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Accelerate data preparation using Amazon SageMaker Data Wrangler for diabetic patient readmission prediction

Patient readmission to hospital after prior visits for the same disease results in an additional burden on healthcare providers, the health system, and patients. Machine learning (ML) models, if built and trained properly, can help understand reasons for readmission, and predict readmission accurately. ML could allow providers to create better treatment plans and care, which…

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Patient readmission to hospital after prior visits for the same disease results in an additional burden on healthcare providers, the health system, and patients. Machine learning (ML) models, if built and trained properly, can help understand reasons for readmission, and predict readmission accurately. ML could allow providers to create better treatment plans and care, which would translate to a reduction of both cost and mental stress for patients. However, ML is a complex technique that has been limiting organizations that don’t have the resources to recruit a team of data engineers and scientists to build ML workloads. In this post, we show you how to build an ML model based on the XGBoost algorithm to predict diabetic patient readmission easily and quickly with a graphical interface from Amazon SageMaker Data Wrangler.

Data Wrangler is an Amazon SageMaker Studio feature designed to allow you to explore and transform tabular data for ML use cases without coding. Data Wrangler is the fastest and easiest way to prepare data for ML. It gives you the ability to use a visual interface to access data and perform exploratory data analysis (EDA) and feature engineering. It also seamlessly operationalizes your data preparation steps by allowing you to export your data flow into Amazon SageMaker Pipelines, a Data Wrangler job, Python file, or Amazon SageMaker Feature Store.

Data Wrangler comes with over 300 built-in transforms and custom transformations using either Python, PySpark, or SparkSQL runtime. It also comes with built-in data analysis capabilities for charts (such as scatter plot or histogram) and time-saving model analysis capabilities such as feature importance, target leakage, and model explainability.

In this post, we explore the key capabilities of Data Wrangler using the UCI diabetic patient readmission dataset. We showcase how you can build ML data transformation steps without writing sophisticated coding, and how to create a model training, feature store, or ML pipeline with reproducibility for a diabetic patient readmission prediction use case.

We also have published a related GitHub project repo that includes the end-to-end ML workflow steps and relevant assets, including Jupyter notebooks.

We walk you through the following high-level steps:

  • Studio prerequisites and input dataset setup
  • Design your Data Wrangler flow file
  • Create processing and training jobs for model building
  • Host a trained model for real-time inference

Studio prerequisites and input dataset setup

To use Studio and Studio notebooks, you must complete the Studio onboarding process. Although you can choose from a few authentication methods, the simplest way to create a Studio domain is to follow the Quick start instructions. The Quick start uses the same default settings as the standard Studio setup. You can also choose to onboard using AWS Single Sign-On (AWS SSO) for authentication (see Onboard to Amazon SageMaker Studio Using AWS SSO).

Dataset

The patient readmission dataset captures 10 years (1999–2008) of clinical care at 130 US hospitals and integrated delivery networks. It includes over 50 features representing patient and hospital outcomes with about 100,000 observations.

You can start by downloading the public dataset and uploading it to an Amazon Simple Storage Service (Amazon S3) bucket. For demonstration purposes, we split the dataset into four tables based on feature categories: diabetic_data_hospital_visits.csv, diabetic_data_demographic.csv, diabetic_data_labs.csv, and diabetic_data_medication.csv. Review and run the code in datawrangler_workshop_pre_requisite.ipynb. If you leave everything at its default inside the notebook, the CSV files will be available in s3://sagemaker-${region}-${account_number}/sagemaker/demo-diabetic-datawrangler/.

Design your Data Wrangler flow file

To get started – on the Studio File menu, choose New, and choose Data Wrangler Flow.

This launches a Data Wrangler instance and configures it with the Data Wrangler app. The process takes a few minutes to complete.

Load the data from Amazon S3 into Data Wrangler

To load the data into Data Wrangler, complete the following steps:

  1. On the Import tab, choose Amazon S3 as the data source.
  2. Choose Add data source.

You could also import data from Amazon Athena, Amazon Redshift, or Snowflake. For more information about the currently supported import sources, see Import.

  1. Select the CSV files from the bucket s3://sagemaker-${region}-${account_number}/sagemaker/demo-diabetic-datawrangler/ one at a time.
  2. Choose Import for each file.

When the import is complete, data in an S3 bucket is available inside Data Wrangler for preprocessing.

Join the CSV files

Now that we have imported multiple CSV source dataset, let’s join them for a consolidated dataset.

  1. On the Data flow tab, for Data types, choose the plus sign.
  2. On the menu, choose Join.
  3. Choose the diabetic_data_hospital_visits.csv dataset as the Right dataset.
  4. Choose Configure to set up the join criteria.
  5. For Name, enter a name for the join.
  6. For Join type¸ choose a join type (for this post, Inner).
  7. Choose the columns for Left and Right.
  8. Choose Apply to preview the joined dataset.
  9. Choose Add to add it to the data flow file.

