# Perform Common Tasks in 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).
The following sections describe how to perform common tasks in Amazon SageMaker Studio Classic. For an overview of the Studio Classic interface, see [Amazon SageMaker Studio Classic UI Overview](studio-ui.md).
**Topics**
+ [Upload Files to Amazon SageMaker Studio Classic](studio-tasks-files.md)
+ [Clone a Git Repository in Amazon SageMaker Studio Classic](studio-tasks-git.md)
+ [Stop a Training Job in Amazon SageMaker Studio Classic](studio-tasks-stop-training-job.md)
+ [Use TensorBoard in Amazon SageMaker Studio Classic](studio-tensorboard.md)
+ [Use Amazon Q Developer with Amazon SageMaker Studio Classic](sm-q.md)
+ [Manage Your Amazon EFS Storage Volume in Amazon SageMaker Studio Classic](studio-tasks-manage-storage.md)
+ [Provide Feedback on Amazon SageMaker Studio Classic](studio-tasks-provide-feedback.md)
+ [Shut Down and Update Amazon SageMaker Studio Classic and Apps](studio-tasks-update.md)
# Upload Files to 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).
When you onboard to Amazon SageMaker Studio Classic, a home directory is created for you in the Amazon Elastic File System (Amazon EFS) volume that was created for your team. Studio Classic can only open files that have been uploaded to your directory. The Studio Classic file browser maps to your home directory.
**Note**
Studio Classic does not support uploading folders. While you can only upload individual files, you can upload multiple files at the same time.
**To upload files to your home directory**
1. In the left sidebar, choose the **File Browser** icon ( ![\[Black square icon representing a placeholder or empty image.\]](http://docs.aws.amazon.com/sagemaker/latest/dg/images/studio/icons/folder.png)).
1. In the file browser, choose the **Upload Files** icon (![\[Black square icon representing a placeholder or empty image.\]](http://docs.aws.amazon.com/sagemaker/latest/dg/images/icons/File_upload_squid.png)).
1. Select the files you want to upload and then choose **Open**.
1. Double-click a file to open the file in a new tab in Studio Classic.
# Clone a Git Repository in 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 Studio Classic can only connect only to a local Git repository (repo). This means that you must clone the Git repo from within Studio Classic to access the files in the repo. Studio Classic offers a Git extension for you to enter the URL of a Git repo, clone it into your environment, push changes, and view commit history. If the repo is private and requires credentials to access, then you are prompted to enter your user credentials. This includes your username and personal access token. For more information about personal access tokens, see [Managing your personal access tokens](https://docs.github.com/en/authentication/keeping-your-account-and-data-secure/managing-your-personal-access-tokens).
Admins can also attach suggested Git repository URLs at the Amazon SageMaker AI domain or user profile level. Users can then select the repo URL from the list of suggestions and clone that into Studio Classic. For more information about attaching suggested repos, see [Attach Suggested Git Repos to Amazon SageMaker Studio Classic](studio-git-attach.md).
The following procedure shows how to clone a GitHub repo from Studio Classic.
**To clone the repo**
1. In the left sidebar, choose the **Git** icon ( ![\[Black square icon representing a placeholder or empty image.\]](http://docs.aws.amazon.com/sagemaker/latest/dg/images/studio/icons/git.png)).
1. Choose **Clone a Repository**. This opens a new window.
1. In the **Clone Git Repository** window, enter the URL in the following format for the Git repo that you want to clone or select a repository from the list of **Suggested repositories**.
```
https://github.com/path-to-git-repo/repo.git
```
1. If you entered the URL of the Git repo manually, select **Clone "*git-url*"** from the dropdown menu.
1. Under **Project directory to clone into**, enter the path to the local directory that you want to clone the Git repo into. If this value is left empty, Studio Classic clones the repo into JupyterLab's root directory.
1. Choose **Clone**. This opens a new terminal window.
1. If the repo requires credentials, you are prompted to enter your username and personal access token. This prompt does not accept passwords, you must use a personal access token. For more information about personal access tokens, see [Managing your personal access tokens](https://docs.github.com/en/authentication/keeping-your-account-and-data-secure/managing-your-personal-access-tokens).
1. Wait for the download to finish. After the repo has been cloned, the **File Browser** opens to display the cloned repo.
1. Double click the repo to open it.
1. Choose the **Git** icon to view the Git user interface which now tracks the repo.
1. To track a different repo, open the repo in the file browser and then choose the **Git** icon.
# Stop a Training Job in 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).
You can stop a training job with the Amazon SageMaker Studio Classic UI. When you stop a training job, its status changes to `Stopping` at which time billing ceases. An algorithm can delay termination in order to save model artifacts after which the job status changes to `Stopped`. For more information, see the [stop\$1training\$1job](https://boto3.amazonaws.com/v1/documentation/api/latest/reference/services/sagemaker.html#SageMaker.Client.stop_training_job) method in the AWS SDK for Python (Boto3).
