# Create and Associate a Lifecycle Configuration with Amazon SageMaker Studio Classic
**Important**
As of November 30, 2023, the previous Amazon SageMaker Studio experience is now named Amazon SageMaker Studio Classic. The following section is specific to using the Studio Classic application. For information about using the updated Studio experience, see [Amazon SageMaker Studio](studio-updated.md).
Studio Classic is still maintained for existing workloads but is no longer available for onboarding. You can only stop or delete existing Studio Classic applications and cannot create new ones. We recommend that you [migrate your workload to the new Studio experience](studio-updated-migrate.md).
Amazon SageMaker AI provides interactive applications that enable Studio Classic's visual interface, code authoring, and run experience. This series shows how to create a lifecycle configuration and associate it with a SageMaker AI domain.
Application types can be either `JupyterServer` or `KernelGateway`.
+ **`JupyterServer` applications:** This application type enables access to the visual interface for Studio Classic. Every user and shared space in Studio Classic gets its own JupyterServer application.
+ **`KernelGateway` applications:** This application type enables access to the code run environment and kernels for your Studio Classic notebooks and terminals. For more information, see [Jupyter Kernel Gateway](https://jupyter-kernel-gateway.readthedocs.io/en/latest/).
For more information about Studio Classic's architecture and Studio Classic applications, see [Use Amazon SageMaker Studio Classic Notebooks](https://docs.aws.amazon.com/sagemaker/latest/dg/notebooks.html).
**Topics**
+ [Create a Lifecycle Configuration from the AWS CLI for Amazon SageMaker Studio Classic](studio-lcc-create-cli.md)
+ [Create a Lifecycle Configuration from the SageMaker AI Console for Amazon SageMaker Studio Classic](studio-lcc-create-console.md)
# Create a Lifecycle Configuration from the AWS CLI for Amazon SageMaker Studio Classic
**Important**
Custom IAM policies that allow Amazon SageMaker Studio or Amazon SageMaker Studio Classic to create Amazon SageMaker resources must also grant permissions to add tags to those resources. The permission to add tags to resources is required because Studio and Studio Classic automatically tag any resources they create. If an IAM policy allows Studio and Studio Classic to create resources but does not allow tagging, "AccessDenied" errors can occur when trying to create resources. For more information, see [Provide permissions for tagging SageMaker AI resources](security_iam_id-based-policy-examples.md#grant-tagging-permissions).
[AWS managed policies for Amazon SageMaker AI](security-iam-awsmanpol.md) that give permissions to create SageMaker resources already include permissions to add tags while creating those resources.
**Important**
As of November 30, 2023, the previous Amazon SageMaker Studio experience is now named Amazon SageMaker Studio Classic. The following section is specific to using the Studio Classic application. For information about using the updated Studio experience, see [Amazon SageMaker Studio](studio-updated.md).
Studio Classic is still maintained for existing workloads but is no longer available for onboarding. You can only stop or delete existing Studio Classic applications and cannot create new ones. We recommend that you [migrate your workload to the new Studio experience](studio-updated-migrate.md).
The following topic shows how to create a lifecycle configuration using the AWS CLI to automate customization for your Studio Classic environment.
## Prerequisites
Before you begin, complete the following prerequisites:
+ Update the AWS CLI by following the steps in [Installing the current AWS CLI Version](https://docs.aws.amazon.com/cli/latest/userguide/install-cliv1.html#install-tool-bundled).
+ From your local machine, run `aws configure` and provide your AWS credentials. For information about AWS credentials, see [Understanding and getting your AWS credentials](https://docs.aws.amazon.com/general/latest/gr/aws-sec-cred-types.html).
+ Onboard to SageMaker AI domain by following the steps in [Amazon SageMaker AI domain overview](gs-studio-onboard.md).
## Step 1: Create a lifecycle configuration
The following procedure shows how to create a lifecycle configuration script that prints `Hello World`.
**Note**
Each script can have up to **16,384 characters**.
1. From your local machine, create a file named `my-script.sh` with the following content.
```
#!/bin/bash
set -eux
echo 'Hello World!'
```
1. Convert your `my-script.sh` file into base64 format. This requirement prevents errors that occur from spacing and line break encoding.
```
LCC_CONTENT=`openssl base64 -A -in my-script.sh`
```
1. Create a lifecycle configuration for use with Studio Classic. The following command creates a lifecycle configuration that runs when you launch an associated `KernelGateway` application.
