aws.sagemaker.StudioLifecycleConfig
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Provides a SageMaker Studio Lifecycle Config resource.
Example Usage
Basic usage
import * as pulumi from "@pulumi/pulumi";
import * as aws from "@pulumi/aws";
import * as std from "@pulumi/std";
const example = new aws.sagemaker.StudioLifecycleConfig("example", {
studioLifecycleConfigName: "example",
studioLifecycleConfigAppType: "JupyterServer",
studioLifecycleConfigContent: std.base64encode({
input: "echo Hello",
}).then(invoke => invoke.result),
});
import pulumi
import pulumi_aws as aws
import pulumi_std as std
example = aws.sagemaker.StudioLifecycleConfig("example",
studio_lifecycle_config_name="example",
studio_lifecycle_config_app_type="JupyterServer",
studio_lifecycle_config_content=std.base64encode(input="echo Hello").result)
package main
import (
"github.com/pulumi/pulumi-aws/sdk/v6/go/aws/sagemaker"
"github.com/pulumi/pulumi-std/sdk/go/std"
"github.com/pulumi/pulumi/sdk/v3/go/pulumi"
)
func main() {
pulumi.Run(func(ctx *pulumi.Context) error {
invokeBase64encode, err := std.Base64encode(ctx, &std.Base64encodeArgs{
Input: "echo Hello",
}, nil)
if err != nil {
return err
}
_, err = sagemaker.NewStudioLifecycleConfig(ctx, "example", &sagemaker.StudioLifecycleConfigArgs{
StudioLifecycleConfigName: pulumi.String("example"),
StudioLifecycleConfigAppType: pulumi.String("JupyterServer"),
StudioLifecycleConfigContent: pulumi.String(invokeBase64encode.Result),
})
if err != nil {
return err
}
return nil
})
}
using System.Collections.Generic;
using System.Linq;
using Pulumi;
using Aws = Pulumi.Aws;
using Std = Pulumi.Std;
return await Deployment.RunAsync(() =>
{
var example = new Aws.Sagemaker.StudioLifecycleConfig("example", new()
{
StudioLifecycleConfigName = "example",
StudioLifecycleConfigAppType = "JupyterServer",
StudioLifecycleConfigContent = Std.Base64encode.Invoke(new()
{
Input = "echo Hello",
}).Apply(invoke => invoke.Result),
});
});
package generated_program;
import com.pulumi.Context;
import com.pulumi.Pulumi;
import com.pulumi.core.Output;
import com.pulumi.aws.sagemaker.StudioLifecycleConfig;
import com.pulumi.aws.sagemaker.StudioLifecycleConfigArgs;
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 example = new StudioLifecycleConfig("example", StudioLifecycleConfigArgs.builder()
.studioLifecycleConfigName("example")
.studioLifecycleConfigAppType("JupyterServer")
.studioLifecycleConfigContent(StdFunctions.base64encode(Base64encodeArgs.builder()
.input("echo Hello")
.build()).result())
.build());
}
}
resources:
example:
type: aws:sagemaker:StudioLifecycleConfig
properties:
studioLifecycleConfigName: example
studioLifecycleConfigAppType: JupyterServer
studioLifecycleConfigContent:
fn::invoke:
Function: std:base64encode
Arguments:
input: echo Hello
Return: result
Create StudioLifecycleConfig Resource
Resources are created with functions called constructors. To learn more about declaring and configuring resources, see Resources.
