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Deployment Instructions

In this section, we walk through the following instructions:

  1. Create a deployment environment.
  2. Install prerequisite software packages.
  3. Create an HealthImaging data store.
  4. Configure the metadata-index solution.
  5. Deploy the metadata-index solution.
  6. (Optional) Launch an Amazon EC2 Windows Server instance and install MySQL Workbench.

We will use the EC2 Windows Server instance and MySQL Workbench to query the Aurora MySQL metadata store.

Create deployment environment

Please set up a local deployment environment that has installed Docker and Node.js on a supported Linux platform. Please make sure that your local environment has sufficient compute power (at least 2 vCPU’s), memory (at least 8 GiB), and storage (at least 10 GB).

Alternatively, you can deploy a m5.large EC2 Amazon Linux instance with 10 GB of gp3 storage as a remote deployment environment. For instructions, please refer to the Get started with EC2 documentation, and install Docker and Node.js on the EC2 instance.

On your deployment environment, please install AWS Command Line Interface (AWS CLI) v2, AWS Cloud Development Kit (AWS CDK), and AWS credentials. Please make sure that your AWS credentials are associated with an IAM role or user with sufficient IAM permissions for CDK deployment.

Install prerequisite software packages

You will need the following software packages installed locally to deploy this solution in your AWS account.

Python3/pip:
The deployment automation code is written in Python.

CDK:
Please refer to CDK documentation to install the framework and bootstrap your AWS environment.

Docker:
When running the CDK deploy command, the container images will automatically be built (via docker build command), and sent to AWS ECR registry. Docker must be present on the machine where the cdk deployment is executed. Docker desktop is sufficient. Refer to Docker Desktop documentation to install it on your machine.

Compatible region:
As of 09/01/2024 this project is compatible in the following regions: US East (N. Virginia), US West (Oregon), Asia Pacific (Sydney), and Europe (Ireland).

Create AWS HealthImaging data store

If you do not have an existing HealthImaging data store, you can create one by following the instructions for creating a data store in the Introduction to AWS HealthImaging workshop.

Configure the metadata-index solution

In this section, we walk through the following instructions:

  1. Download metadata-index solution directory.
  2. Edit config.py file: [project root]/backend/config.py.
  3. Edit cdk.context.json file: [project root]/backend/cdk.context.json.

Download metadata-index solution directory

  1. From your deployment environment, clone the aws-healthimaging-samples repository:
git clone https://github.com/aws-samples/aws-healthimaging-samples.git
  1. Change your working directory to the metadata-index solution directory:
cd aws-healthimaging-samples/metadata-index/backend

Edit config.py file

The config.py file is located at: [project root]/backend/config.py.

At a minimum, you must perform the following changes:

  1. Copy the Amazon Resource Name (ARN) of your HealthImaging data store.
  2. Set the AHI_DATASTORE_ARN parameter to the ARN of your HealthImaging data store.

In addition, you can change the following parameters:

section Parameter Default value Description
ROOT CDK_APP_NAME metadata-index Name of the solution. This name will be use to tag all the resources created by the solution. If you intend to deploy multiple instance of this solution on the same AWS account make sure to change this name for each deployment.
ROOT AHI_DATASTORE_ARN "" The ARN of the AHI datastore for which the metadata should be indexed. The solution will set appropriate privileges for the Lambda parsers to request the metdata.
RDBMS_CONFIG enabled False Enables the RDBMS index mode. Enabling this will deploy an Aurora serverless MYSQL database and the RDBMS Lambda parser.
RDBMS_CONFIG populate_instance_level False Specifies if the index should populate the instance level of the DICOM data. if set to `False` only the issuer, patient, study and series tables will be populated.
RDBMS_CONFIG populate_frame_level False Specifies if the index should populate the frame level of the DICOM data. if set to "True" the instance level will also be populated regardless of the `populate_instance_level` setting.
RDBMS_CONFIG db_name ahiindex Name of the database.
RDBMS_CONFIG db_engine_pause 20 Number of minutes before the Aurora MYSQL goes on sleep if idling. ( no SQL operations done)
RDBMS_CONFIG min_acu_capacity 1 Minimum resource allocation for the Aurora MYSQL engine.
RDBMS_CONFIG max_acu_capacity 16 Maximum resource allocation for the Aurora MYSQL engine.
DATALAKE_CONFIG enabled True Enables the Datalake index mode. Enabling this will deploy an S3 bucket if no exisiting bucket is specified, and the Datalake Lambda parser.
DATALAKE_CONFIG populate_instance_level True Specifies if the index should populate the instance level of the DICOM data. if set to `False` only the issuer, patient, study and series tables will be populated.
DATALAKE_CONFIG destination_bucket_name "" The name of the bucket to be use as the datalake repository. If left empty the solution will create a new bucket for this purpose.
DATALAKE_CONFIG deploy_glue_default_config True Set to True to deploy a default database, tables and crawler in Glue. This allows for using Athena and QuickSight out of the box. table schemas can be modified in the file datalake_tables_config. Set it to False if you plan on using your own schemas.
OPENSEARCH_CONFIG enabled False Enable the OpenSearch index mode. THIS MODE IS NOT IMPLEMENTTED YET.

Edit cdk.context.json file

The cdk.context.json is located at: [project root]/backend/cdk.context.json.

At a minimum, you must perform the following changes:

  1. Set the ACCOUNT_NUMBER to the AWS Account ID of your deployment account.
  2. Set the REGION to the AWS Region of your deployment region. Default value is us-east-1.

In addition, you can change the following parameters:

section Parameter Default value Description
availability-zones ACCOUNT_NUMBER "" AWS Account ID of the deployment account.
availability-zones REGION us-east-1 AWS Region for the deployment region. If you change the default region, please change the list of availability zones to match the changed region.

Deploy the metadata-index solution

Perform the following instructions to deploy the metadata-index solution.

The cdk deploy command in the last step takes about 25 to 30 minutes to complete.

Before proceeding with next steps, confirm that the solution deployment completes successfully.

  1. From [project root]/, create a Python virtual environment on MacOS and Linux:
python3 -m venv .venv
  1. After the init process completes and the Python virtual environment has been created, you can use the following command to activate your Python virtual environment.
source .venv/bin/activate
  1. If you are on a Windows platform, you should use the following command to activate your Python virtual environment:
.venv\Scripts\activate.bat
  1. Once the Python virtual environment has been activated, navigate to the [project root]/backend/ folder.
cd backend
  1. Install the required dependencies.
pip install -r requirements.txt
  1. If it is the first time that you are using CDK to deploy in this account and region, bootstrap for CDK deployment:
cdk bootstrap
  1. Use CDK to synthetize and deploy the CloudFormation template for this code.
cdk deploy

(Optional) Launch an EC2 Windows Server and install MySQL Workbench

In order to facilitate querying and testing on the Aurora MySQL metadata store, you can optionally deploy a m5.large EC2 Windows Server instance (Microsoft Windows Server 2022 Base) with 30 GB of gp3 storage into metadata-index-Lambdas-SG security group, and install MySQL Workbench (version 8.0.41) on the EC2 Windows Server instance.

For instructions, please refer to the Get started with EC2 and Installing MySQL Workbench on Windows documentation.