Model is the Schema for the Models API. Provides a SageMaker model resource.
Type
CRD
Group
sagemaker.aws.upbound.io
Version
v1beta1
apiVersion: sagemaker.aws.upbound.io/v1beta1
kind: Model
ModelSpec defines the desired state of Model
No description provided.
Specifies containers in the inference pipeline. If not specified, the primary_container argument is required. Fields are documented below.
Specifies whether the model container is in Amazon ECR or a private Docker registry accessible from your Amazon Virtual Private Cloud (VPC). For more information see Using a Private Docker Registry for Real-Time Inference Containers. see Image Config.
Specifies an authentication configuration for the private docker registry where your model image is hosted. Specify a value for this property only if you specified Vpc as the value for the RepositoryAccessMode field, and the private Docker registry where the model image is hosted requires authentication. see Repository Auth Config.
The location of model data to deploy. Use this for uncompressed model deployment. For information about how to deploy an uncompressed model, see Deploying uncompressed models in the AWS SageMaker Developer Guide.
The S3 location of model data to deploy.
Reference to a Role in iam to populate executionRoleArn.
Policies for referencing.
Selector for a Role in iam to populate executionRoleArn.
Policies for selection.
Specifies details of how containers in a multi-container endpoint are called. see Inference Execution Config.
The primary docker image containing inference code that is used when the model is deployed for predictions. If not specified, the container argument is required. Fields are documented below.
Specifies whether the model container is in Amazon ECR or a private Docker registry accessible from your Amazon Virtual Private Cloud (VPC). For more information see Using a Private Docker Registry for Real-Time Inference Containers. see Image Config.
Specifies an authentication configuration for the private docker registry where your model image is hosted. Specify a value for this property only if you specified Vpc as the value for the RepositoryAccessMode field, and the private Docker registry where the model image is hosted requires authentication. see Repository Auth Config.
The location of model data to deploy. Use this for uncompressed model deployment. For information about how to deploy an uncompressed model, see Deploying uncompressed models in the AWS SageMaker Developer Guide.
The S3 location of model data to deploy.
Specifies the VPC that you want your model to connect to. VpcConfig is used in hosting services and in batch transform.
No description provided.
No description provided.
THIS IS A BETA FIELD. It will be honored unless the Management Policies feature flag is disabled. InitProvider holds the same fields as ForProvider, with the exception of Identifier and other resource reference fields. The fields that are in InitProvider are merged into ForProvider when the resource is created. The same fields are also added to the terraform ignore_changes hook, to avoid updating them after creation. This is useful for fields that are required on creation, but we do not desire to update them after creation, for example because of an external controller is managing them, like an autoscaler.
Specifies containers in the inference pipeline. If not specified, the primary_container argument is required. Fields are documented below.
Specifies whether the model container is in Amazon ECR or a private Docker registry accessible from your Amazon Virtual Private Cloud (VPC). For more information see Using a Private Docker Registry for Real-Time Inference Containers. see Image Config.
Specifies an authentication configuration for the private docker registry where your model image is hosted. Specify a value for this property only if you specified Vpc as the value for the RepositoryAccessMode field, and the private Docker registry where the model image is hosted requires authentication. see Repository Auth Config.
The location of model data to deploy. Use this for uncompressed model deployment. For information about how to deploy an uncompressed model, see Deploying uncompressed models in the AWS SageMaker Developer Guide.
The S3 location of model data to deploy.
Reference to a Role in iam to populate executionRoleArn.
Policies for referencing.
Selector for a Role in iam to populate executionRoleArn.
Policies for selection.
Specifies details of how containers in a multi-container endpoint are called. see Inference Execution Config.
The primary docker image containing inference code that is used when the model is deployed for predictions. If not specified, the container argument is required. Fields are documented below.
Specifies whether the model container is in Amazon ECR or a private Docker registry accessible from your Amazon Virtual Private Cloud (VPC). For more information see Using a Private Docker Registry for Real-Time Inference Containers. see Image Config.
Specifies an authentication configuration for the private docker registry where your model image is hosted. Specify a value for this property only if you specified Vpc as the value for the RepositoryAccessMode field, and the private Docker registry where the model image is hosted requires authentication. see Repository Auth Config.
The location of model data to deploy. Use this for uncompressed model deployment. For information about how to deploy an uncompressed model, see Deploying uncompressed models in the AWS SageMaker Developer Guide.
The S3 location of model data to deploy.
Specifies the VPC that you want your model to connect to. VpcConfig is used in hosting services and in batch transform.
No description provided.
No description provided.