**To stop a training job**
1. Follow the [View experiments and runs](experiments-view-compare.md) procedure on this page until you open the **Describe Trial Component** tab.
1. At the upper-right side of the tab, choose **Stop training job**. The **Status** at the top left of the tab changes to **Stopped**.
1. To view the training time and billing time, choose **AWS Settings**.
# Use TensorBoard in 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).
The following doc outlines how to install and run TensorBoard in Amazon SageMaker Studio Classic.
**Note**
This guide shows how to open the TensorBoard application through a SageMaker Studio Classic notebook server of an individual SageMaker AI domain user profile. For a more comprehensive TensorBoard experience integrated with SageMaker Training and the access control functionalities of SageMaker AI domain, see [TensorBoard in Amazon SageMaker AI](tensorboard-on-sagemaker.md).
## Prerequisites
This tutorial requires a SageMaker AI domain. For more information, see [Amazon SageMaker AI domain overview](gs-studio-onboard.md)
## Set Up `TensorBoardCallback`
1. Launch Studio Classic, and open the Launcher. For more information, see [Use the Amazon SageMaker Studio Classic Launcher](studio-launcher.md)
1. In the Amazon SageMaker Studio Classic Launcher, under `Notebooks and compute resources`, choose the **Change environment** button.
1. On the **Change environment** dialog, use the dropdown menus to select the `TensorFlow 2.6 Python 3.8 CPU Optimized` Studio Classic **Image**.
1. Back to the Launcher, click the **Create notebook** tile. Your notebook launches and opens in a new Studio Classic tab.
1. Run this code from within your notebook cells.
1. Import the required packages.
```
import os
import datetime
import tensorflow as tf
```
1. Create a Keras model.
```
mnist = tf.keras.datasets.mnist
(x_train, y_train),(x_test, y_test) = mnist.load_data()
x_train, x_test = x_train / 255.0, x_test / 255.0
def create_model():
return tf.keras.models.Sequential([
tf.keras.layers.Flatten(input_shape=(28, 28)),
tf.keras.layers.Dense(512, activation='relu'),
tf.keras.layers.Dropout(0.2),
tf.keras.layers.Dense(10, activation='softmax')
])
```
1. Create a directory for your TensorBoard logs
```
LOG_DIR = os.path.join(os.getcwd(), "logs/fit/" + datetime.datetime.now().strftime("%Y%m%d-%H%M%S"))
```
1. Run training with TensorBoard.
```
model = create_model()
model.compile(optimizer='adam',
loss='sparse_categorical_crossentropy',
metrics=['accuracy'])
tensorboard_callback = tf.keras.callbacks.TensorBoard(log_dir=LOG_DIR, histogram_freq=1)
model.fit(x=x_train,
y=y_train,
epochs=5,
validation_data=(x_test, y_test),
callbacks=[tensorboard_callback])
```
1. Generate the EFS path for the TensorBoard logs. You use this path to set up your logs from the terminal.
```
EFS_PATH_LOG_DIR = "/".join(LOG_DIR.strip("/").split('/')[1:-1])
print (EFS_PATH_LOG_DIR)
```
Retrieve the `EFS_PATH_LOG_DIR`. You will need it in the TensorBoard installation section.
## Install TensorBoard
1. Click on the `Amazon SageMaker Studio Classic` button on the top left corner of Studio Classic to open the Amazon SageMaker Studio Classic Launcher. This launcher must be opened from your root directory. For more information, see [Use the Amazon SageMaker Studio Classic Launcher](studio-launcher.md)
1. In the Launcher, under `Utilities and files`, click `System terminal`.
1. From the terminal, run the following commands. Copy `EFS_PATH_LOG_DIR` from the Jupyter notebook. You must run this from the `/home/sagemaker-user` root directory.
```
pip install tensorboard
tensorboard --logdir
```
## Launch TensorBoard
1. To launch TensorBoard, copy your Studio Classic URL and replace `lab?` with `proxy/6006/` as follows. You must include the trailing `/` character.
```
https://.studio.region.sagemaker.aws/jupyter/default/proxy/6006/
```
1. Navigate to the URL to examine your results.
# Use Amazon Q Developer 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 Studio Classic is an integrated machine learning environment where you can build, train, deploy, and analyze your models all in the same application. You can generate code recommendations and suggest improvements related to code issues by using Amazon Q Developer with Amazon SageMaker AI.