```
aws sagemaker create-studio-lifecycle-config \
--region region \
--studio-lifecycle-config-name my-studio-lcc \
--studio-lifecycle-config-content $LCC_CONTENT \
--studio-lifecycle-config-app-type KernelGateway
```
Note the ARN of the newly created lifecycle configuration that is returned. This ARN is required to attach the lifecycle configuration to your application.
## Step 2: Attach the lifecycle configuration to your domain, user profile, or shared space
To attach the lifecycle configuration, you must update the `UserSettings` for your domain or user profile, or the `SpaceSettings` for a shared space. Lifecycle configuration scripts that are associated at the domain level are inherited by all users. However, scripts that are associated at the user profile level are scoped to a specific user, while scripts that are associated at the shared space level are scoped to the shared space.
The following example shows how to create a new user profile with the lifecycle configuration attached. You can also create a new domain or space with a lifecycle configuration attached by using the [create-domain](https://awscli.amazonaws.com/v2/documentation/api/latest/reference/sagemaker/create-domain.html) and [create-space](https://awscli.amazonaws.com/v2/documentation/api/latest/reference/sagemaker/create-space.html) commands, respectively.
Add the lifecycle configuration ARN from the previous step to the settings for the appropriate app type. For example, place it in the `JupyterServerAppSettings` of the user. You can add multiple lifecycle configurations at the same time by passing a list of lifecycle configurations. When a user launches a JupyterServer application with the AWS CLI, they can pass a lifecycle configuration to use instead of the default. The lifecycle configuration that the user passes must belong to the list of lifecycle configurations in `JupyterServerAppSettings`.
```
# Create a new UserProfile
aws sagemaker create-user-profile --domain-id domain-id \
--user-profile-name user-profile-name \
--region region \
--user-settings '{
"JupyterServerAppSettings": {
"LifecycleConfigArns":
[lifecycle-configuration-arn-list]
}
}'
```
The following example shows how to update an existing shared space to attach the lifecycle configuration. You can also update an existing domain or user profile with a lifecycle configuration attached by using the [update-domain](https://awscli.amazonaws.com/v2/documentation/api/latest/reference/sagemaker/update-domain.html) or [update-user-profile](https://awscli.amazonaws.com/v2/documentation/api/latest/reference/sagemaker/update-user-profile.html) command. When you update the list of lifecycle configurations attached, you must pass all lifecycle configurations as part of the list. If a lifecycle configuration is not part of this list, it will not be attached to the application.
```
aws sagemaker update-space --domain-id domain-id \
--space-name space-name \
--region region \
--space-settings '{
"JupyterServerAppSettings": {
"LifecycleConfigArns":
[lifecycle-configuration-arn-list]
}
}'
```
For information about setting a default lifecycle configuration for a resource, see [Set Default Lifecycle Configurations for Amazon SageMaker Studio Classic](studio-lcc-defaults.md).
## Step 3: Launch application with lifecycle configuration
After you attach a lifecycle configuration to a domain, user profile, or space, the user can select it when launching an application with the AWS CLI. This section describes how to launch an application with an attached lifecycle configuration. For information about changing the default lifecycle configuration after launching a JupyterServer application, see [Set Default Lifecycle Configurations for Amazon SageMaker Studio Classic](studio-lcc-defaults.md).
Launch the desired application type using the `create-app` command and specify the lifecycle configuration ARN in the `resource-spec` argument.
+ The following example shows how to create a `JupyterServer` application with an associated lifecycle configuration. When creating the `JupyterServer`, the `app-name` must be `default`. The lifecycle configuration ARN passed as part of the `resource-spec` parameter must be part of the list of lifecycle configuration ARNs specified in `UserSettings` for your domain or user profile, or `SpaceSettings` for a shared space.
```
aws sagemaker create-app --domain-id domain-id \
--region region \
--user-profile-name user-profile-name \
--app-type JupyterServer \
--resource-spec LifecycleConfigArn=lifecycle-configuration-arn \
--app-name default
```
+ The following example shows how to create a `KernelGateway` application with an associated lifecycle configuration.