THIS IS A BETA FIELD. It is on by default but can be opted out through a Crossplane feature flag. ManagementPolicies specify the array of actions Crossplane is allowed to take on the managed and external resources. This field is planned to replace the DeletionPolicy field in a future release. Currently, both could be set independently and non-default values would be honored if the feature flag is enabled. If both are custom, the DeletionPolicy field will be ignored. See the design doc for more information: https://github.com/crossplane/crossplane/blob/499895a25d1a1a0ba1604944ef98ac7a1a71f197/design/design-doc-observe-only-resources.md?plain=1#L223 and this one: https://github.com/crossplane/crossplane/blob/444267e84783136daa93568b364a5f01228cacbe/design/one-pager-ignore-changes.md
ProviderConfigReference specifies how the provider that will be used to create, observe, update, and delete this managed resource should be configured.
Policies for referencing.
PublishConnectionDetailsTo specifies the connection secret config which contains a name, metadata and a reference to secret store config to which any connection details for this managed resource should be written. Connection details frequently include the endpoint, username, and password required to connect to the managed resource.
WriteConnectionSecretToReference specifies the namespace and name of a Secret to which any connection details for this managed resource should be written. Connection details frequently include the endpoint, username, and password required to connect to the managed resource. This field is planned to be replaced in a future release in favor of PublishConnectionDetailsTo. Currently, both could be set independently and connection details would be published to both without affecting each other.
ModelStatus defines the observed state of Model.
No description provided.
Specifies containers in the inference pipeline. If not specified, the primary_container argument is required. Fields are documented below.
Specifies whether the model container is in Amazon ECR or a private Docker registry accessible from your Amazon Virtual Private Cloud (VPC). For more information see Using a Private Docker Registry for Real-Time Inference Containers. see Image Config.
Specifies an authentication configuration for the private docker registry where your model image is hosted. Specify a value for this property only if you specified Vpc as the value for the RepositoryAccessMode field, and the private Docker registry where the model image is hosted requires authentication. see Repository Auth Config.
The location of model data to deploy. Use this for uncompressed model deployment. For information about how to deploy an uncompressed model, see Deploying uncompressed models in the AWS SageMaker Developer Guide.
The S3 location of model data to deploy.
Specifies details of how containers in a multi-container endpoint are called. see Inference Execution Config.
The primary docker image containing inference code that is used when the model is deployed for predictions. If not specified, the container argument is required. Fields are documented below.
Specifies whether the model container is in Amazon ECR or a private Docker registry accessible from your Amazon Virtual Private Cloud (VPC). For more information see Using a Private Docker Registry for Real-Time Inference Containers. see Image Config.
Specifies an authentication configuration for the private docker registry where your model image is hosted. Specify a value for this property only if you specified Vpc as the value for the RepositoryAccessMode field, and the private Docker registry where the model image is hosted requires authentication. see Repository Auth Config.
The location of model data to deploy. Use this for uncompressed model deployment. For information about how to deploy an uncompressed model, see Deploying uncompressed models in the AWS SageMaker Developer Guide.
The S3 location of model data to deploy.
Specifies the VPC that you want your model to connect to. VpcConfig is used in hosting services and in batch transform.
No description provided.
No description provided.
Conditions of the resource.
example
apiVersion: sagemaker.aws.upbound.io/v1beta1
kind: Model
metadata:
annotations:
meta.upbound.io/example-id: sagemaker/v1beta1/model
upjet.upbound.io/manual-intervention: It requires an image which can be create/deleted only using AWS CLI
labels:
testing.upbound.io/example-name: example
name: example
spec:
forProvider:
executionRoleArnSelector:
matchLabels:
testing.upbound.io/example-name: example
primaryContainer:
- image: ${data.aws_account_id}.dkr.ecr.us-east-1.amazonaws.com/sagemaker-scikit-learn:latest
region: us-east-1
example
apiVersion: sagemaker.aws.upbound.io/v1beta1
kind: Model
metadata:
annotations:
meta.upbound.io/example-id: sagemaker/v1beta1/endpointconfiguration
upjet.upbound.io/manual-intervention: It requires an image which can be create/deleted only using AWS CLI
labels:
testing.upbound.io/example-name: example
name: example
spec:
forProvider:
executionRoleArnSelector:
matchLabels:
testing.upbound.io/example-name: example
primaryContainer:
- image: ${data.aws_account_id}.dkr.ecr.us-east-1.amazonaws.com/sagemaker-scikit-learn:latest
region: us-east-1
example
apiVersion: sagemaker.aws.upbound.io/v1beta1
kind: Model
metadata:
annotations:
meta.upbound.io/example-id: sagemaker/v1beta1/endpoint
upjet.upbound.io/manual-intervention: It requires an image which can be create/deleted only using AWS CLI
labels:
testing.upbound.io/example-name: example
name: example
spec:
forProvider:
executionRoleArnSelector:
matchLabels:
testing.upbound.io/example-name: example
primaryContainer:
- image: ${data.aws_account_id}.dkr.ecr.us-east-1.amazonaws.com/sagemaker-scikit-learn:latest
region: us-east-1
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