = oci.DataScience.get_model_deployments(compartment_id=compartment_id,
    created_by=model_deployment_created_by,
    display_name=model_deployment_display_name,
    id=model_deployment_id,
    project_id=test_project["id"],
    state=model_deployment_state)

package main

import (
	"github.com/pulumi/pulumi-oci/sdk/v2/go/oci/datascience"
	"github.com/pulumi/pulumi/sdk/v3/go/pulumi"
)

func main() {
	pulumi.Run(func(ctx *pulumi.Context) error {
		_, err := datascience.GetModelDeployments(ctx, &datascience.GetModelDeploymentsArgs{
			CompartmentId: compartmentId,
			CreatedBy:     pulumi.StringRef(modelDeploymentCreatedBy),
			DisplayName:   pulumi.StringRef(modelDeploymentDisplayName),
			Id:            pulumi.StringRef(modelDeploymentId),
			ProjectId:     pulumi.StringRef(testProject.Id),
			State:         pulumi.StringRef(modelDeploymentState),
		}, nil)
		if err != nil {
			return err
		}
		return nil
	})
}

using System.Collections.Generic;
using System.Linq;
using Pulumi;
using Oci = Pulumi.Oci;

return await Deployment.RunAsync(() => 
{
    var testModelDeployments = Oci.DataScience.GetModelDeployments.Invoke(new()
    {
        CompartmentId = compartmentId,
        CreatedBy = modelDeploymentCreatedBy,
        DisplayName = modelDeploymentDisplayName,
        Id = modelDeploymentId,
        ProjectId = testProject.Id,
        State = modelDeploymentState,
    });

});

package generated_program;

import com.pulumi.Context;
import com.pulumi.Pulumi;
import com.pulumi.core.Output;
import com.pulumi.oci.DataScience.DataScienceFunctions;
import com.pulumi.oci.DataScience.inputs.GetModelDeploymentsArgs;
import java.util.List;
import java.util.ArrayList;
import java.util.Map;
import java.io.File;
import java.nio.file.Files;
import java.nio.file.Paths;

public class App {
    public static void main(String[] args) {
        Pulumi.run(App::stack);
    }

    public static void stack(Context ctx) {
        final var testModelDeployments = DataScienceFunctions.getModelDeployments(GetModelDeploymentsArgs.builder()
            .compartmentId(compartmentId)
            .createdBy(modelDeploymentCreatedBy)
            .displayName(modelDeploymentDisplayName)
            .id(modelDeploymentId)
            .projectId(testProject.id())
            .state(modelDeploymentState)
            .build());

    }
}

variables:
  testModelDeployments:
    fn::invoke:
      function: oci:DataScience:getModelDeployments
      arguments:
        compartmentId: ${compartmentId}
        createdBy: ${modelDeploymentCreatedBy}
        displayName: ${modelDeploymentDisplayName}
        id: ${modelDeploymentId}
        projectId: ${testProject.id}
        state: ${modelDeploymentState}

Using getModelDeployments

Two invocation forms are available. The direct form accepts plain arguments and either blocks until the result value is available, or returns a Promise-wrapped result. The output form accepts Input-wrapped arguments and returns an Output-wrapped result.

TypeScript
Python
Go
C#
Java
YAML

function getModelDeployments(args: GetModelDeploymentsArgs, opts?: InvokeOptions): Promise<GetModelDeploymentsResult>
function getModelDeploymentsOutput(args: GetModelDeploymentsOutputArgs, opts?: InvokeOptions): Output<GetModelDeploymentsResult>

def get_model_deployments(compartment_id: Optional[str] = None,
                          created_by: Optional[str] = None,
                          display_name: Optional[str] = None,
                          filters: Optional[Sequence[_datascience.GetModelDeploymentsFilter]] = None,
                          id: Optional[str] = None,
                          project_id: Optional[str] = None,
                          state: Optional[str] = None,
                          opts: Optional[InvokeOptions] = None) -> GetModelDeploymentsResult
def get_model_deployments_output(compartment_id: Optional[pulumi.Input[str]] = None,
                          created_by: Optional[pulumi.Input[str]] = None,
                          display_name: Optional[pulumi.Input[str]] = None,
                          filters: Optional[pulumi.Input[Sequence[pulumi.Input[_datascience.GetModelDeploymentsFilterArgs]]]] = None,
                          id: Optional[pulumi.Input[str]] = None,
                          project_id: Optional[pulumi.Input[str]] = None,
                          state: Optional[pulumi.Input[str]] = None,
                          opts: Optional[InvokeOptions] = None) -> Output[GetModelDeploymentsResult]

func GetModelDeployments(ctx *Context, args *GetModelDeploymentsArgs, opts ...InvokeOption) (*GetModelDeploymentsResult, error)
func GetModelDeploymentsOutput(ctx *Context, args *GetModelDeploymentsOutputArgs, opts ...InvokeOption) GetModelDeploymentsResultOutput

> Note: This function is named GetModelDeployments in the Go SDK.

public static class GetModelDeployments 
{
    public static Task<GetModelDeploymentsResult> InvokeAsync(GetModelDeploymentsArgs args, InvokeOptions? opts = null)
    public static Output<GetModelDeploymentsResult> Invoke(GetModelDeploymentsInvokeArgs args, InvokeOptions? opts = null)
}

public static CompletableFuture<GetModelDeploymentsResult> getModelDeployments(GetModelDeploymentsArgs args, InvokeOptions options)
public static Output<GetModelDeploymentsResult> getModelDeployments(GetModelDeploymentsArgs args, InvokeOptions options)

fn::invoke:
  function: oci:DataScience/getModelDeployments:getModelDeployments
  arguments:
    # arguments dictionary

The following arguments are supported:

CompartmentId string: Filter results by the OCID of the compartment.
CreatedBy string: Filter results by the OCID of the user who created the resource.
DisplayName string: Filter results by its user-friendly name.
Filters List<GetModelDeploymentsFilter>
Id string: Filter results by OCID. Must be an OCID of the correct type for the resource type.
ProjectId string: Filter results by the OCID of the project.
State string: Filter results by the specified lifecycle state. Must be a valid state for the resource type.

CompartmentId string: Filter results by the OCID of the compartment.
CreatedBy string: Filter results by the OCID of the user who created the resource.
DisplayName string: Filter results by its user-friendly name.
Filters []GetModelDeploymentsFilter
Id string: Filter results by OCID. Must be an OCID of the correct type for the resource type.
ProjectId string: Filter results by the OCID of the project.
State string: Filter results by the specified lifecycle state. Must be a valid state for the resource type.

compartmentId String: Filter results by the OCID of the compartment.
createdBy String: Filter results by the OCID of the user who created the resource.
displayName String: Filter results by its user-friendly name.
filters List<GetModelDeploymentsFilter>
id String: Filter results by OCID. Must be an OCID of the correct type for the resource type.
projectId String: Filter results by the OCID of the project.
state String: Filter results by the specified lifecycle state. Must be a valid state for the resource type.

compartmentId string: Filter results by the OCID of the compartment.
createdBy string: Filter results by the OCID of the user who created the resource.
displayName string: Filter results by its user-friendly name.
filters GetModelDeploymentsFilter[]
id string: Filter results by OCID. Must be an OCID of the correct type for the resource type.
projectId string: Filter results by the OCID of the project.
state string: Filter results by the specified lifecycle state. Must be a valid state for the resource type.

compartment_id str: Filter results by the OCID of the compartment.
created_by str: Filter results by the OCID of the user who created the resource.
display_name str: Filter results by its user-friendly name.
filters Sequence[datascience.GetModelDeploymentsFilter]
id str: Filter results by OCID. Must be an OCID of the correct type for the resource type.
project_id str: Filter results by the OCID of the project.
state str: Filter results by the specified lifecycle state. Must be a valid state for the resource type.

compartmentId String: Filter results by the OCID of the compartment.
createdBy String: Filter results by the OCID of the user who created the resource.
displayName String: Filter results by its user-friendly name.
filters List<Property Map>
id String: Filter results by OCID. Must be an OCID of the correct type for the resource type.
projectId String: Filter results by the OCID of the project.
state String: Filter results by the specified lifecycle state. Must be a valid state for the resource type.

getModelDeployments Result

The following output properties are available:

CompartmentId string: The OCID of the model deployment's compartment.
ModelDeployments List<GetModelDeploymentsModelDeployment>: The list of model_deployments.
CreatedBy string: The OCID of the user who created the model deployment.
DisplayName string: A user-friendly display name for the resource. Does not have to be unique, and can be modified. Avoid entering confidential information. Example: My ModelDeployment
Filters List<GetModelDeploymentsFilter>
Id string: The OCID of the model deployment.
ProjectId string: The OCID of the project associated with the model deployment.
State string: The state of the model deployment.

