Launch Amazon SageMaker Studio

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.

AWS managed policies for Amazon SageMaker AI 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 updated Studio experience. For information about using the Studio Classic application, see Amazon SageMaker Studio Classic.

This page's topics demonstrate how to launch Amazon SageMaker Studio from the Amazon SageMaker AI console and the AWS Command Line Interface (AWS CLI).

Prerequisites

Before you begin, complete the following prerequisites:

• Onboard to a SageMaker AI domain with Studio access. If you don't have permissions to set Studio as the default experience for your domain, contact your administrator. For more information, see Amazon SageMaker AI domain overview.

• Update the AWS CLI by following the steps in Installing the current AWS CLI Version.

• From your local machine, run aws configure and provide your AWS credentials. For information about AWS credentials, see Understanding and getting your AWS credentials.

Service quotas for Studio

Studio workloads are subject to service quotas. These quotas limit the number of instances you can use or concurrent jobs you can run. Quotas apply to different Studio features including endpoint deployments, training jobs, JupyterLab, and CodeEditor.

After you verify your account, you receive default quotas for supported GPU instance types. You can start using these instances without requesting an increase.

View your Studio quotas using the

Use the Service Quotas console to check your current quota limits.

1. Open the Service Quotas console at https://console.aws.amazon.com/servicequotas/.

2. In the left navigation pane, choose AWS services.

3. From the AWS services list, search for and choose Amazon SageMaker AI.

4. In the Service quotas list, review the quota name, applied value, AWS default quota, and whether you can adjust the quota.

5. Search for quotas relevant to your Studio workload. For example, search for the instance type you plan to use.

Note

Quota limits are also visible within Studio when you choose compute resources. If your quota is too low, choose the limit increase message. This opens the Service Quotas console, where you can request an increase.

Request a quota increase

If your default quota is too low, you can request an increase.

1. In the Service Quotas console, choose the quota you want to increase.

2. If the quota is adjustable, choose Request increase at account level.

3. For Increase quota value, enter the new value. The new value must be greater than the current value.

4. Choose Request.

5. To view pending or recently resolved requests, choose the Request history tab.

6. For pending requests, choose the status to open the receipt. When the status changes to Quota requested, you see the AWS Support case number.

For more information about Amazon SageMaker AI quotas, see Amazon SageMaker AI endpoints and quotas in the AWS General Reference. For more information about requesting a quota increase, see Requesting a quota increase in the Service Quotas User Guide.

Launch from the Amazon SageMaker AI console

Complete the following procedure to launch Studio from the Amazon SageMaker AI console.

1. Open the Amazon SageMaker AI console at https://console.aws.amazon.com/sagemaker/.

2. From the left navigation pane, choose Studio.

3. From the Studio landing page, select the domain and user profile for launching Studio.

4. Choose Open Studio.

5. To launch Studio, choose Launch personal Studio.

Launch using the AWS CLI

This section demonstrates how to launch Studio using the AWS CLI. The procedure to access Studio using the AWS CLI depends if the domain uses AWS Identity and Access Management (IAM) authentication or AWS IAM Identity Center authentication. You can use the AWS CLI to launch Studio by creating a presigned domain URL when your domain uses IAM authentication. For information about launching Studio with IAM Identity Center authentication, see Use custom setup for Amazon SageMaker AI.

Launch if Studio is the default experience

The following code snippet demonstrates how to launch Studio from the AWS CLI using a presigned domain URL if Studio is the default experience. For more information, see create-presigned-domain-url.

aws sagemaker create-presigned-domain-url \
--region region \
--domain-id domain-id \
--user-profile-name user-profile-name \
--session-expiration-duration-in-seconds 43200

Launch if Amazon SageMaker Studio Classic is your default experience

The following code snippet demonstrates how to launch Studio from the AWS CLI using a presigned domain URL if Studio Classic is the default experience. For more information, see create-presigned-domain-url.

aws sagemaker create-presigned-domain-url \
--region region \
--domain-id domain-id \
--user-profile-name user-profile-name \
--session-expiration-duration-in-seconds 43200 \
--landing-uri studio::