Amazon Q Developer is a generative artificial intelligence (AI) powered conversational assistant that can help you understand, build, extend, and operate AWS applications. In the context of an integrated AWS coding environment, Amazon Q can generate code recommendations based on developers' code, as well as their comments in natural language.
Amazon Q has the most support for Java, Python, JavaScript, TypeScript, C\$1, Go, PHP, Rust, Kotlin, and SQL, as well as the Infrastructure as Code (IaC) languages JSON (CloudFormation), YAML (CloudFormation), HCL (Terraform), and CDK (Typescript, Python). It also supports code generation for Ruby, C\$1\$1, C, Shell, and Scala. For examples of how Amazon Q integrates with Amazon SageMaker AI and displays code suggestions in the Amazon SageMaker Studio Classic IDE, see [Code Examples](https://docs.aws.amazon.com/amazonq/latest/qdeveloper-ug/inline-suggestions-code-examples.html) in the *Amazon Q Developer User Guide*.
For more information on using Amazon Q with Amazon SageMaker Studio Classic, see the [Amazon Q Developer User Guide](https://docs.aws.amazon.com/amazonq/latest/qdeveloper-ug/sagemaker-setup.html).
# Manage Your Amazon EFS Storage Volume in 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).
The first time a user on your team onboards to Amazon SageMaker Studio Classic, Amazon SageMaker AI creates an Amazon Elastic File System (Amazon EFS) volume for the team. A home directory is created in the volume for each user who onboards to Studio Classic as part of your team. Notebook files and data files are stored in these directories. Users don't have access to other team member's home directories. Amazon SageMaker AI domain does not support mounting custom or additional Amazon EFS volumes.
**Important**
Don't delete the Amazon EFS volume. If you delete it, the domain will no longer function and all of your users will lose their work.
**To find your Amazon EFS volume**
1. Open the [SageMaker AI console](https://console.aws.amazon.com/sagemaker/).
1. On the left navigation pane, choose **Admin configurations**.
1. Under **Admin configurations**, choose **domains**.
1. From the **Domains** page, select the domain to find the ID for.
1. From the **Domain details** page, select the **Domain settings** tab.
1. Under **General settings**, find the **Domain ID**. The ID will be in the following format: `d-xxxxxxxxxxxx`.
1. Pass the `Domain ID`, as `DomainId`, to the [describe\$1domain](https://boto3.amazonaws.com/v1/documentation/api/latest/reference/services/sagemaker.html#SageMaker.Client.describe_domain) method.
1. In the response from `describe_domain`, note the value for the `HomeEfsFileSystemId` key. This is the Amazon EFS file system ID.
1. Open the [Amazon EFS console](https://console.aws.amazon.com/efs#/file-systems/). Make sure the AWS Region is the same Region that's used by Studio Classic.
1. Under **File systems**, choose the file system ID from the previous step.
1. To verify that you've chosen the correct file system, select the **Tags** heading. The value corresponding to the `ManagedByAmazonSageMakerResource` key should match the `Studio Classic ID`.
For information on how to access the Amazon EFS volume, see [Using file systems in Amazon EFS](https://docs.aws.amazon.com/efs/latest/ug/using-fs.html).
To delete the Amazon EFS volume, see [Deleting an Amazon EFS file system](https://docs.aws.amazon.com/efs/latest/ug/delete-efs-fs.html).
# Provide Feedback on 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 takes your feedback seriously. We encourage you to provide feedback.
**To provide feedback**
1. At the right of SageMaker Studio Classic, find the **Feedback** icon (![\[Speech bubble icon representing messaging or communication functionality.\]](http://docs.aws.amazon.com/sagemaker/latest/dg/images/studio/icons/feedback.png)).
1. Choose a smiley emoji to let us know how satisfied you are with SageMaker Studio Classic and add any feedback you'd care to share with us.
1. Decide whether to share your identity with us, then choose **Submit**.
# Shut Down and Update Amazon SageMaker Studio Classic and Apps
**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 topics show how to shut down and update SageMaker Studio Classic and Studio Classic Apps.
Studio Classic provides a notification icon (![\[Red circle icon with white exclamation mark, indicating an alert or warning.\]](http://docs.aws.amazon.com/sagemaker/latest/dg/images/icons/Notification.png)) in the upper-right corner of the Studio Classic UI. This notification icon displays the number of unread notices. To read the notices, select the icon.
Studio Classic provides two types of notifications:
+ Upgrade – Displayed when Studio Classic or one of the Studio Classic apps have released a new version. To update Studio Classic, see [Shut Down and Update Amazon SageMaker Studio Classic](studio-tasks-update-studio.md). To update Studio Classic apps, see [Shut Down and Update Amazon SageMaker Studio Classic Apps](studio-tasks-update-apps.md).
+ Information – Displayed for new features and other information.