CompartmentId string: The OCID of the model deployment's compartment.
ModelDeployments []GetModelDeploymentsModelDeployment: The list of model_deployments.
CreatedBy string: The OCID of the user who created the model deployment.
DisplayName string: A user-friendly display name for the resource. Does not have to be unique, and can be modified. Avoid entering confidential information. Example: My ModelDeployment
Filters []GetModelDeploymentsFilter
Id string: The OCID of the model deployment.
ProjectId string: The OCID of the project associated with the model deployment.
State string: The state of the model deployment.

compartmentId String: The OCID of the model deployment's compartment.
modelDeployments List<GetModelDeploymentsModelDeployment>: The list of model_deployments.
createdBy String: The OCID of the user who created the model deployment.
displayName String: A user-friendly display name for the resource. Does not have to be unique, and can be modified. Avoid entering confidential information. Example: My ModelDeployment
filters List<GetModelDeploymentsFilter>
id String: The OCID of the model deployment.
projectId String: The OCID of the project associated with the model deployment.
state String: The state of the model deployment.

compartmentId string: The OCID of the model deployment's compartment.
modelDeployments GetModelDeploymentsModelDeployment[]: The list of model_deployments.
createdBy string: The OCID of the user who created the model deployment.
displayName string: A user-friendly display name for the resource. Does not have to be unique, and can be modified. Avoid entering confidential information. Example: My ModelDeployment
filters GetModelDeploymentsFilter[]
id string: The OCID of the model deployment.
projectId string: The OCID of the project associated with the model deployment.
state string: The state of the model deployment.

compartment_id str: The OCID of the model deployment's compartment.
model_deployments Sequence[datascience.GetModelDeploymentsModelDeployment]: The list of model_deployments.
created_by str: The OCID of the user who created the model deployment.
display_name str: A user-friendly display name for the resource. Does not have to be unique, and can be modified. Avoid entering confidential information. Example: My ModelDeployment
filters Sequence[datascience.GetModelDeploymentsFilter]
id str: The OCID of the model deployment.
project_id str: The OCID of the project associated with the model deployment.
state str: The state of the model deployment.

compartmentId String: The OCID of the model deployment's compartment.
modelDeployments List<Property Map>: The list of model_deployments.
createdBy String: The OCID of the user who created the model deployment.
displayName String: A user-friendly display name for the resource. Does not have to be unique, and can be modified. Avoid entering confidential information. Example: My ModelDeployment
filters List<Property Map>
id String: The OCID of the model deployment.
projectId String: The OCID of the project associated with the model deployment.
state String: The state of the model deployment.

Supporting Types

GetModelDeploymentsFilter

Name string
Values List<string>
Regex bool

Name string
Values []string
Regex bool

name String
values List<String>
regex Boolean

name string
values string[]
regex boolean

name str
values Sequence[str]
regex bool

name String
values List<String>
regex Boolean

GetModelDeploymentsModelDeployment

CategoryLogDetails List<GetModelDeploymentsModelDeploymentCategoryLogDetail>: The log details for each category.
CompartmentId string: Filter results by the OCID of the compartment.
CreatedBy string: Filter results by the OCID of the user who created the resource.
DefinedTags Dictionary<string, string>: Defined tags for this resource. Each key is predefined and scoped to a namespace. See Resource Tags. Example: {"Operations.CostCenter": "42"}
Description string: A short description of the model deployment.
DisplayName string: Filter results by its user-friendly name.
FreeformTags Dictionary<string, string>: Free-form tags for this resource. Each tag is a simple key-value pair with no predefined name, type, or namespace. See Resource Tags. Example: {"Department": "Finance"}
Id string: Filter results by OCID. Must be an OCID of the correct type for the resource type.
LifecycleDetails string: Details about the state of the model deployment.
ModelDeploymentConfigurationDetails List<GetModelDeploymentsModelDeploymentModelDeploymentConfigurationDetail>: The model deployment configuration details.
ModelDeploymentSystemDatas List<GetModelDeploymentsModelDeploymentModelDeploymentSystemData>: Model deployment system data.
ModelDeploymentUrl string: The URL to interact with the model deployment.
OpcParentRptUrl string
ProjectId string: Filter results by the OCID of the project.
State string: Filter results by the specified lifecycle state. Must be a valid state for the resource type.
TimeCreated string: The date and time the resource was created, in the timestamp format defined by RFC3339. Example: 2019-08-25T21:10:29.41Z

CategoryLogDetails []GetModelDeploymentsModelDeploymentCategoryLogDetail: The log details for each category.
CompartmentId string: Filter results by the OCID of the compartment.
CreatedBy string: Filter results by the OCID of the user who created the resource.
DefinedTags map[string]string: Defined tags for this resource. Each key is predefined and scoped to a namespace. See Resource Tags. Example: {"Operations.CostCenter": "42"}
Description string: A short description of the model deployment.
DisplayName string: Filter results by its user-friendly name.
FreeformTags map[string]string: Free-form tags for this resource. Each tag is a simple key-value pair with no predefined name, type, or namespace. See Resource Tags. Example: {"Department": "Finance"}
Id string: Filter results by OCID. Must be an OCID of the correct type for the resource type.
LifecycleDetails string: Details about the state of the model deployment.
ModelDeploymentConfigurationDetails []GetModelDeploymentsModelDeploymentModelDeploymentConfigurationDetail: The model deployment configuration details.
ModelDeploymentSystemDatas []GetModelDeploymentsModelDeploymentModelDeploymentSystemData: Model deployment system data.
ModelDeploymentUrl string: The URL to interact with the model deployment.
OpcParentRptUrl string
ProjectId string: Filter results by the OCID of the project.
State string: Filter results by the specified lifecycle state. Must be a valid state for the resource type.
TimeCreated string: The date and time the resource was created, in the timestamp format defined by RFC3339. Example: 2019-08-25T21:10:29.41Z

categoryLogDetails List<GetModelDeploymentsModelDeploymentCategoryLogDetail>: The log details for each category.
compartmentId String: Filter results by the OCID of the compartment.
createdBy String: Filter results by the OCID of the user who created the resource.
definedTags Map<String,String>: Defined tags for this resource. Each key is predefined and scoped to a namespace. See Resource Tags. Example: {"Operations.CostCenter": "42"}
description String: A short description of the model deployment.
displayName String: Filter results by its user-friendly name.
freeformTags Map<String,String>: Free-form tags for this resource. Each tag is a simple key-value pair with no predefined name, type, or namespace. See Resource Tags. Example: {"Department": "Finance"}
id String: Filter results by OCID. Must be an OCID of the correct type for the resource type.
lifecycleDetails String: Details about the state of the model deployment.
modelDeploymentConfigurationDetails List<GetModelDeploymentsModelDeploymentModelDeploymentConfigurationDetail>: The model deployment configuration details.
modelDeploymentSystemDatas List<GetModelDeploymentsModelDeploymentModelDeploymentSystemData>: Model deployment system data.
modelDeploymentUrl String: The URL to interact with the model deployment.
opcParentRptUrl String
projectId String: Filter results by the OCID of the project.
state String: Filter results by the specified lifecycle state. Must be a valid state for the resource type.
timeCreated String: The date and time the resource was created, in the timestamp format defined by RFC3339. Example: 2019-08-25T21:10:29.41Z

categoryLogDetails GetModelDeploymentsModelDeploymentCategoryLogDetail[]: The log details for each category.
compartmentId string: Filter results by the OCID of the compartment.
createdBy string: Filter results by the OCID of the user who created the resource.
definedTags {[key: string]: string}: Defined tags for this resource. Each key is predefined and scoped to a namespace. See Resource Tags. Example: {"Operations.CostCenter": "42"}
description string: A short description of the model deployment.
displayName string: Filter results by its user-friendly name.
freeformTags {[key: string]: string}: Free-form tags for this resource. Each tag is a simple key-value pair with no predefined name, type, or namespace. See Resource Tags. Example: {"Department": "Finance"}
id string: Filter results by OCID. Must be an OCID of the correct type for the resource type.
lifecycleDetails string: Details about the state of the model deployment.
modelDeploymentConfigurationDetails GetModelDeploymentsModelDeploymentModelDeploymentConfigurationDetail[]: The model deployment configuration details.
modelDeploymentSystemDatas GetModelDeploymentsModelDeploymentModelDeploymentSystemData[]: Model deployment system data.
modelDeploymentUrl string: The URL to interact with the model deployment.
opcParentRptUrl string
projectId string: Filter results by the OCID of the project.
state string: Filter results by the specified lifecycle state. Must be a valid state for the resource type.
timeCreated string: The date and time the resource was created, in the timestamp format defined by RFC3339. Example: 2019-08-25T21:10:29.41Z

category_log_details Sequence[datascience.GetModelDeploymentsModelDeploymentCategoryLogDetail]: The log details for each category.
compartment_id str: Filter results by the OCID of the compartment.
created_by str: Filter results by the OCID of the user who created the resource.
defined_tags Mapping[str, str]: Defined tags for this resource. Each key is predefined and scoped to a namespace. See Resource Tags. Example: {"Operations.CostCenter": "42"}
description str: A short description of the model deployment.
display_name str: Filter results by its user-friendly name.
freeform_tags Mapping[str, str]: Free-form tags for this resource. Each tag is a simple key-value pair with no predefined name, type, or namespace. See Resource Tags. Example: {"Department": "Finance"}
id str: Filter results by OCID. Must be an OCID of the correct type for the resource type.
lifecycle_details str: Details about the state of the model deployment.
model_deployment_configuration_details Sequence[datascience.GetModelDeploymentsModelDeploymentModelDeploymentConfigurationDetail]: The model deployment configuration details.
model_deployment_system_datas Sequence[datascience.GetModelDeploymentsModelDeploymentModelDeploymentSystemData]: Model deployment system data.
model_deployment_url str: The URL to interact with the model deployment.
opc_parent_rpt_url str
project_id str: Filter results by the OCID of the project.
state str: Filter results by the specified lifecycle state. Must be a valid state for the resource type.
time_created str: The date and time the resource was created, in the timestamp format defined by RFC3339. Example: 2019-08-25T21:10:29.41Z

categoryLogDetails List<Property Map>: The log details for each category.
compartmentId String: Filter results by the OCID of the compartment.
createdBy String: Filter results by the OCID of the user who created the resource.
definedTags Map<String>: Defined tags for this resource. Each key is predefined and scoped to a namespace. See Resource Tags. Example: {"Operations.CostCenter": "42"}
description String: A short description of the model deployment.
displayName String: Filter results by its user-friendly name.
freeformTags Map<String>: Free-form tags for this resource. Each tag is a simple key-value pair with no predefined name, type, or namespace. See Resource Tags. Example: {"Department": "Finance"}
id String: Filter results by OCID. Must be an OCID of the correct type for the resource type.
lifecycleDetails String: Details about the state of the model deployment.
modelDeploymentConfigurationDetails List<Property Map>: The model deployment configuration details.
modelDeploymentSystemDatas List<Property Map>: Model deployment system data.
modelDeploymentUrl String: The URL to interact with the model deployment.
opcParentRptUrl String
projectId String: Filter results by the OCID of the project.
state String: Filter results by the specified lifecycle state. Must be a valid state for the resource type.
timeCreated String: The date and time the resource was created, in the timestamp format defined by RFC3339. Example: 2019-08-25T21:10:29.41Z

GetModelDeploymentsModelDeploymentCategoryLogDetail

Accesses List<GetModelDeploymentsModelDeploymentCategoryLogDetailAccess>: The log details.
Predicts List<GetModelDeploymentsModelDeploymentCategoryLogDetailPredict>: The log details.

