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PackagesOracle Cloud Infrastructure v1.32.0 published on Thursday, Apr 18, 2024 by Pulumi
oci.DataScience.ModelDeployment
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This resource provides the Model Deployment resource in Oracle Cloud Infrastructure Datascience service.
Creates a new model deployment.
Example Usage
import * as pulumi from "@pulumi/pulumi";
import * as oci from "@pulumi/oci";
const testModelDeployment = new oci.datascience.ModelDeployment("testModelDeployment", {
compartmentId: _var.compartment_id,
modelDeploymentConfigurationDetails: {
deploymentType: _var.model_deployment_model_deployment_configuration_details_deployment_type,
modelConfigurationDetails: {
instanceConfiguration: {
instanceShapeName: oci_core_shape.test_shape.name,
modelDeploymentInstanceShapeConfigDetails: {
cpuBaseline: _var.model_deployment_model_deployment_configuration_details_model_configuration_details_instance_configuration_model_deployment_instance_shape_config_details_cpu_baseline,
memoryInGbs: _var.model_deployment_model_deployment_configuration_details_model_configuration_details_instance_configuration_model_deployment_instance_shape_config_details_memory_in_gbs,
ocpus: _var.model_deployment_model_deployment_configuration_details_model_configuration_details_instance_configuration_model_deployment_instance_shape_config_details_ocpus,
},
subnetId: oci_core_subnet.test_subnet.id,
},
modelId: oci_datascience_model.test_model.id,
bandwidthMbps: _var.model_deployment_model_deployment_configuration_details_model_configuration_details_bandwidth_mbps,
maximumBandwidthMbps: _var.model_deployment_model_deployment_configuration_details_model_configuration_details_maximum_bandwidth_mbps,
scalingPolicy: {
policyType: _var.model_deployment_model_deployment_configuration_details_model_configuration_details_scaling_policy_policy_type,
autoScalingPolicies: [{
autoScalingPolicyType: _var.model_deployment_model_deployment_configuration_details_model_configuration_details_scaling_policy_auto_scaling_policies_auto_scaling_policy_type,
initialInstanceCount: _var.model_deployment_model_deployment_configuration_details_model_configuration_details_scaling_policy_auto_scaling_policies_initial_instance_count,
maximumInstanceCount: _var.model_deployment_model_deployment_configuration_details_model_configuration_details_scaling_policy_auto_scaling_policies_maximum_instance_count,
minimumInstanceCount: _var.model_deployment_model_deployment_configuration_details_model_configuration_details_scaling_policy_auto_scaling_policies_minimum_instance_count,
rules: [{
metricExpressionRuleType: _var.model_deployment_model_deployment_configuration_details_model_configuration_details_scaling_policy_auto_scaling_policies_rules_metric_expression_rule_type,
scaleInConfiguration: {
instanceCountAdjustment: _var.model_deployment_model_deployment_configuration_details_model_configuration_details_scaling_policy_auto_scaling_policies_rules_scale_in_configuration_instance_count_adjustment,
pendingDuration: _var.model_deployment_model_deployment_configuration_details_model_configuration_details_scaling_policy_auto_scaling_policies_rules_scale_in_configuration_pending_duration,
query: _var.model_deployment_model_deployment_configuration_details_model_configuration_details_scaling_policy_auto_scaling_policies_rules_scale_in_configuration_query,
scalingConfigurationType: _var.model_deployment_model_deployment_configuration_details_model_configuration_details_scaling_policy_auto_scaling_policies_rules_scale_in_configuration_scaling_configuration_type,
threshold: _var.model_deployment_model_deployment_configuration_details_model_configuration_details_scaling_policy_auto_scaling_policies_rules_scale_in_configuration_threshold,
},
scaleOutConfiguration: {
instanceCountAdjustment: _var.model_deployment_model_deployment_configuration_details_model_configuration_details_scaling_policy_auto_scaling_policies_rules_scale_out_configuration_instance_count_adjustment,
pendingDuration: _var.model_deployment_model_deployment_configuration_details_model_configuration_details_scaling_policy_auto_scaling_policies_rules_scale_out_configuration_pending_duration,
query: _var.model_deployment_model_deployment_configuration_details_model_configuration_details_scaling_policy_auto_scaling_policies_rules_scale_out_configuration_query,
scalingConfigurationType: _var.model_deployment_model_deployment_configuration_details_model_configuration_details_scaling_policy_auto_scaling_policies_rules_scale_out_configuration_scaling_configuration_type,
threshold: _var.model_deployment_model_deployment_configuration_details_model_configuration_details_scaling_policy_auto_scaling_policies_rules_scale_out_configuration_threshold,
},
metricType: _var.model_deployment_model_deployment_configuration_details_model_configuration_details_scaling_policy_auto_scaling_policies_rules_metric_type,
}],
}],
coolDownInSeconds: _var.model_deployment_model_deployment_configuration_details_model_configuration_details_scaling_policy_cool_down_in_seconds,
instanceCount: _var.model_deployment_model_deployment_configuration_details_model_configuration_details_scaling_policy_instance_count,
isEnabled: _var.model_deployment_model_deployment_configuration_details_model_configuration_details_scaling_policy_is_enabled,
},
},
environmentConfigurationDetails: {
environmentConfigurationType: _var.model_deployment_model_deployment_configuration_details_environment_configuration_details_environment_configuration_type,
cmds: _var.model_deployment_model_deployment_configuration_details_environment_configuration_details_cmd,
entrypoints: _var.model_deployment_model_deployment_configuration_details_environment_configuration_details_entrypoint,
environmentVariables: _var.model_deployment_model_deployment_configuration_details_environment_configuration_details_environment_variables,
healthCheckPort: _var.model_deployment_model_deployment_configuration_details_environment_configuration_details_health_check_port,
image: _var.model_deployment_model_deployment_configuration_details_environment_configuration_details_image,
imageDigest: _var.model_deployment_model_deployment_configuration_details_environment_configuration_details_image_digest,
serverPort: _var.model_deployment_model_deployment_configuration_details_environment_configuration_details_server_port,
},
},
projectId: oci_datascience_project.test_project.id,
categoryLogDetails: {
access: {
logGroupId: oci_logging_log_group.test_log_group.id,
logId: oci_logging_log.test_log.id,
},
predict: {
logGroupId: oci_logging_log_group.test_log_group.id,
logId: oci_logging_log.test_log.id,
},
},
definedTags: {
"Operations.CostCenter": "42",
},
description: _var.model_deployment_description,
displayName: _var.model_deployment_display_name,
freeformTags: {
Department: "Finance",
},
});
import pulumi
import pulumi_oci as oci
test_model_deployment = oci.data_science.ModelDeployment("testModelDeployment",
compartment_id=var["compartment_id"],
model_deployment_configuration_details=oci.data_science.ModelDeploymentModelDeploymentConfigurationDetailsArgs(
deployment_type=var["model_deployment_model_deployment_configuration_details_deployment_type"],
model_configuration_details=oci.data_science.ModelDeploymentModelDeploymentConfigurationDetailsModelConfigurationDetailsArgs(
instance_configuration=oci.data_science.ModelDeploymentModelDeploymentConfigurationDetailsModelConfigurationDetailsInstanceConfigurationArgs(
instance_shape_name=oci_core_shape["test_shape"]["name"],
model_deployment_instance_shape_config_details=oci.data_science.ModelDeploymentModelDeploymentConfigurationDetailsModelConfigurationDetailsInstanceConfigurationModelDeploymentInstanceShapeConfigDetailsArgs(
cpu_baseline=var["model_deployment_model_deployment_configuration_details_model_configuration_details_instance_configuration_model_deployment_instance_shape_config_details_cpu_baseline"],
memory_in_gbs=var["model_deployment_model_deployment_configuration_details_model_configuration_details_instance_configuration_model_deployment_instance_shape_config_details_memory_in_gbs"],
ocpus=var["model_deployment_model_deployment_configuration_details_model_configuration_details_instance_configuration_model_deployment_instance_shape_config_details_ocpus"],
),
subnet_id=oci_core_subnet["test_subnet"]["id"],
),
model_id=oci_datascience_model["test_model"]["id"],
bandwidth_mbps=var["model_deployment_model_deployment_configuration_details_model_configuration_details_bandwidth_mbps"],
maximum_bandwidth_mbps=var["model_deployment_model_deployment_configuration_details_model_configuration_details_maximum_bandwidth_mbps"],
scaling_policy=oci.data_science.ModelDeploymentModelDeploymentConfigurationDetailsModelConfigurationDetailsScalingPolicyArgs(
policy_type=var["model_deployment_model_deployment_configuration_details_model_configuration_details_scaling_policy_policy_type"],
auto_scaling_policies=[oci.data_science.ModelDeploymentModelDeploymentConfigurationDetailsModelConfigurationDetailsScalingPolicyAutoScalingPolicyArgs(
auto_scaling_policy_type=var["model_deployment_model_deployment_configuration_details_model_configuration_details_scaling_policy_auto_scaling_policies_auto_scaling_policy_type"],
initial_instance_count=var["model_deployment_model_deployment_configuration_details_model_configuration_details_scaling_policy_auto_scaling_policies_initial_instance_count"],
maximum_instance_count=var["model_deployment_model_deployment_configuration_details_model_configuration_details_scaling_policy_auto_scaling_policies_maximum_instance_count"],
minimum_instance_count=var["model_deployment_model_deployment_configuration_details_model_configuration_details_scaling_policy_auto_scaling_policies_minimum_instance_count"],
rules=[oci.data_science.ModelDeploymentModelDeploymentConfigurationDetailsModelConfigurationDetailsScalingPolicyAutoScalingPolicyRuleArgs(
metric_expression_rule_type=var["model_deployment_model_deployment_configuration_details_model_configuration_details_scaling_policy_auto_scaling_policies_rules_metric_expression_rule_type"],
scale_in_configuration=oci.data_science.ModelDeploymentModelDeploymentConfigurationDetailsModelConfigurationDetailsScalingPolicyAutoScalingPolicyRuleScaleInConfigurationArgs(
instance_count_adjustment=var["model_deployment_model_deployment_configuration_details_model_configuration_details_scaling_policy_auto_scaling_policies_rules_scale_in_configuration_instance_count_adjustment"],
pending_duration=var["model_deployment_model_deployment_configuration_details_model_configuration_details_scaling_policy_auto_scaling_policies_rules_scale_in_configuration_pending_duration"],
query=var["model_deployment_model_deployment_configuration_details_model_configuration_details_scaling_policy_auto_scaling_policies_rules_scale_in_configuration_query"],
scaling_configuration_type=var["model_deployment_model_deployment_configuration_details_model_configuration_details_scaling_policy_auto_scaling_policies_rules_scale_in_configuration_scaling_configuration_type"],
threshold=var["model_deployment_model_deployment_configuration_details_model_configuration_details_scaling_policy_auto_scaling_policies_rules_scale_in_configuration_threshold"],
),
scale_out_configuration=oci.data_science.ModelDeploymentModelDeploymentConfigurationDetailsModelConfigurationDetailsScalingPolicyAutoScalingPolicyRuleScaleOutConfigurationArgs(
instance_count_adjustment=var["model_deployment_model_deployment_configuration_details_model_configuration_details_scaling_policy_auto_scaling_policies_rules_scale_out_configuration_instance_count_adjustment"],
pending_duration=var["model_deployment_model_deployment_configuration_details_model_configuration_details_scaling_policy_auto_scaling_policies_rules_scale_out_configuration_pending_duration"],
query=var["model_deployment_model_deployment_configuration_details_model_configuration_details_scaling_policy_auto_scaling_policies_rules_scale_out_configuration_query"],
scaling_configuration_type=var["model_deployment_model_deployment_configuration_details_model_configuration_details_scaling_policy_auto_scaling_policies_rules_scale_out_configuration_scaling_configuration_type"],
threshold=var["model_deployment_model_deployment_configuration_details_model_configuration_details_scaling_policy_auto_scaling_policies_rules_scale_out_configuration_threshold"],
),
metric_type=var["model_deployment_model_deployment_configuration_details_model_configuration_details_scaling_policy_auto_scaling_policies_rules_metric_type"],
)],
)],
cool_down_in_seconds=var["model_deployment_model_deployment_configuration_details_model_configuration_details_scaling_policy_cool_down_in_seconds"],
instance_count=var["model_deployment_model_deployment_configuration_details_model_configuration_details_scaling_policy_instance_count"],
is_enabled=var["model_deployment_model_deployment_configuration_details_model_configuration_details_scaling_policy_is_enabled"],
),
),
environment_configuration_details=oci.data_science.ModelDeploymentModelDeploymentConfigurationDetailsEnvironmentConfigurationDetailsArgs(
environment_configuration_type=var["model_deployment_model_deployment_configuration_details_environment_configuration_details_environment_configuration_type"],
cmds=var["model_deployment_model_deployment_configuration_details_environment_configuration_details_cmd"],
entrypoints=var["model_deployment_model_deployment_configuration_details_environment_configuration_details_entrypoint"],
environment_variables=var["model_deployment_model_deployment_configuration_details_environment_configuration_details_environment_variables"],
health_check_port=var["model_deployment_model_deployment_configuration_details_environment_configuration_details_health_check_port"],
image=var["model_deployment_model_deployment_configuration_details_environment_configuration_details_image"],
image_digest=var["model_deployment_model_deployment_configuration_details_environment_configuration_details_image_digest"],
server_port=var["model_deployment_model_deployment_configuration_details_environment_configuration_details_server_port"],
),
),
project_id=oci_datascience_project["test_project"]["id"],
category_log_details=oci.data_science.ModelDeploymentCategoryLogDetailsArgs(
access=oci.data_science.ModelDeploymentCategoryLogDetailsAccessArgs(
log_group_id=oci_logging_log_group["test_log_group"]["id"],
log_id=oci_logging_log["test_log"]["id"],
),
predict=oci.data_science.ModelDeploymentCategoryLogDetailsPredictArgs(
log_group_id=oci_logging_log_group["test_log_group"]["id"],
log_id=oci_logging_log["test_log"]["id"],
),
),
defined_tags={
"Operations.CostCenter": "42",
},
description=var["model_deployment_description"],
display_name=var["model_deployment_display_name"],
freeform_tags={
"Department": "Finance",
})
package main
import (
"github.com/pulumi/pulumi-oci/sdk/go/oci/DataScience"
"github.com/pulumi/pulumi/sdk/v3/go/pulumi"
)
func main() {
pulumi.Run(func(ctx *pulumi.Context) error {
_, err := DataScience.NewModelDeployment(ctx, "testModelDeployment", &DataScience.ModelDeploymentArgs{
CompartmentId: pulumi.Any(_var.Compartment_id),
ModelDeploymentConfigurationDetails: &datascience.ModelDeploymentModelDeploymentConfigurationDetailsArgs{
DeploymentType: pulumi.Any(_var.Model_deployment_model_deployment_configuration_details_deployment_type),
ModelConfigurationDetails: &datascience.ModelDeploymentModelDeploymentConfigurationDetailsModelConfigurationDetailsArgs{
InstanceConfiguration: &datascience.ModelDeploymentModelDeploymentConfigurationDetailsModelConfigurationDetailsInstanceConfigurationArgs{
InstanceShapeName: pulumi.Any(oci_core_shape.Test_shape.Name),
ModelDeploymentInstanceShapeConfigDetails: &datascience.ModelDeploymentModelDeploymentConfigurationDetailsModelConfigurationDetailsInstanceConfigurationModelDeploymentInstanceShapeConfigDetailsArgs{
