Use custom setup for Amazon SageMaker AI - Amazon SageMaker AI
Authentication methodsCustom setupAccess the domain after onboarding

Use custom setup for Amazon SageMaker AI

The Set up for organizations (custom setup) guides you through an advanced setup for your Amazon SageMaker AI domain. This option provides information and recommendations to help you understand and control all aspects of the account configuration, including permissions, integrations, and encryption. Use this option if you want to set up a custom domain. For information about domains, see Amazon SageMaker AI domain overview.

Authentication methods

Before you set up the domain consider the authentication methods for your users to access the domain.

AWS Identity Center:

  • Helps simplify administration of access permissions to groups of users. You can grant or deny permissions to groups of users, instead of applying those permissions to each individual user. If a user moves to a different organization, you can move that user to a different AWS Identity and Access Management Identity center (AWS IAM Identity Center) group. The user then automatically receives the permissions that are needed for the new organization.

    Note that the IAM Identity Center needs to be in the same AWS Region as the domain.

    To set up with IAM Identity Center, use the following instructions from the AWS IAM Identity Center User Guide:

  • The users in IAM Identity Center can access the domain using an AWS access portal URL that is emailed to them. The email provides instructions to create an account to access the domain. For more information, see Sign in to the AWS access portal.

    As an administrator you can find the AWS access portal URL by navigating to the IAM Identity Center and finding the AWS access portal URL under Settings summary.

  • Your domain must use AWS Identity and Access Management (IAM) authentication if you wish to restrict access to your domains exclusively to particular Amazon Virtual Private Clouds (VPCs), interface endpoints, or a predefined set of IP addresses. This feature is not supported for domains that use IAM Identity Center authentication. You can still use IAM Identity Center to enable centralized workforce identity control. For instructions on how to implement these restrictions while keeping IAM Identity Center to provide a consistent user sign-in experience, see Secure access to Amazon SageMaker Studio Classic with IAM Identity Center and a SAML application in the AWS machine learning blog. Note that AWS SSO is IAM Identity Center in this blog.

Login through IAM:

  • The user profiles can access the domain through the SageMaker AI console after logging into the account.

  • You can restrict access to your domains exclusively to particular Amazon Virtual Private Clouds (VPCs), interface endpoints, or a predefined set of IP addresses when using AWS Identity and Access Management (IAM) authentication. For more information, see Allow Access Only from Within Your VPC.

Setup for organizations (custom setup)

After satisfying the prerequisites in Complete Amazon SageMaker AI prerequisites, open the Set up SageMaker AI Domain (custom setup) page and expand the following sections for information on the setup.

Open the Set up SageMaker AI Domain from the SageMaker AI console

  1. Open the SageMaker AI console.

  2. On the left navigation pane, choose Admin configurations to expand the options.

  3. Under Admin configurations, choose Domains.

  4. From the Domains page, choose Create domain.

  5. On the Set up SageMaker AI domain page, choose Set up for organizations.

  6. Choose Set up.

Once you opened the Set up SageMaker AI Domain page, use the following instructions:

  1. For Domain name, enter a unique name for your domain. For example, this can be your project or team name.

  2. Choose Next.

In this step you set up the authentication method, users, and permissions for your domain.

  1. Under How do you want to access Studio?, you can choose one of two options. For information on the authentication methods, see Authentication methods. Details on the options are provided in the following:

    • AWS Identity Center:

      Under Who will use Studio? choose an AWS IAM Identity Center group that will access the domain.

      If you choose No Identity Center user group you create a domain with no users. You can add IAM Identity Center groups to the domain after the domain's creation. For more information, see Edit domain settings.

    • Login through IAM:

      Under Who will use Studio? choose + Add user, enter a new user profile name, and choose Add to create and add a user profile name.

      You can repeat this process to create multiple user profiles.

  2. Under Who will use Studio? select the IAM Identity Center users or groups, then choose Select. You need to set up Amazon SageMaker Studio within the same Region in which your IAM Identity Center is configured. You can change the Region of your domain by choosing the Region from the dropdown list on the top right of the console or you can change your IAM Identity Center Region by navigating to the AWS access portal.

  3. Under What ML activities do they perform? you can use an existing role by choosing Use an existing role or you can create a new role by choosing Create a new role and checking the ML activities you want the role to have access.

  4. While selecting ML activities, you may need to satisfy requirements. To satisfy a requirement, choose Add and complete the requirement.

  5. After all requirements are satisfied, choose Next.

In this step, you can configure the applications you have enabled in the previous step. For more information on the ML activities, see ML activity reference.

If the application has not been enabled, you receive a warning for that application. To enable an application that has not been enabled, return to the previous step by choosing Back and follow the previous instructions.