Accesses []GetModelDeploymentsModelDeploymentCategoryLogDetailAccess: The log details.
Predicts []GetModelDeploymentsModelDeploymentCategoryLogDetailPredict: The log details.

accesses List<GetModelDeploymentsModelDeploymentCategoryLogDetailAccess>: The log details.
predicts List<GetModelDeploymentsModelDeploymentCategoryLogDetailPredict>: The log details.

accesses GetModelDeploymentsModelDeploymentCategoryLogDetailAccess[]: The log details.
predicts GetModelDeploymentsModelDeploymentCategoryLogDetailPredict[]: The log details.

accesses Sequence[datascience.GetModelDeploymentsModelDeploymentCategoryLogDetailAccess]: The log details.
predicts Sequence[datascience.GetModelDeploymentsModelDeploymentCategoryLogDetailPredict]: The log details.

accesses List<Property Map>: The log details.
predicts List<Property Map>: The log details.

GetModelDeploymentsModelDeploymentCategoryLogDetailAccess

LogGroupId string: The OCID of a log group to work with.
LogId string: The OCID of a log to work with.

LogGroupId string: The OCID of a log group to work with.
LogId string: The OCID of a log to work with.

logGroupId String: The OCID of a log group to work with.
logId String: The OCID of a log to work with.

logGroupId string: The OCID of a log group to work with.
logId string: The OCID of a log to work with.

log_group_id str: The OCID of a log group to work with.
log_id str: The OCID of a log to work with.

logGroupId String: The OCID of a log group to work with.
logId String: The OCID of a log to work with.

GetModelDeploymentsModelDeploymentCategoryLogDetailPredict

LogGroupId string: The OCID of a log group to work with.
LogId string: The OCID of a log to work with.

LogGroupId string: The OCID of a log group to work with.
LogId string: The OCID of a log to work with.

logGroupId String: The OCID of a log group to work with.
logId String: The OCID of a log to work with.

logGroupId string: The OCID of a log group to work with.
logId string: The OCID of a log to work with.

log_group_id str: The OCID of a log group to work with.
log_id str: The OCID of a log to work with.

logGroupId String: The OCID of a log group to work with.
logId String: The OCID of a log to work with.

GetModelDeploymentsModelDeploymentModelDeploymentConfigurationDetail

DeploymentType string: The type of the model deployment.
EnvironmentConfigurationDetails List<GetModelDeploymentsModelDeploymentModelDeploymentConfigurationDetailEnvironmentConfigurationDetail>: The configuration to carry the environment details thats used in Model Deployment creation
ModelConfigurationDetails List<GetModelDeploymentsModelDeploymentModelDeploymentConfigurationDetailModelConfigurationDetail>: The model configuration details.

DeploymentType string: The type of the model deployment.
EnvironmentConfigurationDetails []GetModelDeploymentsModelDeploymentModelDeploymentConfigurationDetailEnvironmentConfigurationDetail: The configuration to carry the environment details thats used in Model Deployment creation
ModelConfigurationDetails []GetModelDeploymentsModelDeploymentModelDeploymentConfigurationDetailModelConfigurationDetail: The model configuration details.

deploymentType String: The type of the model deployment.
environmentConfigurationDetails List<GetModelDeploymentsModelDeploymentModelDeploymentConfigurationDetailEnvironmentConfigurationDetail>: The configuration to carry the environment details thats used in Model Deployment creation
modelConfigurationDetails List<GetModelDeploymentsModelDeploymentModelDeploymentConfigurationDetailModelConfigurationDetail>: The model configuration details.

deploymentType string: The type of the model deployment.
environmentConfigurationDetails GetModelDeploymentsModelDeploymentModelDeploymentConfigurationDetailEnvironmentConfigurationDetail[]: The configuration to carry the environment details thats used in Model Deployment creation
modelConfigurationDetails GetModelDeploymentsModelDeploymentModelDeploymentConfigurationDetailModelConfigurationDetail[]: The model configuration details.

deployment_type str: The type of the model deployment.
environment_configuration_details Sequence[datascience.GetModelDeploymentsModelDeploymentModelDeploymentConfigurationDetailEnvironmentConfigurationDetail]: The configuration to carry the environment details thats used in Model Deployment creation
model_configuration_details Sequence[datascience.GetModelDeploymentsModelDeploymentModelDeploymentConfigurationDetailModelConfigurationDetail]: The model configuration details.

deploymentType String: The type of the model deployment.
environmentConfigurationDetails List<Property Map>: The configuration to carry the environment details thats used in Model Deployment creation
modelConfigurationDetails List<Property Map>: The model configuration details.

GetModelDeploymentsModelDeploymentModelDeploymentConfigurationDetailEnvironmentConfigurationDetail

Cmds List<string>: The container image run CMD as a list of strings. Use CMD as arguments to the ENTRYPOINT or the only command to run in the absence of an ENTRYPOINT. The combined size of CMD and ENTRYPOINT must be less than 2048 bytes.
Entrypoints List<string>: The container image run ENTRYPOINT as a list of strings. Accept the CMD as extra arguments. The combined size of CMD and ENTRYPOINT must be less than 2048 bytes. More information on how CMD and ENTRYPOINT interact are here.
EnvironmentConfigurationType string: The environment configuration type
EnvironmentVariables Dictionary<string, string>: Environment variables to set for the web server container. The size of envVars must be less than 2048 bytes. Key should be under 32 characters. Key should contain only letters, digits and underscore (_) Key should start with a letter. Key should have at least 2 characters. Key should not end with underscore eg. TEST_ Key if added cannot be empty. Value can be empty. No specific size limits on individual Values. But overall environment variables is limited to 2048 bytes. Key can't be reserved Model Deployment environment variables.
HealthCheckPort int: The port on which the container HEALTHCHECK would listen. The port can be anything between 1024 and 65535. The following ports cannot be used 24224, 8446, 8447.
Image string: The full path to the Oracle Container Repository (OCIR) registry, image, and tag in a canonical format. Acceptable format: <region>.ocir.io/<registry>/<image>:<tag> <region>.ocir.io/<registry>/<image>:<tag>@digest
ImageDigest string: The digest of the container image. For example, sha256:881303a6b2738834d795a32b4a98eb0e5e3d1cad590a712d1e04f9b2fa90a030
ServerPort int: The port on which the web server serving the inference is running. The port can be anything between 1024 and 65535. The following ports cannot be used 24224, 8446, 8447.

Cmds []string: The container image run CMD as a list of strings. Use CMD as arguments to the ENTRYPOINT or the only command to run in the absence of an ENTRYPOINT. The combined size of CMD and ENTRYPOINT must be less than 2048 bytes.
Entrypoints []string: The container image run ENTRYPOINT as a list of strings. Accept the CMD as extra arguments. The combined size of CMD and ENTRYPOINT must be less than 2048 bytes. More information on how CMD and ENTRYPOINT interact are here.
EnvironmentConfigurationType string: The environment configuration type
EnvironmentVariables map[string]string: Environment variables to set for the web server container. The size of envVars must be less than 2048 bytes. Key should be under 32 characters. Key should contain only letters, digits and underscore (_) Key should start with a letter. Key should have at least 2 characters. Key should not end with underscore eg. TEST_ Key if added cannot be empty. Value can be empty. No specific size limits on individual Values. But overall environment variables is limited to 2048 bytes. Key can't be reserved Model Deployment environment variables.
HealthCheckPort int: The port on which the container HEALTHCHECK would listen. The port can be anything between 1024 and 65535. The following ports cannot be used 24224, 8446, 8447.
Image string: The full path to the Oracle Container Repository (OCIR) registry, image, and tag in a canonical format. Acceptable format: <region>.ocir.io/<registry>/<image>:<tag> <region>.ocir.io/<registry>/<image>:<tag>@digest
ImageDigest string: The digest of the container image. For example, sha256:881303a6b2738834d795a32b4a98eb0e5e3d1cad590a712d1e04f9b2fa90a030
ServerPort int: The port on which the web server serving the inference is running. The port can be anything between 1024 and 65535. The following ports cannot be used 24224, 8446, 8447.

