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Anomaly detection with Amazon SageMaker Edge Manager using AWS IoT Greengrass V2

Deploying and managing machine learning (ML) models at the edge requires a different set of tools and skillsets as compared to the cloud. This is primarily due to the hardware, software, and networking restrictions at the edge sites. This makes deploying and managing these models more complex. An increasing number of applications, such as industrial…

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Deploying and managing machine learning (ML) models at the edge requires a different set of tools and skillsets as compared to the cloud. This is primarily due to the hardware, software, and networking restrictions at the edge sites. This makes deploying and managing these models more complex. An increasing number of applications, such as industrial automation, autonomous vehicles, and automated checkouts, require ML models that run on devices at the edge so predictions can be made in real time when new data is available.

Another common challenge you may face when dealing with computing applications at the edge is how to efficiently manage the fleet of devices at scale. This includes installing applications, deploying application updates, deploying new configurations, monitoring device performance, troubleshooting devices, authenticating and authorizing devices, and securing the data transmission. These are foundational features for any edge application, but creating the infrastructure needed to achieve a secure and scalable solution requires a lot of effort and time.

On a smaller scale, you can adopt solutions such as manually logging in to each device to run scripts, use automated solutions such as Ansible, or build custom applications that rely on services such as AWS IoT Core. Although it can provide the necessary scalability and reliability, building such custom solutions comes at the cost of additional maintenance and requires specialized skills.

Amazon SageMaker, together with AWS IoT Greengrass, can help you overcome these challenges.

SageMaker provides Amazon SageMaker Neo, which is the easiest way to optimize ML models for edge devices, enabling you to train ML models one time in the cloud and run them on any device. As devices proliferate, you may have thousands of deployed models running across your fleets. Amazon SageMaker Edge Manager allows you to optimize, secure, monitor, and maintain ML models on fleets of smart cameras, robots, personal computers, and mobile devices.

This post shows how to train and deploy an anomaly detection ML model to a simulated fleet of wind turbines at the edge using features of SageMaker and AWS IoT Greengrass V2. It takes inspiration from Monitor and Manage Anomaly Detection Models on a fleet of Wind Turbines with Amazon SageMaker Edge Manager by introducing AWS IoT Greengrass for deploying and managing inference application and the model on the edge devices.

In the previous post, the author used custom code relying on AWS IoT services, such as AWS IoT Core and AWS IoT Device Management, to provide the remote management capabilities to the fleet of devices. Although that is a valid approach, developers need to spend a lot of time and effort to implement and maintain such solutions, which they could spend on solving the business problem of providing efficient, performant, and accurate anomaly detection logic for the wind turbines.

The previous post also used a real 3D printed mini wind turbine and Jetson Nano to act as the edge device running the application. Here, we use virtual wind turbines that run as Python threads within a SageMaker notebook. Also, instead of Jetson Nano, we use Amazon Elastic Compute Cloud (Amazon EC2) instances to act as edge devices, running AWS IoT Greengrass software and the application. We also run a simulator to generate measurements for the wind turbines, which are sent to the edge devices using MQTT. We also use the simulator for visualizations and stopping or starting the turbines.

The previous post goes more in detail about the ML aspects of the solution, such as how to build and train the model, which we don’t cover here. We focus primarily on the integration of Edge Manager and AWS IoT Greengrass V2.

Before we go any further, let’s review what AWS IoT Greengrass is and the benefits of using it with Edge Manager.

What is AWS IoT Greengrass V2?

AWS IoT Greengrass is an Internet of Things (IoT) open-source edge runtime and cloud service that helps build, deploy, and manage device software. You can use AWS IoT Greengrass for your IoT applications on millions of devices in homes, factories, vehicles, and businesses. AWS IoT Greengrass V2 offers an open-source edge runtime, improved modularity, new local development tools, and improved fleet deployment features. It provides a component framework that manages dependencies, and allows you to reduce the size of deployments because you can choose to only deploy the components required for the application.