  • Studio configuration:

    Under Studio, you have the option to choose between the newer and classic version of Studio as your default experience. This means choosing which ML environment you interact with when you open Studio.

    • Studio includes multiple integrated development environments (IDEs) and applications, including Amazon SageMaker Studio Classic. If chosen, the Studio Classic IDE has default settings. For information on the default settings, see Default settings.

      For information on Studio, see Amazon SageMaker Studio.

    • Studio Classic includes the Jupyter IDE. If chosen, you may configure your Studio Classic configuration.

      For information on Studio Classic, see Amazon SageMaker Studio Classic.

  • SageMaker Canvas configuration:

    If you have Amazon SageMaker Canvas enabled, see Getting started with using Amazon SageMaker Canvas for the instructions and configuration details for onboarding.

  • Studio Classic configuration:

    If you chose Studio (recommended) as your default experience, the Studio Classic IDE has default settings. For information on the default settings, see Default settings.

    If you chose Studio Classic as your default experience, you can choose to enable or disable notebook resource sharing. Notebook resources include artifacts such as cell output and Git repositories. For more information on Notebook resources, see Share and Use an Amazon SageMaker Studio Classic Notebook.

    If you enabled notebook resource sharing:

    1. Under S3 location for shareable notebook resources, input your Amazon S3 location.

    2. Under Encryption key - optional, leave as No Custom Encryption or choose an existing AWS KMS key or choose Enter a KMS key ARN and enter your AWS KMS key's ARN.

    3. Under Notebook cell output sharing preference, choose Allow users to share cell output or Disable cell output sharing.

  • RStudio configuration:

    To enable RStudio, you need an RStudio license. To set that up, see Get an RStudio license.

    1. Under RStudio Workbench, verify that your RStudio license is automatically detected. For more information about getting an RStudio license and activating it with SageMaker AI, see Get an RStudio license.

    2. Select an instance type to launch your RStudio Server on. For more information, see RStudioServerPro instance type.

    3. Under Permission, create your role or select an existing role. The role must have the following permissions policy. This policy allows the RStudioServerPro application to access necessary resources. It also allows Amazon SageMaker AI to automatically launch an RStudioServerPro application when the existing RStudioServerPro application is in a Deleted or Failed status. For information about adding permissions to a role, see Modifying a role permissions policy (console).

      {
    "Version": "2012-10-17",
    "Statement": [
        {
            "Sid": "VisualEditor0",
            "Effect": "Allow",
            "Action": [
                "license-manager:ExtendLicenseConsumption",
                "license-manager:ListReceivedLicenses",
                "license-manager:GetLicense",
                "license-manager:CheckoutLicense",
                "license-manager:CheckInLicense",
                "logs:CreateLogDelivery",
                "logs:CreateLogGroup",
                "logs:CreateLogStream",
                "logs:DeleteLogDelivery",
                "logs:Describe*",
                "logs:GetLogDelivery",
                "logs:GetLogEvents",
                "logs:ListLogDeliveries",
                "logs:PutLogEvents",
                "logs:PutResourcePolicy",
                "logs:UpdateLogDelivery",
                "sagemaker:CreateApp"
            ],
            "Resource": "*"
        }
    ]
}

    4. Under RStudio Connect, add the URL for your RStudio Connect server. RStudio Connect is a publishing platform for Shiny applications, R Markdown reports, dashboards, plots, and more. When you onboard to RStudio on SageMaker AI, an RStudio Connect server is not created. For more information, see Add an RStudio Connect URL.

    5. Under RStudio Package Manager, add the URL for your RStudio Package Manager. SageMaker AI creates a default package repository for the Package Manager when you onboard RStudio. For more information about RStudio Package Manager, see Update the RStudio Package Manager URL.

    6. Select Next.

  • Code Editor configuration:

    If you have Code Editor enabled, see Code Editor in Amazon SageMaker Studio for an overview and the configuration details.

In this section you can customize the viewable applications and machine learning (ML) tools displayed in Studio. This customization only hides the applications and ML tools in the left navigation pane in Studio. For information on the Studio UI, see Amazon SageMaker Studio UI overview.

For information about the applications, see Applications supported in Amazon SageMaker Studio.

The customize Studio UI feature is not available in Studio Classic. If you wish to set Studio as your default experience, choose Previous and to return to the previous step.

  1. On the Customize Studio UI page you can hide applications and ML tools displayed in Studio by toggling them off.

  2. Once you have reviewed your changes, choose Next.

Choose how you want Studio to connect to other AWS services.