cmds List<String>: The container image run CMD as a list of strings. Use CMD as arguments to the ENTRYPOINT or the only command to run in the absence of an ENTRYPOINT. The combined size of CMD and ENTRYPOINT must be less than 2048 bytes.
entrypoints List<String>: The container image run ENTRYPOINT as a list of strings. Accept the CMD as extra arguments. The combined size of CMD and ENTRYPOINT must be less than 2048 bytes. More information on how CMD and ENTRYPOINT interact are here.
environmentConfigurationType String: The environment configuration type
environmentVariables Map<String,String>: Environment variables to set for the web server container. The size of envVars must be less than 2048 bytes. Key should be under 32 characters. Key should contain only letters, digits and underscore (_) Key should start with a letter. Key should have at least 2 characters. Key should not end with underscore eg. TEST_ Key if added cannot be empty. Value can be empty. No specific size limits on individual Values. But overall environment variables is limited to 2048 bytes. Key can't be reserved Model Deployment environment variables.
healthCheckPort Integer: The port on which the container HEALTHCHECK would listen. The port can be anything between 1024 and 65535. The following ports cannot be used 24224, 8446, 8447.
image String: The full path to the Oracle Container Repository (OCIR) registry, image, and tag in a canonical format. Acceptable format: <region>.ocir.io/<registry>/<image>:<tag> <region>.ocir.io/<registry>/<image>:<tag>@digest
imageDigest String: The digest of the container image. For example, sha256:881303a6b2738834d795a32b4a98eb0e5e3d1cad590a712d1e04f9b2fa90a030
serverPort Integer: The port on which the web server serving the inference is running. The port can be anything between 1024 and 65535. The following ports cannot be used 24224, 8446, 8447.

cmds string[]: The container image run CMD as a list of strings. Use CMD as arguments to the ENTRYPOINT or the only command to run in the absence of an ENTRYPOINT. The combined size of CMD and ENTRYPOINT must be less than 2048 bytes.
entrypoints string[]: The container image run ENTRYPOINT as a list of strings. Accept the CMD as extra arguments. The combined size of CMD and ENTRYPOINT must be less than 2048 bytes. More information on how CMD and ENTRYPOINT interact are here.
environmentConfigurationType string: The environment configuration type
environmentVariables {[key: string]: string}: Environment variables to set for the web server container. The size of envVars must be less than 2048 bytes. Key should be under 32 characters. Key should contain only letters, digits and underscore (_) Key should start with a letter. Key should have at least 2 characters. Key should not end with underscore eg. TEST_ Key if added cannot be empty. Value can be empty. No specific size limits on individual Values. But overall environment variables is limited to 2048 bytes. Key can't be reserved Model Deployment environment variables.
healthCheckPort number: The port on which the container HEALTHCHECK would listen. The port can be anything between 1024 and 65535. The following ports cannot be used 24224, 8446, 8447.
image string: The full path to the Oracle Container Repository (OCIR) registry, image, and tag in a canonical format. Acceptable format: <region>.ocir.io/<registry>/<image>:<tag> <region>.ocir.io/<registry>/<image>:<tag>@digest
imageDigest string: The digest of the container image. For example, sha256:881303a6b2738834d795a32b4a98eb0e5e3d1cad590a712d1e04f9b2fa90a030
serverPort number: The port on which the web server serving the inference is running. The port can be anything between 1024 and 65535. The following ports cannot be used 24224, 8446, 8447.

cmds Sequence[str]: The container image run CMD as a list of strings. Use CMD as arguments to the ENTRYPOINT or the only command to run in the absence of an ENTRYPOINT. The combined size of CMD and ENTRYPOINT must be less than 2048 bytes.
entrypoints Sequence[str]: The container image run ENTRYPOINT as a list of strings. Accept the CMD as extra arguments. The combined size of CMD and ENTRYPOINT must be less than 2048 bytes. More information on how CMD and ENTRYPOINT interact are here.
environment_configuration_type str: The environment configuration type
environment_variables Mapping[str, str]: Environment variables to set for the web server container. The size of envVars must be less than 2048 bytes. Key should be under 32 characters. Key should contain only letters, digits and underscore (_) Key should start with a letter. Key should have at least 2 characters. Key should not end with underscore eg. TEST_ Key if added cannot be empty. Value can be empty. No specific size limits on individual Values. But overall environment variables is limited to 2048 bytes. Key can't be reserved Model Deployment environment variables.
health_check_port int: The port on which the container HEALTHCHECK would listen. The port can be anything between 1024 and 65535. The following ports cannot be used 24224, 8446, 8447.
image str: The full path to the Oracle Container Repository (OCIR) registry, image, and tag in a canonical format. Acceptable format: <region>.ocir.io/<registry>/<image>:<tag> <region>.ocir.io/<registry>/<image>:<tag>@digest
image_digest str: The digest of the container image. For example, sha256:881303a6b2738834d795a32b4a98eb0e5e3d1cad590a712d1e04f9b2fa90a030
server_port int: The port on which the web server serving the inference is running. The port can be anything between 1024 and 65535. The following ports cannot be used 24224, 8446, 8447.

cmds List<String>: The container image run CMD as a list of strings. Use CMD as arguments to the ENTRYPOINT or the only command to run in the absence of an ENTRYPOINT. The combined size of CMD and ENTRYPOINT must be less than 2048 bytes.
entrypoints List<String>: The container image run ENTRYPOINT as a list of strings. Accept the CMD as extra arguments. The combined size of CMD and ENTRYPOINT must be less than 2048 bytes. More information on how CMD and ENTRYPOINT interact are here.
environmentConfigurationType String: The environment configuration type
environmentVariables Map<String>: Environment variables to set for the web server container. The size of envVars must be less than 2048 bytes. Key should be under 32 characters. Key should contain only letters, digits and underscore (_) Key should start with a letter. Key should have at least 2 characters. Key should not end with underscore eg. TEST_ Key if added cannot be empty. Value can be empty. No specific size limits on individual Values. But overall environment variables is limited to 2048 bytes. Key can't be reserved Model Deployment environment variables.
healthCheckPort Number: The port on which the container HEALTHCHECK would listen. The port can be anything between 1024 and 65535. The following ports cannot be used 24224, 8446, 8447.
image String: The full path to the Oracle Container Repository (OCIR) registry, image, and tag in a canonical format. Acceptable format: <region>.ocir.io/<registry>/<image>:<tag> <region>.ocir.io/<registry>/<image>:<tag>@digest
imageDigest String: The digest of the container image. For example, sha256:881303a6b2738834d795a32b4a98eb0e5e3d1cad590a712d1e04f9b2fa90a030
serverPort Number: The port on which the web server serving the inference is running. The port can be anything between 1024 and 65535. The following ports cannot be used 24224, 8446, 8447.

GetModelDeploymentsModelDeploymentModelDeploymentConfigurationDetailModelConfigurationDetail

BandwidthMbps int: The minimum network bandwidth for the model deployment.
InstanceConfigurations List<GetModelDeploymentsModelDeploymentModelDeploymentConfigurationDetailModelConfigurationDetailInstanceConfiguration>: The model deployment instance configuration
MaximumBandwidthMbps int: The maximum network bandwidth for the model deployment.
ModelId string: The OCID of the model you want to deploy.
ScalingPolicies List<GetModelDeploymentsModelDeploymentModelDeploymentConfigurationDetailModelConfigurationDetailScalingPolicy>: The scaling policy to apply to each model of the deployment.

BandwidthMbps int: The minimum network bandwidth for the model deployment.
InstanceConfigurations []GetModelDeploymentsModelDeploymentModelDeploymentConfigurationDetailModelConfigurationDetailInstanceConfiguration: The model deployment instance configuration
MaximumBandwidthMbps int: The maximum network bandwidth for the model deployment.
ModelId string: The OCID of the model you want to deploy.
ScalingPolicies []GetModelDeploymentsModelDeploymentModelDeploymentConfigurationDetailModelConfigurationDetailScalingPolicy: The scaling policy to apply to each model of the deployment.

bandwidthMbps Integer: The minimum network bandwidth for the model deployment.
instanceConfigurations List<GetModelDeploymentsModelDeploymentModelDeploymentConfigurationDetailModelConfigurationDetailInstanceConfiguration>: The model deployment instance configuration
maximumBandwidthMbps Integer: The maximum network bandwidth for the model deployment.
modelId String: The OCID of the model you want to deploy.
scalingPolicies List<GetModelDeploymentsModelDeploymentModelDeploymentConfigurationDetailModelConfigurationDetailScalingPolicy>: The scaling policy to apply to each model of the deployment.

bandwidthMbps number: The minimum network bandwidth for the model deployment.
instanceConfigurations GetModelDeploymentsModelDeploymentModelDeploymentConfigurationDetailModelConfigurationDetailInstanceConfiguration[]: The model deployment instance configuration
maximumBandwidthMbps number: The maximum network bandwidth for the model deployment.
modelId string: The OCID of the model you want to deploy.
scalingPolicies GetModelDeploymentsModelDeploymentModelDeploymentConfigurationDetailModelConfigurationDetailScalingPolicy[]: The scaling policy to apply to each model of the deployment.

bandwidth_mbps int: The minimum network bandwidth for the model deployment.
instance_configurations Sequence[datascience.GetModelDeploymentsModelDeploymentModelDeploymentConfigurationDetailModelConfigurationDetailInstanceConfiguration]: The model deployment instance configuration
maximum_bandwidth_mbps int: The maximum network bandwidth for the model deployment.
model_id str: The OCID of the model you want to deploy.
scaling_policies Sequence[datascience.GetModelDeploymentsModelDeploymentModelDeploymentConfigurationDetailModelConfigurationDetailScalingPolicy]: The scaling policy to apply to each model of the deployment.

bandwidthMbps Number: The minimum network bandwidth for the model deployment.
instanceConfigurations List<Property Map>: The model deployment instance configuration
maximumBandwidthMbps Number: The maximum network bandwidth for the model deployment.
modelId String: The OCID of the model you want to deploy.
scalingPolicies List<Property Map>: The scaling policy to apply to each model of the deployment.