```
aws sagemaker create-app --domain-id domain-id \
--region region \
--user-profile-name user-profile-name \
--app-type KernelGateway \
--resource-spec LifecycleConfigArn=lifecycle-configuration-arn,SageMakerImageArn=sagemaker-image-arn,InstanceType=instance-type \
--app-name app-name
```
# Create a Lifecycle Configuration from the SageMaker AI Console for Amazon SageMaker Studio Classic
**Important**
Custom IAM policies that allow Amazon SageMaker Studio or Amazon SageMaker Studio Classic to create Amazon SageMaker resources must also grant permissions to add tags to those resources. The permission to add tags to resources is required because Studio and Studio Classic automatically tag any resources they create. If an IAM policy allows Studio and Studio Classic to create resources but does not allow tagging, "AccessDenied" errors can occur when trying to create resources. For more information, see [Provide permissions for tagging SageMaker AI resources](security_iam_id-based-policy-examples.md#grant-tagging-permissions).
[AWS managed policies for Amazon SageMaker AI](security-iam-awsmanpol.md) that give permissions to create SageMaker resources already include permissions to add tags while creating those resources.
**Important**
As of November 30, 2023, the previous Amazon SageMaker Studio experience is now named Amazon SageMaker Studio Classic. The following section is specific to using the Studio Classic application. For information about using the updated Studio experience, see [Amazon SageMaker Studio](studio-updated.md).
Studio Classic is still maintained for existing workloads but is no longer available for onboarding. You can only stop or delete existing Studio Classic applications and cannot create new ones. We recommend that you [migrate your workload to the new Studio experience](studio-updated-migrate.md).
The following topic shows how to create a lifecycle configuration from the Amazon SageMaker AI console to automate customization for your Studio Classic environment.
## Prerequisites
Before you can begin this tutorial, complete the following prerequisite:
+ Onboard to Amazon SageMaker Studio Classic. For more information, see [Onboard to Amazon SageMaker Studio Classic](https://docs.aws.amazon.com/sagemaker/latest/dg/gs-studio-onboard.html).
## Step 1: Create a new lifecycle configuration
You can create a lifecycle configuration by entering a script from the Amazon SageMaker AI console.
**Note**
Each script can have up to **16,384 characters**.
The following procedure shows how to create a lifecycle configuration script that prints `Hello World`.
1. Open the Amazon SageMaker AI console at [https://console.aws.amazon.com/sagemaker/](https://console.aws.amazon.com/sagemaker/).
1. On the left navigation pane, choose **Admin configurations**.
1. Under **Admin configurations**, choose **Lifecycle configurations**.
1. Choose the **Studio** tab.
1. Choose **Create configuration**.
1. Under **Select configuration type**, select the type of application that the lifecycle configuration should be attached to. For more information about selecting which application to attach the lifecycle configuration to, see [Set Default Lifecycle Configurations for Amazon SageMaker Studio Classic](studio-lcc-defaults.md).
1. Choose **Next**.
1. In the section called **Configuration settings**, enter a name for your lifecycle configuration.
1. In the **Scripts** section, enter the following content.
```
#!/bin/bash
set -eux
echo 'Hello World!'
```
1. (Optional) Create a tag for your lifecycle configuration.
1. Choose **Submit**.
## Step 2: Attach the lifecycle configuration to a domain or user profile
Lifecycle configuration scripts associated at the domain level are inherited by all users. However, scripts that are associated at the user profile level are scoped to a specific user.
You can attach multiple lifecycle configurations to a domain or user profile for both JupyterServer and KernelGateway applications.
**Note**
To attach a lifecycle configuration to a shared space, you must use the AWS CLI. For more information, see [Create a Lifecycle Configuration from the AWS CLI for Amazon SageMaker Studio Classic](studio-lcc-create-cli.md).
The following sections show how to attach a lifecycle configuration to your domain or user profile.
### Attach to a domain
The following shows how to attach a lifecycle configuration to your existing domain from the SageMaker AI console.
1. Open the Amazon SageMaker AI console at [https://console.aws.amazon.com/sagemaker/](https://console.aws.amazon.com/sagemaker/).
1. On the left navigation pane, choose **Admin configurations**.
1. Under **Admin configurations**, choose **domains**.
1. From the list of domains, select the domain to attach the lifecycle configuration to.
1. From the **Domain details**, choose the **Environment** tab.
1. Under **Lifecycle configurations for personal Studio apps**, choose **Attach**.
1. Under **Source**, choose **Existing configuration**.
1. Under **Studio lifecycle configurations**, select the lifecycle configuration that you created in the previous step.
1. Select **Attach to domain**.
### Attach to your user profile
The following shows how to attach a lifecycle configuration to your existing user profile.
