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Secure multi-account model deployment with Amazon SageMaker: Part 1

Amazon SageMaker Studio is a web-based, integrated development environment (IDE) for machine learning (ML) that lets you build, train, debug, deploy, and monitor your ML models. Although Studio provides all the tools you need to take your models from experimentation to production, you need a robust and secure model deployment process. This process must fulfill…

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Amazon SageMaker Studio is a web-based, integrated development environment (IDE) for machine learning (ML) that lets you build, train, debug, deploy, and monitor your ML models.

Although Studio provides all the tools you need to take your models from experimentation to production, you need a robust and secure model deployment process. This process must fulfill your organization’s operational and security requirements.

Amazon SageMaker and Studio provide a wide range of specialized functionality for building highly secure, scalable, and flexible MLOps platforms to cover your model deployment use cases and requirements. Three SageMaker services, SageMaker Pipelines, SageMaker Projects, and SageMaker Model Registry, build a foundation to implement enterprise-grade secure multi-account model deployment workflow.

In combination with other AWS services, such as Amazon Virtual Private Cloud (Amazon VPC), AWS CloudFormation, and AWS Identity and Access Management (IAM), SageMaker MLOps can deliver solutions for the most demanding security and governance requirements.

Using a multi-account data science environment to meet security, reliability, and operational needs is a good DevOps practice. A multi-account strategy is paramount to achieve strong workload and data isolation, support multiple unrelated teams and projects, ensure fine-grained security and compliance control, facilitate billing, and create cost transparency.

In this two-part post, we offer guidance for using AWS services and SageMaker functionalities, and recommend practices for implementing a production-grade ML platform and secure, automated, multi-account model deployment workflows.

Such ML platforms and workflows can fulfill stringent security requirements, even for regulated industries such as financial services. For example, customers in regulated industries often don’t allow any internet access in ML environments. They often use only VPC endpoints for AWS services. They implement end-to-end data encryption in transit and at rest, and enforce workload isolation for individual teams in a line of business in multi-account organizational structures.

Part 1 of this series focuses on providing a solution architecture overview, in which we explain the security controls employed and how they are implemented. We also look at MLOps automation workflows with SageMaker projects and Pipelines.

In Part 2, we walk through deploying the solution with hands-on SageMaker notebooks.

Solution overview

The post Multi-account model deployment with Amazon SageMaker Pipelines shows a conceptual setup of a multi-account MLOps environment based on Pipelines and SageMaker projects.

The solution presented in this post is built for an actual use case for an AWS customer in the financial services industry. It focuses on the security, automation, and governance aspects of multi-account ML environments. It provides a fully automated provisioning of Studio into your private VPC, subnets and security groups using CloudFormation templates, and stack sets. Compared to the previous post, this solution implements network traffic and access controls with VPC endpoints, security groups, and fine-grained permissions with designated IAM roles. To reflect the real-life ML environment requirements, the solution enforces end-to-end data encryption at rest and in transit.

The following diagram shows the overview of the solution architecture and the deployed components.

Let’s look at each group of components in more detail.

Component 1: AWS Service Catalog

The end-to-end deployment of the data science environment is delivered as an AWS Service Catalog self-provisioned product. One of the main advantages of using AWS Service Catalog for self-provisioning is that authorized users can configure and deploy available products and AWS resources on their own, without needing full privileges or access to AWS services. The deployment of all AWS Service Catalog products happens under a specified service role with the defined set of permissions, which are unrelated to the user’s permissions.

Component 2: Studio domain

The Data Science Environment product in the AWS Service Catalog creates a Studio domain. A Studio domain consists of a list of authorized users, configuration settings, and an Amazon Elastic File System (Amazon EFS) volume. The Amazon EFS volume contains data for the users, including notebooks, resources, and artifacts.

Components 3 and 4: SageMaker MLOps project templates

The solution delivers the customized versions of SageMaker MLOps project templates. Each MLOps template provides an automated model building and deployment pipeline using continuous integration and continuous delivery (CI/CD). The delivered templates are configured for the secure multi-account model deployment and are fully integrated in the provisioned data science environment. The project templates are provisioned in Studio via AWS Service Catalog. The templates include the seed code repository with Studio notebooks, which implements a secure setup of SageMaker workloads such as processing, training jobs, and pipelines.

Components 5 and 6: CI/CD workflows

The MLOps projects implement CI/CD using Pipelines and AWS CodePipeline, AWS CodeCommit, and AWS CodeBuild. SageMaker project templates also support a CI/CD workflow using Jenkins and GitHub as the source repository.

Pipelines is responsible for orchestrating workflows across each step of the ML process and task automation, including data loading, data transformation, training, tuning and validation, and deployment. Each model is tracked via SageMaker Model Registry, which stores the model metadata, such as training and validation metrics and data lineage, and retains model versions and the approval status of the model.

CodePipeline deploys the model to the designated target accounts with staging and production environments. The necessary resources are pre-created by CloudFormation templates during infrastructure creation.

This solution supports secure multi-account model deployment using AWS Organizations or via simple target account lists.

Component 7: Secure infrastructure

The Studio domain is deployed in a dedicated VPC. Each elastic network interface used by a SageMaker domain or workload is created within a private dedicated subnet and attached to the specified security groups. The data science environment VPC can be configured with internet access via an optional NAT gateway. You can also run this VPC in internet-free mode without any inbound or outbound internet access.

All access to the AWS public services is routed via AWS PrivateLink. Traffic between your VPC and the AWS services doesn’t leave the Amazon network and isn’t exposed to the public internet.

Component 8: Data security

All data in the data science environment, which is stored in Amazon Simple Storage Service (Amazon S3) buckets and Amazon Elastic Block Store (Amazon EBS) and EFS volumes, is encrypted at rest using customer managed CMKs. All data transfer between platform components, API calls, and inter-container communication is protected using the Transport Layer Security (TLS 1.2) protocol.

Data access from the Studio notebooks or any SageMaker workload to the environment’s S3 buckets is governed by the combination of the S3 bucket and user policies and S3 VPC endpoint policy.

Multi-account structure

With the goal of illustrating best practices, this solution implements the following three account groups:

  • Development – This account is used by data scientists and ML engineers to perform experimentation and development. Data science tools such as Studio are used in the development account. S3 buckets with data and models, code repositories, and CI/CD pipelines are hosted in this account. Models are built, trained, validated, and registered in the model repository in this account.
  • Testing/staging/UAT – Validated and approved models are first deployed to the staging account, where the automated unit and integration tests are run. Data scientists and ML engineers have read-only access to this account.
  • Production – Fully tested and approved models from the staging accounts are deployed to the production account for both online and batch inference.

Depending on your specific security and governance requirements and your development organization, for the production setup, we recommend using two additional account groups:

  • Shared services – This account hosts common resources like team code repositories, CI/CD pipelines for MLOps workflows, Docker image repositories, service catalog portfolios, model registries, and library package repositories.
  • Data management – A dedicated AWS account to store and manage all data for the ML process. We recommend implementing strong data security and governance practices using AWS Data Lake and AWS Lake Formation.