CpuBaseline: pulumi.Any(_var.Model_deployment_model_deployment_configuration_details_model_configuration_details_instance_configuration_model_deployment_instance_shape_config_details_cpu_baseline),
MemoryInGbs: pulumi.Any(_var.Model_deployment_model_deployment_configuration_details_model_configuration_details_instance_configuration_model_deployment_instance_shape_config_details_memory_in_gbs),
Ocpus: pulumi.Any(_var.Model_deployment_model_deployment_configuration_details_model_configuration_details_instance_configuration_model_deployment_instance_shape_config_details_ocpus),
},
SubnetId: pulumi.Any(oci_core_subnet.Test_subnet.Id),
},
ModelId: pulumi.Any(oci_datascience_model.Test_model.Id),
BandwidthMbps: pulumi.Any(_var.Model_deployment_model_deployment_configuration_details_model_configuration_details_bandwidth_mbps),
MaximumBandwidthMbps: pulumi.Any(_var.Model_deployment_model_deployment_configuration_details_model_configuration_details_maximum_bandwidth_mbps),
ScalingPolicy: &datascience.ModelDeploymentModelDeploymentConfigurationDetailsModelConfigurationDetailsScalingPolicyArgs{
PolicyType: pulumi.Any(_var.Model_deployment_model_deployment_configuration_details_model_configuration_details_scaling_policy_policy_type),
AutoScalingPolicies: datascience.ModelDeploymentModelDeploymentConfigurationDetailsModelConfigurationDetailsScalingPolicyAutoScalingPolicyArray{
&datascience.ModelDeploymentModelDeploymentConfigurationDetailsModelConfigurationDetailsScalingPolicyAutoScalingPolicyArgs{
AutoScalingPolicyType: pulumi.Any(_var.Model_deployment_model_deployment_configuration_details_model_configuration_details_scaling_policy_auto_scaling_policies_auto_scaling_policy_type),
InitialInstanceCount: pulumi.Any(_var.Model_deployment_model_deployment_configuration_details_model_configuration_details_scaling_policy_auto_scaling_policies_initial_instance_count),
MaximumInstanceCount: pulumi.Any(_var.Model_deployment_model_deployment_configuration_details_model_configuration_details_scaling_policy_auto_scaling_policies_maximum_instance_count),
MinimumInstanceCount: pulumi.Any(_var.Model_deployment_model_deployment_configuration_details_model_configuration_details_scaling_policy_auto_scaling_policies_minimum_instance_count),
Rules: datascience.ModelDeploymentModelDeploymentConfigurationDetailsModelConfigurationDetailsScalingPolicyAutoScalingPolicyRuleArray{
&datascience.ModelDeploymentModelDeploymentConfigurationDetailsModelConfigurationDetailsScalingPolicyAutoScalingPolicyRuleArgs{
MetricExpressionRuleType: pulumi.Any(_var.Model_deployment_model_deployment_configuration_details_model_configuration_details_scaling_policy_auto_scaling_policies_rules_metric_expression_rule_type),
ScaleInConfiguration: &datascience.ModelDeploymentModelDeploymentConfigurationDetailsModelConfigurationDetailsScalingPolicyAutoScalingPolicyRuleScaleInConfigurationArgs{
InstanceCountAdjustment: pulumi.Any(_var.Model_deployment_model_deployment_configuration_details_model_configuration_details_scaling_policy_auto_scaling_policies_rules_scale_in_configuration_instance_count_adjustment),
PendingDuration: pulumi.Any(_var.Model_deployment_model_deployment_configuration_details_model_configuration_details_scaling_policy_auto_scaling_policies_rules_scale_in_configuration_pending_duration),
Query: pulumi.Any(_var.Model_deployment_model_deployment_configuration_details_model_configuration_details_scaling_policy_auto_scaling_policies_rules_scale_in_configuration_query),
ScalingConfigurationType: pulumi.Any(_var.Model_deployment_model_deployment_configuration_details_model_configuration_details_scaling_policy_auto_scaling_policies_rules_scale_in_configuration_scaling_configuration_type),
Threshold: pulumi.Any(_var.Model_deployment_model_deployment_configuration_details_model_configuration_details_scaling_policy_auto_scaling_policies_rules_scale_in_configuration_threshold),
},
ScaleOutConfiguration: &datascience.ModelDeploymentModelDeploymentConfigurationDetailsModelConfigurationDetailsScalingPolicyAutoScalingPolicyRuleScaleOutConfigurationArgs{
InstanceCountAdjustment: pulumi.Any(_var.Model_deployment_model_deployment_configuration_details_model_configuration_details_scaling_policy_auto_scaling_policies_rules_scale_out_configuration_instance_count_adjustment),
PendingDuration: pulumi.Any(_var.Model_deployment_model_deployment_configuration_details_model_configuration_details_scaling_policy_auto_scaling_policies_rules_scale_out_configuration_pending_duration),
Query: pulumi.Any(_var.Model_deployment_model_deployment_configuration_details_model_configuration_details_scaling_policy_auto_scaling_policies_rules_scale_out_configuration_query),
ScalingConfigurationType: pulumi.Any(_var.Model_deployment_model_deployment_configuration_details_model_configuration_details_scaling_policy_auto_scaling_policies_rules_scale_out_configuration_scaling_configuration_type),
Threshold: pulumi.Any(_var.Model_deployment_model_deployment_configuration_details_model_configuration_details_scaling_policy_auto_scaling_policies_rules_scale_out_configuration_threshold),
},
MetricType: pulumi.Any(_var.Model_deployment_model_deployment_configuration_details_model_configuration_details_scaling_policy_auto_scaling_policies_rules_metric_type),
},
},
},
},
CoolDownInSeconds: pulumi.Any(_var.Model_deployment_model_deployment_configuration_details_model_configuration_details_scaling_policy_cool_down_in_seconds),
InstanceCount: pulumi.Any(_var.Model_deployment_model_deployment_configuration_details_model_configuration_details_scaling_policy_instance_count),
IsEnabled: pulumi.Any(_var.Model_deployment_model_deployment_configuration_details_model_configuration_details_scaling_policy_is_enabled),
},
},
EnvironmentConfigurationDetails: &datascience.ModelDeploymentModelDeploymentConfigurationDetailsEnvironmentConfigurationDetailsArgs{
EnvironmentConfigurationType: pulumi.Any(_var.Model_deployment_model_deployment_configuration_details_environment_configuration_details_environment_configuration_type),
Cmds: pulumi.Any(_var.Model_deployment_model_deployment_configuration_details_environment_configuration_details_cmd),
Entrypoints: pulumi.Any(_var.Model_deployment_model_deployment_configuration_details_environment_configuration_details_entrypoint),
EnvironmentVariables: pulumi.Any(_var.Model_deployment_model_deployment_configuration_details_environment_configuration_details_environment_variables),
HealthCheckPort: pulumi.Any(_var.Model_deployment_model_deployment_configuration_details_environment_configuration_details_health_check_port),
Image: pulumi.Any(_var.Model_deployment_model_deployment_configuration_details_environment_configuration_details_image),
ImageDigest: pulumi.Any(_var.Model_deployment_model_deployment_configuration_details_environment_configuration_details_image_digest),
ServerPort: pulumi.Any(_var.Model_deployment_model_deployment_configuration_details_environment_configuration_details_server_port),
},
},
ProjectId: pulumi.Any(oci_datascience_project.Test_project.Id),
CategoryLogDetails: &datascience.ModelDeploymentCategoryLogDetailsArgs{
Access: &datascience.ModelDeploymentCategoryLogDetailsAccessArgs{
LogGroupId: pulumi.Any(oci_logging_log_group.Test_log_group.Id),
LogId: pulumi.Any(oci_logging_log.Test_log.Id),
},
Predict: &datascience.ModelDeploymentCategoryLogDetailsPredictArgs{
LogGroupId: pulumi.Any(oci_logging_log_group.Test_log_group.Id),
LogId: pulumi.Any(oci_logging_log.Test_log.Id),
},
},
DefinedTags: pulumi.Map{
"Operations.CostCenter": pulumi.Any("42"),
},
Description: pulumi.Any(_var.Model_deployment_description),
DisplayName: pulumi.Any(_var.Model_deployment_display_name),
FreeformTags: pulumi.Map{
"Department": pulumi.Any("Finance"),
},
})
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 testModelDeployment = new Oci.DataScience.ModelDeployment("testModelDeployment", new()
{
CompartmentId = @var.Compartment_id,
ModelDeploymentConfigurationDetails = new Oci.DataScience.Inputs.ModelDeploymentModelDeploymentConfigurationDetailsArgs
{
DeploymentType = @var.Model_deployment_model_deployment_configuration_details_deployment_type,
ModelConfigurationDetails = new Oci.DataScience.Inputs.ModelDeploymentModelDeploymentConfigurationDetailsModelConfigurationDetailsArgs
{
InstanceConfiguration = new Oci.DataScience.Inputs.ModelDeploymentModelDeploymentConfigurationDetailsModelConfigurationDetailsInstanceConfigurationArgs
{
InstanceShapeName = oci_core_shape.Test_shape.Name,
ModelDeploymentInstanceShapeConfigDetails = new Oci.DataScience.Inputs.ModelDeploymentModelDeploymentConfigurationDetailsModelConfigurationDetailsInstanceConfigurationModelDeploymentInstanceShapeConfigDetailsArgs
{
CpuBaseline = @var.Model_deployment_model_deployment_configuration_details_model_configuration_details_instance_configuration_model_deployment_instance_shape_config_details_cpu_baseline,
MemoryInGbs = @var.Model_deployment_model_deployment_configuration_details_model_configuration_details_instance_configuration_model_deployment_instance_shape_config_details_memory_in_gbs,
Ocpus = @var.Model_deployment_model_deployment_configuration_details_model_configuration_details_instance_configuration_model_deployment_instance_shape_config_details_ocpus,
},
SubnetId = oci_core_subnet.Test_subnet.Id,
},
ModelId = oci_datascience_model.Test_model.Id,
BandwidthMbps = @var.Model_deployment_model_deployment_configuration_details_model_configuration_details_bandwidth_mbps,
MaximumBandwidthMbps = @var.Model_deployment_model_deployment_configuration_details_model_configuration_details_maximum_bandwidth_mbps,
ScalingPolicy = new Oci.DataScience.Inputs.ModelDeploymentModelDeploymentConfigurationDetailsModelConfigurationDetailsScalingPolicyArgs
{
PolicyType = @var.Model_deployment_model_deployment_configuration_details_model_configuration_details_scaling_policy_policy_type,
AutoScalingPolicies = new[]
{
new Oci.DataScience.Inputs.ModelDeploymentModelDeploymentConfigurationDetailsModelConfigurationDetailsScalingPolicyAutoScalingPolicyArgs
{
AutoScalingPolicyType = @var.Model_deployment_model_deployment_configuration_details_model_configuration_details_scaling_policy_auto_scaling_policies_auto_scaling_policy_type,
InitialInstanceCount = @var.Model_deployment_model_deployment_configuration_details_model_configuration_details_scaling_policy_auto_scaling_policies_initial_instance_count,
MaximumInstanceCount = @var.Model_deployment_model_deployment_configuration_details_model_configuration_details_scaling_policy_auto_scaling_policies_maximum_instance_count,
MinimumInstanceCount = @var.Model_deployment_model_deployment_configuration_details_model_configuration_details_scaling_policy_auto_scaling_policies_minimum_instance_count,
Rules = new[]
{
new Oci.DataScience.Inputs.ModelDeploymentModelDeploymentConfigurationDetailsModelConfigurationDetailsScalingPolicyAutoScalingPolicyRuleArgs
{
MetricExpressionRuleType = @var.Model_deployment_model_deployment_configuration_details_model_configuration_details_scaling_policy_auto_scaling_policies_rules_metric_expression_rule_type,
ScaleInConfiguration = new Oci.DataScience.Inputs.ModelDeploymentModelDeploymentConfigurationDetailsModelConfigurationDetailsScalingPolicyAutoScalingPolicyRuleScaleInConfigurationArgs
{
InstanceCountAdjustment = @var.Model_deployment_model_deployment_configuration_details_model_configuration_details_scaling_policy_auto_scaling_policies_rules_scale_in_configuration_instance_count_adjustment,
PendingDuration = @var.Model_deployment_model_deployment_configuration_details_model_configuration_details_scaling_policy_auto_scaling_policies_rules_scale_in_configuration_pending_duration,
Query = @var.Model_deployment_model_deployment_configuration_details_model_configuration_details_scaling_policy_auto_scaling_policies_rules_scale_in_configuration_query,
ScalingConfigurationType = @var.Model_deployment_model_deployment_configuration_details_model_configuration_details_scaling_policy_auto_scaling_policies_rules_scale_in_configuration_scaling_configuration_type,
Threshold = @var.Model_deployment_model_deployment_configuration_details_model_configuration_details_scaling_policy_auto_scaling_policies_rules_scale_in_configuration_threshold,
},
ScaleOutConfiguration = new Oci.DataScience.Inputs.ModelDeploymentModelDeploymentConfigurationDetailsModelConfigurationDetailsScalingPolicyAutoScalingPolicyRuleScaleOutConfigurationArgs
{
InstanceCountAdjustment = @var.Model_deployment_model_deployment_configuration_details_model_configuration_details_scaling_policy_auto_scaling_policies_rules_scale_out_configuration_instance_count_adjustment,
PendingDuration = @var.Model_deployment_model_deployment_configuration_details_model_configuration_details_scaling_policy_auto_scaling_policies_rules_scale_out_configuration_pending_duration,
Query = @var.Model_deployment_model_deployment_configuration_details_model_configuration_details_scaling_policy_auto_scaling_policies_rules_scale_out_configuration_query,
ScalingConfigurationType = @var.Model_deployment_model_deployment_configuration_details_model_configuration_details_scaling_policy_auto_scaling_policies_rules_scale_out_configuration_scaling_configuration_type,
Threshold = @var.Model_deployment_model_deployment_configuration_details_model_configuration_details_scaling_policy_auto_scaling_policies_rules_scale_out_configuration_threshold,
},
MetricType = @var.Model_deployment_model_deployment_configuration_details_model_configuration_details_scaling_policy_auto_scaling_policies_rules_metric_type,
},
},
},
},
CoolDownInSeconds = @var.Model_deployment_model_deployment_configuration_details_model_configuration_details_scaling_policy_cool_down_in_seconds,
InstanceCount = @var.Model_deployment_model_deployment_configuration_details_model_configuration_details_scaling_policy_instance_count,
IsEnabled = @var.Model_deployment_model_deployment_configuration_details_model_configuration_details_scaling_policy_is_enabled,
},
},
EnvironmentConfigurationDetails = new Oci.DataScience.Inputs.ModelDeploymentModelDeploymentConfigurationDetailsEnvironmentConfigurationDetailsArgs
{
EnvironmentConfigurationType = @var.Model_deployment_model_deployment_configuration_details_environment_configuration_details_environment_configuration_type,
Cmds = @var.Model_deployment_model_deployment_configuration_details_environment_configuration_details_cmd,
Entrypoints = @var.Model_deployment_model_deployment_configuration_details_environment_configuration_details_entrypoint,
EnvironmentVariables = @var.Model_deployment_model_deployment_configuration_details_environment_configuration_details_environment_variables,
HealthCheckPort = @var.Model_deployment_model_deployment_configuration_details_environment_configuration_details_health_check_port,
Image = @var.Model_deployment_model_deployment_configuration_details_environment_configuration_details_image,
ImageDigest = @var.Model_deployment_model_deployment_configuration_details_environment_configuration_details_image_digest,
ServerPort = @var.Model_deployment_model_deployment_configuration_details_environment_configuration_details_server_port,
},
},
ProjectId = oci_datascience_project.Test_project.Id,
CategoryLogDetails = new Oci.DataScience.Inputs.ModelDeploymentCategoryLogDetailsArgs
{
Access = new Oci.DataScience.Inputs.ModelDeploymentCategoryLogDetailsAccessArgs
{
LogGroupId = oci_logging_log_group.Test_log_group.Id,
LogId = oci_logging_log.Test_log.Id,
},
Predict = new Oci.DataScience.Inputs.ModelDeploymentCategoryLogDetailsPredictArgs
{
LogGroupId = oci_logging_log_group.Test_log_group.Id,
LogId = oci_logging_log.Test_log.Id,
},
},
DefinedTags =
{
{ "Operations.CostCenter", "42" },
},
Description = @var.Model_deployment_description,
DisplayName = @var.Model_deployment_display_name,
FreeformTags =
{
