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Custom document annotation for extracting named entities in documents using Amazon Comprehend

Intelligent document processing (IDP), as defined by IDC, is an approach by which unstructured content and structured data is analyzed and extracted for use in downstream applications. IDP involves document reading, categorization, and data extraction, by using AI’s processes of computer vision (CV), Optical Character Recognition (OCR), and natural language processing (NLP) on provided texts.[1]…

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Intelligent document processing (IDP), as defined by IDC, is an approach by which unstructured content and structured data is analyzed and extracted for use in downstream applications. IDP involves document reading, categorization, and data extraction, by using AI’s processes of computer vision (CV), Optical Character Recognition (OCR), and natural language processing (NLP) on provided texts.[1] Companies in financial services, healthcare, and manufacturing process millions of documents each year and find the process to be painstaking. Document processing is manual, slow, expensive, and error-prone, and data is often spread across disparate sources. As a result, creating and managing a document processing pipeline remains a challenge for many companies.

Amazon Comprehend is an NLP service that provides APIs to extract key phrases, contextual entities, events, and sentiment from documents. Entities refer to things in your document such as people, places, organizations, credit card numbers, and so on. But what if you want to identify entity types unique to your business, like proprietary product codes or industry-specific terms? Custom entity recognition in Amazon Comprehend enables you to train models to extract entities that are unique to your business in just a few easy steps. Historically, you could only use Amazon Comprehend APIs on plain text documents. If you wanted to process Word or PDF documents, you first needed to preprocess or flatten the documents into a plain text format, which often reduces the quality of the context within the document.

Today, Amazon Comprehend launched a feature to help organizations extract insights and automate processing documents of different formats (PDF, Word, plain text) and layouts (bullets, lists). You can now use custom entity recognition in Amazon Comprehend directly on more document types (scanned PDF, machine-readable PDF, and Word documents) without needing to convert your files to plain text. Custom entity recognition can now extract custom entities and process varying document layouts such as dense text and lists or bullets in PDF, Word, and plain text documents—at no additional cost.

To train a custom entity recognition model that you can use on your PDF, Word, and plain text documents, you need to first annotate PDF documents using a custom Amazon SageMaker Ground Truth annotation template provided by Amazon Comprehend. The custom template renders a PDF and allows you to annotate directly on the document. The custom model leverages both the natural language and structural or positional information (coordinates) of the text to make sure that we accurately extract custom entities that might have been previously impacted when flattening a document.

In this post, we walk through the steps of setting up the custom annotation template and show examples of how to annotate finance documents (SEC filings). After you annotate your data, you can use the generated manifest file to train a custom entity recognition model.

Feature overview

We cover the following steps:

  1. Install the annotation tool.
  2. Upload training documents.
  3. Create a private workforce to label the training documents.
  4. Set up the Ground Truth labeling job.
  5. Access the annotation login portal to start labeling documents.
  6. Label documents with the custom template.

Only PDF files are used for training a custom entity recognition model, but you can use that model on plain text, Word, and PDFs documents during inference (for now, asynchronous processing only).

The custom annotation template is available to download on GitHub. You need to Git clone or download this application and install it in local or in an AWS Cloud 9 integrated development environment (IDE). AWS Cloud9 is a cloud-based IDE that lets you write, run, and debug your code with just a browser. This annotation tool needs to be built and deployed using Python and AWS Serverless Application Model (AWS SAM) (the AWS SAM CLI lets you locally build, test, and debug serverless applications that are defined by AWS SAM templates).

Prerequisites

You need to install the following to build and deploy this solution:

  1. Install Python 3.8.x.
  2. Install jq.
  3. Install the AWS SAM CLI.
  4. Make sure you have pip installed.
  5. Install and configure the AWS CLI.
  6. Configure your AWS credentials.

If you’re setting up this tool in us-east-1, you can skip installing the AWS CLI and AWS SAM CLI, because it’s already installed with Python environment. You just need to create a virtual environment to use Python 3.8 in AWS Cloud9.

Install the annotation tool

Download the annotation tool from GitHub. You can either Git clone this repository or download the setup as a .zip file in your local. The .zip file contains all the installation parts required to build out the custom annotation workflow to annotate PDFs in Ground Truth.

First, you run and configure this tool from the AWS CLI. With AWS SAM, we build and deploy this tool to create an AWS CloudFormation template. The CloudFormation template configures resources such as an AWS Lambda function, AWS Identity and Access Management (IAM) roles and permissions to create an Amazon SageMaker labeling job, and your Amazon Simple Storage Service (Amazon S3) bucket.