Each of these account groups can have multiple AWS accounts and environments for developing and testing services and storing different types of data.

Environment layers

In the following sections, we look at the whole data science environment in terms of layers:

  • Network and security infrastructure
  • IAM roles and cross-account permission setup
  • Application stack consisting of Studio and SageMaker MLOps projects

In Part 2 of this post, you deploy the solution into your AWS account for further experimentation.

Secure infrastructure

We use AWS foundational services such as VPC, security groups, subnets, and NAT gateways to create the secure infrastructure for the data science environment. The following diagram shows the deployment architecture for the solution.

VPC, subnets, routes, and internet access

Our Studio domain is deployed into a dedicated data science VPC using VPC Only mode (Step 1 in the preceding architecture). In this mode, you use your own control flow for the internet traffic, like a NAT gateway or AWS Network Firewall. You can also create an internet-free VPC for your highly secure workloads. Any SageMaker workload launched in the VPC creates an elastic network interface in the specified subnet. You can apply all available layers of security controls—security groups, network ACLs, VPC endpoints, AWS PrivateLink, or Network Firewall endpoints—to the internal network and internet traffic to exercise fine-grained control of network access in Studio. For a detailed description of network configurations and security controls, refer to Securing Amazon SageMaker Studio connectivity using a private VPC. If you must control ingress and egress network traffic or apply any filtering rules, you can use Network Firewall as described in Securing Amazon SageMaker Studio internet traffic using AWS Network Firewall.

All SageMaker workloads, like Studio notebooks, processing or training jobs, and inference endpoints, are placed in the private subnets within the dedicated security group (2). This security group doesn’t allow any ingress from any network interface outside the group except for intra-group communications.

VPC endpoints

All access to Amazon S3 is routed via the gateway-type S3 VPC endpoint (3). You control access to the resources behind a VPC endpoint with a VPC endpoint policy. The combination of the VPC endpoint policy and the S3 bucket policy ensures that only specified buckets can be accessed, and these buckets can be accessed only via the designated VPC endpoints. The solution provisions two buckets: Data and Models. You can extend the CloudFormation templates to accommodate your data storage requirements, create additional S3 buckets, or tighten the data access permissions.

Studio and Studio notebooks communicate with various AWS services, such as the SageMaker backend and APIs, Amazon SageMaker Runtime, AWS Security Token Service (AWS STS), Amazon CloudWatch, AWS Key Management Service (AWS KMS), and others.

The solution uses a private connection over interface-type VPC endpoints (4) to access these AWS services. All VPC endpoints are placed in the dedicated security group to control the inbound and outbound network access. You can find a list with the recommended VPC endpoints to be set up for Studio in the following AWS technical guide.

IAM roles and preventive security controls

The solution uses IAM to set up personas and service execution roles (5). You can assign fine-grained permissions policies on the least privilege principle to various SageMaker execution roles, used to run different workloads, such as processing or training jobs, pipelines, or inference. You can implement preventive security controls using SageMaker-specific IAM condition keys. For example, the solution enforces usage of VPC isolation with private subnets and usage of the security groups for SageMaker notebook instances, processing, training, and tuning jobs, as well as for models for the SageMaker execution role:

{ “Action”: [ “sagemaker:CreateNotebookInstance”, “sagemaker:CreateHyperParameterTuningJob”, “sagemaker:CreateProcessingJob”, “sagemaker:CreateTrainingJob”, “sagemaker:CreateModel” ], “Resource”: “*”, “Effect”: “Deny”, “Condition”: { “Null”: { “sagemaker:VpcSubnets”: “true”, “sagemaker:VpcSecurityGroupIds”: “true” } } }

For a detailed discussion of the security controls and best practices, refer to Building secure machine learning environments with Amazon SageMaker.

Cross-account permission and infrastructure setup

When using a multi-account setup for your data science platform, you must focus on setting up and configuring IAM roles, resource policies, and cross-account trust and permissions polices with special attention to the following topics:

  • How do you set up access to the resources in one account from authorized and authenticated roles and users from another accounts?
  • What roles in one (target) account must be assumed by a role in another (source) account to perform a specific action in the target account?
  • Does the assumed role in the target account have a trust policy for a role in the source account, and does the role in the source account have iam:AssumeRole permission in its permissions policy for the principal in the target account? For more information, see How to use trust policies with IAM roles.
  • Do your AWS CloudFormation deployment roles have iam:PassRole permission for the execution roles they assign to the created resources?
  • How do you configure access control and resource isolation for teams or groups within Studio? For an overview and recipes for the implementation, see Configuring Amazon SageMaker Studio for teams and groups with complete resource isolation.

The solution implements the following IAM roles in its multi-account setup, as shown in the diagram.

User persona IAM roles and various execution roles are created in the development account as we run Studio and perform development work there. We must create the following IAM roles in the staging and production accounts:

  • Stack set execution roles – Used to deploy various resources into target accounts during the initial environment provision and for multi-account CI/CD MLOps workflows
  • Model execution roles – Assumed by SageMaker to access model artifacts and the Docker image for deployment on ML compute instances (SageMaker inference)

These roles are assumed by the roles in the development account.

Configure permissions for multi-account model deployment

In this section, we look closer at the permission setup for multi-account model deployment.

First, we must understand how the multi-account CI/CD model pipeline deploys the model to SageMaker endpoints in the target accounts. The following diagram shows the model deployment process.

After model training and validation, the model is registered in the model registry. The model registry stores the model metadata, and all model artifacts are stored in an S3 bucket (Step 1 in the preceding diagram). The CI/CD pipeline uses CloudFormation stack sets (2) to deploy the model in the target accounts. The CloudFormation service assumes the role StackSetExecutionRole (3) in the target account to perform the deployment. SageMaker also assumes the role ModelExecutionRole (4) to access the model metadata and download the model artifacts from the S3 bucket. The StackSetExecutionRole role must have iam:PassRole permission (5) for ModelExecutionRole to be able to pass the role successfully at stack provisioning time. Finally, the model is deployed to a SageMaker endpoint (6).

For a successful deployment, ModelExecutionRole needs access to the model, which is saved in an S3 bucket, and to the corresponding AWS KMS encryption keys in the development account, because the data in the S3 bucket is encrypted.

Both the S3 bucket and AWS KMS key resource policies have an explicit deny statement if any access request doesn’t arrive via a designated VPC endpoint (following is AWS KMS key policy example):

– Sid: DenyNoVPC Effect: Deny Principal: ‘*’ Action: – kms:Encrypt – kms:Decrypt – kms:ReEncrypt* – kms:GenerateDataKey* – kms:DescribeKey Resource: ‘*’ Condition: StringNotEquals: ‘aws:sourceVpce’: !Ref VPCEndpointKMSId

To access the S3 bucket and AWS KMS key with ModelExecutionRole, the following conditions must be met:

  • ModelExecutionRole must have permissions to access the S3 bucket and AWS KMS key in the development account
  • Both S3 bucket and AWS KMS key policies must allow cross-account access from ModelExecutionRole in the corresponding target account
  • The S3 bucket and AWS KMS key must be accessed only via a designated VPC endpoint in the target account
  • The VPC endpoint ID must be explicitly allowed in both S3 bucket and AWS KMS key policies in the Condition statement

The following diagram shows the infrastructure and IAM configuration for a development, staging, and production account that fulfills these requirements.