Built-in analysis

Before we apply any transformations on the input source, let’s perform a quick analysis of the dataset. Data Wrangler provides several built-in analysis types, like histogram, scatter plot, target leakage, bias report, and quick model. For more information about analysis types, see Analyze and Visualize.

Target leakage

Target leakage occurs when information in an ML training dataset is strongly correlated with the target label, but isn’t available when the model is used for prediction. You might have a column in your dataset that serves as a proxy for the column you want to predict with your model. For classification tasks, Data Wrangler calculates the prediction quality metric of ROC-AUC, which is computed individually for each feature column via cross-validation to generate a target leakage report.

  1. On the Data Flow tab, for Join, choose the plus sign.
  2. Choose Add analysis.
  3. For Analysis type, choose Target Leakage.
  4. For Analysis name¸ enter a name.
  5. For Max features, enter 50.
  6. For Problem Type¸ choose classification.
  7. For Target, choose readmitted.
  8. Choose Preview to generate the report.

As shown in the preceding screenshot, there is no indication of target leakage in our input dataset. However, a few features like encounter_id_1, encounter_id_0, weight, and payer_code are marked as possibly redundant with 0.5 predictive ability of ROC. This means these features by themselves aren’t providing any useful information towards predicting the target. Before making the decision to drop these uninformative features, you should consider whether these could add value when used in tandem with other features. For our use case, we keep them as is and move to the next step.

  1. Choose Save to save the analysis into your Data Wrangler data flow file.

Bias report

AI/ML systems are only as good as the data we put into them. ML-based systems are more accessible than ever before, and with the growth of adoption throughout various industries, further questions arise surrounding fairness and how it is ensured across these ML systems. Understanding how to detect and avoid bias in ML models is imperative and complex. With the built-in bias report in Data Wrangler, data scientists can quickly detect bias during the data preparation stage of the ML workflow. Bias report analysis uses Amazon SageMaker Clarify to perform bias analysis.

To generate a bias report, you must specify the target column that you want to predict and a facet or column that you want to inspect for potential biases. For example, we can generate a bias report on the gender feature for Female values to see whether there is any class imbalance.

  1. On the Analysis tab, choose Create new analysis.
  2. For Analysis type¸ choose Bias Report.
  3. For Analysis name, enter a name.
  4. For Select the column your model predicts, choose readmitted.
  5. For Predicted value, enter NO.
  6. For Column to analyze for bias, choose gender.
  7. For Column value to analyze for bias, choose Female.
  8. Leave remaining settings at their default.
  9. Choose Check for bias to generate the bias report.

As shown in the bias report, there is no significant bias in our input dataset, which means the dataset has a fair amount of representation by gender. For our dataset, we can move forward with a hypothesis that there is no inherent bias in our dataset. However, based on your use case and dataset, you might want to run similar bias reporting on other features of your dataset to identify any potential bias. If any bias is detected, you can consider applying a suitable transformation to address that bias.

  1. Choose Save to add this report to the data flow file.

Histogram

In this section, we use a histogram to gain insights into the target label patterns inside our input dataset.

  1. On the Analysis tab, choose Create new analysis.
  2. For Analysis type¸ choose Histogram.
  3. For Analysis name¸ enter a name.
  4. For X axis, choose readmitted.
  5. For Color by, choose race.
  6. For Facet by, choose gender.
  7. Choose Preview to generate a histogram.

This ML problem is a multi-class classification problem. However, we can observe a major target class imbalance between patients readmitted <30 days, >30 days, and NO readmission. We can also see that these two classifications are proportionate across gender and race. To improve our potential model predictability, we can merge <30 and >30 into a single positive class. This merge of target label classification turns our ML problem into a binary classification. As we demonstrate in the next section, we can do this easily by adding respective transformations.

Transformations

When it comes to training an ML model for structured or tabular data, decision tree-based algorithms are considered best in class. This is due to their inherent technique of applying ensemble tree methods in order to boost weak learners using the gradient descent architecture.

For our medical source dataset, we use the SageMaker built-in XGBoost algorithm because it’s one of the most popular decision tree-based ensemble ML algorithms. The XGBoost algorithm can only accept numerical values as input, therefore as a prerequisite we must apply categorical feature transformations on our source dataset.

Data Wrangler comes with over 300 built-in transforms, which require no coding. Let’s use built-in transforms to apply a few key transformations and prepare our training dataset.