Once you unzip the tool, you need to run commands to build and deploy from with-in the annotation tool. In the CLI, navigate to folder ComprehendSSIEAnnotationTool or run

cd ComprehendSSIEAnnotationTool to change your directory to the annotation tool folder.

  1. Run the following steps to build the tool:

make bootstrap

make build

  1. After the build is successful, run the following command to deploy:

make deploy-guided

  1. For Stack Name¸ enter comprehend-annotation-tool.
  2. For AWS Region, enter the Region you’re in.
  3. Leave the remaining parameters as default.
  4. For Confirm changes before deploy, enter Y.

  1. For Allow SAM CLI IAM role creation, enter Y.

After you complete these steps, the AWS CloudFormation resources are deployed in your AWS account, which you use to set up the annotation jobs. If deployment fails, delete the CloudFormation stack via the AWS CloudFormation console and try make deploy-guided again from your local or AWS Cloud9 IDE to reinstall this tool.

  1. When the status of the CloudFormation deployment changes from In-Progress to Complete (which usually takes a few minutes), note the S3 bucket name created by the CloudFormation template.

You need this S3 bucket to upload training documents that are used for annotation in the next step.

 

Upload the training documents

You can run the following command in your local to copy local files to the S3 bucket created in the previous step. As a reminder, these training documents should be PDF documents only.

aws s3 cp –recursive s3://comprehend-semi-structured-documents-${AWS_REGION}-${AWS_ACCOUNT_ID}/source-semi-structured-documents/

Or you can directly upload documents to this S3 bucket created by the CloudFormation template by creating a folder source-semi-structured-documents. Refer to this link to learn how to upload files in Amazon S3 and creating folders.

Create a private workforce

You need to create a private workforce in Ground Truth, which labels the training documents you uploaded in Amazon S3. You can view or create a private workforce using the Ground Truth console. For detailed instructions, see the section “Creating a private work team” in Developing NER models with Amazon SageMaker Ground Truth and Amazon Comprehend. To follow along with the next steps, we recommend adding yourself to the private workforce. All new workers added to your private workforce receive an enrollment email with the URL, user name, and a temporary password to log in to the labeling portal. Your workers use this login information after we create the labeling job.

After you create a private workforce, note the private workforce name, which we need when creating a Ground Truth labeling job using the AWS CLI.

Set up the Ground Truth labeling job

Ground Truth allows you to create custom labeling workflows, which you can use when the provided templates don’t suit the requirements for your labeling efforts.

To create a Ground Truth labeling job, you need to set up parameters such as the input Amazon S3 path, CloudFormation stack name, work team name, Region, job name prefix, entity types, and annotator metadata.

  • input-s3-path: S3 Uri to the source documents you copied earlier in Upload Source Semi-Structured Documents to S3 bucket
  • cfn-name: The name of the CloudFormation stack name entered in the Package and Deploy step: comprehend-annotation-tool
  • work-team-name: The workforce name created from the previous step
  • job-name-prefix: The prefix to have for the SageMaker Ground Truth labeling job (LIMIT: 29 characters). Extra text will be appended to job name prefix, ex. -labeling-job-task-20210902T232116
  • entity-types: The entities you would like to use during the labeling job (separated by commas)

Run the following AWS CLI command to trigger a Ground Truth labeling job:

python bin/comprehend-ssie-annotation-tool-cli.py –input-s3-path s3://comprehend-semi-structured-documents-${AWS_REGION}-${AWS_ACCOUNT_ID}/source-semi-structured-documents/ –cfn-name comprehend-annotation-tool –work-team-name –region ${AWS_REGION} –job-name-prefix “${USER}-job” –entity-types “EntityTypeA, EnityTypeB, EntityTypeC” –annotator-metadata “key=Info,value=Sample information,key=Due Date,value=Sample date value 12/12/1212”

You can view the labeling job on the SageMaker console. As shown in the following screenshot, our labeling job is created, in progress, and using a custom task type.

You have now created a custom labeling job for annotating your PDF documents.

Historical options for processing PDF documents required converting documents to raw text format before processing through Amazon Comprehend custom entity recognition models. However, the custom Ground Truth template you created allows you to interact with the document in its native PDF format. It does this by rendering a text layer on top of the PDF. This combination allows you to reference the entity within the table or structured format in a familiar, easier, and more efficient manner than using a document that has been converted to a plain text format. In addition to the user-friendly document rendering, the template also helps pick the right span for the text and provides the ability to wrap lines for entities that span several lines within the document.