1. Open the Amazon SageMaker AI console at [https://console.aws.amazon.com/sagemaker/](https://console.aws.amazon.com/sagemaker/).
1. On the left navigation pane, choose **Admin configurations**.
1. Under **Admin configurations**, choose **domains**.
1. From the list of domains, select the domain that contains the user profile to attach the lifecycle configuration to.
1. Under **User profiles**, select the user profile.
1. From the **User Details** page, choose **Edit**.
1. On the left navigation, choose **Studio settings**.
1. Under **Lifecycle configurations attached to user**, choose **Attach**.
1. Under **Source**, choose **Existing configuration**.
1. Under **Studio lifecycle configurations**, select the lifecycle configuration that you created in the previous step.
1. Choose **Attach to user profile**.
## Step 3: Launch an application with the lifecycle configuration
After you attach a lifecycle configuration to a domain or user profile, you can launch an application with that attached lifecycle configuration. Choosing which lifecycle configuration to launch with depends on the application type.
+ **JupyterServer**: When launching a JupyterServer application from the console, SageMaker AI always uses the default lifecycle configuration. You can't use a different lifecycle configuration when launching from the console. For information about changing the default lifecycle configuration after launching a JupyterServer application, see [Set Default Lifecycle Configurations for Amazon SageMaker Studio Classic](studio-lcc-defaults.md).
To select a different attached lifecycle configuration, you must launch with the AWS CLI. For more information about launching a JupyterServer application with an attached lifecycle configuration from the AWS CLI, see [Create a Lifecycle Configuration from the AWS CLI for Amazon SageMaker Studio Classic](studio-lcc-create-cli.md).
+ **KernelGateway**: You can select any of the attached lifecycle configurations when launching a KernelGateway application using the Studio Classic Launcher.
The following procedure describes how to launch a KernelGateway application with an attached lifecycle configuration from the SageMaker AI console.
1. Open the Amazon SageMaker AI console at [https://console.aws.amazon.com/sagemaker/](https://console.aws.amazon.com/sagemaker/).
1. Launch Studio Classic. For more information, see [Launch Amazon SageMaker Studio Classic](studio-launch.md).
1. In the Studio Classic UI, open the Studio Classic Launcher. For more information, see [Use the Amazon SageMaker Studio Classic Launcher](studio-launcher.md).
1. In the Studio Classic Launcher, navigate to the **Notebooks and compute resources** section.
1. Click the **Change environment** button.
1. On the **Change environment** dialog, use the dropdown menus to select your **Image**, **Kernel**, **Instance type**, and a **Start-up script**. If there is no default lifecycle configuration, the **Start-up script** value defaults to `No script`. Otherwise, the **Start-up script** value is your default lifecycle configuration. After you select a lifecycle configuration, you can view the entire script.
1. Click **Select**.
1. Back to the Launcher, click the **Create notebook** to launch a new notebook kernel with your selected image and lifecycle configuration.
## Step 4: View logs for a lifecycle configuration
You can view the logs for your lifecycle configuration after it has been attached to a domain or user profile.
1. First, provide access to CloudWatch for your AWS Identity and Access Management (IAM) role. Add read permissions for the following log group and log stream.
+ **Log group:**`/aws/sagemaker/studio`
+ **Log stream:**`domain/user-profile/app-type/app-name/LifecycleConfigOnStart`
For information about adding permissions, see [Enabling logging from certain AWS services](https://docs.aws.amazon.com/AmazonCloudWatch/latest/logs/AWS-logs-and-resource-policy.html).
1. From within Studio Classic, navigate to the **Running Terminals and Kernels** icon (![\[Black square icon representing a placeholder or empty image.\]](http://docs.aws.amazon.com/sagemaker/latest/dg/images/studio/icons/running-terminals-kernels.png)) to monitor your lifecycle configuration.
1. Select an application from the list of running applications. Applications with attached lifecycle configurations have an attached indicator icon ![\[Code brackets symbol representing programming or markup languages.\]](http://docs.aws.amazon.com/sagemaker/latest/dg/images/studio/studio-lcc-indicator-icon.png).
1. Select the indicator icon for your application. This opens a new panel that lists the lifecycle configuration.
1. From the new panel, select `View logs`. This opens a new tab that displays the logs.