Constructor syntax
new StudioLifecycleConfig(name: string, args: StudioLifecycleConfigArgs, opts?: CustomResourceOptions);
@overload
def StudioLifecycleConfig(resource_name: str,
args: StudioLifecycleConfigArgs,
opts: Optional[ResourceOptions] = None)
@overload
def StudioLifecycleConfig(resource_name: str,
opts: Optional[ResourceOptions] = None,
studio_lifecycle_config_app_type: Optional[str] = None,
studio_lifecycle_config_content: Optional[str] = None,
studio_lifecycle_config_name: Optional[str] = None,
tags: Optional[Mapping[str, str]] = None)
func NewStudioLifecycleConfig(ctx *Context, name string, args StudioLifecycleConfigArgs, opts ...ResourceOption) (*StudioLifecycleConfig, error)
public StudioLifecycleConfig(string name, StudioLifecycleConfigArgs args, CustomResourceOptions? opts = null)
public StudioLifecycleConfig(String name, StudioLifecycleConfigArgs args)
public StudioLifecycleConfig(String name, StudioLifecycleConfigArgs args, CustomResourceOptions options)
type: aws:sagemaker:StudioLifecycleConfig
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 StudioLifecycleConfigArgs
- 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 StudioLifecycleConfigArgs
- 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 StudioLifecycleConfigArgs
- The arguments to resource properties.
- opts ResourceOption
- Bag of options to control resource's behavior.
- name string
- The unique name of the resource.
- args StudioLifecycleConfigArgs
- The arguments to resource properties.
- opts CustomResourceOptions
- Bag of options to control resource's behavior.
- name String
- The unique name of the resource.
- args StudioLifecycleConfigArgs
- The arguments to resource properties.
- options CustomResourceOptions
- Bag of options to control resource's behavior.
Constructor example
The following reference example uses placeholder values for all input properties.
var studioLifecycleConfigResource = new Aws.Sagemaker.StudioLifecycleConfig("studioLifecycleConfigResource", new()
{
StudioLifecycleConfigAppType = "string",
StudioLifecycleConfigContent = "string",
StudioLifecycleConfigName = "string",
Tags =
{
{ "string", "string" },
},
});
example, err := sagemaker.NewStudioLifecycleConfig(ctx, "studioLifecycleConfigResource", &sagemaker.StudioLifecycleConfigArgs{
StudioLifecycleConfigAppType: pulumi.String("string"),
StudioLifecycleConfigContent: pulumi.String("string"),
StudioLifecycleConfigName: pulumi.String("string"),
Tags: pulumi.StringMap{
"string": pulumi.String("string"),
},
})
var studioLifecycleConfigResource = new StudioLifecycleConfig("studioLifecycleConfigResource", StudioLifecycleConfigArgs.builder()
.studioLifecycleConfigAppType("string")
.studioLifecycleConfigContent("string")
.studioLifecycleConfigName("string")
.tags(Map.of("string", "string"))
.build());
studio_lifecycle_config_resource = aws.sagemaker.StudioLifecycleConfig("studioLifecycleConfigResource",
studio_lifecycle_config_app_type="string",
studio_lifecycle_config_content="string",
studio_lifecycle_config_name="string",
tags={
"string": "string",
})
const studioLifecycleConfigResource = new aws.sagemaker.StudioLifecycleConfig("studioLifecycleConfigResource", {
studioLifecycleConfigAppType: "string",
studioLifecycleConfigContent: "string",
studioLifecycleConfigName: "string",
tags: {
string: "string",
},
});
type: aws:sagemaker:StudioLifecycleConfig
properties:
studioLifecycleConfigAppType: string
studioLifecycleConfigContent: string
studioLifecycleConfigName: string
tags:
string: string
StudioLifecycleConfig Resource Properties
To learn more about resource properties and how to use them, see Inputs and Outputs in the Architecture and Concepts docs.
Inputs
In Python, inputs that are objects can be passed either as argument classes or as dictionary literals.
The StudioLifecycleConfig resource accepts the following input properties:
- Studio
Lifecycle stringConfig App Type - The App type that the Lifecycle Configuration is attached to. Valid values are
JupyterServer
,JupyterLab
,CodeEditor
andKernelGateway
. - Studio
Lifecycle stringConfig Content - The content of your Studio Lifecycle Configuration script. This content must be base64 encoded.
- Studio
Lifecycle stringConfig Name - The name of the Studio Lifecycle Configuration to create.
- Dictionary<string, string>
- 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.
- Studio
Lifecycle stringConfig App Type - The App type that the Lifecycle Configuration is attached to. Valid values are
JupyterServer
,JupyterLab
,CodeEditor
andKernelGateway
. - Studio
Lifecycle stringConfig Content - The content of your Studio Lifecycle Configuration script. This content must be base64 encoded.