Access the annotation login portal

You can access the annotation portal either by using the URL in your private workforce enrollment email or via the SageMaker console. To use the SageMaker console, complete the following steps:

  1. Choose Labeling workforces in the navigation pane.
  2. Choose Private.
  3. Choose the link under Labeling portal sign-in URL.

  1. Enter the email and password you received in your enrollment email.
  2. Choose Login to start labeling.

Label documents with the custom template

After you create the labeling job, the labeling workforce can access the documents and begin annotating the required entities. First, we walk you through the annotation UI template, then three examples of how to annotate documents with finance documents (Securities and Exchange Commission (SEC) filings).

Explanation of the annotation UI template

On the annotation user interface, you can see one of your PDF documents open (see the following screenshot). On the top left, you can see the custom entity types that you provided during setup. To the right of your entity types, you can see arrows to navigate through your training documents. The document in the example below is blurred out to focus on the annotation UI.

 

Additionally, you have options to remove, undo, or auto tag your annotations on each document. To use auto tag, simply annotate a word and associate it with one of your custom entity types. If you choose Auto Tag, all other instances of that entity type (for example, all addresses) are automatically annotated with that entity type. After you annotate each PDF, choose Submit to save annotations before moving to the next document.

The following screenshot shows the options within the right panel of the annotation interface. You can edit, copy, remove, and reset your annotations.

Now with this understanding of the annotation UI, we show end-to-end examples of how to annotate custom entities for finance documents (SEC filings).

Finance

Finance customers can now easily process bank statements and SEC filings to extract custom entities such as board of director names, acquisition price, earnings per share, and more. In the following example, we show how to extract the following entities from SEC form S3 (registration) and SEC form 424B5 (prospectus filing): OFFERING_PRICE and OFFERED_SHARES. More specifically, we demonstrate how to annotate these forms so you can extract custom entities that appear in both a table and paragraph and have multiple occurrences.

The SEC dataset is available for download here s3://aws-ml-blog/artifacts/custom-document-annotation-comprehend/sources/. You can use this dataset to train a custom entity recognition model after you finish annotating. Run the below command to copy the S3 data from s3://aws-ml-blog/artifacts/custom-document-annotation-comprehend/sources/ to the AWS CloudFormation created bucket s3://comprehend-semi-structured-documents-${AWS_REGION}-${AWS_ACCOUNT_ID}/source-semi-structured-documents/ by running following command:

aws s3 cp –recursive s3://aws-ml-blog/artifacts/custom-document-annotation-comprehend/sources/ s3://comprehend-semi-structured-documents-${AWS_REGION}-${AWS_ACCOUNT_ID}/source-semi-structured-documents/

Go to the command prompt where you set up annotation tool and run the following command to set up a training job for the entities OFFERING_PRICE and OFFERED_SHARES in the entity-types parameter:

python bin/comprehend-ssie-annotation-tool-cli.py –input-s3-path s3://comprehend-semi-structured-documents-${AWS_REGION}-${AWS_ACCOUNT_ID}/source-semi-structured-documents/ –cfn-name annotationtool –work-team-name –region ${AWS_REGION} –job-name-prefix “${USER}-job” –entity-types “OFFERING_PRICE,OFFERED_SHARES ” –annotator-metadata “key=Info,value=Sample information,key=Due Date,value=Sample date value 12/12/1212” –use-textract-only

Additional customizable options:

  1. Include –-annotator-metadata parameter to reveal key-value information to annotators. Default metadata about the document is already revealed to the annotator within the UI side panel.
  2. Specify –-use-textract-only flag to instruct the annotation tool to only use Amazon Textract AnalyzeDocument API to parse the PDF document. By default, the tool tries to auto-detect what types of source PDF document format is, and use either PDFPlumber (native PDF) or Amazon Textract (scanned PDF) to parse the PDF Documents. When creating the labeling job, a customer has the option of only using Amazon Textract for both the scenarios, which may be more accurate for text extraction, but comes at an additional cost (see Textract Pricing).

This command triggers a Ground Truth labeling job for your private workforce. Now log in to your labeling portal. You’re redirected to the annotation UI to annotate entities we specified in the labeling job. In the following screenshot, we show how entities such as OFFERING_PRICE and OFFERED_SHARES are annotated. Your labeling workforce uses the pointer to draw bounding boxes around the appropriate entities and labels them with the appropriate custom entity label. You must annotate the OFFERING_PRICE entity in every occurrence, which in this case, is within the dense text paragraph and in semi-structured (columnar) format.

After you label all the pages, you can find annotations in JSON format in the Amazon S3 location s3://comprehend-semi-structured-documents-us-east-1-/output//annotations/.

