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Copy file name to clipboardExpand all lines: content/patterns/medical-diagnosis/_index.adoc
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:_content-type: ASSEMBLY
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include::modules/comm-attributes.adoc[]
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== Background
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//Module to be included
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//:_content-type: CONCEPT
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//:imagesdir: ../../images
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[id="about-med-diag-pattern"]
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= About the {med-pattern}
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This Validated Pattern is based on a demo implementation of an automated data pipeline for chest Xray
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analysis previously developed by Red Hat. The original demo can be found link:https://github.com/red-hat-data-services/jumpstart-library[here]. It was developed for the US Department of Veteran Affairs.
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Background::
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This validated pattern includes the same functionality as the original demonstration. The difference is
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that we use the _GitOps_ framework to deploy the pattern including operators, creation of namespaces,
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and cluster configuration. Using GitOps provides a much more efficient means of doing continuous deployment.
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This validated pattern is based on a demo implementation of an automated data pipeline for chest Xray analysis previously developed by Red Hat. You can find the original demonstration link:https://github.com/red-hat-data-services/jumpstart-library[here]. It was developed for the US Department of Veteran Affairs.
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What does this pattern do?:
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This validated pattern includes the same functionality as the original demonstration. The difference is that we use the GitOps framework to deploy the pattern including Operators, creation of namespaces, and cluster configuration. Using GitOps provides an efficient means of implementing continuous deployment.
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* Ingest chest Xrays from a simulated Xray machine and puts them into an objectStore based on Ceph.
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* The objectStore sends a notification to a Kafka topic.
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Workflow::
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* Ingest chest Xrays from a simulated Xray machine and puts them into an `objectStore` based on Ceph.
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* The `objectStore` sends a notification to a Kafka topic.
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* A KNative Eventing Listener to the topic triggers a KNative Serving function.
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* An ML-trained model running in a container makes a risk assessment of Pneumonia for incoming images.
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* A Grafana dashboard displays the pipeline in real time, along with images incoming, processed and anonymized, as well as full metrics collected from Prometheus.
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* A Grafana dashboard displays the pipeline in real time, along with images incoming, processed, and anonymized, as well as full metrics collected from Prometheus.
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This pipeline is showcased link:https://www.youtube.com/watch?v=zja83FVsm14[in this video].
This validated pattern is still under development. Any questions or concerns
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please contact mailto:jrickard@redhat.com[Jonny Rickard] or mailto:claudiol@redhat.com[Lester Claudio].
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[WARNING]
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====
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This validated pattern is still under development. If you have any questions or concerns contact mailto:jrickard@redhat.com[Jonny Rickard] or mailto:claudiol@redhat.com[Lester Claudio]
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====
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=== Solution elements
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[id="about-solution-med"]
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== About the solution elements
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* How to use a GitOps approach to keep in control of configuration and operations
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The solution aids the understanding of the following:
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* How to use a GitOps approach to keep in control of configuration and operations.
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* How to deploy AI/ML technologies for medical diagnosis using GitOps.
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=== Red Hat Technologies
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The {med-pattern} uses the following Red Hat products and technologies:
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* {rh-ocp} (Kubernetes)
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* {rh-gitops} (ArgoCD)
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* Red Hat AMQ Streams (Apache Kafka Event Broker)
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* Red Hat OpenShift Serverless (Knative Eventing, Knative Serving)
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* Red Hat OpenShift Data Foundations (Cloud Native storage)
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* Grafana dashboard (OpenShift Grafana Operator)
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* {rh-ocp} for container orchestration
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* {rh-gitops}, a GitOps continuous delivery (CD) product for Kubernetes.
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* {rh-amq-first}, an event streaming platform based on the Apache Kafka
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* {rh-serverless-first} for event-driven applications
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* {rh-ocp-data-first} for cloud native storage capabilities
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* {grafana-op} to manage and share Grafana dashboards, datasources, and so on
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* Open Data Hub
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* S3 storage
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== Architecture
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[id="about-architecture-med"]
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== About the architecture
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In this iteration of the pattern *there is no edge component* . Future releases have planned Edge deployment capabilities as part of the pattern architecture.
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[IMPORTANT]
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====
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There is no edge component in this iteration of the pattern. Edge deployment capabilities are planned part of the pattern architecture for a future release.
To know the minimum requirements for an {ocp} cluster, for example on bare-metal, see link: https://docs.openshift.com/container-platform/4.13/installing/installing_bare_metal/installing-bare-metal.html#installation-minimum-resource-requirements_installing-bare-metal[Minimum resource requirements for cluster installation].
=== Medical Diagnosis Pattern OpenShift Cluster Size
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=== About {med-pattern} OpenShift Cluster Size
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The Medical Diagnosis pattern has been tested with a defined set of configurations that represent the most common combinations that Red Hat OpenShift Container Platform (OCP) customers are using or deploying for the x86_64 architecture.
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| Standard_D8s_v3
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|===
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[id="aws-instance-types-cluster-sizing"]
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=== AWS Instance Types
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The *Medical Diagnosis* pattern was tested with the highlighted AWS instances in *bold*. The OpenShift installer will let you know if the instance type meets the minimum requirements for a cluster.
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The message that the openshift installer will give you will be similar to this message
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[,text]
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----
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INFO Credentials loaded from default AWS environment variables
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FATAL failed to fetch Metadata: failed to load asset "Install Config": [controlPlane.platform.aws.type: Invalid value: "m4.large": instance type does not meet minimum resource requirements of 4 vCPUs, controlPlane.platform.aws.type: Invalid value: "m4.large": instance type does not meet minimum resource requirements of 16384 MiB Memory]
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----
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Below you can find a list of the AWS instance types that can be used to deploy the *Medical Diagnosis* pattern.
The OpenShift cluster is made of 3 Control Plane nodes and 3 Workers. For the node sizes we used the *m5.4xlarge* on AWS and this instance type met the minimum requirements to deploy the *Medical Diagnosis* pattern successfully.
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To understand better what types of nodes you can use on other Cloud Providers we provide some of the details below.
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[id="azure-instance-types-cluster-sizing"]
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=== Azure Instance Types
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The *Medical Diagnosis* pattern was also deployed on Azure using the *Standard_D8s_v3* VM size. Below is a table of different VM sizes available for Azure. Keep in mind that due to limited access to Azure we only used the *Standard_D8s_v3* VM size.
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The OpenShift cluster is made of 3 Control Plane nodes and 3 Workers.
| Specialized virtual machines targeted for heavy graphic rendering and video editing, as well as model training and inferencing (ND) with deep learning. Available with single or multiple GPUs.
The *Medical Diagnosis* pattern was also deployed on GCP using the *n1-standard-8* VM size. Below is a table of different VM sizes available for GCP. Keep in mind that due to limited access to GCP we only used the *n1-standard-8* VM size.
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The OpenShift cluster is made of 3 Control Plane and 3 Workers cluster.
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The following table provides VM recommendations for different workloads.
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