All access to the model artifacts is made via the S3 VPC endpoint (Step 1 in the preceding architecture). This VPC endpoint allows access to the model and data in your S3 buckets. The bucket policy (2) for the bucket where the models are stored grants access to the ModelExecutionRole principals (5) in each of the target accounts:

“Sid”: “AllowCrossAccount”, “Effect”: “Allow”, “Principal”: { “AWS”: [ “arn:aws:iam:::role/SageMakerModelExecutionRole”, “arn:aws:iam:::role/SageMakerModelExecutionRole”, “arn:aws:iam:::root” ] }

We apply the same setup for the data encryption key (3), whose policy (4) grants access to the principals in the target accounts.

SageMaker model-hosting endpoints are placed in the VPC (6) in each of the target accounts. Any access to S3 buckets and AWS KMS keys is made via the corresponding VPC endpoints. The IDs of these VPC endpoints are added to the Condition statement of the bucket and the AWS KMS key’s resource policies:

“Sid”: “DenyNoVPC”, “Effect”: “Deny”, “Principal”: “*”, “Action”: [ “s3:GetObject”, “s3:PutObject”, “s3:ListBucket”, “s3:GetBucketAcl”, “s3:GetObjectAcl”, “s3:PutBucketAcl”, “s3:PutObjectAcl” ], “Resource”: [ “arn:aws:s3:::sm-mlops-dev-us-east-1-models/*”, “arn:aws:s3:::sm-mlops-dev-us-east-1-models” ], “Condition”: { “StringNotEquals”: { “aws:sourceVpce”: [ “vpce-0b82e29a828790da2”, “vpce-07ef65869ca950e14”, “vpce-03d9ed0a1ba396ff5” ] } }

SageMaker MLOps projects: Automation pipelines

This solution delivers two MLOps projects as SageMaker project templates:

  • Model build, train, and validate pipeline
  • Multi-account model deploy pipeline

These projects are fully functional examples that are integrated with the solution infrastructure and multi-layer security controls such as VPC, subnets, security groups, AWS account boundaries, and the dedicated IAM execution roles.

You can find a detailed description of the SageMaker MLOps projects in Building, automating, managing, and scaling ML workflows using Amazon SageMaker Pipelines.

MLOps project template to build, train, validate model

This project is based on the SageMaker project template but has been adapted for this particular solution infrastructure and security controls. The following diagram shows the functional setup of the CI/CD pipeline.

The project creates the following resources comprising the MLOps pipeline:

  1. An MLOps template, made available through SageMaker projects and provided via an AWS Service Catalog portfolio.
  2. A CodePipeline pipeline with two stages: Source to get the source code of the ML pipeline, and Build to build and run the pipeline.
  3. A pipeline to implement a repeatable DAG workflow with individual steps for processing, training, validation, and model registration.
  4. A seed code repository in CodeCommit.

The seed code repository contains code to create a multi-step model building pipeline that includes data processing, model training, model evaluation, and conditional model registration (depending on model accuracy) steps. The pipeline implementation in the pipeline.py file trains a linear regression model using the XGBoost algorithm on the well-known UCI Abalone dataset. This repository also includes a build specification file, used by CodePipeline and CodeBuild to run the pipeline automatically.

MLOps project template for multi-account model deployment

This project is based on the SageMaker MLOps template for model deployment, but implements secure multi-account deployment from SageMaker Model Registry to SageMaker hosted endpoints for real-time inference in the staging and production accounts.

The following diagram shows the functional components of the project.

The components are as follows:

  1. The MLOps project template, which is deployable as a SageMaker project in Studio.
  2. A CodeCommit repository with seed code.
  3. The model deployment multi-stage CI/CD CodePipeline pipeline.
  4. A staging AWS account or accounts where the model is deployed and tested.
  5. A production AWS account or accounts where the model is deployed for production serving.
  6. SageMaker endpoints with the approved model hosted in your private VPC.

You can use the delivered seed code to implement your own customized model deployment pipelines with additional tests or approval steps.

Multi-account ML development best practices

In addition to the already discussed MLOps approaches, security controls, and infrastructure setup, the following resources provide a detailed description and overview of the ML development and deployment best practices:

Conclusion

In this post, we presented the main building blocks and patterns for implementing a multi-account, secure, and governed ML environment. In Part 2 of this series, you deploy the solution from the source code GitHub repository into your account and experiment with the hands-on SageMaker notebooks.

About the Author

Yevgeniy Ilyin is a Solutions Architect at AWS. He has over 20 years of experience working at all levels of software development and solutions architecture and has used programming languages from COBOL and Assembler to .NET, Java, and Python. He develops and codes cloud native solutions with a focus on big data, analytics, and data engineering.

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Secure Amazon SageMaker Studio presigned URLs Part 2: Private API with JWT authentication

In part 1 of this series, we demonstrated how to resolve an Amazon SageMaker Studio presigned URL from a corporate network using Amazon private VPC endpoints without traversing the internet. In this post, we will continue to build on top of the previous solution to demonstrate how to build a private API Gateway via Amazon API…

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In part 1 of this series, we demonstrated how to resolve an Amazon SageMaker Studio presigned URL from a corporate network using Amazon private VPC endpoints without traversing the internet. In this post, we will continue to build on top of the previous solution to demonstrate how to build a private API Gateway via Amazon API Gateway as a proxy interface to generate and access Amazon SageMaker presigned URLs. Furthermore, we add an additional guardrail to ensure presigned URLs are only generated and accessed for the authenticated end-user within the corporate network.

Solution overview

The following diagram illustrates the architecture of the solution.

The process includes the following steps:

  1. In the Amazon Cognito user pool, first set up a user with the name matching their Studio user profile and register Studio as the app client in the user pool.
  2. The user federates from their corporate identity provider (IdP) and authenticates with the Amazon Cognito user pool for accessing Studio.
  3. Amazon Cognito returns a token to the user authorizing access to the Studio application.
  4. The user invokes createStudioPresignedUrl API on API Gateway along with a token in the header.
  5. API Gateway invokes a custom AWS Lambda authorizer and validates the token.
  6. When the token is valid, Amazon Cognito returns an access grant policy with studio user profile id to API Gateway.
  7. API Gateway invokes the createStudioPresignedUrl Lambda function for creating the studio presigned url.
  8. The createStudioPresignedUrl function creates a presigned URL using the SageMaker API VPC endpoint and returns to caller.
  9. User accesses the presigned URL from their corporate network that resolves over the Studio VPC endpoint.
  10. The function’s AWS Identity and Access Management (IAM) policy makes sure that the presigned URL creation and access are performed via VPC endpoints.