Handle missing values

To address missing values, complete the following steps:

  1. Switch to Data tab to bring up all the built-in transforms
  2. Expand Handle missing in the list of transforms.
  3. For Transform, choose Impute.
  4. For Column type¸ choose Numeric.
  5. For Input column, choose diag_1.
  6. For Imputing strategy, choose Mean.
  7. By default, the operation is performed in-place, but you can provide an optional Output column name, which creates a new column with imputed values. For our blog we go with default in-place update.
  8. Choose Preview to preview the results.
  9. Choose Add to include this transformation step into the data flow file.
  10. Repeat these steps for the diag_2 and diag_3 features and impute missing values.

Search and edit features with special characters

Because our source dataset has features with special characters, we need to clean them before training. Let’s use the search and edit transform.

  1. Expand Search and edit in the list of transforms.
  2. For Transform, choose Find and replace substring.
  3. For Input column, choose race.
  4. For Pattern, enter ?.
  5. For Replacement string¸ choose Other.
  6. Leave Output column blank for in-place replacements.
  7. Choose Preview.
  8. Choose Add to add the transform to your data flow.
  9. Repeat the same steps for other features to replace weight and payer_code with 0 and medical_specialty with Other.

One-hot encoding for categorical features

To use one-hot encoding for categorical features, complete the following steps:

  1. Expand Encode categorical in the list of transforms.
  2. For Transform, choose One-hot encode.
  3. For Input column, choose race.
  4. For Output style, choose Columns.
  5. Choose Preview.
  6. Choose Add to add the change to the data flow.
  7. Repeat these steps for age and medical_specialty_filler to one-hot encode those categorical features as well.

Ordinal encoding for categorical features

To use ordinal encoding for categorical features, complete the following steps:

  1. Expand Encode categorical in the list of transforms.
  2. For Transform, choose Ordinal encode.
  3. For Input column, choose gender.
  4. For Invalid handling strategy, choose Keep.
  5. Choose Preview.
  6. Choose Add to add the change to the data flow.

Custom transformations: Add new features to your dataset

If we decide to store our transformed features in Feature Store, a prerequisite is to insert the eventTime feature into the dataset. We can easily do that using a custom transformation.

  1. Expand Custom Transform in the list of transforms.
  2. Choose Python (Pandas) and enter the following line of code: # Table is available as variable `df` import time df[‘eventTime’] = time.time()
  3. Choose Preview to view the results.
  4. Choose Add to add the change to the data flow.

Transform the target Label

The target label readmitted has three classes: NO readmission, readmitted <30 days, and readmitted >30 days. We saw in our histogram analysis that there is a strong class imbalance because the majority of the patients didn’t readmit. We can combine the latter two classes into a positive class to denote the patients being readmitted, and turn the classification problem into a binary case instead of multi-class. Let’s use the search and edit transform to convert string values to binary values.

  1. Expand Search and edit in the list of transforms.
  2. For Transform, choose Find and replace substring.
  3. For Input column, choose readmitted.
  4. For Pattern, enter >30|<30.
  5. For the Replacement string, enter 1.

This converts all the values that have either >30 or <30 values to 1.

  1. Choose Preview to view the results.
  2. Choose Add to add this transform to the data flow.

Let’s repeat the same steps to convert NO values to 0.

  1. Expand Search and edit in the list of transforms.
  2. For Transform, choose Find and replace substring.
  3. For Input column, choose readmitted.
  4. For Pattern, enter NO.
  5. For Replacement string, enter 0.
  6. Choose Preview to review the converted column.
  7. Choose Add to add the transform to our data flow.

Now our target label readmitted is ready for ML training.

Position the target label as the first column to utilize XGBoost algorithm

Because we’re going to use the XGBoost built-in SageMaker algorithm to train the model, the algorithm assumes that the target label is in the first column. Let’s position the target label as such in order to use this algorithm.

  1. Expand Manage columns in the list of transforms.
  2. For Transform, choose Move column.
  3. For Move type, choose Move to start.
  4. For Column to move, choose readmitted.
  5. Choose Preview.
  6. Choose Add to add the change to your data flow.

Drop redundant columns

Next, we drop any redundant columns.

  1. Expand Manage columns in the list of transforms.
  2. For Transform, choose Drop column.
  3. For Column to drop, choose encounter_id_0.
  4. Choose Preview.
  5. Choose Add to add the changes to the flow file.
  6. Repeat these steps for the other redundant columns: patient_nbr_0, encounter_id_1, and patient_nbr_1.

At this stage, we have done a few analyses and applied a few transformations on our raw input dataset. If we choose to preserve the transformed state of the input dataset, like checkpoint, you can do so by choosing Export data. This option allows you to persist the transformed dataset to an S3 bucket.