{ "Department", "Finance" },
},
});
});
package generated_program;
import com.pulumi.Context;
import com.pulumi.Pulumi;
import com.pulumi.core.Output;
import com.pulumi.oci.DataScience.ModelDeployment;
import com.pulumi.oci.DataScience.ModelDeploymentArgs;
import com.pulumi.oci.DataScience.inputs.ModelDeploymentModelDeploymentConfigurationDetailsArgs;
import com.pulumi.oci.DataScience.inputs.ModelDeploymentModelDeploymentConfigurationDetailsModelConfigurationDetailsArgs;
import com.pulumi.oci.DataScience.inputs.ModelDeploymentModelDeploymentConfigurationDetailsModelConfigurationDetailsInstanceConfigurationArgs;
import com.pulumi.oci.DataScience.inputs.ModelDeploymentModelDeploymentConfigurationDetailsModelConfigurationDetailsInstanceConfigurationModelDeploymentInstanceShapeConfigDetailsArgs;
import com.pulumi.oci.DataScience.inputs.ModelDeploymentModelDeploymentConfigurationDetailsModelConfigurationDetailsScalingPolicyArgs;
import com.pulumi.oci.DataScience.inputs.ModelDeploymentModelDeploymentConfigurationDetailsEnvironmentConfigurationDetailsArgs;
import com.pulumi.oci.DataScience.inputs.ModelDeploymentCategoryLogDetailsArgs;
import com.pulumi.oci.DataScience.inputs.ModelDeploymentCategoryLogDetailsAccessArgs;
import com.pulumi.oci.DataScience.inputs.ModelDeploymentCategoryLogDetailsPredictArgs;
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) {
var testModelDeployment = new ModelDeployment("testModelDeployment", ModelDeploymentArgs.builder()
.compartmentId(var_.compartment_id())
.modelDeploymentConfigurationDetails(ModelDeploymentModelDeploymentConfigurationDetailsArgs.builder()
.deploymentType(var_.model_deployment_model_deployment_configuration_details_deployment_type())
.modelConfigurationDetails(ModelDeploymentModelDeploymentConfigurationDetailsModelConfigurationDetailsArgs.builder()
.instanceConfiguration(ModelDeploymentModelDeploymentConfigurationDetailsModelConfigurationDetailsInstanceConfigurationArgs.builder()
.instanceShapeName(oci_core_shape.test_shape().name())
.modelDeploymentInstanceShapeConfigDetails(ModelDeploymentModelDeploymentConfigurationDetailsModelConfigurationDetailsInstanceConfigurationModelDeploymentInstanceShapeConfigDetailsArgs.builder()
.cpuBaseline(var_.model_deployment_model_deployment_configuration_details_model_configuration_details_instance_configuration_model_deployment_instance_shape_config_details_cpu_baseline())
.memoryInGbs(var_.model_deployment_model_deployment_configuration_details_model_configuration_details_instance_configuration_model_deployment_instance_shape_config_details_memory_in_gbs())
.ocpus(var_.model_deployment_model_deployment_configuration_details_model_configuration_details_instance_configuration_model_deployment_instance_shape_config_details_ocpus())
.build())
.subnetId(oci_core_subnet.test_subnet().id())
.build())
.modelId(oci_datascience_model.test_model().id())
.bandwidthMbps(var_.model_deployment_model_deployment_configuration_details_model_configuration_details_bandwidth_mbps())
.maximumBandwidthMbps(var_.model_deployment_model_deployment_configuration_details_model_configuration_details_maximum_bandwidth_mbps())
.scalingPolicy(ModelDeploymentModelDeploymentConfigurationDetailsModelConfigurationDetailsScalingPolicyArgs.builder()
.policyType(var_.model_deployment_model_deployment_configuration_details_model_configuration_details_scaling_policy_policy_type())
.autoScalingPolicies(ModelDeploymentModelDeploymentConfigurationDetailsModelConfigurationDetailsScalingPolicyAutoScalingPolicyArgs.builder()
.autoScalingPolicyType(var_.model_deployment_model_deployment_configuration_details_model_configuration_details_scaling_policy_auto_scaling_policies_auto_scaling_policy_type())
.initialInstanceCount(var_.model_deployment_model_deployment_configuration_details_model_configuration_details_scaling_policy_auto_scaling_policies_initial_instance_count())
.maximumInstanceCount(var_.model_deployment_model_deployment_configuration_details_model_configuration_details_scaling_policy_auto_scaling_policies_maximum_instance_count())
.minimumInstanceCount(var_.model_deployment_model_deployment_configuration_details_model_configuration_details_scaling_policy_auto_scaling_policies_minimum_instance_count())
.rules(ModelDeploymentModelDeploymentConfigurationDetailsModelConfigurationDetailsScalingPolicyAutoScalingPolicyRuleArgs.builder()
.metricExpressionRuleType(var_.model_deployment_model_deployment_configuration_details_model_configuration_details_scaling_policy_auto_scaling_policies_rules_metric_expression_rule_type())
.scaleInConfiguration(ModelDeploymentModelDeploymentConfigurationDetailsModelConfigurationDetailsScalingPolicyAutoScalingPolicyRuleScaleInConfigurationArgs.builder()
.instanceCountAdjustment(var_.model_deployment_model_deployment_configuration_details_model_configuration_details_scaling_policy_auto_scaling_policies_rules_scale_in_configuration_instance_count_adjustment())
.pendingDuration(var_.model_deployment_model_deployment_configuration_details_model_configuration_details_scaling_policy_auto_scaling_policies_rules_scale_in_configuration_pending_duration())
.query(var_.model_deployment_model_deployment_configuration_details_model_configuration_details_scaling_policy_auto_scaling_policies_rules_scale_in_configuration_query())
.scalingConfigurationType(var_.model_deployment_model_deployment_configuration_details_model_configuration_details_scaling_policy_auto_scaling_policies_rules_scale_in_configuration_scaling_configuration_type())
.threshold(var_.model_deployment_model_deployment_configuration_details_model_configuration_details_scaling_policy_auto_scaling_policies_rules_scale_in_configuration_threshold())
.build())
.scaleOutConfiguration(ModelDeploymentModelDeploymentConfigurationDetailsModelConfigurationDetailsScalingPolicyAutoScalingPolicyRuleScaleOutConfigurationArgs.builder()
.instanceCountAdjustment(var_.model_deployment_model_deployment_configuration_details_model_configuration_details_scaling_policy_auto_scaling_policies_rules_scale_out_configuration_instance_count_adjustment())
.pendingDuration(var_.model_deployment_model_deployment_configuration_details_model_configuration_details_scaling_policy_auto_scaling_policies_rules_scale_out_configuration_pending_duration())
.query(var_.model_deployment_model_deployment_configuration_details_model_configuration_details_scaling_policy_auto_scaling_policies_rules_scale_out_configuration_query())
.scalingConfigurationType(var_.model_deployment_model_deployment_configuration_details_model_configuration_details_scaling_policy_auto_scaling_policies_rules_scale_out_configuration_scaling_configuration_type())
.threshold(var_.model_deployment_model_deployment_configuration_details_model_configuration_details_scaling_policy_auto_scaling_policies_rules_scale_out_configuration_threshold())
.build())
.metricType(var_.model_deployment_model_deployment_configuration_details_model_configuration_details_scaling_policy_auto_scaling_policies_rules_metric_type())
.build())
.build())
.coolDownInSeconds(var_.model_deployment_model_deployment_configuration_details_model_configuration_details_scaling_policy_cool_down_in_seconds())
.instanceCount(var_.model_deployment_model_deployment_configuration_details_model_configuration_details_scaling_policy_instance_count())
.isEnabled(var_.model_deployment_model_deployment_configuration_details_model_configuration_details_scaling_policy_is_enabled())
.build())
.build())
.environmentConfigurationDetails(ModelDeploymentModelDeploymentConfigurationDetailsEnvironmentConfigurationDetailsArgs.builder()
.environmentConfigurationType(var_.model_deployment_model_deployment_configuration_details_environment_configuration_details_environment_configuration_type())
.cmds(var_.model_deployment_model_deployment_configuration_details_environment_configuration_details_cmd())
.entrypoints(var_.model_deployment_model_deployment_configuration_details_environment_configuration_details_entrypoint())
.environmentVariables(var_.model_deployment_model_deployment_configuration_details_environment_configuration_details_environment_variables())
.healthCheckPort(var_.model_deployment_model_deployment_configuration_details_environment_configuration_details_health_check_port())
.image(var_.model_deployment_model_deployment_configuration_details_environment_configuration_details_image())
.imageDigest(var_.model_deployment_model_deployment_configuration_details_environment_configuration_details_image_digest())
.serverPort(var_.model_deployment_model_deployment_configuration_details_environment_configuration_details_server_port())
.build())
.build())
.projectId(oci_datascience_project.test_project().id())
.categoryLogDetails(ModelDeploymentCategoryLogDetailsArgs.builder()
.access(ModelDeploymentCategoryLogDetailsAccessArgs.builder()
.logGroupId(oci_logging_log_group.test_log_group().id())
.logId(oci_logging_log.test_log().id())
.build())
.predict(ModelDeploymentCategoryLogDetailsPredictArgs.builder()
.logGroupId(oci_logging_log_group.test_log_group().id())
.logId(oci_logging_log.test_log().id())
.build())
.build())
.definedTags(Map.of("Operations.CostCenter", "42"))
.description(var_.model_deployment_description())
.displayName(var_.model_deployment_display_name())
.freeformTags(Map.of("Department", "Finance"))
.build());
}
}
resources:
testModelDeployment:
type: oci:DataScience:ModelDeployment
properties:
#Required
compartmentId: ${var.compartment_id}
modelDeploymentConfigurationDetails:
deploymentType: ${var.model_deployment_model_deployment_configuration_details_deployment_type}
modelConfigurationDetails:
instanceConfiguration:
instanceShapeName: ${oci_core_shape.test_shape.name}
modelDeploymentInstanceShapeConfigDetails:
cpuBaseline: ${var.model_deployment_model_deployment_configuration_details_model_configuration_details_instance_configuration_model_deployment_instance_shape_config_details_cpu_baseline}
memoryInGbs: ${var.model_deployment_model_deployment_configuration_details_model_configuration_details_instance_configuration_model_deployment_instance_shape_config_details_memory_in_gbs}
ocpus: ${var.model_deployment_model_deployment_configuration_details_model_configuration_details_instance_configuration_model_deployment_instance_shape_config_details_ocpus}
subnetId: ${oci_core_subnet.test_subnet.id}
modelId: ${oci_datascience_model.test_model.id}
bandwidthMbps: ${var.model_deployment_model_deployment_configuration_details_model_configuration_details_bandwidth_mbps}
maximumBandwidthMbps: ${var.model_deployment_model_deployment_configuration_details_model_configuration_details_maximum_bandwidth_mbps}
scalingPolicy:
policyType: ${var.model_deployment_model_deployment_configuration_details_model_configuration_details_scaling_policy_policy_type}
autoScalingPolicies:
- autoScalingPolicyType: ${var.model_deployment_model_deployment_configuration_details_model_configuration_details_scaling_policy_auto_scaling_policies_auto_scaling_policy_type}
initialInstanceCount: ${var.model_deployment_model_deployment_configuration_details_model_configuration_details_scaling_policy_auto_scaling_policies_initial_instance_count}
maximumInstanceCount: ${var.model_deployment_model_deployment_configuration_details_model_configuration_details_scaling_policy_auto_scaling_policies_maximum_instance_count}
minimumInstanceCount: ${var.model_deployment_model_deployment_configuration_details_model_configuration_details_scaling_policy_auto_scaling_policies_minimum_instance_count}
rules:
- metricExpressionRuleType: ${var.model_deployment_model_deployment_configuration_details_model_configuration_details_scaling_policy_auto_scaling_policies_rules_metric_expression_rule_type}
scaleInConfiguration:
instanceCountAdjustment: ${var.model_deployment_model_deployment_configuration_details_model_configuration_details_scaling_policy_auto_scaling_policies_rules_scale_in_configuration_instance_count_adjustment}
pendingDuration: ${var.model_deployment_model_deployment_configuration_details_model_configuration_details_scaling_policy_auto_scaling_policies_rules_scale_in_configuration_pending_duration}
query: ${var.model_deployment_model_deployment_configuration_details_model_configuration_details_scaling_policy_auto_scaling_policies_rules_scale_in_configuration_query}
scalingConfigurationType: ${var.model_deployment_model_deployment_configuration_details_model_configuration_details_scaling_policy_auto_scaling_policies_rules_scale_in_configuration_scaling_configuration_type}
threshold: ${var.model_deployment_model_deployment_configuration_details_model_configuration_details_scaling_policy_auto_scaling_policies_rules_scale_in_configuration_threshold}
scaleOutConfiguration:
instanceCountAdjustment: ${var.model_deployment_model_deployment_configuration_details_model_configuration_details_scaling_policy_auto_scaling_policies_rules_scale_out_configuration_instance_count_adjustment}
pendingDuration: ${var.model_deployment_model_deployment_configuration_details_model_configuration_details_scaling_policy_auto_scaling_policies_rules_scale_out_configuration_pending_duration}
query: ${var.model_deployment_model_deployment_configuration_details_model_configuration_details_scaling_policy_auto_scaling_policies_rules_scale_out_configuration_query}
scalingConfigurationType: ${var.model_deployment_model_deployment_configuration_details_model_configuration_details_scaling_policy_auto_scaling_policies_rules_scale_out_configuration_scaling_configuration_type}
threshold: ${var.model_deployment_model_deployment_configuration_details_model_configuration_details_scaling_policy_auto_scaling_policies_rules_scale_out_configuration_threshold}
metricType: ${var.model_deployment_model_deployment_configuration_details_model_configuration_details_scaling_policy_auto_scaling_policies_rules_metric_type}
coolDownInSeconds: ${var.model_deployment_model_deployment_configuration_details_model_configuration_details_scaling_policy_cool_down_in_seconds}
instanceCount: ${var.model_deployment_model_deployment_configuration_details_model_configuration_details_scaling_policy_instance_count}
isEnabled: ${var.model_deployment_model_deployment_configuration_details_model_configuration_details_scaling_policy_is_enabled}
environmentConfigurationDetails:
environmentConfigurationType: ${var.model_deployment_model_deployment_configuration_details_environment_configuration_details_environment_configuration_type}
cmds: ${var.model_deployment_model_deployment_configuration_details_environment_configuration_details_cmd}
entrypoints: ${var.model_deployment_model_deployment_configuration_details_environment_configuration_details_entrypoint}
environmentVariables: ${var.model_deployment_model_deployment_configuration_details_environment_configuration_details_environment_variables}
healthCheckPort: ${var.model_deployment_model_deployment_configuration_details_environment_configuration_details_health_check_port}
image: ${var.model_deployment_model_deployment_configuration_details_environment_configuration_details_image}
imageDigest: ${var.model_deployment_model_deployment_configuration_details_environment_configuration_details_image_digest}
serverPort: ${var.model_deployment_model_deployment_configuration_details_environment_configuration_details_server_port}
projectId: ${oci_datascience_project.test_project.id}
categoryLogDetails:
access:
logGroupId: ${oci_logging_log_group.test_log_group.id}
logId: ${oci_logging_log.test_log.id}
predict:
logGroupId: ${oci_logging_log_group.test_log_group.id}
logId: ${oci_logging_log.test_log.id}
definedTags:
Operations.CostCenter: '42'
description: ${var.model_deployment_description}
displayName: ${var.model_deployment_display_name}
freeformTags:
Department: Finance
Create ModelDeployment Resource
Resources are created with functions called constructors. To learn more about declaring and configuring resources, see Resources.