You can choose to disable internet access to your Studio by specifying using Virtual Private Cloud (VPC) Only network access type. If you choose this option, you cannot run a Studio notebook unless your VPC has an interface endpoint to the SageMaker API and runtime, or a Network Address Translation (NAT) gateway with internet access, and your security groups allow outbound connections. For more information on Amazon VPCs, see Choose an Amazon VPC.

If you choose Virtual Private Cloud (VPC) Only the following steps are required. If you choose Public internet access, the first two of the following steps are required.

  1. Under VPC, choose the Amazon VPC ID.

  2. Under Subnet, choose one or more subnets. If you don't choose any subnets, SageMaker AI uses all the subnets in the Amazon VPC. We recommend that you use multiple subnets that are not created in constrained Availability Zones. Using subnets in these constrained Availability Zones can result in insufficient capacity errors and longer application creation times. For more information about constrained Availability Zones, see Availability Zones.

  3. Under Security group(s), choose one or more subnets.

If VPC only is selected, SageMaker AI automatically applies the security group settings defined for the domain to all shared spaces created in the domain. If Public internet only is selected, SageMaker AI does not apply the security group settings to shared spaces created in the domain.

You have the option to encrypt your data. The Amazon Elastic File System (Amazon EFS) and Amazon Elastic Block Store (Amazon EBS) file systems that are created for you when you create a domain. Amazon EBS sizes are used by both Code Editor and JupyterLab spaces.

You cannot change the encryption key after you encrypt your Amazon EFS and Amazon EBS file systems. To encrypt your Amazon EFS and Amazon EBS file systems, you can use the following configurations.

  • Under Encryption key - optional, leave as No Custom Encryption or choose an existing KMS key or choose Enter a KMS key ARN and enter the ARN of your KMS key.

  • Under Default space size - optional, enter the default space size.

  • Under Maximum space size - optional, enter the maximum space size.

Review your domain settings. If you need to change the settings, choose Edit next to the relevant step. Once you confirm that your domain settings are accurate, choose Submit and the domain is created for you. This process may take a few minutes.

The following sections provide AWS CLI instructions for the custom setup your domain using the IAM Identity Center or IAM authentication methods.

After satisfying the prerequisites, including setting up your AWS CLI credentials, in Complete Amazon SageMaker AI prerequisites, use the following the steps.

  1. Create an execution role that is used to create a domain and attach the AmazonSageMakerFullAccess policy. You can also use an existing role that has, at a minimum, an attached trust policy that grants SageMaker AI permission to assume the role. For more information, see How to use SageMaker AI execution roles.

    aws iam create-role --role-name execution-role-name --assume-role-policy-document file://execution-role-trust-policy.json
aws iam attach-role-policy --role-name execution-role-name --policy-arn arn:aws:iam::aws:policy/AmazonSageMakerFullAccess

  2. Get the default Amazon Virtual Private Cloud (Amazon VPC) of your account.

    aws --region region ec2 describe-vpcs --filters Name=isDefault,Values=true --query "Vpcs[0].VpcId" --output text

  3. Get the list of subnets in the default Amazon VPC.

    aws --region region ec2 describe-subnets --filters Name=vpc-id,Values=default-vpc-id --query "Subnets[*].SubnetId" --output json

  4. Create a domain by passing the default Amazon VPC ID, subnets, and execution role ARN. You must also pass a SageMaker AI image ARN. For information on the available JupyterLab version ARNs, see Setting a default JupyterLab version.

    For authentication-mode, use SSO for IAM Identity Center authentication or IAM for IAM authentication.

    aws --region region sagemaker create-domain --domain-name domain-name --vpc-id default-vpc-id --subnet-ids subnet-ids --auth-mode authentication-mode --default-user-settings "ExecutionRole=arn:aws:iam::account-number:role/execution-role-name,JupyterServerAppSettings={DefaultResourceSpec={InstanceType=system,SageMakerImageArn=image-arn}}" \ --query DomainArn --output text

    You can use the AWS CLI to customize the applications and ML tools displayed in Studio for the domain, using StudioWebPortalSettings. Use HiddenAppTypes to hide applications and HiddenMlTools to hide ML tools. For more information on customizing the left navigation of the Studio UI, see Hide machine learning tools and applications in the Amazon SageMaker Studio UI. This feature is not available for Studio Classic.

  5. Verify that the domain has been created.

    aws --region region sagemaker list-domains

For information about creating a domain using AWS CloudFormation, see AWS::SageMaker::Domain in the AWS CloudFormation User Guide.

For an example of an AWS CloudFormation template that you can use to set up your domain, see Creating Amazon SageMaker AI domains using AWS CloudFormation in the aws-samples GitHub repository.

After the domain is set up, the administrative user can view and edit the domain. For information, see View domains and Edit domain settings.

Access the domain after onboarding

The users can access SageMaker AI using: