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Enhance your machine learning development by using a modular architecture with Amazon SageMaker projects

One of the main challenges in a machine learning (ML) project implementation is the variety and high number of development artifacts and tools used. This includes code in notebooks, modules for data processing and transformation, environment configuration, inference pipeline, and orchestration code. In production workloads, the ML model created within your development framework is almost…

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One of the main challenges in a machine learning (ML) project implementation is the variety and high number of development artifacts and tools used. This includes code in notebooks, modules for data processing and transformation, environment configuration, inference pipeline, and orchestration code. In production workloads, the ML model created within your development framework is almost never the end of the work, but is a part of a larger application or workflow.

Another challenge is the varied nature of ML development activities performed by different user roles. For example, the DevOps engineer develops infrastructure components, such as CI/CD automation, builds production inference pipelines, and configures security and networking. The data engineer is typically focused on data processing and transformation workflows. The data scientist or ML engineer delivers ML models and model building, training, and validation pipelines.

These challenges call for an architecture and framework that facilitate separation of concerns by allowing each development role to work on their own part of the system, and hide the complexity of integration, security, and environment configuration.

This post illustrates how to introduce a modular component-based architecture in your ML application by implementing reusable, self-contained, and consistent components with Amazon SageMaker.

Solution overview

As an example of an ML workflow that spans several development domains, the proposed solution implements a use case of an automated pipeline for data transformation, feature extraction, and ingestion into Amazon SageMaker Feature Store.

On a high level, the workflow comprises the following functional steps:

  1. An upstream data ingestion component uploads data objects to an Amazon Simple Storage Service (Amazon S3) bucket.
  2. The data upload event launches a data processing and transformation process.
  3. The data transformation process extracts, processes, and transforms features, and ingests them into a designated feature group in Feature Store.

Terminology

This section introduces the following important concepts and definitions.

ML component

An ML component is a construction unit that contains all the required resources, configuration, and workflows to perform a specific ML task. For example, the proposed data transformation and ingestion pipeline can be delivered as an ML component. ML components have a better integration capability to help you to implement reproducible, governed, and secure ML applications. An ML component can encapsulate all the boilerplate code required to properly set up data access permissions, security keys, tagging, naming, and logging requirements for all resources.

A process of implementing an ML component assumes that a dedicated DevOps or MLOps team performs the design, building, testing, and distribution of components. The recipients of ML components are data scientists, data engineers, and ML engineers.

This separation of development responsibilities brings higher agility, a faster time to market, and less manual heavy lifting, and results in a higher quality and consistency of your ML workflows.

Amazon SageMaker project

SageMaker facilitates the development and distribution of ML components with SageMaker projects.

A SageMaker project is a self-sufficient collection of resources, which can be instantiated and used by the entitled users. A project contains all the resources, artifacts, source code, orchestration, and permissions that are needed to perform a designated ML task or workflow. For example, SageMaker provides MLOps project templates to automate setup and implementation of MLOps for your applications.

You can implement a custom SageMaker project template to deliver a packaged ML workflow, which can be distributed and provisioned via an Amazon SageMaker Studio IDE.

When you implement custom reusable components with SageMaker projects, you can separate the development, testing, and distribution process for ML components from their employment, and follow MLOps best practices.

Product portfolio

A project works together with two other AWS services, AWS Service Catalog and AWS CloudFormation, to provide an end-to-end, user-friendly integration in your SageMaker environment and Studio. You can combine multiple projects in a portfolio. A SageMaker project is called product in the portfolio scope. A product portfolio is delivered via AWS Service Catalog into Studio. You can control who can view and provision specific products by associating user roles with a designated portfolio.

Solution architecture

The detailed component architecture of the solution is presented in the following diagram.

A product portfolio (1) defines the automated Feature Store data ingestion product (2) together with the associated user roles that are allowed to use the portfolio and the containing products. CloudFormation templates define both the product portfolio (1) and the product (2). A CloudFormation template (3) contains all the resources, source code, configuration, and permissions that are needed to provision the product in your SageMaker environment.

When AWS CloudFormation deploys the product, it creates a new SageMaker project (4).

The SageMaker project implements the feature ingestion workflow (5). The workflow contains an AWS Lambda function, which is launched by an Amazon EventBridge rule each time new objects are uploaded into a monitored S3 bucket. The Lambda function starts a SageMaker pipeline (6), which is defined and provisioned as a part of the SageMaker project. The pipeline implements data transformation and ingestion in Feature Store.