Constructor syntax
new ModelDeployment(name: string, args: ModelDeploymentArgs, opts?: CustomResourceOptions);@overload
def ModelDeployment(resource_name: str,
args: ModelDeploymentArgs,
opts: Optional[ResourceOptions] = None)
@overload
def ModelDeployment(resource_name: str,
opts: Optional[ResourceOptions] = None,
compartment_id: Optional[str] = None,
model_deployment_configuration_details: Optional[_datascience.ModelDeploymentModelDeploymentConfigurationDetailsArgs] = None,
project_id: Optional[str] = None,
category_log_details: Optional[_datascience.ModelDeploymentCategoryLogDetailsArgs] = None,
defined_tags: Optional[Mapping[str, Any]] = None,
description: Optional[str] = None,
display_name: Optional[str] = None,
freeform_tags: Optional[Mapping[str, Any]] = None,
state: Optional[str] = None)func NewModelDeployment(ctx *Context, name string, args ModelDeploymentArgs, opts ...ResourceOption) (*ModelDeployment, error)public ModelDeployment(string name, ModelDeploymentArgs args, CustomResourceOptions? opts = null)
public ModelDeployment(String name, ModelDeploymentArgs args)
public ModelDeployment(String name, ModelDeploymentArgs args, CustomResourceOptions options)
type: oci:DataScience:ModelDeployment
properties: # The arguments to resource properties.
options: # Bag of options to control resource's behavior.
Parameters
- name string
- The unique name of the resource.
- args ModelDeploymentArgs
- The arguments to resource properties.
- opts CustomResourceOptions
- Bag of options to control resource's behavior.
- resource_name str
- The unique name of the resource.
- args ModelDeploymentArgs
- The arguments to resource properties.
- opts ResourceOptions
- Bag of options to control resource's behavior.
- ctx Context
- Context object for the current deployment.
- name string
- The unique name of the resource.
- args ModelDeploymentArgs
- The arguments to resource properties.
- opts ResourceOption
- Bag of options to control resource's behavior.
- name string
- The unique name of the resource.
- args ModelDeploymentArgs
- The arguments to resource properties.
- opts CustomResourceOptions
- Bag of options to control resource's behavior.
- name String
- The unique name of the resource.
- args ModelDeploymentArgs
- The arguments to resource properties.
- options CustomResourceOptions
- Bag of options to control resource's behavior.
Example
The following reference example uses placeholder values for all input properties.
var modelDeploymentResource = new Oci.DataScience.ModelDeployment("modelDeploymentResource", new()
{
CompartmentId = "string",
ModelDeploymentConfigurationDetails = new Oci.DataScience.Inputs.ModelDeploymentModelDeploymentConfigurationDetailsArgs
{
DeploymentType = "string",
ModelConfigurationDetails = new Oci.DataScience.Inputs.ModelDeploymentModelDeploymentConfigurationDetailsModelConfigurationDetailsArgs
{
InstanceConfiguration = new Oci.DataScience.Inputs.ModelDeploymentModelDeploymentConfigurationDetailsModelConfigurationDetailsInstanceConfigurationArgs
{
InstanceShapeName = "string",
ModelDeploymentInstanceShapeConfigDetails = new Oci.DataScience.Inputs.ModelDeploymentModelDeploymentConfigurationDetailsModelConfigurationDetailsInstanceConfigurationModelDeploymentInstanceShapeConfigDetailsArgs
{
CpuBaseline = "string",
MemoryInGbs = 0,
Ocpus = 0,
},
SubnetId = "string",
},
ModelId = "string",
BandwidthMbps = 0,
MaximumBandwidthMbps = 0,
ScalingPolicy = new Oci.DataScience.Inputs.ModelDeploymentModelDeploymentConfigurationDetailsModelConfigurationDetailsScalingPolicyArgs
{
PolicyType = "string",
AutoScalingPolicies = new[]
{
new Oci.DataScience.Inputs.ModelDeploymentModelDeploymentConfigurationDetailsModelConfigurationDetailsScalingPolicyAutoScalingPolicyArgs
{
AutoScalingPolicyType = "string",
InitialInstanceCount = 0,
MaximumInstanceCount = 0,
MinimumInstanceCount = 0,
Rules = new[]
{
new Oci.DataScience.Inputs.ModelDeploymentModelDeploymentConfigurationDetailsModelConfigurationDetailsScalingPolicyAutoScalingPolicyRuleArgs
{
MetricExpressionRuleType = "string",
ScaleInConfiguration = new Oci.DataScience.Inputs.ModelDeploymentModelDeploymentConfigurationDetailsModelConfigurationDetailsScalingPolicyAutoScalingPolicyRuleScaleInConfigurationArgs
{
InstanceCountAdjustment = 0,
PendingDuration = "string",
Query = "string",
ScalingConfigurationType = "string",
Threshold = 0,
},
ScaleOutConfiguration = new Oci.DataScience.Inputs.ModelDeploymentModelDeploymentConfigurationDetailsModelConfigurationDetailsScalingPolicyAutoScalingPolicyRuleScaleOutConfigurationArgs
{
InstanceCountAdjustment = 0,
PendingDuration = "string",
Query = "string",
ScalingConfigurationType = "string",
Threshold = 0,
},
MetricType = "string",
},
},
},
},
CoolDownInSeconds = 0,
InstanceCount = 0,
IsEnabled = false,
},
},
EnvironmentConfigurationDetails = new Oci.DataScience.Inputs.ModelDeploymentModelDeploymentConfigurationDetailsEnvironmentConfigurationDetailsArgs
{
EnvironmentConfigurationType = "string",
Cmds = new[]
{
"string",
},
Entrypoints = new[]
{
"string",
},
EnvironmentVariables =
{
{ "string", "any" },
},
HealthCheckPort = 0,
Image = "string",
ImageDigest = "string",
ServerPort = 0,
},
},
ProjectId = "string",
CategoryLogDetails = new Oci.DataScience.Inputs.ModelDeploymentCategoryLogDetailsArgs
{
Access = new Oci.DataScience.Inputs.ModelDeploymentCategoryLogDetailsAccessArgs
{
LogGroupId = "string",
LogId = "string",
},
Predict = new Oci.DataScience.Inputs.ModelDeploymentCategoryLogDetailsPredictArgs
{
LogGroupId = "string",
LogId = "string",
},
},
DefinedTags =
{
{ "string", "any" },
},
Description = "string",
DisplayName = "string",
FreeformTags =
{
{ "string", "any" },
},
State = "string",
});
example, err := DataScience.NewModelDeployment(ctx, "modelDeploymentResource", &DataScience.ModelDeploymentArgs{
CompartmentId: pulumi.String("string"),
ModelDeploymentConfigurationDetails: &datascience.ModelDeploymentModelDeploymentConfigurationDetailsArgs{
DeploymentType: pulumi.String("string"),
ModelConfigurationDetails: &datascience.ModelDeploymentModelDeploymentConfigurationDetailsModelConfigurationDetailsArgs{
InstanceConfiguration: &datascience.ModelDeploymentModelDeploymentConfigurationDetailsModelConfigurationDetailsInstanceConfigurationArgs{
InstanceShapeName: pulumi.String("string"),
ModelDeploymentInstanceShapeConfigDetails: &datascience.ModelDeploymentModelDeploymentConfigurationDetailsModelConfigurationDetailsInstanceConfigurationModelDeploymentInstanceShapeConfigDetailsArgs{
CpuBaseline: pulumi.String("string"),
MemoryInGbs: pulumi.Float64(0),
Ocpus: pulumi.Float64(0),
},
SubnetId: pulumi.String("string"),
},
ModelId: pulumi.String("string"),
BandwidthMbps: pulumi.Int(0),
MaximumBandwidthMbps: pulumi.Int(0),
ScalingPolicy: &datascience.ModelDeploymentModelDeploymentConfigurationDetailsModelConfigurationDetailsScalingPolicyArgs{
PolicyType: pulumi.String("string"),
AutoScalingPolicies: datascience.ModelDeploymentModelDeploymentConfigurationDetailsModelConfigurationDetailsScalingPolicyAutoScalingPolicyArray{
&datascience.ModelDeploymentModelDeploymentConfigurationDetailsModelConfigurationDetailsScalingPolicyAutoScalingPolicyArgs{
AutoScalingPolicyType: pulumi.String("string"),
InitialInstanceCount: pulumi.Int(0),
MaximumInstanceCount: pulumi.Int(0),
MinimumInstanceCount: pulumi.Int(0),
Rules: datascience.ModelDeploymentModelDeploymentConfigurationDetailsModelConfigurationDetailsScalingPolicyAutoScalingPolicyRuleArray{
&datascience.ModelDeploymentModelDeploymentConfigurationDetailsModelConfigurationDetailsScalingPolicyAutoScalingPolicyRuleArgs{
MetricExpressionRuleType: pulumi.String("string"),
ScaleInConfiguration: &datascience.ModelDeploymentModelDeploymentConfigurationDetailsModelConfigurationDetailsScalingPolicyAutoScalingPolicyRuleScaleInConfigurationArgs{
InstanceCountAdjustment: pulumi.Int(0),
PendingDuration: pulumi.String("string"),
Query: pulumi.String("string"),
ScalingConfigurationType: pulumi.String("string"),
Threshold: pulumi.Int(0),
},
ScaleOutConfiguration: &datascience.ModelDeploymentModelDeploymentConfigurationDetailsModelConfigurationDetailsScalingPolicyAutoScalingPolicyRuleScaleOutConfigurationArgs{
InstanceCountAdjustment: pulumi.Int(0),
PendingDuration: pulumi.String("string"),
Query: pulumi.String("string"),
ScalingConfigurationType: pulumi.String("string"),
Threshold: pulumi.Int(0),
},
MetricType: pulumi.String("string"),
},
},
},
},
CoolDownInSeconds: pulumi.Int(0),
InstanceCount: pulumi.Int(0),
IsEnabled: pulumi.Bool(false),
},
},
EnvironmentConfigurationDetails: &datascience.ModelDeploymentModelDeploymentConfigurationDetailsEnvironmentConfigurationDetailsArgs{
EnvironmentConfigurationType: pulumi.String("string"),
Cmds: pulumi.StringArray{
pulumi.String("string"),
},
Entrypoints: pulumi.StringArray{
pulumi.String("string"),
},
EnvironmentVariables: pulumi.Map{
"string": pulumi.Any("any"),
},
HealthCheckPort: pulumi.Int(0),
Image: pulumi.String("string"),
ImageDigest: pulumi.String("string"),
ServerPort: pulumi.Int(0),
},
},
ProjectId: pulumi.String("string"),
CategoryLogDetails: &datascience.ModelDeploymentCategoryLogDetailsArgs{
Access: &datascience.ModelDeploymentCategoryLogDetailsAccessArgs{
LogGroupId: pulumi.String("string"),
LogId: pulumi.String("string"),
},
Predict: &datascience.ModelDeploymentCategoryLogDetailsPredictArgs{
LogGroupId: pulumi.String("string"),
LogId: pulumi.String("string"),
},
},
DefinedTags: pulumi.Map{
"string": pulumi.Any("any"),
},
Description: pulumi.String("string"),
DisplayName: pulumi.String("string"),
FreeformTags: pulumi.Map{
"string": pulumi.Any("any"),
},
State: pulumi.String("string"),
})
var modelDeploymentResource = new ModelDeployment("modelDeploymentResource", ModelDeploymentArgs.builder()
.compartmentId("string")
.modelDeploymentConfigurationDetails(ModelDeploymentModelDeploymentConfigurationDetailsArgs.builder()
.deploymentType("string")
.modelConfigurationDetails(ModelDeploymentModelDeploymentConfigurationDetailsModelConfigurationDetailsArgs.builder()
.instanceConfiguration(ModelDeploymentModelDeploymentConfigurationDetailsModelConfigurationDetailsInstanceConfigurationArgs.builder()
.instanceShapeName("string")
.modelDeploymentInstanceShapeConfigDetails(ModelDeploymentModelDeploymentConfigurationDetailsModelConfigurationDetailsInstanceConfigurationModelDeploymentInstanceShapeConfigDetailsArgs.builder()
.cpuBaseline("string")
.memoryInGbs(0)
.ocpus(0)
.build())
.subnetId("string")
.build())
.modelId("string")
.bandwidthMbps(0)
.maximumBandwidthMbps(0)
.scalingPolicy(ModelDeploymentModelDeploymentConfigurationDetailsModelConfigurationDetailsScalingPolicyArgs.builder()
.policyType("string")
.autoScalingPolicies(ModelDeploymentModelDeploymentConfigurationDetailsModelConfigurationDetailsScalingPolicyAutoScalingPolicyArgs.builder()
.autoScalingPolicyType("string")
.initialInstanceCount(0)
.maximumInstanceCount(0)
.minimumInstanceCount(0)
.rules(ModelDeploymentModelDeploymentConfigurationDetailsModelConfigurationDetailsScalingPolicyAutoScalingPolicyRuleArgs.builder()
.metricExpressionRuleType("string")
.scaleInConfiguration(ModelDeploymentModelDeploymentConfigurationDetailsModelConfigurationDetailsScalingPolicyAutoScalingPolicyRuleScaleInConfigurationArgs.builder()
.instanceCountAdjustment(0)
.pendingDuration("string")
.query("string")
.scalingConfigurationType("string")
.threshold(0)
.build())
.scaleOutConfiguration(ModelDeploymentModelDeploymentConfigurationDetailsModelConfigurationDetailsScalingPolicyAutoScalingPolicyRuleScaleOutConfigurationArgs.builder()
.instanceCountAdjustment(0)
.pendingDuration("string")
.query("string")
.scalingConfigurationType("string")
.threshold(0)
.build())
.metricType("string")
.build())
.build())
.coolDownInSeconds(0)
.instanceCount(0)
.isEnabled(false)
.build())
.build())
.environmentConfigurationDetails(ModelDeploymentModelDeploymentConfigurationDetailsEnvironmentConfigurationDetailsArgs.builder()
.environmentConfigurationType("string")
.cmds("string")
.entrypoints("string")
.environmentVariables(Map.of("string", "any"))
.healthCheckPort(0)
.image("string")
.imageDigest("string")
.serverPort(0)
.build())
.build())
.projectId("string")
.categoryLogDetails(ModelDeploymentCategoryLogDetailsArgs.builder()
.access(ModelDeploymentCategoryLogDetailsAccessArgs.builder()
.logGroupId("string")
.logId("string")
.build())
.predict(ModelDeploymentCategoryLogDetailsPredictArgs.builder()
.logGroupId("string")
.logId("string")
.build())
.build())
.definedTags(Map.of("string", "any"))
.description("string")
.displayName("string")
.freeformTags(Map.of("string", "any"))
.state("string")
.build());
model_deployment_resource = oci.data_science.ModelDeployment("modelDeploymentResource",
compartment_id="string",
model_deployment_configuration_details=oci.data_science.ModelDeploymentModelDeploymentConfigurationDetailsArgs(
deployment_type="string",
model_configuration_details=oci.data_science.ModelDeploymentModelDeploymentConfigurationDetailsModelConfigurationDetailsArgs(
instance_configuration=oci.data_science.ModelDeploymentModelDeploymentConfigurationDetailsModelConfigurationDetailsInstanceConfigurationArgs(
instance_shape_name="string",
model_deployment_instance_shape_config_details=oci.data_science.ModelDeploymentModelDeploymentConfigurationDetailsModelConfigurationDetailsInstanceConfigurationModelDeploymentInstanceShapeConfigDetailsArgs(
cpu_baseline="string",
memory_in_gbs=0,
ocpus=0,
),
subnet_id="string",
),
model_id="string",
bandwidth_mbps=0,
maximum_bandwidth_mbps=0,
scaling_policy=oci.data_science.ModelDeploymentModelDeploymentConfigurationDetailsModelConfigurationDetailsScalingPolicyArgs(
policy_type="string",
auto_scaling_policies=[oci.data_science.ModelDeploymentModelDeploymentConfigurationDetailsModelConfigurationDetailsScalingPolicyAutoScalingPolicyArgs(
auto_scaling_policy_type="string",
initial_instance_count=0,
maximum_instance_count=0,
minimum_instance_count=0,
rules=[oci.data_science.ModelDeploymentModelDeploymentConfigurationDetailsModelConfigurationDetailsScalingPolicyAutoScalingPolicyRuleArgs(
metric_expression_rule_type="string",
scale_in_configuration=oci.data_science.ModelDeploymentModelDeploymentConfigurationDetailsModelConfigurationDetailsScalingPolicyAutoScalingPolicyRuleScaleInConfigurationArgs(
instance_count_adjustment=0,
pending_duration="string",
query="string",
scaling_configuration_type="string",
threshold=0,
),
scale_out_configuration=oci.data_science.ModelDeploymentModelDeploymentConfigurationDetailsModelConfigurationDetailsScalingPolicyAutoScalingPolicyRuleScaleOutConfigurationArgs(
instance_count_adjustment=0,
pending_duration="string",
query="string",
scaling_configuration_type="string",
threshold=0,
),
metric_type="string",
)],
)],
cool_down_in_seconds=0,
instance_count=0,
is_enabled=False,
),
),
environment_configuration_details=oci.data_science.ModelDeploymentModelDeploymentConfigurationDetailsEnvironmentConfigurationDetailsArgs(
environment_configuration_type="string",
cmds=["string"],
entrypoints=["string"],
environment_variables={
"string": "any",
},
health_check_port=0,
image="string",
image_digest="string",
server_port=0,
),
),
project_id="string",
category_log_details=oci.data_science.ModelDeploymentCategoryLogDetailsArgs(
access=oci.data_science.ModelDeploymentCategoryLogDetailsAccessArgs(
log_group_id="string",
log_id="string",
),
predict=oci.data_science.ModelDeploymentCategoryLogDetailsPredictArgs(
log_group_id="string",
log_id="string",
),
),
defined_tags={
"string": "any",
},
description="string",
display_name="string",
freeform_tags={
"string": "any",
},
state="string")
const modelDeploymentResource = new oci.datascience.ModelDeployment("modelDeploymentResource", {
compartmentId: "string",