Let’s go through some of the concepts of AWS IoT Greengrass to understand how it works:

  • AWS IoT Greengrass core device – A device that runs the AWS IoT Greengrass Core software. The device is registered into the AWS IoT Core registry as an AWS IoT thing.
  • AWS IoT Greengrass component – A software module that is deployed to and runs on a core device. All software that is developed and deployed with AWS IoT Greengrass is modeled as a component.
  • Deployment – The process to send components and apply the desired component configuration to a destination target device, which can be a single core device or a group of core devices.
  • AWS IoT Greengrass core software – The set of all AWS IoT Greengrass software that you install on a core device.

To enable remote application management on a device (or thousands of them), we first install the core software. This software runs as a background process and listens to deployments configurations sent from the cloud.

To run specific applications on the devices, we model the application as one or more components. For example, we can have a component providing a database feature, another component providing a local UX, or we can use public components provided by AWS, such as LogManager to push the components logs to Amazon CloudWatch.

We then create a deployment containing the necessary components and their specific configuration and send it to the target devices, either on a device-by-device basis or as a fleet.

To learn more, refer to What is AWS IoT Greengrass?

Why use AWS IoT Greengrass with Edge Manager?

The post Monitor and Manage Anomaly Detection Models on a fleet of Wind Turbines with Amazon SageMaker Edge Manager already explains why we use Edge Manager to provide the ML model runtime for the application. But let’s understand why we should use AWS IoT Greengrass to deploy applications to edge devices:

  • With AWS IoT Greengrass, you can automate the tasks needed to deploy the Edge Manager software onto the devices and manage the ML models. AWS IoT Greengrass provides a SageMaker Edge Agent as an AWS IoT Greengrass component, which provides model management and data capture APIs on the edge. Without AWS IoT Greengrass, setting up devices and fleets to use Edge Manager requires you to manually copy the Edge Manager agent from an Amazon Simple Storage Service (Amazon S3) release bucket. The agent is used to make predictions with models loaded onto edge devices.
  • With AWS IoT Greengrass and Edge Manager integration, you use AWS IoT Greengrass components. Components are pre-built software modules that can connect edge devices to AWS services or third-party services via AWS IoT Greengrass.
  • The solution takes a modular approach in which the inference application, model, and any other business logic can be packaged as a component where the dependencies can also be specified. You can manage the lifecycle, updates, and reinstalls of each of the components independently rather than treat everything as a monolith.
  • To make it easier to maintain AWS Identity and Access Management (IAM) roles, Edge Manager allows you to reuse the existing AWS IoT Core role alias. If it doesn’t exist, Edge Manager generates a role alias as part of the Edge Manager packaging job. You no longer need to associate a role alias generated from the Edge Manager packaging job with an AWS IoT Core role. This simplifies the deployment process for existing AWS IoT Greengrass customers.
  • You can manage the models and other components with less code and configurations because AWS IoT Greengrass takes care of provisioning, updating, and stopping the components.

Solution overview

The following diagram is the architecture implemented for the solution:

We can broadly divide the architecture into the following phases:

  • Model training
    • Prepare the data and train an anomaly detection model using Amazon SageMaker Pipelines. SageMaker Pipelines helps orchestrate your training pipeline with your own custom code. It also outputs the Mean Absolute Error (MAE) and other threshold values used to calculate anomalies.
  • Compile and package the model
    • Compile the model using Neo, so that it can be optimized for the target hardware (in this case, an EC2 instance).
    • Use the SageMaker Edge packaging job API to package the model as an AWS IoT Greengrass component. The Edge Manager API has a native integration with AWS IoT Greengrass APIs.
  • Build and package the inference application
    • Build and package the inference application as an AWS IoT Greengrass component. This application uses the computed threshold, the model, and some custom code to accept the data coming from turbines, perform anomaly detection, and return results.
  • Set up AWS IoT Greengrass on edge devices
  • Deploy to edge devices
    • Deploy the following on each edge device:
      • An ML model packaged as an AWS IoT Greengrass component.
      • An inference application packaged an AWS IoT Greengrass component. This also sets up the connection to AWS IoT Core MQTT.
      • The AWS-provided Edge Manager Greengrass component.
      • The AWS-provided AWS IoT Greengrass CLI component (only needed for development and debugging purposes).
  • Run the end-to-end solution
    • Run the simulator, which generates measurements for the wind turbines, which are sent to the edge devices using MQTT.
    • Because the notebook and the EC2 instances running AWS IoT Greengrass are on different networks, we use AWS IoT Core to relay MQTT messages between them. In a real scenario, the wind turbine would send the data to the anomaly detection device using a local communication, for example, an AWS IoT Greengrass MQTT broker component.
    • The inference app and model running in the anomaly detection device predicts if the received data is anomalous or not, and sends the result to the monitoring application via MQTT through AWS IoT Core.
    • The application displays the data and anomaly signal on the simulator dashboard.