The following sections walk you through solution deployment, configuration, and validation for the API Gateway private API for creating and resolving a Studio presigned URL from a corporate network using VPC endpoints.

  1. Deploy the solution
  2. Configure the Amazon Cognito user
  3. Authenticating the private API for the presigned URL using a JSON Web Token
  4. Configure the corporate DNS server for accessing the private API
  5. Test the API Gateway private API for a presigned URL from the corporate network
  6. Pre-Signed URL Lambda Auth Policy
  7. Cleanup

Deploy the solution

You can deploy the solution through either the AWS Management Console or the AWS Serverless Application Model (AWS SAM).

To deploy the solution via the console, launch the following AWS CloudFormation template in your account by choosing Launch Stack. It takes approximately 10 minutes for the CloudFormation stack to complete.

To deploy the solution using AWS SAM, you can find the latest code in the aws-samples GitHub repository, where you can also contribute to the sample code. The following commands show how to deploy the solution using the AWS SAM CLI. If not currently installed, install the AWS SAM CLI.

  1. Clone the repository at https://github.com/aws-samples/secure-sagemaker-studio-presigned-url.
  2. After you clone the repo, navigate to the source and run the following code:

Configure the Amazon Cognito user

To configure your Amazon Cognito user, complete the following steps:

  1. Create an Amazon Cognito user with the same name as a SageMaker user profile: aws cognito-idp admin-create-user –user-pool-id –username
  2. Set the user password: aws cognito-idp admin-set-user-password –user-pool-id –username –password –permanent
  3. Get an access token: aws cognito-idp initiate-auth –auth-flow USER_PASSWORD_AUTH –client-id –auth-parameters USERNAME=,PASSWORD=

Authenticating the private API for the presigned URL using a JSON Web Token

When you deployed a private API for creating a SageMaker presigned URL, you added a guardrail to restrict access to access the presigned URL by anyone outside the corporate network and VPC endpoint. However, without implementing another control to the private API within the corporate network, any internal user within the corporate network would be able to pass unauthenticated parameters for the SageMaker user profile and access any SageMaker app.

To mitigate this issue, we propose passing a JSON Web Token (JWT) for the authenticated caller to the API Gateway and validating that token with a JWT authorizer. There are multiple options for implementing an authorizer for the private API Gateway, using either a custom Lambda authorizer or Amazon Cognito.

With a custom Lambda authorizer, you can embed a SageMaker user profile name in the returned policy. This prevents any users within the corporate network from being able to send any SageMaker user profile name for creating a presigned URL that they’re not authorized to create. We use Amazon Cognito to generate our tokens and a custom Lambda authorizer to validate and return the appropriate policy. For more information, refer to Building fine-grained authorization using Amazon Cognito, API Gateway, and IAM. The Lambda authorizer uses the Amazon Cognito user name as the user profile name.

If you’re unable to use Amazon Cognito, you can develop a custom application to authenticate and pass end-user tokens to the Lambda authorizer. For more information, refer to Use API Gateway Lambda authorizers.

Configure the corporate DNS server for accessing the private API

To configure your corporate DNS server, complete the following steps:

  1. On the Amazon Elastic Compute Cloud (Amazon EC2) console, choose your on-premises DNSA EC2 instance and connect via Systems Manager Session Manager.
  2. Add a zone record in the /etc/named.conf file for resolving to the API Gateway’s DNS name via your Amazon Route 53 inbound resolver, as shown in the following code: zone “zxgua515ef.execute-api..amazonaws.com” { type forward; forward only; forwarders { 10.16.43.122; 10.16.102.163; }; };
  3. Restart the named service using the following command: sudo service named restart

Validate requesting a presigned URL from the API Gateway private API for authorized users

In a real-world scenario, you would implement a front-end interface that would pass the appropriate Authorization headers for authenticated and authorized resources using either a custom solution or leverage AWS Amplify. For brevity of this blog post, the following steps leverages Postman to quickly validate the solution we deployed actually restricts requesting the presigned URL for an internal user, unless authorized to do so.

To validate the solution with Postman, complete the following steps:

  1. Install Postman on the WINAPP EC2 instance. See instructions here
  2. Open Postman and add the access token to your Authorization header: Authorization: Bearer
  3. Modify the API Gateway URL to access it from your internal EC2 instance:
    1. Add the VPC endpoint into your API Gateway URL: https://.execute-api..amazonaws.com/dev/EMPLOYEE_ID
    2. Add the Host header with a value of your API Gateway URL: .execute-api..amazonaws.com
    3. First, change the EMPLOYEE_ID to your Amazon Cognito user and SageMaker user profile name. Make sure you receive an authorized presigned URL.
    4. Then change the EMPLOYEE_ID to a user that is not yours and make sure you receive an access failure.
  4. On the Amazon EC2 console, choose your on-premises WINAPP instance and connect via your RDP client.
  5. Open a Chrome browser and navigate to your authorized presigned URL to launch Studio.

Studio is launched over VPC endpoint with remote address as the Studio VPC endpoint IP.

If the presigned URL is accessed outside of the corporate network, the resolution fails because the IAM policy condition for the presigned URL enforces creation and access from a VPC endpoint.

Pre-Signed URL Lambda Auth Policy

Above solution created the following Auth Policy for the Lambda that generated Pre-Signed URL for accessing SageMaker Studio.

{ “Version”: “2012-10-17”, “Statement”: [ { “Condition”: { “IpAddress”: { “aws:VpcSourceIp”: “10.16.0.0/16” } }, “Action”: “sagemaker:CreatePresignedDomainUrl”, “Resource”: “arn:aws:sagemaker:::user-profile/*/*”, “Effect”: “Allow” }, { “Condition”: { “IpAddress”: { “aws:SourceIp”: “192.168.10.0/24” } }, “Action”: “sagemaker:CreatePresignedDomainUrl”, “Resource”: “arn:aws:sagemaker:::user-profile/*/*”, “Effect”: “Allow” }, { “Condition”: { “StringEquals”: { “aws:sourceVpce”: [ “vpce-sm-api-xx”, “vpce-sm-api-yy” ] } }, “Action”: “sagemaker:CreatePresignedDomainUrl”, “Resource”: “arn:aws:sagemaker:::user-profile/*/*”, “Effect”: “Allow” } ] }

The above policy enforces Studio pre-signed URL is both generated and accessed via one of these three entrypoints:

  1. aws:VpcSourceIp as your AWS VPC CIDR
  2. aws:SourceIp as your corporate network CIDR
  3. aws:sourceVpce as your SageMaker API VPC endpoints

Cleanup

To avoid incurring ongoing charges, delete the CloudFormation stacks you created. Alternatively, if you deployed the solution using SAM, you need to authenticate to the AWS account the solution was deployed and run sam delete.

Conclusion

In this post, we demonstrated how to access Studio using a private API Gateway from a corporate network using Amazon private VPC endpoints, preventing access to presigned URLs outside the corporate network, and securing the API Gateway with a JWT authorizer using Amazon Cognito and custom Lambda authorizers.