GetModelDeploymentsModelDeploymentModelDeploymentConfigurationDetailModelConfigurationDetailInstanceConfiguration

InstanceShapeName string: The shape used to launch the model deployment instances.
ModelDeploymentInstanceShapeConfigDetails List<GetModelDeploymentsModelDeploymentModelDeploymentConfigurationDetailModelConfigurationDetailInstanceConfigurationModelDeploymentInstanceShapeConfigDetail>: Details for the model-deployment instance shape configuration.
PrivateEndpointId string: The OCID of a Data Science private endpoint.
SubnetId string: A model deployment instance is provided with a VNIC for network access. This specifies the OCID of the subnet to create a VNIC in. The subnet should be in a VCN with a NAT/SGW gateway for egress.

InstanceShapeName string: The shape used to launch the model deployment instances.
ModelDeploymentInstanceShapeConfigDetails []GetModelDeploymentsModelDeploymentModelDeploymentConfigurationDetailModelConfigurationDetailInstanceConfigurationModelDeploymentInstanceShapeConfigDetail: Details for the model-deployment instance shape configuration.
PrivateEndpointId string: The OCID of a Data Science private endpoint.
SubnetId string: A model deployment instance is provided with a VNIC for network access. This specifies the OCID of the subnet to create a VNIC in. The subnet should be in a VCN with a NAT/SGW gateway for egress.

instanceShapeName String: The shape used to launch the model deployment instances.
modelDeploymentInstanceShapeConfigDetails List<GetModelDeploymentsModelDeploymentModelDeploymentConfigurationDetailModelConfigurationDetailInstanceConfigurationModelDeploymentInstanceShapeConfigDetail>: Details for the model-deployment instance shape configuration.
privateEndpointId String: The OCID of a Data Science private endpoint.
subnetId String: A model deployment instance is provided with a VNIC for network access. This specifies the OCID of the subnet to create a VNIC in. The subnet should be in a VCN with a NAT/SGW gateway for egress.

instanceShapeName string: The shape used to launch the model deployment instances.
modelDeploymentInstanceShapeConfigDetails GetModelDeploymentsModelDeploymentModelDeploymentConfigurationDetailModelConfigurationDetailInstanceConfigurationModelDeploymentInstanceShapeConfigDetail[]: Details for the model-deployment instance shape configuration.
privateEndpointId string: The OCID of a Data Science private endpoint.
subnetId string: A model deployment instance is provided with a VNIC for network access. This specifies the OCID of the subnet to create a VNIC in. The subnet should be in a VCN with a NAT/SGW gateway for egress.

instance_shape_name str: The shape used to launch the model deployment instances.
model_deployment_instance_shape_config_details Sequence[datascience.GetModelDeploymentsModelDeploymentModelDeploymentConfigurationDetailModelConfigurationDetailInstanceConfigurationModelDeploymentInstanceShapeConfigDetail]: Details for the model-deployment instance shape configuration.
private_endpoint_id str: The OCID of a Data Science private endpoint.
subnet_id str: A model deployment instance is provided with a VNIC for network access. This specifies the OCID of the subnet to create a VNIC in. The subnet should be in a VCN with a NAT/SGW gateway for egress.

instanceShapeName String: The shape used to launch the model deployment instances.
modelDeploymentInstanceShapeConfigDetails List<Property Map>: Details for the model-deployment instance shape configuration.
privateEndpointId String: The OCID of a Data Science private endpoint.
subnetId String: A model deployment instance is provided with a VNIC for network access. This specifies the OCID of the subnet to create a VNIC in. The subnet should be in a VCN with a NAT/SGW gateway for egress.

GetModelDeploymentsModelDeploymentModelDeploymentConfigurationDetailModelConfigurationDetailInstanceConfigurationModelDeploymentInstanceShapeConfigDetail

CpuBaseline string: The baseline OCPU utilization for a subcore burstable VM instance. If this attribute is left blank, it will default to BASELINE_1_1. The following values are supported: BASELINE_1_8 - baseline usage is 1/8 of an OCPU. BASELINE_1_2 - baseline usage is 1/2 of an OCPU. BASELINE_1_1 - baseline usage is an entire OCPU. This represents a non-burstable instance.
MemoryInGbs double: A model-deployment instance of type VM.Standard.E3.Flex or VM.Standard.E4.Flex allows the memory to be specified with in the range of 6 to 1024 GB. VM.Standard3.Flex memory range is between 6 to 512 GB and VM.Optimized3.Flex memory range is between 6 to 256 GB.
Ocpus double: A model-deployment instance of type VM.Standard.E3.Flex or VM.Standard.E4.Flex allows the ocpu count to be specified with in the range of 1 to 64 ocpu. VM.Standard3.Flex OCPU range is between 1 to 32 ocpu and for VM.Optimized3.Flex OCPU range is 1 to 18 ocpu.

CpuBaseline string: The baseline OCPU utilization for a subcore burstable VM instance. If this attribute is left blank, it will default to BASELINE_1_1. The following values are supported: BASELINE_1_8 - baseline usage is 1/8 of an OCPU. BASELINE_1_2 - baseline usage is 1/2 of an OCPU. BASELINE_1_1 - baseline usage is an entire OCPU. This represents a non-burstable instance.
MemoryInGbs float64: A model-deployment instance of type VM.Standard.E3.Flex or VM.Standard.E4.Flex allows the memory to be specified with in the range of 6 to 1024 GB. VM.Standard3.Flex memory range is between 6 to 512 GB and VM.Optimized3.Flex memory range is between 6 to 256 GB.
Ocpus float64: A model-deployment instance of type VM.Standard.E3.Flex or VM.Standard.E4.Flex allows the ocpu count to be specified with in the range of 1 to 64 ocpu. VM.Standard3.Flex OCPU range is between 1 to 32 ocpu and for VM.Optimized3.Flex OCPU range is 1 to 18 ocpu.

cpuBaseline String: The baseline OCPU utilization for a subcore burstable VM instance. If this attribute is left blank, it will default to BASELINE_1_1. The following values are supported: BASELINE_1_8 - baseline usage is 1/8 of an OCPU. BASELINE_1_2 - baseline usage is 1/2 of an OCPU. BASELINE_1_1 - baseline usage is an entire OCPU. This represents a non-burstable instance.
memoryInGbs Double: A model-deployment instance of type VM.Standard.E3.Flex or VM.Standard.E4.Flex allows the memory to be specified with in the range of 6 to 1024 GB. VM.Standard3.Flex memory range is between 6 to 512 GB and VM.Optimized3.Flex memory range is between 6 to 256 GB.
ocpus Double: A model-deployment instance of type VM.Standard.E3.Flex or VM.Standard.E4.Flex allows the ocpu count to be specified with in the range of 1 to 64 ocpu. VM.Standard3.Flex OCPU range is between 1 to 32 ocpu and for VM.Optimized3.Flex OCPU range is 1 to 18 ocpu.

cpuBaseline string: The baseline OCPU utilization for a subcore burstable VM instance. If this attribute is left blank, it will default to BASELINE_1_1. The following values are supported: BASELINE_1_8 - baseline usage is 1/8 of an OCPU. BASELINE_1_2 - baseline usage is 1/2 of an OCPU. BASELINE_1_1 - baseline usage is an entire OCPU. This represents a non-burstable instance.
memoryInGbs number: A model-deployment instance of type VM.Standard.E3.Flex or VM.Standard.E4.Flex allows the memory to be specified with in the range of 6 to 1024 GB. VM.Standard3.Flex memory range is between 6 to 512 GB and VM.Optimized3.Flex memory range is between 6 to 256 GB.
ocpus number: A model-deployment instance of type VM.Standard.E3.Flex or VM.Standard.E4.Flex allows the ocpu count to be specified with in the range of 1 to 64 ocpu. VM.Standard3.Flex OCPU range is between 1 to 32 ocpu and for VM.Optimized3.Flex OCPU range is 1 to 18 ocpu.

cpu_baseline str: The baseline OCPU utilization for a subcore burstable VM instance. If this attribute is left blank, it will default to BASELINE_1_1. The following values are supported: BASELINE_1_8 - baseline usage is 1/8 of an OCPU. BASELINE_1_2 - baseline usage is 1/2 of an OCPU. BASELINE_1_1 - baseline usage is an entire OCPU. This represents a non-burstable instance.
memory_in_gbs float: A model-deployment instance of type VM.Standard.E3.Flex or VM.Standard.E4.Flex allows the memory to be specified with in the range of 6 to 1024 GB. VM.Standard3.Flex memory range is between 6 to 512 GB and VM.Optimized3.Flex memory range is between 6 to 256 GB.
ocpus float: A model-deployment instance of type VM.Standard.E3.Flex or VM.Standard.E4.Flex allows the ocpu count to be specified with in the range of 1 to 64 ocpu. VM.Standard3.Flex OCPU range is between 1 to 32 ocpu and for VM.Optimized3.Flex OCPU range is 1 to 18 ocpu.

cpuBaseline String: The baseline OCPU utilization for a subcore burstable VM instance. If this attribute is left blank, it will default to BASELINE_1_1. The following values are supported: BASELINE_1_8 - baseline usage is 1/8 of an OCPU. BASELINE_1_2 - baseline usage is 1/2 of an OCPU. BASELINE_1_1 - baseline usage is an entire OCPU. This represents a non-burstable instance.
memoryInGbs Number: A model-deployment instance of type VM.Standard.E3.Flex or VM.Standard.E4.Flex allows the memory to be specified with in the range of 6 to 1024 GB. VM.Standard3.Flex memory range is between 6 to 512 GB and VM.Optimized3.Flex memory range is between 6 to 256 GB.
ocpus Number: A model-deployment instance of type VM.Standard.E3.Flex or VM.Standard.E4.Flex allows the ocpu count to be specified with in the range of 1 to 64 ocpu. VM.Standard3.Flex OCPU range is between 1 to 32 ocpu and for VM.Optimized3.Flex OCPU range is 1 to 18 ocpu.

