StudioLifecycleConfig class

Provides a SageMaker AI 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)

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 main

import (
	"github.com/pulumi/pulumi-aws/sdk/v7/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
	})
}

pulumi {
  required_providers {
    aws = {
      source = "pulumi/aws"
    }
    std = {
      source = "pulumi/std"
    }
  }
}

resource "aws_sagemaker_studiolifecycleconfig" "example" {
  studio_lifecycle_config_name     = "example"
  studio_lifecycle_config_app_type = "JupyterServer"
  studio_lifecycle_config_content  = base64encode("echo Hello")
}

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 com.pulumi.std.StdFunctions;
import com.pulumi.std.inputs.Base64encodeArgs;
import java.util.ArrayList;
import java.util.Arrays;
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

Import

Using pulumi import, import SageMaker AI Studio Lifecycle Configs using the studioLifecycleConfigName. For example:

$ pulumi import aws:sagemaker/studioLifecycleConfig:StudioLifecycleConfig example example