Try out with this solution and experiment integrating this with your corporate portal, and leave your feedback in the comments!

About the Authors

Ram Vittal is a machine learning solutions architect at AWS. He has over 20+ years of experience architecting and building distributed, hybrid and cloud applications. He is passionate about building secure and scalable AI/ML and Big Data solutions to help enterprise customers with their cloud adoption and optimization journey to improve their business outcomes. In his spare time, he enjoys tennis, photography, and action movies.

Jonathan Nguyen is a Shared Delivery Team Senior Security Consultant at AWS. His background is in AWS Security with a focus on Threat Detection and Incident Response. Today, he helps enterprise customers develop a comprehensive AWS Security strategy, deploy security solutions at scale, and train customers on AWS Security best practices.

Chris Childers is a Cloud Infrastructure Architect in Professional Services at AWS. He works with AWS customers to design and automate their cloud infrastructure and improve their adoption of DevOps culture and processes.



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Secure Amazon SageMaker Studio presigned URLs Part 1: Foundational infrastructure

You can access Amazon SageMaker Studio notebooks from the Amazon SageMaker console via AWS Identity and Access Management (IAM) authenticated federation from your identity provider (IdP), such as Okta. When a Studio user opens the notebook link, Studio validates the federated user’s IAM policy to authorize access, and generates and resolves the presigned URL for…

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You can access Amazon SageMaker Studio notebooks from the Amazon SageMaker console via AWS Identity and Access Management (IAM) authenticated federation from your identity provider (IdP), such as Okta. When a Studio user opens the notebook link, Studio validates the federated user’s IAM policy to authorize access, and generates and resolves the presigned URL for the user. Because the SageMaker console runs on an internet domain, this generated presigned URL is visible in the browser session. This presents an undesired threat vector for exfiltration and gaining access to customer data when proper access controls are not enforced.

Studio supports a few methods for enforcing access controls against presigned URL data exfiltration:

  • Client IP validation using the IAM policy condition aws:sourceIp
  • Client VPC validation using the IAM condition aws:sourceVpc
  • Client VPC endpoint validation using the IAM policy condition aws:sourceVpce

When you access Studio notebooks from the SageMaker console, the only available option is to use client IP validation with the IAM policy condition aws:sourceIp. However, you can use browser traffic routing products such as Zscaler to ensure scale and compliance for your workforce internet access. These traffic routing products generate their own source IP, whose IP range is not controlled by the enterprise customer. This makes it impossible for these enterprise customers to use the aws:sourceIp condition.

To use client VPC endpoint validation using the IAM policy condition aws:sourceVpce, the creation of a presigned URL needs to originate in the same customer VPC where Studio is deployed, and resolution of the presigned URL needs to happen via a Studio VPC endpoint on the customer VPC. This resolution of the presigned URL during access time for corporate network users can be accomplished using DNS forwarding rules (both in Zscaler and corporate DNS) and then into the customer VPC endpoint using an AWS Route 53 inbound resolver.

In this part, we discuss the overarching architecture for securing studio pre-signed url and demonstrate how to set up the foundational infrastructure to create and launch a Studio presigned URL through your VPC endpoint over a private network without traversing the internet. This serves as the foundational layer for preventing data exfiltration by external bad actors gaining access to Studio pre-signed URL and unauthorized or spoofed corporate user access within a corporate environment.

Solution overview

The following diagram illustrates over-arching solution architecture.

The process includes the following steps:

  1. A corporate user authenticates via their IdP, connects to their corporate portal, and opens the Studio link from the corporate portal.
  2. The corporate portal application makes a private API call using an API Gateway VPC endpoint to create a presigned URL.
  3. The API Gateway VPC endpoint “create presigned URL” call is forwarded to the Route 53 inbound resolver on the customer VPC as configured in the corporate DNS.
  4. The VPC DNS resolver resolves it to the API Gateway VPC endpoint IP. Optionally, it looks up a private hosted zone record if it exists.
  5. The API Gateway VPC endpoint routes the request via the Amazon private network to the “create presigned URL API” running in the API Gateway service account.
  6. API Gateway invokes the create-pre-signedURL private API and proxies the request to the create-pre-signedURL Lambda function.
  7. The create-pre-signedURL Lambda call is invoked via the Lambda VPC endpoint.
  8. The create-pre-signedURL function runs in the service account, retrieves authenticated user context (user ID, Region, and so on), looks up a mapping table to identify the SageMaker domain and user profile identifier, makes a sagemaker createpre-signedDomainURL API call, and generates a presigned URL. The Lambda service role has the source VPC endpoint conditions defined for the SageMaker API and Studio.
  9. The generated presigned URL is resolved over the Studio VPC endpoint.
  10. Studio validates that the presigned URL is being accessed via the customer’s VPC endpoint defined in the policy, and returns the result.
  11. The Studio notebook is returned to the user’s browser session over the corporate network without traversing the internet.

The following sections walk you through how to implement this architecture to resolve Studio presigned URLs from a corporate network using VPC endpoints. We demonstrate a complete implementation by showing the following steps:

  1. Set up the foundational architecture.
  2. Configure the corporate app server to access a SageMaker presigned URL via a VPC endpoint.
  3. Set up and launch Studio from the corporate network.

Set up the foundational architecture

In the post Access an Amazon SageMaker Studio notebook from a corporate network, we demonstrated how to resolve a presigned URL domain name for a Studio notebook from a corporate network without traversing the internet. You can follow the instructions in that post to set up the foundational architecture, and then return to this post and proceed to the next step.

Configure the corporate app server to access a SageMaker presigned URL via a VPC endpoint

To enable accessing Studio from your internet browser, we set up an on-premises app server on Windows Server on the on-premises VPC public subnet. However, the DNS queries for accessing Studio are routed through the corporate (private) network. Complete the following steps to configure routing Studio traffic through the corporate network:

  1. Connect to your on-premises Windows app server.

  2. Choose Get Password then browse and upload your private key to decrypt your password.
  3. Use an RDP client and connect to the Windows Server using your credentials.
    Resolving Studio DNS from the Windows Server command prompt results in using public DNS servers, as shown in the following screenshot.
    Now we update Windows Server to use the on-premises DNS server that we set up earlier.
  4. Navigate to Control Panel, Network and Internet, and choose Network Connections.
  5. Right-click Ethernet and choose the Properties tab.
  6. Update Windows Server to use the on-premises DNS server.
  7. Now you update your preferred DNS server with your DNS server IP.
  8. Navigate to VPC and Route Tables and choose your STUDIO-ONPREM-PUBLIC-RT route table.
  9. Add a route to 10.16.0.0/16 with the target as the peering connection that we created during the foundational architecture setup.