To know more on how to deploy this solution architecture, please refer to the GitHub Repository related to this post.

In the following sections, we go deeper into the details of how to implement this solution.

Dataset

The solution uses raw turbine data collected from real wind turbines. The dataset is provided as part of the solution. It has the following features:

  • nanoId – ID of the edge device that collected the data
  • turbineId – ID of the turbine that produced this data
  • arduino_timestamp – Timestamp of the Arduino that was operating this turbine
  • nanoFreemem: Amount of free memory in bytes
  • eventTime – Timestamp of the row
  • rps – Rotation of the rotor in rotations per second
  • voltage – Voltage produced by the generator in milivolts
  • qw, qx, qy, qz – Quaternion angular acceleration
  • gx, gy, gz – Gravity acceleration
  • ax, ay, az – Linear acceleration
  • gearboxtemp – Internal temperature
  • ambtemp – External temperature
  • humidity – Air humidity
  • pressure – Air pressure
  • gas – Air quality
  • wind_speed_rps – Wind speed in rotations per second

For more information, refer to Monitor and Manage Anomaly Detection Models on a fleet of Wind Turbines with Amazon SageMaker Edge Manager.

Data preparation and training

The data preparation and training are performed using SageMaker Pipelines. Pipelines is the first purpose-built, easy-to-use continuous integration and continuous delivery (CI/CD) service for ML. With Pipelines, you can create, automate, and manage end-to-end ML workflows at scale. Because it’s purpose-built for ML, Pipelines helps automate different steps of the ML workflow, including data loading, data transformation, training and tuning, and deployment. For more information, refer to Amazon SageMaker Model Building Pipelines.

Model compilation

We use Neo for model compilation. It automatically optimizes ML models for inference on cloud instances and edge devices to run faster with no loss in accuracy. ML models are optimized for a target hardware platform, which can be a SageMaker hosting instance or an edge device based on processor type and capabilities, for example if there is a GPU or not. The compiler uses ML to apply the performance optimizations that extract the best available performance for your model on the cloud instance or edge device. For more information, see Compile and Deploy Models with Neo.

Model packaging

To use a compiled model with Edge Manager, you first need to package it. In this step, SageMaker creates an archive consisting of the compiled model and the Neo DLR runtime required to run it. It also signs the model for integrity verification. When you deploy the model via AWS IoT Greengrass, the create_edge_packaging_job API automatically creates an AWS IoT Greengrass component containing the model package, which is ready to be deployed to the devices.

The following code snippet shows how to invoke this API:

model_version = ‘1.0.0’ # use this for semantic versioning the model. Must increment for every new model model_name = ‘WindTurbineAnomalyDetection’ edge_packaging_job_name=’wind-turbine-anomaly-%d’ % int(time.time()*1000) component_name = ‘aws.samples.windturbine.model’ component_version = model_version resp = sm_client.create_edge_packaging_job( EdgePackagingJobName=edge_packaging_job_name, CompilationJobName=compilation_job_name, ModelName=model_name, ModelVersion=model_version, RoleArn=role, OutputConfig={ ‘S3OutputLocation’: ‘s3://%s/%s/model/’ % (bucket_name, prefix), “PresetDeploymentType”: “GreengrassV2Component”, “PresetDeploymentConfig”: json.dumps( {“ComponentName”: component_name, “ComponentVersion”: component_version} ), } )

To allow the API to create an AWS IoT Greengrass component, you must provide the following additional parameters under OutputConfig:

  • The PresetDeploymentType as GreengrassV2Component
  • PresetDeploymentConfig to provide the ComponentName and ComponentVersion that AWS IoT Greengrass uses to publish the component
  • The ComponentVersion and ModelVersion must be in major.minor.patch format

The model is then published as an AWS IoT Greengrass component.