The user-labeled document information is also saved to an output manifest file in the Amazon S3 location provided during the setup of the custom labeling job. The output manifest file references all the annotations within your training documents. You can find your output manifest file in the Amazon S3 location s3://comprehend-semi-structured-documents-us-east-1–/output///manifests/. You use this manifest file to create an Amazon Comprehend custom entity recognition training job and train your custom model. For instructions, see Extract custom entities from more document types with Amazon Comprehend.

Review the output manifest file

When examining the output manifest file for the custom labeling training job on PDF documents, we can see the contextual information (natural language and positional or structural information) that is gathered and used to train the Amazon Comprehend custom entity recognition model.

First, let’s look at the output generated from the custom entity labeling job of a different dataset. In the following screenshot, the output manifest file for the labeling job has generated starting and ending offsets but also uses references to block IDs and child block IDs for each labeled entity to capture the associated 2-D information of the entity (what the entity is and where it’s located in the document). A block represents layout coordinate positional information of the tokens within the document. A child block ID identifies the word blocks within the referenced block ID. As shown in the highlighted code, the entity labeled contains information relating to where the entity is placed within the overall structure of the document.

To compare, let’s examine the output from a standard Ground Truth template for named entity recognition (NER). As shown in the following screenshot, the starting and ending offsets are given for each of the three entities labeled. However, no other contextual information, such as the coordinates or position of the text within the document, is provided. This is expected because the Ground Truth template for NER takes input files in as plain text format, which has removed all spatial information from within the PDF document in regards to positioning of words relative to each other and the document.

Labeling best practices

The following are best practices while annotating your data:

  • Annotate your data with care and verify that you annotate every mention of the entity. You can use the Auto Tag feature and every reference of the entity will be annotated. Imprecise annotations can lead to poor results. In general, more annotations lead to better results.
  • Input data should not contain duplicates, like a duplicate of a PDF you are going to annotate. Presence of a duplicate sample might result in test set contamination and could negatively affect the training process, model metrics, and model behavior.
  • Make sure that all documents in the corpus are annotated, and that the documents without annotations are due to lack of legitimate entities, not due to negligence. For example, if you have a document containing “J Doe has been an engineer for 14 years,” you should provide an annotation for “J Doe” as well as “John Doe.” Failing to do so confuses the model and might lead to not recognizing “J Doe” as an ENGINEER. This should be consistent within the same document and across documents.
  • Provide documents that resemble real use cases as closely as possible. Synthesized data with repetitive patterns should be avoided. The input data should be as diverse as possible to avoid overfitting and help the underlying model better generalize on real examples.
  • It’s important that documents should be diverse in terms of word count. For example, if all documents in the training data are short, the resulting model has difficulty predicting entities in longer documents.
  • Try to give the same data distribution for training as you expect to be using when you’re actually detecting your custom entities (inference time). For example, at inference time, if you expect to be sending documents that have no entities in them, this should also be part of your training document set.
  • We recommend a minimum of 250 documents and 100 annotations per entity to ensure good quality predictions. With more training data, you’re more likely to produce a higher-quality model. If you want higher accuracy, we recommend increasing the volume of annotated data by 10% to further improve the accuracy. This improvement might be best visible to you by running inference on a held-out test set that remains unchanged and can be tested by different models. In this way, you can compare successive models.

For additional suggestions, see Improving Custom Entity Recognizer Performance

Conclusion

With this new contextual information included within labeled annotations, you can now train Amazon Comprehend custom entity recognition models with semi-structured information (bullets, lists, dense text). This additional structural context provides more information to the model to help identify the relevant entities within documents.

For more information about how to train your custom model and the impact this additional information can have on custom Amazon Comprehend model performance, see Extract custom entities from documents in their native format with Amazon Comprehend and our documentation.

References

[1] IDC Survey Spotlight, What Is the Landscape of the Emerging Document Artificial Intelligence Market?, Doc # US47701421, July 2021

About the Authors

Mona Mona is an AI/ML Specialist Solutions Architect based out of Arlington, VA. She helps customers adopt machine learning on a large scale. She is passionate about NLP and ML Explainability areas in AI/ML.

Anant Patel is a Sr. Product Manager-Tech on the Amazon Comprehend team within AWS AI/ML.

Andrea Morton-Youmans is a Product Marketing Manager on the AI Services team at AWS. Over the past 10 years she has worked in the technology and telecommunications industries, focused on developer storytelling and marketing campaigns. In her spare time, she enjoys heading to the lake with her husband and Aussie dog Oakley, tasting wine and enjoying a movie from time to time.



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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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