Provides a SageMaker Studio Lifecycle Config resource.
resource "aws_sagemaker_studio_lifecycle_config" "example" {
studio_lifecycle_config_name = "example"
studio_lifecycle_config_app_type = "JupyterServer"
studio_lifecycle_config_content = base64encode("echo Hello")
}
This resource supports the following arguments:
studio_lifecycle_config_name
- (Required) The name of the Studio Lifecycle Configuration to create.studio_lifecycle_config_app_type
- (Required) The App type that the Lifecycle Configuration is attached to. Valid values are JupyterServer
, JupyterLab
, CodeEditor
and KernelGateway
.studio_lifecycle_config_content
- (Required) The content of your Studio Lifecycle Configuration script. This content must be base64 encoded.tags
- (Optional) A map of tags to assign to the resource. If configured with a provider default_tags
configuration block present, tags with matching keys will overwrite those defined at the provider-level.This resource exports the following attributes in addition to the arguments above:
id
- The name of the Studio Lifecycle Config.arn
- The Amazon Resource Name (ARN) assigned by AWS to this Studio Lifecycle Config.tags_all
- A map of tags assigned to the resource, including those inherited from the provider default_tags
configuration block.In Terraform v1.5.0 and later, use an import
block to import SageMaker Studio Lifecycle Configs using the studio_lifecycle_config_name
. For example:
import {
to = aws_sagemaker_studio_lifecycle_config.example
id = "example"
}
Using terraform import
, import SageMaker Studio Lifecycle Configs using the studio_lifecycle_config_name
. For example:
% terraform import aws_sagemaker_studio_lifecycle_config.example example