The project also provisions CI/CD automation (7) with an AWS CodeCommit repository with source code, AWS CodeBuild with a pipeline build script, and AWS CodePipeline to orchestrate the build and deployment of the SageMaker pipeline (6).

ML pipeline

This solution implements an ML pipeline by using Amazon SageMaker Pipelines, an ML workflow creation and orchestration framework. The pipeline contains a single step with an Amazon SageMaker Data Wrangler processor for data transformation and ingestion into a feature group in Feature Store. The following diagram shows a data processing pipeline implemented by this solution.

Refer to Build, tune, and deploy an end-to-end churn prediction model using Amazon SageMaker Pipelines for an example of how to build and use a SageMaker pipeline.

The rest of this post walks you through the implementation of a custom SageMaker project. We discuss how to do the following:

  • Create a project with your resources
  • Understand the project lifecycle
  • View project resources
  • Create a Studio domain and deploy a product portfolio
  • Work with the project and run a data transformation and ingestion pipeline

The GitHub repository provides the full source code for the end-to-end solution. You can use this code as a starting point for your own custom ML components to deploy using this same reference architecture.

Author a SageMaker project template

To get started with a custom SageMaker project, you need the following resources, artifacts, and AWS Identity and Access Management (IAM) roles and permissions:

  • A CloudFormation template that defines an AWS Service Catalog portfolio.
  • A CloudFormation template that defines a SageMaker project.
  • IAM roles and permissions needed to run your project components and perform the project’s tasks and workflows.
  • If your project contains any source code delivered as a part of the project, this code must be also delivered. The solution refers to this source code as the seed code.

Files in this solution

This solution contains all the source code needed to create your custom SageMaker project. The structure of the code repository is as follows:

  • cfn-templates folder: This folder contains the following:
    • project-s3-fs-ingestion.yaml – A CloudFormation template with the SageMaker project
    • sm-project-sc-portfolio.yaml – A CloudFormation template with the product portfolio and managed policies with permissions needed to deploy the product
  • project-seed-code/s3-fs-ingestion folder – Contains the project seed code, including the SageMaker pipeline definition code, build scripts for the CI/CD CodeBuild project, and source code for the Lambda function
  • notebooks folder – Contains the SageMaker notebooks to experiment with the project

The following sections describe each part of the project authoring process and give examples of the source code.

AWS Service Catalog portfolio

An AWS Service Catalog portfolio is delivered as a CloudFormation template, which defines the following resources:

  • Portfolio definition.
  • Product definition.
  • Product to portfolio association for each product.
  • Portfolio to IAM principle association. This defines which IAM principles are allowed to deploy portfolio products.
  • Product launch role constraint. This defines which IAM role AWS CloudFormation assumes when a user provisions the template.

To make your project template available in Studio, you must add the following tag to the product:

Tags: – Key: ‘sagemaker:studio-visibility’ Value: ‘true’

Refer to Create Custom Project Templates for more details on custom project templates.

This solution contains an example of an AWS Service Catalog portfolio that contains a single product.

Product CloudFormation template

A CloudFormation template defines the product. The product’s template is self-sufficient and contains all the resources, permissions, and artifacts that are needed to deliver the product’s functionality.

For the product to work with SageMaker projects, you must add the following parameters to your product template:

SageMakerProjectName: Type: String Description: Name of the project MinLength: 1 MaxLength: 32 AllowedPattern: ^[a-zA-Z](-*[a-zA-Z0-9])* SageMakerProjectId: Type: String Description: Service generated Id of the project.

This solution contains a product template that creates several resources.

For the data transformation and ingestion pipeline, the template creates the following:

  • A SageMaker pipeline definition source code.
  • A Lambda function to start the SageMaker pipeline whenever a new object is uploaded to the monitored S3 bucket.
  • An IAM execution role for the Lambda function.
  • An S3 bucket to keep an AWS CloudTrail log. You need a CloudTrail log to enable EventBridge notification for object put events on the monitored bucket. You use the CloudTrail-based notification instead of Amazon S3 notifications because you must not overwrite an existing Amazon S3 notification on the monitored bucket.
  • A CloudTrail log configured to capture WriteOnly events on S3 objects under a specified S3 prefix.
  • An EventBridge rule to launch the Lambda function whenever a new object is uploaded to the monitored S3 bucket. The EventBridge rule pattern monitors the events PutObject and CompleteMultipartUpload.