GetModelDeploymentsModelDeploymentModelDeploymentConfigurationDetailModelConfigurationDetailScalingPolicy

AutoScalingPolicies List<GetModelDeploymentsModelDeploymentModelDeploymentConfigurationDetailModelConfigurationDetailScalingPolicyAutoScalingPolicy>: The list of autoscaling policy details.
CoolDownInSeconds int: For threshold-based autoscaling policies, this value is the minimum period of time to wait between scaling actions. The cooldown period gives the system time to stabilize before rescaling. The minimum value is 600 seconds, which is also the default. The cooldown period starts when the model deployment becomes ACTIVE after the scaling operation.
InstanceCount int: The number of instances for the model deployment.
IsEnabled bool: Whether the autoscaling policy is enabled.
PolicyType string: The type of scaling policy.

AutoScalingPolicies []GetModelDeploymentsModelDeploymentModelDeploymentConfigurationDetailModelConfigurationDetailScalingPolicyAutoScalingPolicy: The list of autoscaling policy details.
CoolDownInSeconds int: For threshold-based autoscaling policies, this value is the minimum period of time to wait between scaling actions. The cooldown period gives the system time to stabilize before rescaling. The minimum value is 600 seconds, which is also the default. The cooldown period starts when the model deployment becomes ACTIVE after the scaling operation.
InstanceCount int: The number of instances for the model deployment.
IsEnabled bool: Whether the autoscaling policy is enabled.
PolicyType string: The type of scaling policy.

autoScalingPolicies List<GetModelDeploymentsModelDeploymentModelDeploymentConfigurationDetailModelConfigurationDetailScalingPolicyAutoScalingPolicy>: The list of autoscaling policy details.
coolDownInSeconds Integer: For threshold-based autoscaling policies, this value is the minimum period of time to wait between scaling actions. The cooldown period gives the system time to stabilize before rescaling. The minimum value is 600 seconds, which is also the default. The cooldown period starts when the model deployment becomes ACTIVE after the scaling operation.
instanceCount Integer: The number of instances for the model deployment.
isEnabled Boolean: Whether the autoscaling policy is enabled.
policyType String: The type of scaling policy.

autoScalingPolicies GetModelDeploymentsModelDeploymentModelDeploymentConfigurationDetailModelConfigurationDetailScalingPolicyAutoScalingPolicy[]: The list of autoscaling policy details.
coolDownInSeconds number: For threshold-based autoscaling policies, this value is the minimum period of time to wait between scaling actions. The cooldown period gives the system time to stabilize before rescaling. The minimum value is 600 seconds, which is also the default. The cooldown period starts when the model deployment becomes ACTIVE after the scaling operation.
instanceCount number: The number of instances for the model deployment.
isEnabled boolean: Whether the autoscaling policy is enabled.
policyType string: The type of scaling policy.

auto_scaling_policies Sequence[datascience.GetModelDeploymentsModelDeploymentModelDeploymentConfigurationDetailModelConfigurationDetailScalingPolicyAutoScalingPolicy]: The list of autoscaling policy details.
cool_down_in_seconds int: For threshold-based autoscaling policies, this value is the minimum period of time to wait between scaling actions. The cooldown period gives the system time to stabilize before rescaling. The minimum value is 600 seconds, which is also the default. The cooldown period starts when the model deployment becomes ACTIVE after the scaling operation.
instance_count int: The number of instances for the model deployment.
is_enabled bool: Whether the autoscaling policy is enabled.
policy_type str: The type of scaling policy.

autoScalingPolicies List<Property Map>: The list of autoscaling policy details.
coolDownInSeconds Number: For threshold-based autoscaling policies, this value is the minimum period of time to wait between scaling actions. The cooldown period gives the system time to stabilize before rescaling. The minimum value is 600 seconds, which is also the default. The cooldown period starts when the model deployment becomes ACTIVE after the scaling operation.
instanceCount Number: The number of instances for the model deployment.
isEnabled Boolean: Whether the autoscaling policy is enabled.
policyType String: The type of scaling policy.

GetModelDeploymentsModelDeploymentModelDeploymentConfigurationDetailModelConfigurationDetailScalingPolicyAutoScalingPolicy

AutoScalingPolicyType string: The type of autoscaling policy.
InitialInstanceCount int: For a threshold-based autoscaling policy, this value is the initial number of instances to launch in the model deployment immediately after autoscaling is enabled. Note that anytime this value is updated, the number of instances will be reset to this value. After autoscaling retrieves performance metrics, the number of instances is automatically adjusted from this initial number to a number that is based on the limits that you set.
MaximumInstanceCount int: For a threshold-based autoscaling policy, this value is the maximum number of instances the model deployment is allowed to increase to (scale out).
MinimumInstanceCount int: For a threshold-based autoscaling policy, this value is the minimum number of instances the model deployment is allowed to decrease to (scale in).
Rules List<GetModelDeploymentsModelDeploymentModelDeploymentConfigurationDetailModelConfigurationDetailScalingPolicyAutoScalingPolicyRule>: The list of autoscaling policy rules.

AutoScalingPolicyType string: The type of autoscaling policy.
InitialInstanceCount int: For a threshold-based autoscaling policy, this value is the initial number of instances to launch in the model deployment immediately after autoscaling is enabled. Note that anytime this value is updated, the number of instances will be reset to this value. After autoscaling retrieves performance metrics, the number of instances is automatically adjusted from this initial number to a number that is based on the limits that you set.
MaximumInstanceCount int: For a threshold-based autoscaling policy, this value is the maximum number of instances the model deployment is allowed to increase to (scale out).
MinimumInstanceCount int: For a threshold-based autoscaling policy, this value is the minimum number of instances the model deployment is allowed to decrease to (scale in).
Rules []GetModelDeploymentsModelDeploymentModelDeploymentConfigurationDetailModelConfigurationDetailScalingPolicyAutoScalingPolicyRule: The list of autoscaling policy rules.

autoScalingPolicyType String: The type of autoscaling policy.
initialInstanceCount Integer: For a threshold-based autoscaling policy, this value is the initial number of instances to launch in the model deployment immediately after autoscaling is enabled. Note that anytime this value is updated, the number of instances will be reset to this value. After autoscaling retrieves performance metrics, the number of instances is automatically adjusted from this initial number to a number that is based on the limits that you set.
maximumInstanceCount Integer: For a threshold-based autoscaling policy, this value is the maximum number of instances the model deployment is allowed to increase to (scale out).
minimumInstanceCount Integer: For a threshold-based autoscaling policy, this value is the minimum number of instances the model deployment is allowed to decrease to (scale in).
rules List<GetModelDeploymentsModelDeploymentModelDeploymentConfigurationDetailModelConfigurationDetailScalingPolicyAutoScalingPolicyRule>: The list of autoscaling policy rules.

autoScalingPolicyType string: The type of autoscaling policy.
initialInstanceCount number: For a threshold-based autoscaling policy, this value is the initial number of instances to launch in the model deployment immediately after autoscaling is enabled. Note that anytime this value is updated, the number of instances will be reset to this value. After autoscaling retrieves performance metrics, the number of instances is automatically adjusted from this initial number to a number that is based on the limits that you set.
maximumInstanceCount number: For a threshold-based autoscaling policy, this value is the maximum number of instances the model deployment is allowed to increase to (scale out).
minimumInstanceCount number: For a threshold-based autoscaling policy, this value is the minimum number of instances the model deployment is allowed to decrease to (scale in).
rules GetModelDeploymentsModelDeploymentModelDeploymentConfigurationDetailModelConfigurationDetailScalingPolicyAutoScalingPolicyRule[]: The list of autoscaling policy rules.

auto_scaling_policy_type str: The type of autoscaling policy.
initial_instance_count int: For a threshold-based autoscaling policy, this value is the initial number of instances to launch in the model deployment immediately after autoscaling is enabled. Note that anytime this value is updated, the number of instances will be reset to this value. After autoscaling retrieves performance metrics, the number of instances is automatically adjusted from this initial number to a number that is based on the limits that you set.
maximum_instance_count int: For a threshold-based autoscaling policy, this value is the maximum number of instances the model deployment is allowed to increase to (scale out).
minimum_instance_count int: For a threshold-based autoscaling policy, this value is the minimum number of instances the model deployment is allowed to decrease to (scale in).
rules Sequence[datascience.GetModelDeploymentsModelDeploymentModelDeploymentConfigurationDetailModelConfigurationDetailScalingPolicyAutoScalingPolicyRule]: The list of autoscaling policy rules.

autoScalingPolicyType String: The type of autoscaling policy.
initialInstanceCount Number: For a threshold-based autoscaling policy, this value is the initial number of instances to launch in the model deployment immediately after autoscaling is enabled. Note that anytime this value is updated, the number of instances will be reset to this value. After autoscaling retrieves performance metrics, the number of instances is automatically adjusted from this initial number to a number that is based on the limits that you set.
maximumInstanceCount Number: For a threshold-based autoscaling policy, this value is the maximum number of instances the model deployment is allowed to increase to (scale out).
minimumInstanceCount Number: For a threshold-based autoscaling policy, this value is the minimum number of instances the model deployment is allowed to decrease to (scale in).
rules List<Property Map>: The list of autoscaling policy rules.