Set up and launch Studio from your corporate network

To set up and launch Studio, complete the following steps:

  1. Download Chrome and launch the browser on this Windows instance.
    You may need to turn off Internet Explorer Enhanced Security Configuration to allow file downloads and then enable file downloads.
  2. In your local device Chrome browser, navigate to the SageMaker console and open the Chrome developer tools Network tab.
  3. Launch the Studio app and observe the Network tab for the authtokenparameter value, which includes the generated presigned URL along with the remote server address that the URL is routed to for resolution.In this example, the remote address 100.21.12.108 is one of the public DNS server addresses to resolve the SageMaker DNS domain name d-h4cy01pxticj.studio.us-west-2.sagemaker.aws.
  4. Repeat these steps from the Amazon Elastic Compute Cloud (Amazon EC2) Windows instance that you configured as part of the foundational architecture.

We can observe that the remote address is not the public DNS IP, instead it’s the Studio VPC endpoint 10.16.42.74.

Conclusion

In this post, we demonstrated how to resolve a Studio presigned URL from a corporate network using Amazon private VPC endpoints without exposing the presigned URL resolution to the internet. This further secures your enterprise security posture for accessing Studio from a corporate network for building highly secure machine learning workloads on SageMaker. In part 2 of this series, we further extend this solution to demonstrate how to build a private API for accessing Studio with aws:sourceVPCE IAM policy validation and token authentication. Try out this solution and leave your feedback in the comments!

About the Authors

Ram Vittal is a machine learning solutions architect at AWS. He has over 20+ years of experience architecting and building distributed, hybrid and cloud applications. He is passionate about building secure and scalable AI/ML and Big Data solutions to help enterprise customers with their cloud adoption and optimization journey to improve their business outcomes. In his spare time, he enjoys tennis and photography.

Neelam Koshiya is an enterprise solution architect at AWS. Her current focus is to help enterprise customers with their cloud adoption journey for strategic business outcomes. In her spare time, she enjoys reading and being outdoors.



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Use a custom image to bring your own development environment to RStudio on Amazon SageMaker

RStudio on Amazon SageMaker is the industry’s first fully managed RStudio Workbench in cloud. You can quickly launch the familiar RStudio integrated development environment (IDE), and dial up and down the underlying compute resources without interrupting your work, making it easy to build machine learning (ML) and analytics solutions in R at scale. RStudio on…

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RStudio on Amazon SageMaker is the industry’s first fully managed RStudio Workbench in cloud. You can quickly launch the familiar RStudio integrated development environment (IDE), and dial up and down the underlying compute resources without interrupting your work, making it easy to build machine learning (ML) and analytics solutions in R at scale. RStudio on SageMaker already comes with a built-in image preconfigured with R programming and data science tools; however, you often need to customize your IDE environment. Starting today, you can bring your own custom image with packages and tools of your choice, and make them available to all the users of RStudio on SageMaker in a few clicks.

Bringing your own custom image has several benefits. You can standardize and simplify the getting started experience for data scientists and developers by providing a starter image, preconfigure the drivers required for connecting to data stores, or pre-install specialized data science software for your business domain. Furthermore, organizations that have previously hosted their own RStudio Workbench may have existing containerized environments that they want to continue to use in RStudio on SageMaker.

In this post, we share step-by-step instructions to create a custom image and bring it to RStudio on SageMaker using the AWS Management Console or AWS Command Line Interface (AWS CLI). You can get your first custom IDE environment up and running in few simple steps. For more information on the content discussed in this post, refer to Bring your own RStudio image.

Solution overview

When a data scientist starts a new session in RStudio on SageMaker, a new on-demand ML compute instance is provisioned and a container image that defines the runtime environment (operating system, libraries, R versions, and so on) is run on the ML instance. You can provide your data scientists multiple choices for the runtime environment by creating custom container images and making them available on the RStudio Workbench launcher, as shown in the following screenshot.

The following diagram describes the process to bring your custom image. First you build a custom container image from a Dockerfile and push it to a repository in Amazon Elastic Container Registry (Amazon ECR). Next, you create a SageMaker image that points to the container image in Amazon ECR, and attach that image to your SageMaker domain. This makes the custom image available for launching a new session in RStudio.

Prerequisites

To implement this solution, you must have the following prerequisites:

We provide more details on each in this section.

RStudio on SageMaker domain

If you have an existing SageMaker domain with RStudio enabled prior to April 7, 2022, you must delete and recreate the RStudioServerPro app under the user profile name domain-shared to get the latest updates for bring your own custom image capability. The AWS CLI commands are as follows. Note that this action interrupts RStudio users on SageMaker.

aws sagemaker delete-app –domain-id –app-type RStudioServerPro –app-name default –user-profile-name domain-shared aws sagemaker create-app –domain-id –app-type RStudioServerPro –app-name default –user-profile-name domain-shared

If this is your first time using RStudio on SageMaker, follow the step-by-step setup process described in Get started with RStudio on Amazon SageMaker, or run the following AWS CloudFormation template to set up your first RStudio on SageMaker domain. If you already have a working RStudio on SageMaker domain, you can skip this step.

The following RStudio on SageMaker CloudFormation template requires an RStudio license approved through AWS License Manager. For more about licensing, refer to RStudio license. Also note that only one SageMaker domain is permitted per AWS Region, so you’ll need to use an AWS account and Region that doesn’t have an existing domain.

  1. Choose Launch Stack.
    Launch stack button
    The link takes you to the us-east-1 Region, but you can change to your preferred Region.
  2. In the Specify template section, choose Next.
  3. In the Specify stack details section, for Stack name, enter a name.
  4. For Parameters, enter a SageMaker user profile name.
  5. Choose Next.
  6. In the Configure stack options section, choose Next.
  7. In the Review section, select I acknowledge that AWS CloudFormation might create IAM resources and choose Next.
  8. When the stack status changes to CREATE_COMPLETE, go to the Control Panel on the SageMaker console to find the domain and the new user.

IAM policies to interact with Amazon ECR

To interact with your private Amazon ECR repositories, you need the following IAM permissions in the IAM user or role you’ll use to build and push Docker images:

{ “Version”:”2012-10-17″, “Statement”:[ { “Sid”: “VisualEditor0”, “Effect”:”Allow”, “Action”:[ “ecr:CreateRepository”, “ecr:BatchGetImage”, “ecr:CompleteLayerUpload”, “ecr:DescribeImages”, “ecr:DescribeRepositories”, “ecr:UploadLayerPart”, “ecr:ListImages”, “ecr:InitiateLayerUpload”, “ecr:BatchCheckLayerAvailability”, “ecr:PutImage” ], “Resource”: “*” } ] }

To initially build from a public Amazon ECR image as shown in this post, you need to attach the AWS-managed AmazonElasticContainerRegistryPublicReadOnly policy to your IAM user or role as well.

To build a Docker container image, you can use either a local Docker client or the SageMaker Docker Build CLI tool from a terminal within RStudio on SageMaker. For the latter, follow the prerequisites in Using the Amazon SageMaker Studio Image Build CLI to build container images from your Studio notebooks to set up the IAM permissions and CLI tool.