To reset the notification icon, you must select the link in each notice. Read notifications may still display in the icon. This does not indicate that updates are still needed after you have updated Studio Classic and Studio Classic Apps.
To learn how to update [Amazon SageMaker Data Wrangler](https://docs.aws.amazon.com/sagemaker/latest/dg/data-wrangler.html), see [Shut Down and Update Amazon SageMaker Studio Classic Apps](studio-tasks-update-apps.md).
To ensure that you have the most recent software updates, update Amazon SageMaker Studio Classic and your Studio Classic apps using the methods outlined in the following topics.
**Topics**
+ [Shut Down and Update Amazon SageMaker Studio Classic](studio-tasks-update-studio.md)
+ [Shut Down and Update Amazon SageMaker Studio Classic Apps](studio-tasks-update-apps.md)
# Shut Down and Update 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).
To update Amazon SageMaker Studio Classic to the latest release, you must shut down the JupyterServer app. You can shut down the JupyterServer app from the SageMaker AI console, from Amazon SageMaker Studio or from within Studio Classic. After the JupyterServer app is shut down, you must reopen Studio Classic through the SageMaker AI console or from Studio which creates a new version of the JupyterServer app.
You cannot delete the JupyterServer application while the Studio Classic UI is still open in the browser. If you delete the JupyterServer application while the Studio Classic UI is still open in the browser, SageMaker AI automatically re-creates the JupyterServer application.
Any unsaved notebook information is lost in the process. The user data in the Amazon EFS volume isn't impacted.
Some of the services within Studio Classic, like Data Wrangler, run on their own app. To update these services you must delete the app for that service. To learn more, see [Shut Down and Update Amazon SageMaker Studio Classic Apps](studio-tasks-update-apps.md).
**Note**
A JupyterServer app is associated with a single Studio Classic user. When you update the app for one user it doesn't affect other users.
The following page shows how to update the JupyterServer App from the SageMaker AI console, from Studio, or from inside Studio Classic.
## Shut down and update from the SageMaker AI console
1. Navigate to [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. Select the domain that includes the Studio Classic application that you want to update.
1. Under **User profiles**, select your user name.
1. Under **Apps**, in the row displaying **JupyterServer**, choose **Action**, then choose **Delete**.
1. Choose **Yes, delete app**.
1. Type **delete** in the confirmation box.
1. Choose **Delete**.
1. After the app has been deleted, launch a new Studio Classic app to get the latest version.
## Shut down and update from Studio
1. Navigate to Studio following the steps in [Launch Amazon SageMaker Studio](studio-updated-launch.md).
1. From the Studio UI, find the applications pane on the left side.
1. From the applications pane, select **Studio Classic**.
1. From the Studio Classic landing page, select the Studio Classic instance to stop.
1. Choose **Stop**.
1. After the app has been stopped, select **Run** to use the latest version.
## Shut down and update from inside Studio Classic
1. Launch Studio Classic.
1. On the top menu, choose **File** then **Shut Down**.
1. Choose one of the following options:
+ **Shutdown Server** – Shuts down the JupyterServer app. Terminal sessions, kernel sessions, SageMaker images, and instances aren't shut down. These resources continue to accrue charges.
+ **Shutdown All** – Shuts down all apps, terminal sessions, kernel sessions, SageMaker images, and instances. These resources no longer accrue charges.
1. Close the window.
1. After the app has been deleted, launch a new Studio Classic app to use the latest version.
# Shut Down and Update Amazon SageMaker Studio Classic Apps
**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).
To update an Amazon SageMaker Studio Classic app to the latest release, you must first shut down the corresponding KernelGateway app from the SageMaker AI console. After the KernelGateway app is shut down, you must reopen it through SageMaker Studio Classic by running a new kernel. The kernel automatically updates. Any unsaved notebook information is lost in the process. The user data in the Amazon EFS volume isn't impacted.
After an application has been shut down for 24 hours, SageMaker AI deletes all metadata for the application. To be considered an update and retain application metadata, applications must be restarted within 24 hours after the previous application has been shut down. After this time window, creation of an application is considered a new application rather than an update of the previous application.
**Note**
A KernelGateway app is associated with a single Studio Classic user. When you update the app for one user it doesn't effect other users.
**To update the KernelGateway app**
1. Navigate to [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. Select the domain that includes the application that you want to update.
1. Under **User profiles**, select your user name.
1. Under **Apps**, in the row displaying the **App name**, choose **Action**, then choose **Delete**
To update Data Wrangler, delete the app that starts with **sagemaker-data-wrang**.
1. Choose **Yes, delete app**.
1. Type **delete** in the confirmation box.
1. Choose **Delete**.
1. After the app has been deleted, launch a new kernel from within Studio Classic to use the latest version.