For CI/CD automation, the template creates the following:

  • An S3 bucket to store CodePipeline artifacts
  • A CodeCommit repository with the SageMaker pipeline definition
  • An EventBridge rule to launch CodePipeline when the CodeCommit repository is updated
  • A CodeBuild project to build the SageMaker pipeline
  • A CodePipeline pipeline to orchestrate the build of the SageMaker pipeline

IAM roles and permissions

To launch and use a SageMaker project, you need two IAM roles:

  • An IAM role to launch a product from AWS Service Catalog – This rule is assumed by AWS Service Catalog and contains permission specifically needed to deploy resources using CloudFormation templates. The AWS Service Catalog-based approach allows data scientists and ML engineers to provision custom ML components and workflows centrally without requiring each ML user to have high-profile permissions policies or going via a manual and non-reproducible individual deployment process.
  • An IAM role to use resources created by a SageMaker project – These resources include a CodePipeline pipeline, a SageMaker pipeline, and an EventBridge rule. The project’s CloudFormation template explicitly specifies which resource uses which role.

When you enable SageMaker projects for Studio users, the provisioning process creates two IAM roles in your AWS account: AmazonSageMakerServiceCatalogProductsLaunchRole and AmazonSageMakerServiceCatalogProductsUseRole. The SageMaker-provided project templates use these roles to deploy and operate the created resources. You can use these roles for your custom SageMaker projects, or you can create your own roles with a specific set of IAM permissions suited to your requirements. Make sure these roles are given all necessary permissions, specifically S3 bucket access, to perform their tasks.

Refer to AWS Managed Policies for SageMaker projects and JumpStart for more details on the default roles.

If you create and assign any IAM roles to resources created by the project provisioning via AWS Service Catalog and AWS CloudFormation, the role AmazonSageMakerServiceCatalogProductsLaunchRole must have iam:PassRole permission for a role you pass to a resource. For example, this solution creates an IAM execution role for the Lambda function. The managed policy for AmazonSageMakerServiceCatalogProductsLaunchRole contains the corresponding permission statement:

– Sid: FSIngestionPermissionPassRole Effect: Allow Action: – ‘iam:PassRole’ Resource: – !Sub ‘arn:aws:iam::${AWS::AccountId}:role/*StartIngestionPipeline*’

The following diagram shows all the IAM roles involved and which service or resource assumes which role.

The architecture contains the following components:

  1. The SageMaker Service Catalog products launch role. This role calls the iam:PassRole API for the SageMaker Service Catalog products use role (2) and the Lambda execution role (4).
  2. The SageMaker Service Catalog products use role. Project resources assume this role to perform their tasks.
  3. The SageMaker execution role. Studio notebooks use this role to access all resources, including S3 buckets.
  4. The Lambda execution role. The Lambda function assumes this role.
  5. The Lambda function resource policy allows EventBridge to invoke the function.

Refer to SageMaker Studio Permissions Required to Use Projects for more details on the Studio permission setup for projects.

Project seed code

If your custom SageMaker project uses CI/CD workflow automation or contains any source code-based resources, you can deliver the seed code as a CodeCommit or third-party Git repository such as GitHub and Bitbucket. The project user owns the code and can customize it to implement their requirements.

This solution delivers the seed code, which contains a SageMaker pipeline definition. The project also creates a CI/CD workflow to build the SageMaker pipeline. Any commit to the source code repository launches the CodePipeline pipeline.

Project lifecycle

A project passes through distinct lifecycle stages: you create a project, use it and its resources, and delete the project when you don’t need it anymore. Studio UX integrates end-to-end SageMaker projects including project resources, data lineage, and lifecycle control.

Create a project

You can provision a SageMaker project directly in your Studio IDE or via the SageMaker API.

To create a new SageMaker project in Studio, complete the following steps:

  1. On the SageMaker resources page, choose Projects on the drop-down menu.
  2. Choose Create project.
  3. Choose Organization templates.
  4. Choose the template for the project you want to provision.
  5. Enter a name and optional description for your project.
  6. Under Project template parameters, provide your project-specific parameters.