- Studio
Lifecycle stringConfig Name - The name of the Studio Lifecycle Configuration to create.
- map[string]string
- 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.
- studio
Lifecycle StringConfig App Type - The App type that the Lifecycle Configuration is attached to. Valid values are
JupyterServer
,JupyterLab
,CodeEditor
andKernelGateway
. - studio
Lifecycle StringConfig Content - The content of your Studio Lifecycle Configuration script. This content must be base64 encoded.
- studio
Lifecycle StringConfig Name - The name of the Studio Lifecycle Configuration to create.
- Map<String,String>
- 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.
- studio
Lifecycle stringConfig App Type - The App type that the Lifecycle Configuration is attached to. Valid values are
JupyterServer
,JupyterLab
,CodeEditor
andKernelGateway
. - studio
Lifecycle stringConfig Content - The content of your Studio Lifecycle Configuration script. This content must be base64 encoded.
- studio
Lifecycle stringConfig Name - The name of the Studio Lifecycle Configuration to create.
- {[key: string]: string}
- 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.
- studio_
lifecycle_ strconfig_ app_ type - The App type that the Lifecycle Configuration is attached to. Valid values are
JupyterServer
,JupyterLab
,CodeEditor
andKernelGateway
. - studio_
lifecycle_ strconfig_ content - The content of your Studio Lifecycle Configuration script. This content must be base64 encoded.
- studio_
lifecycle_ strconfig_ name - The name of the Studio Lifecycle Configuration to create.
- Mapping[str, str]
- 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.
- studio
Lifecycle StringConfig App Type - The App type that the Lifecycle Configuration is attached to. Valid values are
JupyterServer
,JupyterLab
,CodeEditor
andKernelGateway
. - studio
Lifecycle StringConfig Content - The content of your Studio Lifecycle Configuration script. This content must be base64 encoded.
- studio
Lifecycle StringConfig Name - The name of the Studio Lifecycle Configuration to create.
- Map<String>
- 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.
Outputs
All input properties are implicitly available as output properties. Additionally, the StudioLifecycleConfig resource produces the following output properties:
- Arn string
- The Amazon Resource Name (ARN) assigned by AWS to this Studio Lifecycle Config.
- Id string
- The provider-assigned unique ID for this managed resource.
- Dictionary<string, string>
- A map of tags assigned to the resource, including those inherited from the provider
default_tags
configuration block.
Look up Existing StudioLifecycleConfig Resource
Get an existing StudioLifecycleConfig 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?: StudioLifecycleConfigState, opts?: CustomResourceOptions): StudioLifecycleConfig
@staticmethod
def get(resource_name: str,
id: str,
opts: Optional[ResourceOptions] = None,
arn: Optional[str] = None,
studio_lifecycle_config_app_type: Optional[str] = None,
studio_lifecycle_config_content: Optional[str] = None,
studio_lifecycle_config_name: Optional[str] = None,
tags: Optional[Mapping[str, str]] = None,
tags_all: Optional[Mapping[str, str]] = None) -> StudioLifecycleConfig
func GetStudioLifecycleConfig(ctx *Context, name string, id IDInput, state *StudioLifecycleConfigState, opts ...ResourceOption) (*StudioLifecycleConfig, error)
public static StudioLifecycleConfig Get(string name, Input<string> id, StudioLifecycleConfigState? state, CustomResourceOptions? opts = null)
public static StudioLifecycleConfig get(String name, Output<String> id, StudioLifecycleConfigState 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.
- Arn string
- The Amazon Resource Name (ARN) assigned by AWS to this Studio Lifecycle Config.
- Studio
Lifecycle stringConfig App Type - The App type that the Lifecycle Configuration is attached to. Valid values are
JupyterServer
,JupyterLab
,CodeEditor
andKernelGateway
. - Studio
Lifecycle stringConfig Content - The content of your Studio Lifecycle Configuration script. This content must be base64 encoded.