modelDeploymentConfigurationDetails: {
deploymentType: "string",
modelConfigurationDetails: {
instanceConfiguration: {
instanceShapeName: "string",
modelDeploymentInstanceShapeConfigDetails: {
cpuBaseline: "string",
memoryInGbs: 0,
ocpus: 0,
},
subnetId: "string",
},
modelId: "string",
bandwidthMbps: 0,
maximumBandwidthMbps: 0,
scalingPolicy: {
policyType: "string",
autoScalingPolicies: [{
autoScalingPolicyType: "string",
initialInstanceCount: 0,
maximumInstanceCount: 0,
minimumInstanceCount: 0,
rules: [{
metricExpressionRuleType: "string",
scaleInConfiguration: {
instanceCountAdjustment: 0,
pendingDuration: "string",
query: "string",
scalingConfigurationType: "string",
threshold: 0,
},
scaleOutConfiguration: {
instanceCountAdjustment: 0,
pendingDuration: "string",
query: "string",
scalingConfigurationType: "string",
threshold: 0,
},
metricType: "string",
}],
}],
coolDownInSeconds: 0,
instanceCount: 0,
isEnabled: false,
},
},
environmentConfigurationDetails: {
environmentConfigurationType: "string",
cmds: ["string"],
entrypoints: ["string"],
environmentVariables: {
string: "any",
},
healthCheckPort: 0,
image: "string",
imageDigest: "string",
serverPort: 0,
},
},
projectId: "string",
categoryLogDetails: {
access: {
logGroupId: "string",
logId: "string",
},
predict: {
logGroupId: "string",
logId: "string",
},
},
definedTags: {
string: "any",
},
description: "string",
displayName: "string",
freeformTags: {
string: "any",
},
state: "string",
});
type: oci:DataScience:ModelDeployment
properties:
categoryLogDetails:
access:
logGroupId: string
logId: string
predict:
logGroupId: string
logId: string
compartmentId: string
definedTags:
string: any
description: string
displayName: string
freeformTags:
string: any
modelDeploymentConfigurationDetails:
deploymentType: string
environmentConfigurationDetails:
cmds:
- string
entrypoints:
- string
environmentConfigurationType: string
environmentVariables:
string: any
healthCheckPort: 0
image: string
imageDigest: string
serverPort: 0
modelConfigurationDetails:
bandwidthMbps: 0
instanceConfiguration:
instanceShapeName: string
modelDeploymentInstanceShapeConfigDetails:
cpuBaseline: string
memoryInGbs: 0
ocpus: 0
subnetId: string
maximumBandwidthMbps: 0
modelId: string
scalingPolicy:
autoScalingPolicies:
- autoScalingPolicyType: string
initialInstanceCount: 0
maximumInstanceCount: 0
minimumInstanceCount: 0
rules:
- metricExpressionRuleType: string
metricType: string
scaleInConfiguration:
instanceCountAdjustment: 0
pendingDuration: string
query: string
scalingConfigurationType: string
threshold: 0
scaleOutConfiguration:
instanceCountAdjustment: 0
pendingDuration: string
query: string
scalingConfigurationType: string
threshold: 0
coolDownInSeconds: 0
instanceCount: 0
isEnabled: false
policyType: string
projectId: string
state: string
ModelDeployment Resource Properties
To learn more about resource properties and how to use them, see Inputs and Outputs in the Architecture and Concepts docs.
Inputs
The ModelDeployment resource accepts the following input properties:
- Compartment
Id string - (Updatable) The OCID of the compartment where you want to create the model deployment.
- Model
Deployment ModelConfiguration Details Deployment Model Deployment Configuration Details - (Updatable) The model deployment configuration details.
- Project
Id string - The OCID of the project to associate with the model deployment.
- Category
Log ModelDetails Deployment Category Log Details - (Updatable) The log details for each category.
- Dictionary<string, object>
- (Updatable) Defined tags for this resource. Each key is predefined and scoped to a namespace. See Resource Tags. Example:
{"Operations.CostCenter": "42"} - Description string
- (Updatable) A short description of the model deployment.
- Display
Name string - (Updatable) 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 - Dictionary<string, object>
- (Updatable) 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"} - State string
(Updatable) The target state for the Model Deployment. Could be set to
ACTIVEorINACTIVE.** IMPORTANT ** Any change to a property that does not support update will force the destruction and recreation of the resource with the new property values
- Compartment
Id string - (Updatable) The OCID of the compartment where you want to create the model deployment.
- Model
Deployment ModelConfiguration Details Deployment Model Deployment Configuration Details Args - (Updatable) The model deployment configuration details.
- Project
Id string - The OCID of the project to associate with the model deployment.
- Category
Log ModelDetails Deployment Category Log Details Args - (Updatable) The log details for each category.
- map[string]interface{}
- (Updatable) Defined tags for this resource. Each key is predefined and scoped to a namespace. See Resource Tags. Example:
{"Operations.CostCenter": "42"} - Description string
- (Updatable) A short description of the model deployment.
- Display
Name string - (Updatable) 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 - map[string]interface{}
- (Updatable) 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"} - State string
(Updatable) The target state for the Model Deployment. Could be set to
ACTIVEorINACTIVE.** IMPORTANT ** Any change to a property that does not support update will force the destruction and recreation of the resource with the new property values
- compartment
Id String - (Updatable) The OCID of the compartment where you want to create the model deployment.
- model
Deployment ModelConfiguration Details Deployment Model Deployment Configuration Details - (Updatable) The model deployment configuration details.
- project
Id String - The OCID of the project to associate with the model deployment.
- category
Log ModelDetails Deployment Category Log Details - (Updatable) The log details for each category.
- Map<String,Object>
- (Updatable) Defined tags for this resource. Each key is predefined and scoped to a namespace. See Resource Tags. Example:
{"Operations.CostCenter": "42"} - description String
- (Updatable) A short description of the model deployment.
- display
Name String - (Updatable) 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 - Map<String,Object>
- (Updatable) 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"} - state String
(Updatable) The target state for the Model Deployment. Could be set to
ACTIVEorINACTIVE.** IMPORTANT ** Any change to a property that does not support update will force the destruction and recreation of the resource with the new property values
- compartment
Id string - (Updatable) The OCID of the compartment where you want to create the model deployment.
- model
Deployment ModelConfiguration Details Deployment Model Deployment Configuration Details - (Updatable) The model deployment configuration details.
- project
Id string - The OCID of the project to associate with the model deployment.
- category
Log ModelDetails Deployment Category Log Details - (Updatable) The log details for each category.
- {[key: string]: any}
- (Updatable) Defined tags for this resource. Each key is predefined and scoped to a namespace. See Resource Tags. Example:
{"Operations.CostCenter": "42"} - description string
- (Updatable) A short description of the model deployment.
- display
Name string - (Updatable) 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 - {[key: string]: any}
- (Updatable) 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"} - state string
(Updatable) The target state for the Model Deployment. Could be set to
ACTIVEorINACTIVE.** IMPORTANT ** Any change to a property that does not support update will force the destruction and recreation of the resource with the new property values
- compartment_
id str - (Updatable) The OCID of the compartment where you want to create the model deployment.
- model_
deployment_ datascience.configuration_ details Model Deployment Model Deployment Configuration Details Args - (Updatable) The model deployment configuration details.
- project_
id str - The OCID of the project to associate with the model deployment.
- category_
log_ datascience.details Model Deployment Category Log Details Args - (Updatable) The log details for each category.
- Mapping[str, Any]
- (Updatable) Defined tags for this resource. Each key is predefined and scoped to a namespace. See Resource Tags. Example:
{"Operations.CostCenter": "42"} - description str
- (Updatable) A short description of the model deployment.
- display_
name str - (Updatable) 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 - Mapping[str, Any]
- (Updatable) 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"} - state str
(Updatable) The target state for the Model Deployment. Could be set to
ACTIVEorINACTIVE.** IMPORTANT ** Any change to a property that does not support update will force the destruction and recreation of the resource with the new property values
- compartment
Id String - (Updatable) The OCID of the compartment where you want to create the model deployment.
- model
Deployment Property MapConfiguration Details - (Updatable) The model deployment configuration details.
- project
Id String - The OCID of the project to associate with the model deployment.
- category
Log Property MapDetails - (Updatable) The log details for each category.
- Map<Any>
- (Updatable) Defined tags for this resource. Each key is predefined and scoped to a namespace. See Resource Tags. Example:
{"Operations.CostCenter": "42"} - description String
- (Updatable) A short description of the model deployment.
- display
Name String - (Updatable) 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 - Map<Any>
- (Updatable) 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"} - state String
(Updatable) The target state for the Model Deployment. Could be set to
ACTIVEorINACTIVE.** IMPORTANT ** Any change to a property that does not support update will force the destruction and recreation of the resource with the new property values
Outputs
All input properties are implicitly available as output properties. Additionally, the ModelDeployment resource produces the following output properties:
- Created
By string - The OCID of the user who created the model deployment.
- Id string
- The provider-assigned unique ID for this managed resource.
- Lifecycle
Details string - Details about the state of the model deployment.
- Model
Deployment List<ModelSystem Datas Deployment Model Deployment System Data> - Model deployment system data.
- Model
Deployment stringUrl - The URL to interact with the model deployment.
- Time
Created string - The date and time the resource was created, in the timestamp format defined by RFC3339. Example: 2019-08-25T21:10:29.41Z
- Created
By string - The OCID of the user who created the model deployment.
- Id string
- The provider-assigned unique ID for this managed resource.
- Lifecycle
Details string - Details about the state of the model deployment.
- Model
Deployment []ModelSystem Datas Deployment Model Deployment System Data - Model deployment system data.
- Model
Deployment stringUrl - The URL to interact with the model deployment.
- Time
Created string - The date and time the resource was created, in the timestamp format defined by RFC3339. Example: 2019-08-25T21:10:29.41Z
- created
By String - The OCID of the user who created the model deployment.
- id String
- The provider-assigned unique ID for this managed resource.
- lifecycle
Details String - Details about the state of the model deployment.
- model
Deployment List<ModelSystem Datas Deployment Model Deployment System Data> - Model deployment system data.
- model
Deployment StringUrl - The URL to interact with the model deployment.
- time
Created String - The date and time the resource was created, in the timestamp format defined by RFC3339. Example: 2019-08-25T21:10:29.41Z
- created
By string - The OCID of the user who created the model deployment.
- id string
- The provider-assigned unique ID for this managed resource.
- lifecycle
Details string - Details about the state of the model deployment.
- model
Deployment ModelSystem Datas Deployment Model Deployment System Data[] - Model deployment system data.
- model
Deployment stringUrl - The URL to interact with the model deployment.
- time
Created string - The date and time the resource was created, in the timestamp format defined by RFC3339. Example: 2019-08-25T21:10:29.41Z
- created_
by str - The OCID of the user who created the model deployment.
- id str
- The provider-assigned unique ID for this managed resource.
- lifecycle_
details str - Details about the state of the model deployment.
- model_
deployment_ Sequence[datascience.system_ datas Model Deployment Model Deployment System Data] - Model deployment system data.
- model_
deployment_ strurl - The URL to interact with the model deployment.
- 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
- created
By String - The OCID of the user who created the model deployment.
- id String
- The provider-assigned unique ID for this managed resource.
- lifecycle
Details String - Details about the state of the model deployment.
- model
Deployment List<Property Map>System Datas - Model deployment system data.
- model
Deployment StringUrl - The URL to interact with the model deployment.
- time
Created String - The date and time the resource was created, in the timestamp format defined by RFC3339. Example: 2019-08-25T21:10:29.41Z
Look up Existing ModelDeployment Resource
Get an existing ModelDeployment resource’s state with the given name, ID, and optional extra properties used to qualify the lookup.
public static get(name: string, id: Input<ID>, state?: ModelDeploymentState, opts?: CustomResourceOptions): ModelDeployment@staticmethod
def get(resource_name: str,
id: str,
opts: Optional[ResourceOptions] = None,
category_log_details: Optional[_datascience.ModelDeploymentCategoryLogDetailsArgs] = None,
compartment_id: Optional[str] = None,
created_by: Optional[str] = None,
defined_tags: Optional[Mapping[str, Any]] = None,
description: Optional[str] = None,
display_name: Optional[str] = None,
freeform_tags: Optional[Mapping[str, Any]] = None,
lifecycle_details: Optional[str] = None,
model_deployment_configuration_details: Optional[_datascience.ModelDeploymentModelDeploymentConfigurationDetailsArgs] = None,
model_deployment_system_datas: Optional[Sequence[_datascience.ModelDeploymentModelDeploymentSystemDataArgs]] = None,
model_deployment_url: Optional[str] = None,
project_id: Optional[str] = None,
state: Optional[str] = None,
time_created: Optional[str] = None) -> ModelDeploymentfunc GetModelDeployment(ctx *Context, name string, id IDInput, state *ModelDeploymentState, opts ...ResourceOption) (*ModelDeployment, error)public static ModelDeployment Get(string name, Input<string> id, ModelDeploymentState? state, CustomResourceOptions? opts = null)public static ModelDeployment get(String name, Output<String> id, ModelDeploymentState state, CustomResourceOptions options)Resource lookup is not supported in YAML- name
- The unique name of the resulting resource.
- id
- The unique provider ID of the resource to lookup.
- state
- Any extra arguments used during the lookup.
- opts
- A bag of options that control this resource's behavior.
- resource_name
- The unique name of the resulting resource.
- id
- The unique provider ID of the resource to lookup.
- name
- The unique name of the resulting resource.
- id
- The unique provider ID of the resource to lookup.
- state
- Any extra arguments used during the lookup.
- opts
- A bag of options that control this resource's behavior.
- name
- The unique name of the resulting resource.
- id
- The unique provider ID of the resource to lookup.