You can also use the Python SDK to create a project programmatically, as shown in this code snippet from the 01-feature-store-ingest-pipeline notebook:

sm = boto3.client(“sagemaker”) # set project_parameters # project_parameters = [ # { # ‘Key’: ‘PipelineDescription’, # ‘Value’: ‘Feature Store ingestion pipeline’ # }, # … #] r = sm.create_project( ProjectName=project_name, ProjectDescription=”Feature Store ingestion from S3″, ServiceCatalogProvisioningDetails={ ‘ProductId’: product_id, ‘ProvisioningArtifactId’: provisioning_artifact_ids, ‘ProvisioningParameters’: project_parameters }, )

Each project is provisioned via an AWS Service Catalog and AWS CloudFormation process. Because you have the corresponding IAM access policy, for example AWSCloudFormationReadOnlyAccess, you can observe the project deployment on the AWS CloudFormation console. As shown in the following screenshot, you can browse stack info, events, resources, outputs, parameters, and the template.

View project resources

After you provision the project, you can browse SageMaker-specific project resources in the Studio IDE.

You can also see all the resources created by the project deployment process on the AWS CloudFormation console.

Any resource created by the project is automatically tagged with two tags: sagemaker:project-name and sagemaker:project-id, allowing for data and resource lineage.

You can add your own tags to project resources, for example, to fulfill your specific resource tagging and naming requirements.

Delete project

If you don’t need the provisioned project any more, to stop incurring charges, you must delete it to clean up the resources created by the project.

At the time of writing this post, you must use the SageMaker API to delete a project. A sample Python code looks like the following:

import boto3 sm_client=boto3.client(“sagemaker”) sm_client.delete_project(ProjectName=”MyProject”)

Deleting the project also initiates the deletion of the CloudFormation stack with the project template.

A project can create other resources, such as objects in S3 buckets, ML models, feature groups, inference endpoints, or CloudFormation stacks. These resources may not be removed upon project deletion. Refer to the specific project documentation for how to perform a full cleanup.

This solution provides a Studio notebook to delete all the resources created by the project.

Deploy the solution

To deploy the solution, you must have administrator (or power user) permissions to package the CloudFormation templates, upload the templates in your S3 bucket, and run the deployment commands.

To start working with the solution’s notebooks, provision a project, and run a data transformation and ingestion pipeline, you must complete the following deployment steps from the solution’s GitHub README file:

  1. Clone the solution’s GitHub repo to your local development environment.
  2. Create a Studio domain (instructions in the README file).
  3. Deploy the SageMaker project portfolio (instructions in the README file).
  4. Add custom permissions to the AWS Service Catalog launch and SageMaker execution IAM roles (instructions in the README file).
  5. Start Studio and clone the GitHub repository into your SageMaker environment (instructions in the README file).

Solution walkthrough

The delivered notebooks take you through the following solution steps:

  • Setup:
    • Set up the working environment, create an S3 bucket for data upload, download and explore the test dataset
    • Optionally, create a Data Wrangler flow for data transformation and feature ingestion
    • Create a feature group in Feature Store where features are kept
    • Query the data from the feature group
  • Feature Store ingestion pipeline:
    • Provision a SageMaker project with a data pipeline
    • Explore the project resources
    • Test the data pipeline by uploading new data to the monitored S3 bucket
    • Run the data pipeline on demand via Python SDK
    • Query the data from the feature group
  • Clean up:
    • Delete the project and project’s resources
    • Delete the feature group
    • Delete project-provisioned S3 buckets and S3 objects

Clean up

To avoid charges, you must remove all project-provisioned and generated resources from your AWS account.

Follow the instructions in the solution’s README file.

Call to action

In this post, you learned how to create ML components for your modular architecture using SageMaker projects. SageMaker projects offer a convenient and AWS-native method to package and deliver reusable units to implement ML workflows. Integrating SageMaker projects with SageMaker Pipelines and CI/CD CodePipeline automation gives you power tools to follow MLOps best practices and increase the speed and quality of your development work.

Your ML workflows and pipelines may benefit from being encapsulated into a reusable and parametrizable component. Now you can implement this component using the described approach with SageMaker projects.

Additional references

For more hands-on examples of using SageMaker projects and pipelines for various use cases, see the following resources:

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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