Quick Model analysis

Now that we have applied transformations to our initial dataset, let’s explore the Quick Model analysis feature. Quick model helps you quickly evaluate the training dataset and produce importance scores for each feature. A feature importance score indicates how useful a feature is at predicting a target label. The feature importance score is between 0–1; a higher number indicates that the feature is more important to the whole dataset. Because our use case relates to the classification problem type, the quick model also generates an F1 score for the current dataset.

  1. Switch back to Analysis Tab and click Create new analysis to bring-up built-in analysis
  2. For Analysis type, choose Quick Model.
  3. Enter a name for your analysis.
  4. For Label, choose readmitted.
  5. Choose Preview and wait for the model to be trained and the results to appear.

The resulting quick model F1 score shows 0.618 (your generated score might be different) with the transformed dataset. Data Wrangler performs several steps to generate the F1 score, including preprocessing, training, evaluating, and finally calculating feature importance. For more details about these steps, see Quick Model.

With the quick model analysis feature, data scientists can iterate through applicable transformations until they have their desired transformed dataset that can potentially lead to better business accuracy and expectations.

  1. Choose Save to add the quick model analysis to the data flow.

Export options

We’re now ready to export our data flow for further processing.

  1. Navigate back to data flow designer by clicking Back to data flow on the top left
  2. On the Export tab, choose Steps to reveal the Data Wrangler flow steps.
  3. Choose the last step to mark it with a check.
  4. Choose Export step to reveal the export options.

As of this writing, you have four export options:

  • Save to S3 – Save the data to an S3 bucket using a SageMaker processing job
  • Pipeline – Export a Jupyter notebook that creates a SageMaker pipeline with your data flow
  • Python Code – Export your data flow to Python code
  • Feature Store – Export a Jupyter notebook that creates a Feature Store feature group and adds features to an offline or online feature store
  1. Choose Save to S3 to generate a fully implemented Jupyter notebook that creates a processing job using your data flow file.

Run processing and training jobs for model building

In this section, we show how to run processing and training jobs using the generated Jupyter notebook from Data Wrangler.

Submit a processing job

We’re now ready to submit a SageMaker processing job using our data flow file.

Run all the cells up to and including the Create Processing Job cell inside the exported notebook.

The cell Create Processing Job triggers a new SageMaker processing job by provisioning managed infrastructure and running the required Data Wrangler Docker container on that infrastructure.

You can check the status of the submitted processing job by running the next cell Job Status & S3 Output Location.

You can also check the status of the submitted processing job on the SageMaker console.

Train a model with SageMaker

Now that the data has been processed, let’s train a model using the data. The same notebook has sample steps to train a model using the SageMaker built-in XGBoost algorithm. Because our use case is a binary classification ML problem, we need to change the objective to binary:logistic inside the sample training steps.

Now we’re ready to run our training job using the SageMaker managed infrastructure. Run the cell Start the Training Job.

You can monitor the status of the submitted training job on the SageMaker console, on the Training jobs page.

Host a trained model for real-time inference

We now use another notebook available on GitHub under the project folder hosting/Model_deployment_Steps.ipynb. This is a simple notebook with two cells: the first cell has code for deploying your model to a persistent endpoint. You need to update model_url with your training job output S3 model artifact.

The second cell in the notebook runs inference on the sample test file under test_data/test_data_UCI_sample.csv. As you can see, we are able to generate predictions for our synthetic observations inside csv file. That concludes the ML workflow.

Clean up

After you have experimented with the steps in this post, perform the following cleanup steps to stop incurring charges:

  1. On the SageMaker console, under Inference in the navigation pane, choose Endpoints.
  2. Select your hosted endpoint.
  3. On the Actions menu, choose Delete.
  4. On the SageMaker Studio Control Panel, navigate to your SageMaker user profile.
  5. Under Apps, locate your Data Wrangler app and choose Delete app.

Conclusion

In this post, we explored Data Wrangler capabilities using a public medical dataset related to patient readmission and demonstrated how to perform feature transformations using built-in transforms and quick analysis. We showed how, without much coding, to generate the required steps to trigger data processing and ML training. This no-code/low-code capability of Data Wrangler accelerates training data preparation and increases data scientist agility with faster iterative data preparation. In the end, we hosted our trained model and ran inferences against synthetic test data. We encourage you to check out our GitHub repository to get hands-on practice and find new ways to improve model accuracy! To learn more about SageMaker, visit the SageMaker Development Guide.

About the Authors

Shyam Namavaram is a Senior Solutions Architect at AWS. He has over 20 years of experience architecting and building distributed, hybrid, and cloud-native applications. He passionately works with customers accelerating their AI/ML adoption by providing technical guidance and helping them innovate and build secure cloud solutions on AWS. He specializes in AI/ML, containers, and analytics technologies. Outside of work, he loves playing sports and exploring nature with trekking.

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



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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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