- state
- Any extra arguments used during the lookup.
- opts
- A bag of options that control this resource's behavior.
- name
- The unique name of the resulting resource.
- id
- The unique provider ID of the resource to lookup.
- state
- Any extra arguments used during the lookup.
- opts
- A bag of options that control this resource's behavior.
- Category
Log ModelDetails Deployment Category Log Details - (Updatable) The log details for each category.
- Compartment
Id string - (Updatable) The OCID of the compartment where you want to create the model deployment.
- Created
By string - The OCID of the user who created the model deployment.
- Dictionary<string, object>
- (Updatable) Defined tags for this resource. Each key is predefined and scoped to a namespace. See Resource Tags. Example:
{"Operations.CostCenter": "42"} - Description string
- (Updatable) A short description of the model deployment.
- Display
Name string - (Updatable) 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 - Dictionary<string, object>
- (Updatable) 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"} - Lifecycle
Details string - Details about the state of the model deployment.
- Model
Deployment ModelConfiguration Details Deployment Model Deployment Configuration Details - (Updatable) The model deployment configuration details.
- Model
Deployment List<ModelSystem Datas Deployment Model Deployment System Data> - Model deployment system data.
- Model
Deployment stringUrl - The URL to interact with the model deployment.
- Project
Id string - The OCID of the project to associate with the model deployment.
- State string
(Updatable) The target state for the Model Deployment. Could be set to
ACTIVEorINACTIVE.** IMPORTANT ** Any change to a property that does not support update will force the destruction and recreation of the resource with the new property values
- Time
Created 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 ModelDetails Deployment Category Log Details Args - (Updatable) The log details for each category.
- Compartment
Id string - (Updatable) The OCID of the compartment where you want to create the model deployment.
- Created
By string - The OCID of the user who created the model deployment.
- map[string]interface{}
- (Updatable) Defined tags for this resource. Each key is predefined and scoped to a namespace. See Resource Tags. Example:
{"Operations.CostCenter": "42"} - Description string
- (Updatable) A short description of the model deployment.
- Display
Name string - (Updatable) 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 - map[string]interface{}
- (Updatable) 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"} - Lifecycle
Details string - Details about the state of the model deployment.
- Model
Deployment ModelConfiguration Details Deployment Model Deployment Configuration Details Args - (Updatable) The model deployment configuration details.
- Model
Deployment []ModelSystem Datas Deployment Model Deployment System Data Args - Model deployment system data.
- Model
Deployment stringUrl - The URL to interact with the model deployment.
- Project
Id string - The OCID of the project to associate with the model deployment.
- State string
(Updatable) The target state for the Model Deployment. Could be set to
ACTIVEorINACTIVE.** IMPORTANT ** Any change to a property that does not support update will force the destruction and recreation of the resource with the new property values
- Time
Created 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 ModelDetails Deployment Category Log Details - (Updatable) The log details for each category.
- compartment
Id String - (Updatable) The OCID of the compartment where you want to create the model deployment.
- created
By String - The OCID of the user who created the model deployment.
- Map<String,Object>
- (Updatable) Defined tags for this resource. Each key is predefined and scoped to a namespace. See Resource Tags. Example:
{"Operations.CostCenter": "42"} - description String
- (Updatable) A short description of the model deployment.
- display
Name String - (Updatable) 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 - Map<String,Object>
- (Updatable) 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"} - lifecycle
Details String - Details about the state of the model deployment.
- model
Deployment ModelConfiguration Details Deployment Model Deployment Configuration Details - (Updatable) The model deployment configuration details.
- model
Deployment List<ModelSystem Datas Deployment Model Deployment System Data> - Model deployment system data.
- model
Deployment StringUrl - The URL to interact with the model deployment.
- project
Id String - The OCID of the project to associate with the model deployment.
- state String
(Updatable) The target state for the Model Deployment. Could be set to
ACTIVEorINACTIVE.** IMPORTANT ** Any change to a property that does not support update will force the destruction and recreation of the resource with the new property values
- time
Created 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 ModelDetails Deployment Category Log Details - (Updatable) The log details for each category.
- compartment
Id string - (Updatable) The OCID of the compartment where you want to create the model deployment.
- created
By string - The OCID of the user who created the model deployment.
- {[key: string]: any}
- (Updatable) Defined tags for this resource. Each key is predefined and scoped to a namespace. See Resource Tags. Example:
{"Operations.CostCenter": "42"} - description string
- (Updatable) A short description of the model deployment.
- display
Name string - (Updatable) 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 - {[key: string]: any}
- (Updatable) 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"} - lifecycle
Details string - Details about the state of the model deployment.
- model
Deployment ModelConfiguration Details Deployment Model Deployment Configuration Details - (Updatable) The model deployment configuration details.
- model
Deployment ModelSystem Datas Deployment Model Deployment System Data[] - Model deployment system data.
- model
Deployment stringUrl - The URL to interact with the model deployment.
- project
Id string - The OCID of the project to associate with the model deployment.
- state string
(Updatable) The target state for the Model Deployment. Could be set to
ACTIVEorINACTIVE.** IMPORTANT ** Any change to a property that does not support update will force the destruction and recreation of the resource with the new property values
- time
Created 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_ datascience.details Model Deployment Category Log Details Args - (Updatable) The log details for each category.
- compartment_
id str - (Updatable) The OCID of the compartment where you want to create the model deployment.
- created_
by str - The OCID of the user who created the model deployment.
- Mapping[str, Any]
- (Updatable) Defined tags for this resource. Each key is predefined and scoped to a namespace. See Resource Tags. Example:
{"Operations.CostCenter": "42"} - description str
- (Updatable) A short description of the model deployment.
- display_
name str - (Updatable) 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 - Mapping[str, Any]
- (Updatable) 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"} - lifecycle_
details str - Details about the state of the model deployment.
- model_
deployment_ datascience.configuration_ details Model Deployment Model Deployment Configuration Details Args - (Updatable) The model deployment configuration details.
- model_
deployment_ Sequence[datascience.system_ datas Model Deployment Model Deployment System Data Args] - Model deployment system data.
- model_
deployment_ strurl - The URL to interact with the model deployment.
- project_
id str - The OCID of the project to associate with the model deployment.
- state str
(Updatable) The target state for the Model Deployment. Could be set to
ACTIVEorINACTIVE.** IMPORTANT ** Any change to a property that does not support update will force the destruction and recreation of the resource with the new property values
- 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
- category
Log Property MapDetails - (Updatable) The log details for each category.
- compartment
Id String - (Updatable) The OCID of the compartment where you want to create the model deployment.
- created
By String - The OCID of the user who created the model deployment.
- Map<Any>
- (Updatable) Defined tags for this resource. Each key is predefined and scoped to a namespace. See Resource Tags. Example:
{"Operations.CostCenter": "42"} - description String
- (Updatable) A short description of the model deployment.
- display
Name String - (Updatable) 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 - Map<Any>
- (Updatable) 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"} - lifecycle
Details String - Details about the state of the model deployment.
- model
Deployment Property MapConfiguration Details - (Updatable) The model deployment configuration details.
- model
Deployment List<Property Map>System Datas - Model deployment system data.
- model
Deployment StringUrl - The URL to interact with the model deployment.
- project
Id String - The OCID of the project to associate with the model deployment.
- state String
(Updatable) The target state for the Model Deployment. Could be set to
ACTIVEorINACTIVE.** IMPORTANT ** Any change to a property that does not support update will force the destruction and recreation of the resource with the new property values
- time
Created String - The date and time the resource was created, in the timestamp format defined by RFC3339. Example: 2019-08-25T21:10:29.41Z
Supporting Types
ModelDeploymentCategoryLogDetails, ModelDeploymentCategoryLogDetailsArgs
- Access
Model
Deployment Category Log Details Access - (Updatable) The log details.
- Predict
Model
Deployment Category Log Details Predict - (Updatable) The log details.
- Access
Model
Deployment Category Log Details Access - (Updatable) The log details.
- Predict
Model
Deployment Category Log Details Predict - (Updatable) The log details.
- access
Model
Deployment Category Log Details Access - (Updatable) The log details.
- predict
Model
Deployment Category Log Details Predict - (Updatable) The log details.
- access
Model
Deployment Category Log Details Access - (Updatable) The log details.
- predict
Model
Deployment Category Log Details Predict - (Updatable) The log details.
- access
datascience.
Model Deployment Category Log Details Access - (Updatable) The log details.
- predict
datascience.
Model Deployment Category Log Details Predict - (Updatable) The log details.
- access Property Map
- (Updatable) The log details.
- predict Property Map
- (Updatable) The log details.
ModelDeploymentCategoryLogDetailsAccess, ModelDeploymentCategoryLogDetailsAccessArgs
- Log
Group stringId - (Updatable) The OCID of a log group to work with.
- Log
Id string - (Updatable) The OCID of a log to work with.
- Log
Group stringId - (Updatable) The OCID of a log group to work with.
- Log
Id string - (Updatable) The OCID of a log to work with.
- log
Group StringId - (Updatable) The OCID of a log group to work with.
- log
Id String - (Updatable) The OCID of a log to work with.
- log
Group stringId - (Updatable) The OCID of a log group to work with.
- log
Id string - (Updatable) The OCID of a log to work with.
- log_
group_ strid - (Updatable) The OCID of a log group to work with.
- log_
id str - (Updatable) The OCID of a log to work with.
- log
Group StringId - (Updatable) The OCID of a log group to work with.
- log
Id String - (Updatable) The OCID of a log to work with.
ModelDeploymentCategoryLogDetailsPredict, ModelDeploymentCategoryLogDetailsPredictArgs
- Log
Group stringId - (Updatable) The OCID of a log group to work with.
- Log
Id string - (Updatable) The OCID of a log to work with.
- Log
Group stringId - (Updatable) The OCID of a log group to work with.
- Log
Id string - (Updatable) The OCID of a log to work with.
- log
Group StringId - (Updatable) The OCID of a log group to work with.
- log
Id String - (Updatable) The OCID of a log to work with.
- log
Group stringId - (Updatable) The OCID of a log group to work with.
- log
Id string - (Updatable) The OCID of a log to work with.
- log_
group_ strid - (Updatable) The OCID of a log group to work with.
- log_
id str - (Updatable) The OCID of a log to work with.
- log
Group StringId - (Updatable) The OCID of a log group to work with.
- log
Id String - (Updatable) The OCID of a log to work with.
ModelDeploymentModelDeploymentConfigurationDetails, ModelDeploymentModelDeploymentConfigurationDetailsArgs
- Deployment
Type string - (Updatable) The type of the model deployment.
- Model
Configuration ModelDetails Deployment Model Deployment Configuration Details Model Configuration Details - (Updatable) The model configuration details.
- Environment
Configuration ModelDetails Deployment Model Deployment Configuration Details Environment Configuration Details - (Updatable) The configuration to carry the environment details thats used in Model Deployment creation
- Deployment
Type string - (Updatable) The type of the model deployment.
- Model
Configuration ModelDetails Deployment Model Deployment Configuration Details Model Configuration Details - (Updatable) The model configuration details.
- Environment
Configuration ModelDetails Deployment Model Deployment Configuration Details Environment Configuration Details - (Updatable) The configuration to carry the environment details thats used in Model Deployment creation
- deployment
Type String - (Updatable) The type of the model deployment.
- model
Configuration ModelDetails Deployment Model Deployment Configuration Details Model Configuration Details - (Updatable) The model configuration details.
- environment
Configuration ModelDetails Deployment Model Deployment Configuration Details Environment Configuration Details - (Updatable) The configuration to carry the environment details thats used in Model Deployment creation
- deployment
Type string - (Updatable) The type of the model deployment.
- model
Configuration ModelDetails Deployment Model Deployment Configuration Details Model Configuration Details - (Updatable) The model configuration details.
- environment
Configuration ModelDetails Deployment Model Deployment Configuration Details Environment Configuration Details - (Updatable) The configuration to carry the environment details thats used in Model Deployment creation
- deployment_
type str - (Updatable) The type of the model deployment.
- model_
configuration_ datascience.details Model Deployment Model Deployment Configuration Details Model Configuration Details - (Updatable) The model configuration details.
- environment_
configuration_ datascience.details Model Deployment Model Deployment Configuration Details Environment Configuration Details - (Updatable) The configuration to carry the environment details thats used in Model Deployment creation
- deployment
Type String - (Updatable) The type of the model deployment.
- model
Configuration Property MapDetails - (Updatable) The model configuration details.
- environment
Configuration Property MapDetails - (Updatable) The configuration to carry the environment details thats used in Model Deployment creation
ModelDeploymentModelDeploymentConfigurationDetailsEnvironmentConfigurationDetails, ModelDeploymentModelDeploymentConfigurationDetailsEnvironmentConfigurationDetailsArgs
- Environment
Configuration stringType - (Updatable) The environment configuration type
- Cmds List<string>
- (Updatable) The container image run CMD as a list of strings. Use
CMDas arguments to theENTRYPOINTor the only command to run in the absence of anENTRYPOINT. The combined size ofCMDandENTRYPOINTmust be less than 2048 bytes. - Entrypoints List<string>
- (Updatable) The container image run ENTRYPOINT as a list of strings. Accept the
CMDas extra arguments. The combined size ofCMDandENTRYPOINTmust be less than 2048 bytes. More information on howCMDandENTRYPOINTinteract are here. - Environment
Variables Dictionary<string, object> - (Updatable) 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 intPort - (Updatable) The port on which the container HEALTHCHECK would listen. The port can be anything between
1024and65535. The following ports cannot be used24224,8446,8447. - Image string
- (Updatable) 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 string - (Updatable) The digest of the container image. For example,
sha256:881303a6b2738834d795a32b4a98eb0e5e3d1cad590a712d1e04f9b2fa90a030 - Server
Port int - (Updatable) The port on which the web server serving the inference is running. The port can be anything between
1024and65535. The following ports cannot be used24224,8446,8447.
- Environment
Configuration stringType - (Updatable) The environment configuration type
- Cmds []string
- (Updatable) The container image run CMD as a list of strings. Use
CMDas arguments to theENTRYPOINTor the only command to run in the absence of anENTRYPOINT. The combined size ofCMDandENTRYPOINTmust be less than 2048 bytes. - Entrypoints []string
- (Updatable) The container image run ENTRYPOINT as a list of strings. Accept the
CMDas extra arguments. The combined size ofCMDandENTRYPOINTmust be less than 2048 bytes. More information on howCMDandENTRYPOINTinteract are here. - Environment
Variables map[string]interface{} - (Updatable) 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 intPort - (Updatable) The port on which the container HEALTHCHECK would listen. The port can be anything between
1024and65535. The following ports cannot be used24224,8446,8447. - Image string
- (Updatable) 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 string - (Updatable) The digest of the container image. For example,
sha256:881303a6b2738834d795a32b4a98eb0e5e3d1cad590a712d1e04f9b2fa90a030 - Server
Port int - (Updatable) The port on which the web server serving the inference is running. The port can be anything between
1024and65535. The following ports cannot be used24224,8446,8447.
- environment
Configuration StringType - (Updatable) The environment configuration type
- cmds List<String>
- (Updatable) The container image run CMD as a list of strings. Use
CMDas arguments to theENTRYPOINTor the only command to run in the absence of anENTRYPOINT. The combined size ofCMDandENTRYPOINTmust be less than 2048 bytes. - entrypoints List<String>
- (Updatable) The container image run ENTRYPOINT as a list of strings. Accept the
CMDas extra arguments. The combined size ofCMDandENTRYPOINTmust be less than 2048 bytes. More information on howCMDandENTRYPOINTinteract are here. - environment
Variables Map<String,Object> - (Updatable) 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 IntegerPort - (Updatable) The port on which the container HEALTHCHECK would listen. The port can be anything between
1024and65535. The following ports cannot be used24224,8446,8447. - image String
- (Updatable) 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 String - (Updatable) The digest of the container image. For example,
sha256:881303a6b2738834d795a32b4a98eb0e5e3d1cad590a712d1e04f9b2fa90a030 - server
Port Integer - (Updatable) The port on which the web server serving the inference is running. The port can be anything between
1024and65535. The following ports cannot be used24224,8446,8447.