AWS CLI versions

There are minimum version requirements for the AWS CLI tool to run the commands mentioned in this post. Make sure to upgrade AWS CLI on your terminal of choice:

  • AWS CLI v1 >= 1.23.6
  • AWS CLI v2 >= 2.6.2

Prepare a Dockerfile

You can customize your runtime environment in RStudio in a Dockerfile. Because the customization depends on your use case and requirements, we show you the essentials and the most common customizations in this example. You can download the full sample Dockerfile.

Install RStudio Workbench session components

The most important software to install in your custom container image is RStudio Workbench. We download from the public S3 bucket hosted by RStudio PBC. There are many version releases and OS distributions for use. The version of the installation needs to be compatible with the RStudio Workbench version used in RStudio on SageMaker, which is 1.4.1717-3 at the time of writing. The OS (argument OS in the following snippet) needs to match the base OS used in the container image. In our sample Dockerfile, the base image we use is Amazon Linux 2 from an AWS-managed public Amazon ECR repository. The compatible RStudio Workbench OS is centos7.

FROM public.ecr.aws/amazonlinux/amazonlinux … ARG RSW_VERSION=1.4.1717-3 ARG RSW_NAME=rstudio-workbench-rhel ARG OS=centos7 ARG RSW_DOWNLOAD_URL=https://s3.amazonaws.com/rstudio-ide-build/server/${OS}/x86_64 RUN RSW_VERSION_URL=`echo -n “${RSW_VERSION}” | sed ‘s/+/-/g’` && curl -o rstudio-workbench.rpm ${RSW_DOWNLOAD_URL}/${RSW_NAME}-${RSW_VERSION_URL}-x86_64.rpm && yum install -y rstudio-workbench.rpm

You can find all the OS release options with the following command:

aws s3 ls s3://rstudio-ide-build/server/

Install R (and versions of R)

The runtime for your custom RStudio container image needs at least one version of R. We can first install a version of R and make it the default R by creating soft links to /usr/local/bin/:

# Install main R version ARG R_VERSION=4.1.3 RUN curl -O https://cdn.rstudio.com/r/centos-7/pkgs/R-${R_VERSION}-1-1.x86_64.rpm && yum install -y R-${R_VERSION}-1-1.x86_64.rpm && yum clean all && rm -rf R-${R_VERSION}-1-1.x86_64.rpm RUN ln -s /opt/R/${R_VERSION}/bin/R /usr/local/bin/R && ln -s /opt/R/${R_VERSION}/bin/Rscript /usr/local/bin/Rscript

Data scientists often need multiple versions of R so that they can easily switch between projects and code base. RStudio on SageMaker supports easy switching between R versions, as shown in the following screenshot.

RStudio on SageMaker automatically scans and discovers versions of R in the following directories:

/usr/lib/R /usr/lib64/R /usr/local/lib/R /usr/local/lib64/R /opt/local/lib/R /opt/local/lib64/R /opt/R/* /opt/local/R/*

We can install more versions in the container image, as shown in the following snippet. They will be installed in /opt/R/.

RUN curl -O https://cdn.rstudio.com/r/centos-7/pkgs/R-4.0.5-1-1.x86_64.rpm && yum install -y R-4.0.5-1-1.x86_64.rpm && yum clean all && rm -rf R-4.0.5-1-1.x86_64.rpm RUN curl -O https://cdn.rstudio.com/r/centos-7/pkgs/R-3.6.3-1-1.x86_64.rpm && yum install -y R-3.6.3-1-1.x86_64.rpm && yum clean all && rm -rf R-3.6.3-1-1.x86_64.rpm RUN curl -O https://cdn.rstudio.com/r/centos-7/pkgs/R-3.5.3-1-1.x86_64.rpm && yum install -y R-3.5.3-1-1.x86_64.rpm && yum clean all && rm -rf R-3.5.3-1-1.x86_64.rpm

Install RStudio Professional Drivers

Data scientists often need to access data from sources such as Amazon Athena and Amazon Redshift within RStudio on SageMaker. You can do so using RStudio Professional Drivers and RStudio Connections. Make sure you install the relevant libraries and drivers as shown in the following snippet:

# Install RStudio Professional Drivers —————————————-# RUN yum update -y && yum install -y unixODBC unixODBC-devel && yum clean all ARG DRIVERS_VERSION=2021.10.0-1 RUN curl -O https://drivers.rstudio.org/7C152C12/installer/rstudio-drivers-${DRIVERS_VERSION}.el7.x86_64.rpm && yum install -y rstudio-drivers-${DRIVERS_VERSION}.el7.x86_64.rpm && yum clean all && rm -f rstudio-drivers-${DRIVERS_VERSION}.el7.x86_64.rpm && cp /opt/rstudio-drivers/odbcinst.ini.sample /etc/odbcinst.ini RUN /opt/R/${R_VERSION}/bin/R -e ‘install.packages(“odbc”, repos=”https://packagemanager.rstudio.com/cran/__linux__/centos7/latest”)’

Install custom libraries

You can also install additional R and Python libraries so that data scientists don’t need to install them on the fly:

RUN /opt/R/${R_VERSION}/bin/R -e “install.packages(c(‘reticulate’, ‘readr’, ‘curl’, ‘ggplot2’, ‘dplyr’, ‘stringr’, ‘fable’, ‘tsibble’, ‘dplyr’, ‘feasts’, ‘remotes’, ‘urca’, ‘sodium’, ‘plumber’, ‘jsonlite’), repos=’https://packagemanager.rstudio.com/cran/__linux__/centos7/latest’)” RUN /opt/python/${PYTHON_VERSION}/bin/pip install –upgrade ‘boto3>1.0<2.0' 'awscli>1.0<2.0' 'sagemaker[local]<3' 'sagemaker-studio-image-build' 'numpy'

When you’ve finished your customization in a Dockerfile, it’s time to build a container image and push it to Amazon ECR.

Build and push to Amazon ECR

You can build a container image from the Dockerfile from a terminal where the Docker engine is installed, such as your local terminal or AWS Cloud9. If you’re building it from a terminal within RStudio on SageMaker, you can use SageMaker Studio Image Build. We demonstrate the steps for both approaches.

In a local terminal where the Docker engine is present, you can run the following commands from where the Dockerfile is. You can use the sample script create-and-update-image.sh.

IMAGE_NAME=r-4.1.3-rstudio-1.4.1717-3 # the name for SageMaker Image REPO=rstudio-custom # ECR repository name TAG=$IMAGE_NAME # login to your Amazon ECR aws ecr get-login-password | docker login –username AWS –password-stdin ${ACCOUNT_ID}.dkr.ecr.${REGION}.amazonaws.com # create a repo aws ecr create-repository –repository-name ${REPO} # build a docker image and push it to the repo docker build . -t ${REPO}:${TAG} -t ${ACCOUNT_ID}.dkr.ecr.${REGION}.amazonaws.com/${REPO}:${TAG} docker push ${ACCOUNT_ID}.dkr.ecr.${REGION}.amazonaws.com/${REPO}:${TAG}

In a terminal on RStudio on SageMaker, run the following commands:

pip install sagemaker-studio-image-build sm-docker build . –repository ${REPO}:${IMAGE_NAME}

After these commands, you have a repository and a Docker container image in Amazon ECR for our next step, in which we attach the container image for use in RStudio on SageMaker. Note the image URI in Amazon ECR .dkr.ecr..amazonaws.com/: for later use.