GetModelDeploymentsModelDeploymentModelDeploymentConfigurationDetailModelConfigurationDetailScalingPolicyAutoScalingPolicyRule

MetricExpressionRuleType string: The metric expression for creating the alarm used to trigger autoscaling actions on the model deployment.
MetricType string: Metric type
ScaleInConfigurations List<GetModelDeploymentsModelDeploymentModelDeploymentConfigurationDetailModelConfigurationDetailScalingPolicyAutoScalingPolicyRuleScaleInConfiguration>: The scaling configuration for the predefined metric expression rule.
ScaleOutConfigurations List<GetModelDeploymentsModelDeploymentModelDeploymentConfigurationDetailModelConfigurationDetailScalingPolicyAutoScalingPolicyRuleScaleOutConfiguration>: The scaling configuration for the predefined metric expression rule.

MetricExpressionRuleType string: The metric expression for creating the alarm used to trigger autoscaling actions on the model deployment.
MetricType string: Metric type
ScaleInConfigurations []GetModelDeploymentsModelDeploymentModelDeploymentConfigurationDetailModelConfigurationDetailScalingPolicyAutoScalingPolicyRuleScaleInConfiguration: The scaling configuration for the predefined metric expression rule.
ScaleOutConfigurations []GetModelDeploymentsModelDeploymentModelDeploymentConfigurationDetailModelConfigurationDetailScalingPolicyAutoScalingPolicyRuleScaleOutConfiguration: The scaling configuration for the predefined metric expression rule.

metricExpressionRuleType String: The metric expression for creating the alarm used to trigger autoscaling actions on the model deployment.
metricType String: Metric type
scaleInConfigurations List<GetModelDeploymentsModelDeploymentModelDeploymentConfigurationDetailModelConfigurationDetailScalingPolicyAutoScalingPolicyRuleScaleInConfiguration>: The scaling configuration for the predefined metric expression rule.
scaleOutConfigurations List<GetModelDeploymentsModelDeploymentModelDeploymentConfigurationDetailModelConfigurationDetailScalingPolicyAutoScalingPolicyRuleScaleOutConfiguration>: The scaling configuration for the predefined metric expression rule.

metricExpressionRuleType string: The metric expression for creating the alarm used to trigger autoscaling actions on the model deployment.
metricType string: Metric type
scaleInConfigurations GetModelDeploymentsModelDeploymentModelDeploymentConfigurationDetailModelConfigurationDetailScalingPolicyAutoScalingPolicyRuleScaleInConfiguration[]: The scaling configuration for the predefined metric expression rule.
scaleOutConfigurations GetModelDeploymentsModelDeploymentModelDeploymentConfigurationDetailModelConfigurationDetailScalingPolicyAutoScalingPolicyRuleScaleOutConfiguration[]: The scaling configuration for the predefined metric expression rule.

metric_expression_rule_type str: The metric expression for creating the alarm used to trigger autoscaling actions on the model deployment.
metric_type str: Metric type
scale_in_configurations Sequence[datascience.GetModelDeploymentsModelDeploymentModelDeploymentConfigurationDetailModelConfigurationDetailScalingPolicyAutoScalingPolicyRuleScaleInConfiguration]: The scaling configuration for the predefined metric expression rule.
scale_out_configurations Sequence[datascience.GetModelDeploymentsModelDeploymentModelDeploymentConfigurationDetailModelConfigurationDetailScalingPolicyAutoScalingPolicyRuleScaleOutConfiguration]: The scaling configuration for the predefined metric expression rule.

metricExpressionRuleType String: The metric expression for creating the alarm used to trigger autoscaling actions on the model deployment.
metricType String: Metric type
scaleInConfigurations List<Property Map>: The scaling configuration for the predefined metric expression rule.
scaleOutConfigurations List<Property Map>: The scaling configuration for the predefined metric expression rule.

GetModelDeploymentsModelDeploymentModelDeploymentConfigurationDetailModelConfigurationDetailScalingPolicyAutoScalingPolicyRuleScaleInConfiguration

InstanceCountAdjustment int: The value is used for adjusting the count of instances by.
PendingDuration string: The period of time that the condition defined in the alarm must persist before the alarm state changes from "OK" to "FIRING" or vice versa. For example, a value of 5 minutes means that the alarm must persist in breaching the condition for five minutes before the alarm updates its state to "FIRING"; likewise, the alarm must persist in not breaching the condition for five minutes before the alarm updates its state to "OK."
Query string: The Monitoring Query Language (MQL) expression to evaluate for the alarm. The Alarms feature of the Monitoring service interprets results for each returned time series as Boolean values, where zero represents false and a non-zero value represents true. A true value means that the trigger rule condition has been met. The query must specify a metric, statistic, interval, and trigger rule (threshold or absence). Supported values for interval: 1m-60m (also 1h). You can optionally specify dimensions and grouping functions. Supported grouping functions: grouping(), groupBy().
ScalingConfigurationType string: The type of scaling configuration.
Threshold int: A metric value at which the scaling operation will be triggered.

InstanceCountAdjustment int: The value is used for adjusting the count of instances by.
PendingDuration string: The period of time that the condition defined in the alarm must persist before the alarm state changes from "OK" to "FIRING" or vice versa. For example, a value of 5 minutes means that the alarm must persist in breaching the condition for five minutes before the alarm updates its state to "FIRING"; likewise, the alarm must persist in not breaching the condition for five minutes before the alarm updates its state to "OK."
Query string: The Monitoring Query Language (MQL) expression to evaluate for the alarm. The Alarms feature of the Monitoring service interprets results for each returned time series as Boolean values, where zero represents false and a non-zero value represents true. A true value means that the trigger rule condition has been met. The query must specify a metric, statistic, interval, and trigger rule (threshold or absence). Supported values for interval: 1m-60m (also 1h). You can optionally specify dimensions and grouping functions. Supported grouping functions: grouping(), groupBy().
ScalingConfigurationType string: The type of scaling configuration.
Threshold int: A metric value at which the scaling operation will be triggered.

instanceCountAdjustment Integer: The value is used for adjusting the count of instances by.
pendingDuration String: The period of time that the condition defined in the alarm must persist before the alarm state changes from "OK" to "FIRING" or vice versa. For example, a value of 5 minutes means that the alarm must persist in breaching the condition for five minutes before the alarm updates its state to "FIRING"; likewise, the alarm must persist in not breaching the condition for five minutes before the alarm updates its state to "OK."
query String: The Monitoring Query Language (MQL) expression to evaluate for the alarm. The Alarms feature of the Monitoring service interprets results for each returned time series as Boolean values, where zero represents false and a non-zero value represents true. A true value means that the trigger rule condition has been met. The query must specify a metric, statistic, interval, and trigger rule (threshold or absence). Supported values for interval: 1m-60m (also 1h). You can optionally specify dimensions and grouping functions. Supported grouping functions: grouping(), groupBy().
scalingConfigurationType String: The type of scaling configuration.
threshold Integer: A metric value at which the scaling operation will be triggered.

instanceCountAdjustment number: The value is used for adjusting the count of instances by.
pendingDuration string: The period of time that the condition defined in the alarm must persist before the alarm state changes from "OK" to "FIRING" or vice versa. For example, a value of 5 minutes means that the alarm must persist in breaching the condition for five minutes before the alarm updates its state to "FIRING"; likewise, the alarm must persist in not breaching the condition for five minutes before the alarm updates its state to "OK."
query string: The Monitoring Query Language (MQL) expression to evaluate for the alarm. The Alarms feature of the Monitoring service interprets results for each returned time series as Boolean values, where zero represents false and a non-zero value represents true. A true value means that the trigger rule condition has been met. The query must specify a metric, statistic, interval, and trigger rule (threshold or absence). Supported values for interval: 1m-60m (also 1h). You can optionally specify dimensions and grouping functions. Supported grouping functions: grouping(), groupBy().
scalingConfigurationType string: The type of scaling configuration.
threshold number: A metric value at which the scaling operation will be triggered.

instance_count_adjustment int: The value is used for adjusting the count of instances by.
pending_duration str: The period of time that the condition defined in the alarm must persist before the alarm state changes from "OK" to "FIRING" or vice versa. For example, a value of 5 minutes means that the alarm must persist in breaching the condition for five minutes before the alarm updates its state to "FIRING"; likewise, the alarm must persist in not breaching the condition for five minutes before the alarm updates its state to "OK."
query str: The Monitoring Query Language (MQL) expression to evaluate for the alarm. The Alarms feature of the Monitoring service interprets results for each returned time series as Boolean values, where zero represents false and a non-zero value represents true. A true value means that the trigger rule condition has been met. The query must specify a metric, statistic, interval, and trigger rule (threshold or absence). Supported values for interval: 1m-60m (also 1h). You can optionally specify dimensions and grouping functions. Supported grouping functions: grouping(), groupBy().
scaling_configuration_type str: The type of scaling configuration.
threshold int: A metric value at which the scaling operation will be triggered.

instanceCountAdjustment Number: The value is used for adjusting the count of instances by.
pendingDuration String: The period of time that the condition defined in the alarm must persist before the alarm state changes from "OK" to "FIRING" or vice versa. For example, a value of 5 minutes means that the alarm must persist in breaching the condition for five minutes before the alarm updates its state to "FIRING"; likewise, the alarm must persist in not breaching the condition for five minutes before the alarm updates its state to "OK."
query String: The Monitoring Query Language (MQL) expression to evaluate for the alarm. The Alarms feature of the Monitoring service interprets results for each returned time series as Boolean values, where zero represents false and a non-zero value represents true. A true value means that the trigger rule condition has been met. The query must specify a metric, statistic, interval, and trigger rule (threshold or absence). Supported values for interval: 1m-60m (also 1h). You can optionally specify dimensions and grouping functions. Supported grouping functions: grouping(), groupBy().
scalingConfigurationType String: The type of scaling configuration.
threshold Number: A metric value at which the scaling operation will be triggered.

GetModelDeploymentsModelDeploymentModelDeploymentConfigurationDetailModelConfigurationDetailScalingPolicyAutoScalingPolicyRuleScaleOutConfiguration

InstanceCountAdjustment int: The value is used for adjusting the count of instances by.
PendingDuration string: The period of time that the condition defined in the alarm must persist before the alarm state changes from "OK" to "FIRING" or vice versa. For example, a value of 5 minutes means that the alarm must persist in breaching the condition for five minutes before the alarm updates its state to "FIRING"; likewise, the alarm must persist in not breaching the condition for five minutes before the alarm updates its state to "OK."
Query string: The Monitoring Query Language (MQL) expression to evaluate for the alarm. The Alarms feature of the Monitoring service interprets results for each returned time series as Boolean values, where zero represents false and a non-zero value represents true. A true value means that the trigger rule condition has been met. The query must specify a metric, statistic, interval, and trigger rule (threshold or absence). Supported values for interval: 1m-60m (also 1h). You can optionally specify dimensions and grouping functions. Supported grouping functions: grouping(), groupBy().
ScalingConfigurationType string: The type of scaling configuration.
Threshold int: A metric value at which the scaling operation will be triggered.

InstanceCountAdjustment int: The value is used for adjusting the count of instances by.
PendingDuration string: The period of time that the condition defined in the alarm must persist before the alarm state changes from "OK" to "FIRING" or vice versa. For example, a value of 5 minutes means that the alarm must persist in breaching the condition for five minutes before the alarm updates its state to "FIRING"; likewise, the alarm must persist in not breaching the condition for five minutes before the alarm updates its state to "OK."
Query string: The Monitoring Query Language (MQL) expression to evaluate for the alarm. The Alarms feature of the Monitoring service interprets results for each returned time series as Boolean values, where zero represents false and a non-zero value represents true. A true value means that the trigger rule condition has been met. The query must specify a metric, statistic, interval, and trigger rule (threshold or absence). Supported values for interval: 1m-60m (also 1h). You can optionally specify dimensions and grouping functions. Supported grouping functions: grouping(), groupBy().
ScalingConfigurationType string: The type of scaling configuration.
Threshold int: A metric value at which the scaling operation will be triggered.

instanceCountAdjustment Integer: The value is used for adjusting the count of instances by.
pendingDuration String: The period of time that the condition defined in the alarm must persist before the alarm state changes from "OK" to "FIRING" or vice versa. For example, a value of 5 minutes means that the alarm must persist in breaching the condition for five minutes before the alarm updates its state to "FIRING"; likewise, the alarm must persist in not breaching the condition for five minutes before the alarm updates its state to "OK."
query String: The Monitoring Query Language (MQL) expression to evaluate for the alarm. The Alarms feature of the Monitoring service interprets results for each returned time series as Boolean values, where zero represents false and a non-zero value represents true. A true value means that the trigger rule condition has been met. The query must specify a metric, statistic, interval, and trigger rule (threshold or absence). Supported values for interval: 1m-60m (also 1h). You can optionally specify dimensions and grouping functions. Supported grouping functions: grouping(), groupBy().
scalingConfigurationType String: The type of scaling configuration.
threshold Integer: A metric value at which the scaling operation will be triggered.

instanceCountAdjustment number: The value is used for adjusting the count of instances by.
pendingDuration string: The period of time that the condition defined in the alarm must persist before the alarm state changes from "OK" to "FIRING" or vice versa. For example, a value of 5 minutes means that the alarm must persist in breaching the condition for five minutes before the alarm updates its state to "FIRING"; likewise, the alarm must persist in not breaching the condition for five minutes before the alarm updates its state to "OK."
query string: The Monitoring Query Language (MQL) expression to evaluate for the alarm. The Alarms feature of the Monitoring service interprets results for each returned time series as Boolean values, where zero represents false and a non-zero value represents true. A true value means that the trigger rule condition has been met. The query must specify a metric, statistic, interval, and trigger rule (threshold or absence). Supported values for interval: 1m-60m (also 1h). You can optionally specify dimensions and grouping functions. Supported grouping functions: grouping(), groupBy().
scalingConfigurationType string: The type of scaling configuration.
threshold number: A metric value at which the scaling operation will be triggered.

instance_count_adjustment int: The value is used for adjusting the count of instances by.
pending_duration str: The period of time that the condition defined in the alarm must persist before the alarm state changes from "OK" to "FIRING" or vice versa. For example, a value of 5 minutes means that the alarm must persist in breaching the condition for five minutes before the alarm updates its state to "FIRING"; likewise, the alarm must persist in not breaching the condition for five minutes before the alarm updates its state to "OK."
query str: The Monitoring Query Language (MQL) expression to evaluate for the alarm. The Alarms feature of the Monitoring service interprets results for each returned time series as Boolean values, where zero represents false and a non-zero value represents true. A true value means that the trigger rule condition has been met. The query must specify a metric, statistic, interval, and trigger rule (threshold or absence). Supported values for interval: 1m-60m (also 1h). You can optionally specify dimensions and grouping functions. Supported grouping functions: grouping(), groupBy().
scaling_configuration_type str: The type of scaling configuration.
threshold int: A metric value at which the scaling operation will be triggered.

instanceCountAdjustment Number: The value is used for adjusting the count of instances by.
pendingDuration String: The period of time that the condition defined in the alarm must persist before the alarm state changes from "OK" to "FIRING" or vice versa. For example, a value of 5 minutes means that the alarm must persist in breaching the condition for five minutes before the alarm updates its state to "FIRING"; likewise, the alarm must persist in not breaching the condition for five minutes before the alarm updates its state to "OK."
query String: The Monitoring Query Language (MQL) expression to evaluate for the alarm. The Alarms feature of the Monitoring service interprets results for each returned time series as Boolean values, where zero represents false and a non-zero value represents true. A true value means that the trigger rule condition has been met. The query must specify a metric, statistic, interval, and trigger rule (threshold or absence). Supported values for interval: 1m-60m (also 1h). You can optionally specify dimensions and grouping functions. Supported grouping functions: grouping(), groupBy().
scalingConfigurationType String: The type of scaling configuration.
threshold Number: A metric value at which the scaling operation will be triggered.

GetModelDeploymentsModelDeploymentModelDeploymentSystemData

CurrentInstanceCount int: This value is the current count of the model deployment instances.
SystemInfraType string: The infrastructure type of the model deployment.

CurrentInstanceCount int: This value is the current count of the model deployment instances.
SystemInfraType string: The infrastructure type of the model deployment.

currentInstanceCount Integer: This value is the current count of the model deployment instances.
systemInfraType String: The infrastructure type of the model deployment.

currentInstanceCount number: This value is the current count of the model deployment instances.
systemInfraType string: The infrastructure type of the model deployment.

current_instance_count int: This value is the current count of the model deployment instances.
system_infra_type str: The infrastructure type of the model deployment.

currentInstanceCount Number: This value is the current count of the model deployment instances.
systemInfraType String: The infrastructure type of the model deployment.

Package Details

Repository: oci pulumi/pulumi-oci
License: Apache-2.0
Notes: This Pulumi package is based on the oci Terraform Provider.

Oracle Cloud Infrastructure v2.29.0 published on Wednesday, Apr 9, 2025 by Pulumi

pulumi/pulumi-oci

oci.DataScience.getModelDeployments

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Example Usage

Using getModelDeployments

getModelDeployments Result

Supporting Types

GetModelDeploymentsFilter

GetModelDeploymentsModelDeployment

GetModelDeploymentsModelDeploymentCategoryLogDetail

GetModelDeploymentsModelDeploymentCategoryLogDetailAccess

GetModelDeploymentsModelDeploymentCategoryLogDetailPredict

GetModelDeploymentsModelDeploymentModelDeploymentConfigurationDetail

GetModelDeploymentsModelDeploymentModelDeploymentConfigurationDetailEnvironmentConfigurationDetail

GetModelDeploymentsModelDeploymentModelDeploymentConfigurationDetailModelConfigurationDetail

GetModelDeploymentsModelDeploymentModelDeploymentConfigurationDetailModelConfigurationDetailInstanceConfiguration

GetModelDeploymentsModelDeploymentModelDeploymentConfigurationDetailModelConfigurationDetailInstanceConfigurationModelDeploymentInstanceShapeConfigDetail

GetModelDeploymentsModelDeploymentModelDeploymentConfigurationDetailModelConfigurationDetailScalingPolicy

GetModelDeploymentsModelDeploymentModelDeploymentConfigurationDetailModelConfigurationDetailScalingPolicyAutoScalingPolicy

GetModelDeploymentsModelDeploymentModelDeploymentConfigurationDetailModelConfigurationDetailScalingPolicyAutoScalingPolicyRule

GetModelDeploymentsModelDeploymentModelDeploymentConfigurationDetailModelConfigurationDetailScalingPolicyAutoScalingPolicyRuleScaleInConfiguration

GetModelDeploymentsModelDeploymentModelDeploymentConfigurationDetailModelConfigurationDetailScalingPolicyAutoScalingPolicyRuleScaleOutConfiguration

GetModelDeploymentsModelDeploymentModelDeploymentSystemData

Package Details

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