- environment
Configuration stringType - (Updatable) The environment configuration type
- cmds string[]
- (Updatable) The container image run CMD as a list of strings. Use
CMDas arguments to theENTRYPOINTor the only command to run in the absence of anENTRYPOINT. The combined size ofCMDandENTRYPOINTmust be less than 2048 bytes. - entrypoints string[]
- (Updatable) The container image run ENTRYPOINT as a list of strings. Accept the
CMDas extra arguments. The combined size ofCMDandENTRYPOINTmust be less than 2048 bytes. More information on howCMDandENTRYPOINTinteract are here. - environment
Variables {[key: string]: any} - (Updatable) 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 numberPort - (Updatable) The port on which the container HEALTHCHECK would listen. The port can be anything between
1024and65535. The following ports cannot be used24224,8446,8447. - image string
- (Updatable) 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 string - (Updatable) The digest of the container image. For example,
sha256:881303a6b2738834d795a32b4a98eb0e5e3d1cad590a712d1e04f9b2fa90a030 - server
Port number - (Updatable) The port on which the web server serving the inference is running. The port can be anything between
1024and65535. The following ports cannot be used24224,8446,8447.
- environment_
configuration_ strtype - (Updatable) The environment configuration type
- cmds Sequence[str]
- (Updatable) The container image run CMD as a list of strings. Use
CMDas arguments to theENTRYPOINTor the only command to run in the absence of anENTRYPOINT. The combined size ofCMDandENTRYPOINTmust be less than 2048 bytes. - entrypoints Sequence[str]
- (Updatable) The container image run ENTRYPOINT as a list of strings. Accept the
CMDas extra arguments. The combined size ofCMDandENTRYPOINTmust be less than 2048 bytes. More information on howCMDandENTRYPOINTinteract are here. - environment_
variables Mapping[str, Any] - (Updatable) 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_ intport - (Updatable) The port on which the container HEALTHCHECK would listen. The port can be anything between
1024and65535. The following ports cannot be used24224,8446,8447. - image str
- (Updatable) 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 - (Updatable) The digest of the container image. For example,
sha256:881303a6b2738834d795a32b4a98eb0e5e3d1cad590a712d1e04f9b2fa90a030 - server_
port int - (Updatable) The port on which the web server serving the inference is running. The port can be anything between
1024and65535. The following ports cannot be used24224,8446,8447.
- environment
Configuration StringType - (Updatable) The environment configuration type
- cmds List<String>
- (Updatable) The container image run CMD as a list of strings. Use
CMDas arguments to theENTRYPOINTor the only command to run in the absence of anENTRYPOINT. The combined size ofCMDandENTRYPOINTmust be less than 2048 bytes. - entrypoints List<String>
- (Updatable) The container image run ENTRYPOINT as a list of strings. Accept the
CMDas extra arguments. The combined size ofCMDandENTRYPOINTmust be less than 2048 bytes. More information on howCMDandENTRYPOINTinteract are here. - environment
Variables Map<Any> - (Updatable) 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 NumberPort - (Updatable) The port on which the container HEALTHCHECK would listen. The port can be anything between
1024and65535. The following ports cannot be used24224,8446,8447. - image String
- (Updatable) 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 String - (Updatable) The digest of the container image. For example,
sha256:881303a6b2738834d795a32b4a98eb0e5e3d1cad590a712d1e04f9b2fa90a030 - server
Port Number - (Updatable) The port on which the web server serving the inference is running. The port can be anything between
1024and65535. The following ports cannot be used24224,8446,8447.
ModelDeploymentModelDeploymentConfigurationDetailsModelConfigurationDetails, ModelDeploymentModelDeploymentConfigurationDetailsModelConfigurationDetailsArgs
- Instance
Configuration ModelDeployment Model Deployment Configuration Details Model Configuration Details Instance Configuration - (Updatable) The model deployment instance configuration
- Model
Id string - (Updatable) The OCID of the model you want to deploy.
- Bandwidth
Mbps int - (Updatable) The minimum network bandwidth for the model deployment.
- Maximum
Bandwidth intMbps - (Updatable) The maximum network bandwidth for the model deployment.
- Scaling
Policy ModelDeployment Model Deployment Configuration Details Model Configuration Details Scaling Policy - (Updatable) The scaling policy to apply to each model of the deployment.
- Instance
Configuration ModelDeployment Model Deployment Configuration Details Model Configuration Details Instance Configuration - (Updatable) The model deployment instance configuration
- Model
Id string - (Updatable) The OCID of the model you want to deploy.
- Bandwidth
Mbps int - (Updatable) The minimum network bandwidth for the model deployment.
- Maximum
Bandwidth intMbps - (Updatable) The maximum network bandwidth for the model deployment.
- Scaling
Policy ModelDeployment Model Deployment Configuration Details Model Configuration Details Scaling Policy - (Updatable) The scaling policy to apply to each model of the deployment.
- instance
Configuration ModelDeployment Model Deployment Configuration Details Model Configuration Details Instance Configuration - (Updatable) The model deployment instance configuration
- model
Id String - (Updatable) The OCID of the model you want to deploy.
- bandwidth
Mbps Integer - (Updatable) The minimum network bandwidth for the model deployment.
- maximum
Bandwidth IntegerMbps - (Updatable) The maximum network bandwidth for the model deployment.
- scaling
Policy ModelDeployment Model Deployment Configuration Details Model Configuration Details Scaling Policy - (Updatable) The scaling policy to apply to each model of the deployment.
- instance
Configuration ModelDeployment Model Deployment Configuration Details Model Configuration Details Instance Configuration - (Updatable) The model deployment instance configuration
- model
Id string - (Updatable) The OCID of the model you want to deploy.
- bandwidth
Mbps number - (Updatable) The minimum network bandwidth for the model deployment.
- maximum
Bandwidth numberMbps - (Updatable) The maximum network bandwidth for the model deployment.
- scaling
Policy ModelDeployment Model Deployment Configuration Details Model Configuration Details Scaling Policy - (Updatable) The scaling policy to apply to each model of the deployment.
- instance_
configuration datascience.Model Deployment Model Deployment Configuration Details Model Configuration Details Instance Configuration - (Updatable) The model deployment instance configuration
- model_
id str - (Updatable) The OCID of the model you want to deploy.
- bandwidth_
mbps int - (Updatable) The minimum network bandwidth for the model deployment.
- maximum_
bandwidth_ intmbps - (Updatable) The maximum network bandwidth for the model deployment.
- scaling_
policy datascience.Model Deployment Model Deployment Configuration Details Model Configuration Details Scaling Policy - (Updatable) The scaling policy to apply to each model of the deployment.
- instance
Configuration Property Map - (Updatable) The model deployment instance configuration
- model
Id String - (Updatable) The OCID of the model you want to deploy.
- bandwidth
Mbps Number - (Updatable) The minimum network bandwidth for the model deployment.
- maximum
Bandwidth NumberMbps - (Updatable) The maximum network bandwidth for the model deployment.
- scaling
Policy Property Map - (Updatable) The scaling policy to apply to each model of the deployment.
ModelDeploymentModelDeploymentConfigurationDetailsModelConfigurationDetailsInstanceConfiguration, ModelDeploymentModelDeploymentConfigurationDetailsModelConfigurationDetailsInstanceConfigurationArgs
- Instance
Shape stringName - (Updatable) The shape used to launch the model deployment instances.
- Model
Deployment ModelInstance Shape Config Details Deployment Model Deployment Configuration Details Model Configuration Details Instance Configuration Model Deployment Instance Shape Config Details - (Updatable) Details for the model-deployment instance shape configuration.
- Subnet
Id string - (Updatable) 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 stringName - (Updatable) The shape used to launch the model deployment instances.
- Model
Deployment ModelInstance Shape Config Details Deployment Model Deployment Configuration Details Model Configuration Details Instance Configuration Model Deployment Instance Shape Config Details - (Updatable) Details for the model-deployment instance shape configuration.
- Subnet
Id string - (Updatable) 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 StringName - (Updatable) The shape used to launch the model deployment instances.
- model
Deployment ModelInstance Shape Config Details Deployment Model Deployment Configuration Details Model Configuration Details Instance Configuration Model Deployment Instance Shape Config Details - (Updatable) Details for the model-deployment instance shape configuration.
- subnet
Id String - (Updatable) 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 stringName - (Updatable) The shape used to launch the model deployment instances.
- model
Deployment ModelInstance Shape Config Details Deployment Model Deployment Configuration Details Model Configuration Details Instance Configuration Model Deployment Instance Shape Config Details - (Updatable) Details for the model-deployment instance shape configuration.
- subnet
Id string - (Updatable) 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_ strname - (Updatable) The shape used to launch the model deployment instances.
- model_
deployment_ datascience.instance_ shape_ config_ details Model Deployment Model Deployment Configuration Details Model Configuration Details Instance Configuration Model Deployment Instance Shape Config Details - (Updatable) Details for the model-deployment instance shape configuration.
- subnet_
id str - (Updatable) 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 StringName - (Updatable) The shape used to launch the model deployment instances.
- model
Deployment Property MapInstance Shape Config Details - (Updatable) Details for the model-deployment instance shape configuration.
- subnet
Id String - (Updatable) 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.
ModelDeploymentModelDeploymentConfigurationDetailsModelConfigurationDetailsInstanceConfigurationModelDeploymentInstanceShapeConfigDetails, ModelDeploymentModelDeploymentConfigurationDetailsModelConfigurationDetailsInstanceConfigurationModelDeploymentInstanceShapeConfigDetailsArgs
- Cpu
Baseline string - (Updatable) 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 doubleGbs - (Updatable) 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
- (Updatable) 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 string - (Updatable) 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 float64Gbs - (Updatable) 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
- (Updatable) 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 String - (Updatable) 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 DoubleGbs - (Updatable) 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
- (Updatable) 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 string - (Updatable) 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 numberGbs - (Updatable) 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
- (Updatable) 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 - (Updatable) 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_ floatgbs - (Updatable) 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
- (Updatable) 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 String - (Updatable) 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 NumberGbs - (Updatable) 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
- (Updatable) 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.
ModelDeploymentModelDeploymentConfigurationDetailsModelConfigurationDetailsScalingPolicy, ModelDeploymentModelDeploymentConfigurationDetailsModelConfigurationDetailsScalingPolicyArgs
- Policy
Type string - (Updatable) The type of scaling policy.
- Auto
Scaling List<ModelPolicies Deployment Model Deployment Configuration Details Model Configuration Details Scaling Policy Auto Scaling Policy> - (Updatable) The list of autoscaling policy details.
- Cool
Down intIn Seconds - (Updatable) 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 - (Updatable) The number of instances for the model deployment.
- Is
Enabled bool - (Updatable) Whether the autoscaling policy is enabled.
- Policy
Type string - (Updatable) The type of scaling policy.
- Auto
Scaling []ModelPolicies Deployment Model Deployment Configuration Details Model Configuration Details Scaling Policy Auto Scaling Policy - (Updatable) The list of autoscaling policy details.
- Cool
Down intIn Seconds - (Updatable) 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 - (Updatable) The number of instances for the model deployment.
- Is
Enabled bool - (Updatable) Whether the autoscaling policy is enabled.
- policy
Type String - (Updatable) The type of scaling policy.
- auto
Scaling List<ModelPolicies Deployment Model Deployment Configuration Details Model Configuration Details Scaling Policy Auto Scaling Policy> - (Updatable) The list of autoscaling policy details.
- cool
Down IntegerIn Seconds - (Updatable) 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 Integer - (Updatable) The number of instances for the model deployment.
- is
Enabled Boolean - (Updatable) Whether the autoscaling policy is enabled.
- policy
Type string - (Updatable) The type of scaling policy.
- auto
Scaling ModelPolicies Deployment Model Deployment Configuration Details Model Configuration Details Scaling Policy Auto Scaling Policy[] - (Updatable) The list of autoscaling policy details.
- cool
Down numberIn Seconds - (Updatable) 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 number - (Updatable) The number of instances for the model deployment.
- is
Enabled boolean - (Updatable) Whether the autoscaling policy is enabled.
- policy_
type str - (Updatable) The type of scaling policy.
- auto_
scaling_ Sequence[datascience.policies Model Deployment Model Deployment Configuration Details Model Configuration Details Scaling Policy Auto Scaling Policy] - (Updatable) The list of autoscaling policy details.
- cool_
down_ intin_ seconds - (Updatable) 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 - (Updatable) The number of instances for the model deployment.
- is_
enabled bool - (Updatable) Whether the autoscaling policy is enabled.
- policy
Type String - (Updatable) The type of scaling policy.
- auto
Scaling List<Property Map>Policies - (Updatable) The list of autoscaling policy details.
- cool
Down NumberIn Seconds - (Updatable) 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 Number - (Updatable) The number of instances for the model deployment.
- is
Enabled Boolean - (Updatable) Whether the autoscaling policy is enabled.
ModelDeploymentModelDeploymentConfigurationDetailsModelConfigurationDetailsScalingPolicyAutoScalingPolicy, ModelDeploymentModelDeploymentConfigurationDetailsModelConfigurationDetailsScalingPolicyAutoScalingPolicyArgs
- Auto
Scaling stringPolicy Type - (Updatable) The type of autoscaling policy.
- Initial
Instance intCount - (Updatable) 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 intCount - (Updatable) 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 intCount - (Updatable) 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<Model
Deployment Model Deployment Configuration Details Model Configuration Details Scaling Policy Auto Scaling Policy Rule> - (Updatable) The list of autoscaling policy rules.
- Auto
Scaling stringPolicy Type - (Updatable) The type of autoscaling policy.
- Initial
Instance intCount - (Updatable) 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 intCount - (Updatable) 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 intCount - (Updatable) 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
[]Model
Deployment Model Deployment Configuration Details Model Configuration Details Scaling Policy Auto Scaling Policy Rule - (Updatable) The list of autoscaling policy rules.
- auto
Scaling StringPolicy Type - (Updatable) The type of autoscaling policy.
- initial
Instance IntegerCount - (Updatable) 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 IntegerCount - (Updatable) 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 IntegerCount - (Updatable) 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<Model
Deployment Model Deployment Configuration Details Model Configuration Details Scaling Policy Auto Scaling Policy Rule> - (Updatable) The list of autoscaling policy rules.
- auto
Scaling stringPolicy Type - (Updatable) The type of autoscaling policy.
- initial
Instance numberCount - (Updatable) 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 numberCount - (Updatable) 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 numberCount - (Updatable) 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
Model
Deployment Model Deployment Configuration Details Model Configuration Details Scaling Policy Auto Scaling Policy Rule[] - (Updatable) The list of autoscaling policy rules.
- auto_
scaling_ strpolicy_ type - (Updatable) The type of autoscaling policy.
- initial_
instance_ intcount - (Updatable) 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_ intcount - (Updatable) 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_ intcount - (Updatable) 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.
Model Deployment Model Deployment Configuration Details Model Configuration Details Scaling Policy Auto Scaling Policy Rule] - (Updatable) The list of autoscaling policy rules.
- auto
Scaling StringPolicy Type - (Updatable) The type of autoscaling policy.
- initial
Instance NumberCount - (Updatable) 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 NumberCount - (Updatable) 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 NumberCount - (Updatable) 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>
- (Updatable) The list of autoscaling policy rules.
ModelDeploymentModelDeploymentConfigurationDetailsModelConfigurationDetailsScalingPolicyAutoScalingPolicyRule, ModelDeploymentModelDeploymentConfigurationDetailsModelConfigurationDetailsScalingPolicyAutoScalingPolicyRuleArgs
- Metric
Expression stringRule Type (Updatable) The metric expression for creating the alarm used to trigger autoscaling actions on the model deployment.