Update RStudio on SageMaker through the console

RStudio on SageMaker allows runtime customization through the use of a custom SageMaker image. A SageMaker image is a holder for a set of SageMaker image versions. Each image version represents a container image that is compatible with RStudio on SageMaker and stored in an Amazon ECR repository. To make a custom SageMaker image available to all RStudio users within a domain, you can attach the image to the domain following the steps in this section.

  1. On the SageMaker console, navigate to the Custom SageMaker Studio images attached to domain page, and choose Attach image.
  2. Select New image, and enter your Amazon ECR image URI.
  3. Choose Next.
  4. In the Image properties section, provide an Image name (required), Image display name (optional), Description (optional), IAM role, and tags.
    The image display name, if provided, is shown in the session launcher in RStudio on SageMaker. If the Image display name field is left empty, the image name is shown in RStudio on SageMaker instead.
  5. Leave EFS mount path and Advanced configuration (User ID and Group ID) as default because RStudio on SageMaker manages the configuration for us.
  6. In the Image type section, select RStudio image.
  7. Choose Submit.

You can now see a new entry in the list. It’s worth noting that, with the introduction of the support of custom RStudio images, you can see a new Usage type column in the table to denote whether an image is an RStudio image or an Amazon SageMaker Studio image.

It may take up to 5–10 minutes for the custom images to be available in the session launcher UI. You can then launch a new R session in RStudio on SageMaker with your custom images.

Over time, you may want to retire old and outdated images. To remove the custom images from the list of custom images in RStudio, select the images in the list and choose Detach.

Choose Detach again to confirm.

Update RStudio on SageMaker via the AWS CLI

The following sections describe the steps to create a SageMaker image and attach it for use in RStudio on SageMaker on the SageMaker console and using the AWS CLI. You can use the sample script create-and-update-image.sh.

Create the SageMaker image and image version

The first step is to create a SageMaker image from the custom container image in Amazon ECR by running the following two commands:

ROLE_ARN= DISPLAY_NAME=RSession-r-4.1.3-rstudio-1.4.1717-3 aws sagemaker create-image –image-name ${IMAGE_NAME} –display-name ${DISPLAY_NAME} –role-arn ${ROLE_ARN} aws sagemaker create-image-version –image-name ${IMAGE_NAME} –base-image “${ACCOUNT_ID}.dkr.ecr.${REGION}.amazonaws.com/${REPO}:${TAG}”

Note that the custom image displayed in the session launcher in RStudio on SageMaker is determined by the input of –display-name. If the optional display name is not provided, the input of –image-name is used instead. Also note that the IAM role allows SageMaker to attach an Amazon ECR image to RStudio on SageMaker.

Create an AppImageConfig

In addition to a SageMaker image, which captures the image URI from Amazon ECR, an app image configuration (AppImageConfig) is required for use in a SageMaker domain. We simplify the configuration for an RSessionApp image so we can just create a placeholder configuration with the following command:

IMAGE_CONFIG_NAME=r-4-1-3-rstudio-1-4-1717-3 aws sagemaker create-app-image-config –app-image-config-name ${IMAGE_CONFIG_NAME}

Attach to a SageMaker domain

With the SageMaker image and the app image configuration created, we’re ready to attach the custom container image to the SageMaker domain. To make a custom SageMaker image available to all RStudio users within a domain, you attach the image to the domain as a default user setting. All existing users and any new users will be able to use the custom image.

For better readability, we place the following configuration into the JSON file default-user-settings.json:

“DefaultUserSettings”: { “RSessionAppSettings”: { “CustomImages”: [ { “ImageName”: “r-4.1.3-rstudio-2022”, “AppImageConfigName”: “r-4-1-3-rstudio-2022” }, { “ImageName”: “r-4.1.3-rstudio-1.4.1717-3”, “AppImageConfigName”: “r-4-1-3-rstudio-1-4-1717-3” } ] } } }

In this file, we can specify the image and AppImageConfig name pairs in a list in DefaultUserSettings.RSessionAppSettings.CustomImages. This preceding snippet assumes two custom images are being created.

Then run the following command to update the SageMaker domain:

aws sagemaker update-domain –domain-id –cli-input-json file://default-user-settings.json

After you update the domaim, it may take up to 5–10 minutes for the custom images to be available in the session launcher UI. You can then launch a new R session in RStudio on SageMaker with your custom images.

Detach images from a SageMaker domain

You can detach images simply by removing the ImageName and AppImageConfigName pairs from default-user-settings.json and updating the domain.

For example, updating the domain with the following default-user-settings.json removes r-4.1.3-rstudio-2022 from the R session launching UI and leaves r-4.1.3-rstudio-1.4.1717-3 as the only custom image available to all users in a domain:

{ “DefaultUserSettings”: { “RSessionAppSettings”: { “CustomImages”: [ { “ImageName”: “r-4.1.3-rstudio-1.4.1717-3”, “AppImageConfigName”: “r-4-1-3-rstudio-1-4-1717-3” } ] } } }

Clean up

To safely remove images and resources in the SageMaker domain, complete the following steps in Clean up image resources.

To safely remove the RStudio on SageMaker and the SageMaker domain, complete the following steps in Delete an Amazon SageMaker Domain to delete any RSessionGateway app, user profile and the domain.

To safely remove images and repositories in Amazon ECR, complete the following steps in Deleting an image.

Finally, to delete the CloudFormation template:

  1. On the AWS CloudFormation console, choose Stacks.
  2. Select the stack you deployed for this solution.
  3. Choose Delete.

Conclusion

RStudio on SageMaker makes it simple for data scientists to build ML and analytic solutions in R at scale, and for administrators to manage a robust data science environment for their developers. Data scientists want to customize the environment so that they can use the right libraries for the right job and achieve the desired reproducibility for each ML project. Administrators need to standardize the data science environment for regulatory and security reasons. You can now create custom container images that meet your organizational requirements and allow data scientists to use them in RStudio on SageMaker.

We encourage you to try it out. Happy developing!

About the Authors

Michael Hsieh is a Senior AI/ML Specialist Solutions Architect. He works with customers to advance their ML journey with a combination of AWS ML offerings and his ML domain knowledge. As a Seattle transplant, he loves exploring the great Mother Nature the city has to offer, such as the hiking trails, scenery kayaking in the SLU, and the sunset at Shilshole Bay.

Declan Kelly is a Software Engineer on the Amazon SageMaker Studio team. He has been working on Amazon SageMaker Studio since its launch at AWS re:Invent 2019. Outside of work, he enjoys hiking and climbing.

Sean MorganSean Morgan is an AI/ML Solutions Architect at AWS. He has experience in the semiconductor and academic research fields, and uses his experience to help customers reach their goals on AWS. In his free time, Sean is an active open-source contributor and maintainer, and is the special interest group lead for TensorFlow Add-ons.



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