- Studio
Lifecycle stringConfig Name - The name of the Studio Lifecycle Configuration to create.
- Dictionary<string, string>
- 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. - Dictionary<string, string>
- A map of tags assigned to the resource, including those inherited from the provider
default_tags
configuration block.
- Arn string
- The Amazon Resource Name (ARN) assigned by AWS to this Studio Lifecycle Config.
- Studio
Lifecycle stringConfig App Type - The App type that the Lifecycle Configuration is attached to. Valid values are
JupyterServer
,JupyterLab
,CodeEditor
andKernelGateway
. - Studio
Lifecycle stringConfig Content - The content of your Studio Lifecycle Configuration script. This content must be base64 encoded.
- Studio
Lifecycle stringConfig Name - The name of the Studio Lifecycle Configuration to create.
- map[string]string
- 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. - map[string]string
- A map of tags assigned to the resource, including those inherited from the provider
default_tags
configuration block.
- arn String
- The Amazon Resource Name (ARN) assigned by AWS to this Studio Lifecycle Config.
- studio
Lifecycle StringConfig App Type - The App type that the Lifecycle Configuration is attached to. Valid values are
JupyterServer
,JupyterLab
,CodeEditor
andKernelGateway
. - studio
Lifecycle StringConfig Content - The content of your Studio Lifecycle Configuration script. This content must be base64 encoded.
- studio
Lifecycle StringConfig Name - The name of the Studio Lifecycle Configuration to create.
- Map<String,String>
- 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. - Map<String,String>
- A map of tags assigned to the resource, including those inherited from the provider
default_tags
configuration block.
- arn string
- The Amazon Resource Name (ARN) assigned by AWS to this Studio Lifecycle Config.
- studio
Lifecycle stringConfig App Type - The App type that the Lifecycle Configuration is attached to. Valid values are
JupyterServer
,JupyterLab
,CodeEditor
andKernelGateway
. - studio
Lifecycle stringConfig Content - The content of your Studio Lifecycle Configuration script. This content must be base64 encoded.
- studio
Lifecycle stringConfig Name - The name of the Studio Lifecycle Configuration to create.
- {[key: string]: string}
- 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. - {[key: string]: string}
- A map of tags assigned to the resource, including those inherited from the provider
default_tags
configuration block.
- arn str
- The Amazon Resource Name (ARN) assigned by AWS to this Studio Lifecycle Config.
- studio_
lifecycle_ strconfig_ app_ type - The App type that the Lifecycle Configuration is attached to. Valid values are
JupyterServer
,JupyterLab
,CodeEditor
andKernelGateway
. - studio_
lifecycle_ strconfig_ content - The content of your Studio Lifecycle Configuration script. This content must be base64 encoded.
- studio_
lifecycle_ strconfig_ name - The name of the Studio Lifecycle Configuration to create.
- Mapping[str, str]
- 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. - Mapping[str, str]
- A map of tags assigned to the resource, including those inherited from the provider
default_tags
configuration block.
- arn String
- The Amazon Resource Name (ARN) assigned by AWS to this Studio Lifecycle Config.
- studio
Lifecycle StringConfig App Type - The App type that the Lifecycle Configuration is attached to. Valid values are
JupyterServer
,JupyterLab
,CodeEditor
andKernelGateway
. - studio
Lifecycle StringConfig Content - The content of your Studio Lifecycle Configuration script. This content must be base64 encoded.
- studio
Lifecycle StringConfig Name - The name of the Studio Lifecycle Configuration to create.
- Map<String>
- 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. - Map<String>
- A map of tags assigned to the resource, including those inherited from the provider
default_tags
configuration block.
Import
Using pulumi import
, import SageMaker Studio Lifecycle Configs using the studio_lifecycle_config_name
. For example:
$ pulumi import aws:sagemaker/studioLifecycleConfig:StudioLifecycleConfig example example
To learn more about importing existing cloud resources, see Importing resources.
Package Details
- Repository
- AWS Classic pulumi/pulumi-aws
- License
- Apache-2.0
- Notes
- This Pulumi package is based on the
aws
Terraform Provider.