validatedpatterns-sandbox/amx-accelerated-medical-diagnosis

Hardware Acceleration Support for the Image Classification/Medical Diagnosis Pattern

Medical Diagnosis Validated Pattern

Live build status

On-Prem customization

Before deploying validated pattern:

All required fields to fill are described in 'values-global.yaml'.

If Validated pattern is installed using operator from OperatorHub user must type in secrets (from values-secret.yaml) into vault manually. Root token to vault can be found here:

oc -n imperative get secrets vaultkeys -ojsonpath='{.data.vault_data_json}' | base64 -d

To make secrets populate into vault automatically, please install validated pattern using make command.

After validated pattern is deployed please execute following steps:

1. Install aws-cli

Instead of external S3 bucket we set up ceph rgw object storage. To communicate with its API user can utilize aws-cli. Installation instruction: https://docs.aws.amazon.com/cli/latest/userguide/getting-started-install.html#getting-started-install-instructions

1. Set up local s3 object storage bucket using Ceph RGW. Ceph RGW should be deployed by default by validated pattern

User can get CEPH_RGW_ENDPOINT by executing command:

oc -n openshift-storage get route ocs-storagecluster-cephobjectstore -ojsonpath='{.spec.host}'

AWS_ACCESS_KEY_ID and AWS_SECRET_ACCESS_KEY of RGW object store can be found by performing following commands:

oc -n xraylab-1 get secret s3-secret-bck -ojsonpath='{.data.AWS_ACCESS_KEY_ID}' | base64 -d
oc -n xraylab-1 get secret s3-secret-bck -ojsonpath='{.data.AWS_SECRET_ACCESS_KEY}' | base64 -d

Ceph rgw bucket needs specific bucket policy to be applied 'bucket-policy.json':

{
"Statement": [
                {
                "Sid": "listobjs",
                "Effect": "Allow",
                "Principal": "*",
                "Action": "s3:*",
                "Resource": "*"
                }
                ]
}

To apply bucket policy execute command:

export AWS_ACCESS_KEY_ID=xxxx
export AWS_SECRET_ACCESS_KEY=xxx

aws --endpoint https://CEPH_RGW_ENDPOINT --no-verify-ssl s3api put-bucket-policy --bucket CEPH_BUCKET_NAME --policy file://PATH_TO_BUCKET_POLICY/bucket-policy.json

1. Clone repository with xray images and push them to the bucket

https://github.com/red-hat-data-services/jumpstart-library/tree/main/demo1-xray-pipeline/base_elements/containers/image-init/base_images

export AWS_ACCESS_KEY_ID=xxxx
export AWS_SECRET_ACCESS_KEY=xxx

aws --endpoint https://CEPH_RGW_ENDPOINT --no-verify-ssl s3api create-bucket --bucket CEPH_BUCKET_NAME
aws --endpoint https://CEPH_RGW_ENDPOINT --no-verify-ssl s3 cp base_images/ s3://CEPH_BUCKET_NAME/ --recursive

Cluster HW requirements

On-prem version was tested with 3 master and 3 worker nodes:

• 12 vCPU each (for future 36 vCPU per worker may be required)
• 40 GB memory
• 100 GB disk space for OS
• Storage: 400 GB

XRay analysis automated pipeline

This Validated Pattern is based on a demo implemetation of an automated data pipeline for chest Xray analysis previously developed by Red Hat. The original demo can be found here.

The Validated Pattern includes the same functionality as the original demonstration. The difference is that we use the GitOps to deploy most of the components which includes operators, creation of namespaces, and cluster configuration.

The Validated Pattern includes:

• Ingest chest Xrays into an object store based on Ceph.
• The Object store sends notifications to a Kafka topic.
• A KNative Eventing Listener to the topic triggers a KNative Serving function.
• An ML-trained model running in a container makes a risk of Pneumonia assessment for incoming images.
• A Grafana dashboard displays the pipeline in real time, along with images incoming, processed and anonymized, as well as full metrics.

This pipeline is showcased in this video.

Check the values files before deployment

You can run a check before deployment to make sure that you have the required variables to deploy the Medical Diagnosis Validated Pattern.

You can run make predeploy to check your values. This will allow you to review your values and changed them in the case there are typos or old values. The values files that should be reviewed prior to deploying the Medical Diagnosis Validated Pattern are:

Values File	Description
values-secret.yaml	This is the values file that will include the xraylab section with all the database secrets
values-global.yaml	File that is used to contain all the global values used by Helm

Make sure you have the correct domain, clustername, externalUrl, targetBucket and bucketSource values.

Then you can run make install to deploy the Medical Diagnosis Validated Pattern.

This validated pattern is still being developed. More to come in the next few weeks. Any questions or concerns please contact Jonny Rickard or Lester Claudio.