Create the inference application as an AWS IoT Greengrass component

Now we create an inference application component that we can deploy to the device. This application component loads the ML model, receives data from wind turbines, performs anomaly detections, and sends the result back to the simulator. This application can be a native application that receives the data locally on the edge devices from the turbines or any other client application over a gRPC interface.

To create a custom AWS IoT Greengrass component, perform the following steps:

  1. Provide the code for the application as single files or as an archive. The code needs to be uploaded to an S3 bucket in the same Region where we registered the AWS IoT Greengrass devices.
  2. Create a recipe file, which specifies the component’s configuration parameters, component dependencies, lifecycle, and platform compatibility.

The component lifecycle defines the commands that install, run, and shut down the component. For more information, see AWS IoT Greengrass component recipe reference. We can define the recipe either in JSON or YAML format. Because the inference application requires the model and Edge Manager agent to be available on the device, we need to specify dependencies to the ML model packaged as an AWS IoT Greengrass component and the Edge Manager Greengrass component.

  1. When the recipe file is ready, create the inference component by invoking the create_component_version API. See the following code: ggv2_client = boto3.client(‘greengrassv2’) with open(‘recipes/aws.samples.windturbine.detector-recipe.json’) as f: recipe = f.read() recipe = recipe.replace(‘_BUCKET_’, bucket_name) ggv2_client.create_component_version(inlineRecipe=recipe )

Inference application

The inference application connects to AWS IoT Core to receive messages from the simulated wind turbine and send the prediction results to the simulator dashboard.

It publishes to the following topics:

  • wind-turbine/{turbine_id}/dashboard/update – Updates the simulator dashboard
  • wind-turbine/{turbine_id}/label/update – Updates the model loaded status on simulator
  • wind-turbine/{turbine_id}/anomalies – Publishes anomaly results to the simulator dashboard

It subscribes to the following topic:

  • wind-turbine/{turbine_id}/raw-data – Receives raw data from the turbine

Set up AWS IoT Core devices

Next, we need to set up the devices that run the anomaly detection application by installing the AWS IoT Greengrass core software. For this post, we use five EC2 instances that act as the anomaly detection devices. We use AWS CloudFormation to launch the instances. To install the AWS IoT Greengrass core software, we provide a script in the instance UserData as shown in the following code:

UserData: Fn::Base64: !Sub “#!/bin/bash wget -O- https://apt.corretto.aws/corretto.key | apt-key add – add-apt-repository ‘deb https://apt.corretto.aws stable main’ apt-get update; apt-get install -y java-11-amazon-corretto-jdk apt install unzip -y apt install python3-pip -y apt-get install python3.8-venv -y ec2_region=$(curl http://169.254.169.254/latest/meta-data/placement/region) curl -s https://d2s8p88vqu9w66.cloudfront.net/releases/greengrass-nucleus-latest.zip > greengrass-nucleus-latest.zip && unzip greengrass-nucleus-latest.zip -d GreengrassCore java -Droot=”/greengrass/v2″ -Dlog.store=FILE -jar ./GreengrassCore/lib/Greengrass.jar –aws-region $ec2_region –thing-name edge-device-0 –thing-group-name ${ThingGroupName} –tes-role-name SageMaker-WindturbinesStackTESRole –tes-role-alias-name SageMaker-WindturbinesStackTESRoleAlias –component-default-user ggc_user:ggc_group –provision true –setup-system-service true –deploy-dev-tools true ”

Each EC2 instance is associated to a single virtual wind turbine. In a real scenario, multiple wind turbines could also communicate to a single device in order to reduce the solution costs.

To learn more about how to set up AWS IoT Greengrass software on a core device, refer to Install the AWS IoT Greengrass Core software. The complete CloudFormation template is available in the GitHub repository.

Create an AWS IoT Greengrass deployment

When the devices are up and running, we can deploy the application. We create a deployment with a configuration containing the following components:

  • ML model
  • Inference application
  • Edge Manager
  • AWS IoT Greengrass CLI (only needed for debugging purposes)

For each component, we must specify the component version. We can also provide additional configuration data, if necessary. We create the deployment by invoking the create_deployment API. See the following code:

ggv2_deployment = ggv2_client.create_deployment( targetArn=wind_turbine_thing_group_arn, deploymentName=”Deployment for ” + project_id, components={ “aws.greengrass.Cli”: { “componentVersion”: “2.5.3” }, “aws.greengrass.SageMakerEdgeManager”: { “componentVersion”: “1.1.0”, “configurationUpdate”: { “merge”: json.dumps({“DeviceFleetName”:wind_turbine_device_fleet_name,”BucketName”:bucket_name}) }, “runWith”: {} }, “aws.samples.windturbine.detector”: { “componentVersion”: component_version }, “aws.samples.windturbine.model”: { “componentVersion”: component_version } })

The targetArn argument defines where to run the deployment. The thing group ARN is specified to deploy this configuration to all devices belonging to the thing group. The thing group is created already as part of the setup of the solution architecture.

The aws.greengrass.SageMakerEdgeManager component is an AWS-provided component by AWS IoT Greengrass. At the time of writing, the latest version is 1.1.0. You need to configure this component with the SageMaker edge device fleet name and S3 bucket location. You can find these parameters on the Edge Manager console, where the fleet was created during the setup of the solution architecture.

aws.samples.windturbine.detector is the inference application component created earlier.

aws.samples.windturbine.model is the anomaly detection ML model component created earlier.

Run the simulator

Now that everything is in place, we can start the simulator. The simulator is run from a Python notebook and performs two tasks:

  1. Simulate the physical wind turbine and display a dashboard for each wind turbine.
  2. Exchange data with the devices via AWS IoT MQTT using the following topics:
    1. wind-turbine/{turbine_id}/raw-data – Publishes the raw turbine data.
    2. wind-turbine/{turbine_id}/label/update – Receives model loaded or not loaded status from the inference application.
    3. wind-turbine/{turbine_id}/anomalies – Receives anomalies published by inference application.
    4. wind-turbine/{turbine_id}/dashboard/update – Receives recent buffered data by the turbines.

We can use the simulator UI to start and stop the virtual wind turbine and inject noise in the Volt, Rot, and Vib measurements to simulate anomalies that are detected by the application running on the device. In the following screenshot, the simulator shows a virtual representation of five wind turbines that are currently running. We can choose Stop to stop any of the turbines, or choose Volt, Rot, or Vib to inject noise in the turbines. For example, if we choose Volt for turbine with ID 0, the Voltage status changes from a green check mark to a red x, denoting the voltage readings of the turbine are anomalous.

Conclusion

Securely and reliably maintaining the lifecycle of an ML model deployed across a fleet of devices isn’t an easy task. However, with Edge Manager and AWS IoT Greengrass, we can reduce the implementation effort and operational cost of such a solution. This solution increases the agility in experimenting and optimizing the ML model with full automation of the ML pipelines, from data acquisition, data preparation, model training, model validation, and deployment to the devices.

In addition to the benefits described, Edge Manager offers further benefits, like having access to a device fleet dashboard on the Edge Manager console, which can display near-real-time health of the devices by capturing heartbeat requests. You can use this inference data with Amazon SageMaker Model Monitor to check for data and model quality drift issues.

To build a solution for your own needs, get the code and artifacts from the GitHub repo. The repository shows two different ways of deploying the models:

  • Using IoT jobs
  • Using AWS IoT Greengrass (covered in this post)

Although this post focuses on deployment using AWS IoT Greengrass, interested readers look at the solution using IoT jobs as well to better understand the differences.

About the Authors

Vikesh Pandey is a Machine Learning Specialist Specialist Solutions Architect at AWS, helping customers in the Nordics and wider EMEA region design and build ML solutions. Outside of work, Vikesh enjoys trying out different cuisines and playing outdoor sports.

Massimiliano Angelino is Lead Architect for the EMEA Prototyping team. During the last 3 and half years he has been an IoT Specialist Solution Architect with a particular focus on edge computing, and he contributed to the launch of AWS IoT Greengrass v2 service and its integration with Amazon SageMaker Edge Manager. Based in Stockholm, he enjoys skating on frozen lakes.



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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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