The following values are supported:
- Scale
In ModelConfiguration Deployment Model Deployment Configuration Details Model Configuration Details Scaling Policy Auto Scaling Policy Rule Scale In Configuration - (Updatable) The scaling configuration for the predefined metric expression rule.
- Scale
Out ModelConfiguration Deployment Model Deployment Configuration Details Model Configuration Details Scaling Policy Auto Scaling Policy Rule Scale Out Configuration - (Updatable) The scaling configuration for the predefined metric expression rule.
- Metric
Type string - (Updatable) Metric type
- Metric
Expression stringRule Type (Updatable) The metric expression for creating the alarm used to trigger autoscaling actions on the model deployment.
The following values are supported:
- Scale
In ModelConfiguration Deployment Model Deployment Configuration Details Model Configuration Details Scaling Policy Auto Scaling Policy Rule Scale In Configuration - (Updatable) The scaling configuration for the predefined metric expression rule.
- Scale
Out ModelConfiguration Deployment Model Deployment Configuration Details Model Configuration Details Scaling Policy Auto Scaling Policy Rule Scale Out Configuration - (Updatable) The scaling configuration for the predefined metric expression rule.
- Metric
Type string - (Updatable) Metric type
- metric
Expression StringRule Type (Updatable) The metric expression for creating the alarm used to trigger autoscaling actions on the model deployment.
The following values are supported:
- scale
In ModelConfiguration Deployment Model Deployment Configuration Details Model Configuration Details Scaling Policy Auto Scaling Policy Rule Scale In Configuration - (Updatable) The scaling configuration for the predefined metric expression rule.
- scale
Out ModelConfiguration Deployment Model Deployment Configuration Details Model Configuration Details Scaling Policy Auto Scaling Policy Rule Scale Out Configuration - (Updatable) The scaling configuration for the predefined metric expression rule.
- metric
Type String - (Updatable) Metric type
- metric
Expression stringRule Type (Updatable) The metric expression for creating the alarm used to trigger autoscaling actions on the model deployment.
The following values are supported:
- scale
In ModelConfiguration Deployment Model Deployment Configuration Details Model Configuration Details Scaling Policy Auto Scaling Policy Rule Scale In Configuration - (Updatable) The scaling configuration for the predefined metric expression rule.
- scale
Out ModelConfiguration Deployment Model Deployment Configuration Details Model Configuration Details Scaling Policy Auto Scaling Policy Rule Scale Out Configuration - (Updatable) The scaling configuration for the predefined metric expression rule.
- metric
Type string - (Updatable) Metric type
- metric_
expression_ strrule_ type (Updatable) The metric expression for creating the alarm used to trigger autoscaling actions on the model deployment.
The following values are supported:
- scale_
in_ datascience.configuration Model Deployment Model Deployment Configuration Details Model Configuration Details Scaling Policy Auto Scaling Policy Rule Scale In Configuration - (Updatable) The scaling configuration for the predefined metric expression rule.
- scale_
out_ datascience.configuration Model Deployment Model Deployment Configuration Details Model Configuration Details Scaling Policy Auto Scaling Policy Rule Scale Out Configuration - (Updatable) The scaling configuration for the predefined metric expression rule.
- metric_
type str - (Updatable) Metric type
- metric
Expression StringRule Type (Updatable) The metric expression for creating the alarm used to trigger autoscaling actions on the model deployment.
The following values are supported:
- scale
In Property MapConfiguration - (Updatable) The scaling configuration for the predefined metric expression rule.
- scale
Out Property MapConfiguration - (Updatable) The scaling configuration for the predefined metric expression rule.
- metric
Type String - (Updatable) Metric type
ModelDeploymentModelDeploymentConfigurationDetailsModelConfigurationDetailsScalingPolicyAutoScalingPolicyRuleScaleInConfiguration, ModelDeploymentModelDeploymentConfigurationDetailsModelConfigurationDetailsScalingPolicyAutoScalingPolicyRuleScaleInConfigurationArgs
- Instance
Count intAdjustment - (Updatable) The value is used for adjusting the count of instances by.
- Pending
Duration string (Updatable) 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."
The duration is specified as a string in ISO 8601 format (
PT10Mfor ten minutes orPT1Hfor one hour). Minimum: PT3M. Maximum: PT1H. Default: PT3M.- Query string
(Updatable) 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(also1h). You can optionally specify dimensions and grouping functions. Supported grouping functions:grouping(),groupBy().Example of threshold alarm:
CPUUtilization[1m]{resourceId = "MODEL_DEPLOYMENT_OCID"}.grouping().mean() < 25 CPUUtilization[1m]{resourceId = "MODEL_DEPLOYMENT_OCID"}.grouping().mean() > 75
- Scaling
Configuration stringType - (Updatable) The type of scaling configuration.
- Threshold int
- (Updatable) A metric value at which the scaling operation will be triggered.
- Instance
Count intAdjustment - (Updatable) The value is used for adjusting the count of instances by.
- Pending
Duration string (Updatable) 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."
The duration is specified as a string in ISO 8601 format (
PT10Mfor ten minutes orPT1Hfor one hour). Minimum: PT3M. Maximum: PT1H. Default: PT3M.- Query string
(Updatable) 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(also1h). You can optionally specify dimensions and grouping functions. Supported grouping functions:grouping(),groupBy().Example of threshold alarm:
CPUUtilization[1m]{resourceId = "MODEL_DEPLOYMENT_OCID"}.grouping().mean() < 25 CPUUtilization[1m]{resourceId = "MODEL_DEPLOYMENT_OCID"}.grouping().mean() > 75
- Scaling
Configuration stringType - (Updatable) The type of scaling configuration.
- Threshold int
- (Updatable) A metric value at which the scaling operation will be triggered.
- instance
Count IntegerAdjustment - (Updatable) The value is used for adjusting the count of instances by.
- pending
Duration String (Updatable) 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."
The duration is specified as a string in ISO 8601 format (
PT10Mfor ten minutes orPT1Hfor one hour). Minimum: PT3M. Maximum: PT1H. Default: PT3M.- query String
(Updatable) 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(also1h). You can optionally specify dimensions and grouping functions. Supported grouping functions:grouping(),groupBy().Example of threshold alarm:
CPUUtilization[1m]{resourceId = "MODEL_DEPLOYMENT_OCID"}.grouping().mean() < 25 CPUUtilization[1m]{resourceId = "MODEL_DEPLOYMENT_OCID"}.grouping().mean() > 75
- scaling
Configuration StringType - (Updatable) The type of scaling configuration.
- threshold Integer
- (Updatable) A metric value at which the scaling operation will be triggered.
- instance
Count numberAdjustment - (Updatable) The value is used for adjusting the count of instances by.
- pending
Duration string (Updatable) 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."
The duration is specified as a string in ISO 8601 format (
PT10Mfor ten minutes orPT1Hfor one hour). Minimum: PT3M. Maximum: PT1H. Default: PT3M.- query string
(Updatable) 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(also1h). You can optionally specify dimensions and grouping functions. Supported grouping functions:grouping(),groupBy().Example of threshold alarm:
CPUUtilization[1m]{resourceId = "MODEL_DEPLOYMENT_OCID"}.grouping().mean() < 25 CPUUtilization[1m]{resourceId = "MODEL_DEPLOYMENT_OCID"}.grouping().mean() > 75
- scaling
Configuration stringType - (Updatable) The type of scaling configuration.
- threshold number
- (Updatable) A metric value at which the scaling operation will be triggered.
- instance_
count_ intadjustment - (Updatable) The value is used for adjusting the count of instances by.
- pending_
duration str (Updatable) 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."
The duration is specified as a string in ISO 8601 format (
PT10Mfor ten minutes orPT1Hfor one hour). Minimum: PT3M. Maximum: PT1H. Default: PT3M.- query str
(Updatable) 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(also1h). You can optionally specify dimensions and grouping functions. Supported grouping functions:grouping(),groupBy().Example of threshold alarm:
CPUUtilization[1m]{resourceId = "MODEL_DEPLOYMENT_OCID"}.grouping().mean() < 25 CPUUtilization[1m]{resourceId = "MODEL_DEPLOYMENT_OCID"}.grouping().mean() > 75
- scaling_
configuration_ strtype - (Updatable) The type of scaling configuration.
- threshold int
- (Updatable) A metric value at which the scaling operation will be triggered.
- instance
Count NumberAdjustment - (Updatable) The value is used for adjusting the count of instances by.
- pending
Duration String (Updatable) 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."
The duration is specified as a string in ISO 8601 format (
PT10Mfor ten minutes orPT1Hfor one hour). Minimum: PT3M. Maximum: PT1H. Default: PT3M.- query String
(Updatable) 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(also1h). You can optionally specify dimensions and grouping functions. Supported grouping functions:grouping(),groupBy().Example of threshold alarm:
CPUUtilization[1m]{resourceId = "MODEL_DEPLOYMENT_OCID"}.grouping().mean() < 25 CPUUtilization[1m]{resourceId = "MODEL_DEPLOYMENT_OCID"}.grouping().mean() > 75
- scaling
Configuration StringType - (Updatable) The type of scaling configuration.
- threshold Number
- (Updatable) A metric value at which the scaling operation will be triggered.
ModelDeploymentModelDeploymentConfigurationDetailsModelConfigurationDetailsScalingPolicyAutoScalingPolicyRuleScaleOutConfiguration, ModelDeploymentModelDeploymentConfigurationDetailsModelConfigurationDetailsScalingPolicyAutoScalingPolicyRuleScaleOutConfigurationArgs
- Instance
Count intAdjustment - (Updatable) The value is used for adjusting the count of instances by.
- Pending
Duration string (Updatable) 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."
The duration is specified as a string in ISO 8601 format (
PT10Mfor ten minutes orPT1Hfor one hour). Minimum: PT3M. Maximum: PT1H. Default: PT3M.- Query string
(Updatable) 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(also1h). You can optionally specify dimensions and grouping functions. Supported grouping functions:grouping(),groupBy().Example of threshold alarm:
CPUUtilization[1m]{resourceId = "MODEL_DEPLOYMENT_OCID"}.grouping().mean() < 25 CPUUtilization[1m]{resourceId = "MODEL_DEPLOYMENT_OCID"}.grouping().mean() > 75
- Scaling
Configuration stringType - (Updatable) The type of scaling configuration.
- Threshold int
- (Updatable) A metric value at which the scaling operation will be triggered.
- Instance
Count intAdjustment - (Updatable) The value is used for adjusting the count of instances by.
- Pending
Duration string (Updatable) 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."
The duration is specified as a string in ISO 8601 format (
PT10Mfor ten minutes orPT1Hfor one hour). Minimum: PT3M. Maximum: PT1H. Default: PT3M.- Query string
(Updatable) 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(also1h). You can optionally specify dimensions and grouping functions. Supported grouping functions:grouping(),groupBy().Example of threshold alarm:
CPUUtilization[1m]{resourceId = "MODEL_DEPLOYMENT_OCID"}.grouping().mean() < 25 CPUUtilization[1m]{resourceId = "MODEL_DEPLOYMENT_OCID"}.grouping().mean() > 75
- Scaling
Configuration stringType - (Updatable) The type of scaling configuration.
- Threshold int
- (Updatable) A metric value at which the scaling operation will be triggered.
- instance
Count IntegerAdjustment - (Updatable) The value is used for adjusting the count of instances by.
- pending
Duration String (Updatable) 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."
The duration is specified as a string in ISO 8601 format (
PT10Mfor ten minutes orPT1Hfor one hour). Minimum: PT3M. Maximum: PT1H. Default: PT3M.- query String
(Updatable) 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(also1h). You can optionally specify dimensions and grouping functions. Supported grouping functions:grouping(),groupBy().Example of threshold alarm:
CPUUtilization[1m]{resourceId = "MODEL_DEPLOYMENT_OCID"}.grouping().mean() < 25 CPUUtilization[1m]{resourceId = "MODEL_DEPLOYMENT_OCID"}.grouping().mean() > 75
- scaling
Configuration StringType - (Updatable) The type of scaling configuration.
- threshold Integer
- (Updatable) A metric value at which the scaling operation will be triggered.
- instance
Count numberAdjustment - (Updatable) The value is used for adjusting the count of instances by.
- pending
Duration string (Updatable) 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."
The duration is specified as a string in ISO 8601 format (
PT10Mfor ten minutes orPT1Hfor one hour). Minimum: PT3M. Maximum: PT1H. Default: PT3M.- query string
(Updatable) 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(also1h). You can optionally specify dimensions and grouping functions. Supported grouping functions:grouping(),groupBy().Example of threshold alarm:
CPUUtilization[1m]{resourceId = "MODEL_DEPLOYMENT_OCID"}.grouping().mean() < 25 CPUUtilization[1m]{resourceId = "MODEL_DEPLOYMENT_OCID"}.grouping().mean() > 75
- scaling
Configuration stringType - (Updatable) The type of scaling configuration.
- threshold number
- (Updatable) A metric value at which the scaling operation will be triggered.
- instance_
count_ intadjustment - (Updatable) The value is used for adjusting the count of instances by.
- pending_
duration str (Updatable) 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."
The duration is specified as a string in ISO 8601 format (
PT10Mfor ten minutes orPT1Hfor one hour). Minimum: PT3M. Maximum: PT1H. Default: PT3M.- query str
(Updatable) 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(also1h). You can optionally specify dimensions and grouping functions. Supported grouping functions:grouping(),groupBy().Example of threshold alarm:
CPUUtilization[1m]{resourceId = "MODEL_DEPLOYMENT_OCID"}.grouping().mean() < 25 CPUUtilization[1m]{resourceId = "MODEL_DEPLOYMENT_OCID"}.grouping().mean() > 75
- scaling_
configuration_ strtype - (Updatable) The type of scaling configuration.
- threshold int
- (Updatable) A metric value at which the scaling operation will be triggered.
- instance
Count NumberAdjustment - (Updatable) The value is used for adjusting the count of instances by.
- pending
Duration String (Updatable) 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."
The duration is specified as a string in ISO 8601 format (
PT10Mfor ten minutes orPT1Hfor one hour). Minimum: PT3M. Maximum: PT1H. Default: PT3M.- query String
(Updatable) 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(also1h). You can optionally specify dimensions and grouping functions. Supported grouping functions:grouping(),groupBy().Example of threshold alarm:
CPUUtilization[1m]{resourceId = "MODEL_DEPLOYMENT_OCID"}.grouping().mean() < 25 CPUUtilization[1m]{resourceId = "MODEL_DEPLOYMENT_OCID"}.grouping().mean() > 75
- scaling
Configuration StringType - (Updatable) The type of scaling configuration.
- threshold Number
- (Updatable) A metric value at which the scaling operation will be triggered.
ModelDeploymentModelDeploymentSystemData, ModelDeploymentModelDeploymentSystemDataArgs
- Current
Instance intCount - This value is the current count of the model deployment instances.
- System
Infra stringType - The infrastructure type of the model deployment.
- Current
Instance intCount - This value is the current count of the model deployment instances.
- System
Infra stringType - The infrastructure type of the model deployment.
- current
Instance IntegerCount - This value is the current count of the model deployment instances.
- system
Infra StringType - The infrastructure type of the model deployment.
- current
Instance numberCount - This value is the current count of the model deployment instances.
- system
Infra stringType - The infrastructure type of the model deployment.
- current_
instance_ intcount - This value is the current count of the model deployment instances.
- system_
infra_ strtype - The infrastructure type of the model deployment.
- current
Instance NumberCount - This value is the current count of the model deployment instances.
- system
Infra StringType - The infrastructure type of the model deployment.
Import
ModelDeployments can be imported using the id, e.g.
$ pulumi import oci:DataScience/modelDeployment:ModelDeployment test_model_deployment "id"
To learn more about importing existing cloud resources, see Importing resources.
Package Details
- Repository
- oci pulumi/pulumi-oci
- License
- Apache-2.0
- Notes
- This Pulumi package is based on the
ociTerraform Provider.