remerged from internal repo

Author: amgowda-oci

GETTING_STARTED_CONSOLE_DEPLOY.md

@@ -124,6 +124,7 @@ helm install lens oci-ai-incubations/lens -n lens --create-namespace \
   --set grafana.adminPassword="access password for grafana portal. User name is admin by default" \
  
 ```
+
 ## Verify for successful install
 
 Once the installation is complete you should see the following pods in the "lens" namespace. If you don't please uninstall and reinstall or check the helm install events/logs. 
@@ -226,23 +227,17 @@ helm install lens ./helm -n lens \
   --set backend.image.tag=stable
 ```
 
-### Uninstall the control plane components 
-```bash
-helm uninstall lens -n lens
-```
-
-
 ## Step 2: OCI GPU Data Plane Plugin installation on GPU Nodes
 
-1. **Navigate to Dashboards**: Go to the dashboard section
+1. **Navigate to Dashboards**: Go to the dashboard section of the OCI GPU Scanner Portal
 2. **Go to Tab - OCI GPU Scanner Install Script**:
    - You can use the script there and deploy the oci-scanner plugin on to your gpus nodes manually. 
    - Embed them into a slurm script if you run a slurm cluster.
    - Use the kubernetes objects for the plugin under the `oci_scanner_plugin` folder for a Kubernetes cluster. Refer to [Readme](oci_scanner_plugin/README.md).
    - use the same scripts to be added as part of your new GPU compute deployments through cloud-init scripts.
 ---
 
-## Step 4: Explore Monitoring Dashboards
+## Step 3: Explore Monitoring Dashboards
 
 1. **Navigate to Dashboards**: Go to the dashboard section
 2. **View Available Dashboards**:
@@ -255,46 +250,31 @@ helm uninstall lens -n lens
 6. **Access Additional Features**:
    - **Custom Queries**: Use Prometheus queries to create custom visualizations
    - **Alerting**: Set up alerts for critical GPU or cluster issues
-
 ---
 
-## Architecture
-
-The Helm chart deploys the following components:
-
-1. **Frontend (Portal)**
-   - React/Node.js application
-   - Served on port 3000
-   - Service for internal/external access
-
-2. **Backend (Control Plane)**
-   - Django application
-   - Served on port 5000 (container), 80 (service)
-   - External access via LoadBalancer service
-   - Connects to Postgres
-   - Configured with Prometheus Pushgateway and Grafana URLs
-
-3. **Postgres Database**
-   - Managed via StatefulSet/Deployment
-   - Persistent storage via PVC
-   - Service for backend connectivity
-
-4. **ConfigMaps and Secrets**
-   - All environment variables and sensitive data are managed via ConfigMaps and Kubernetes Secrets
-
 ## Cleanup
 
 You can remove all control plane resources in **one step**:
 
-1. **Destroy the Control Plane Components**
-   - Go to **Resource Manager → Stacks** in the OCI Console.
-   - Select your **OCI GPU Scanner stack**.
-   - Click **Destroy**, confirm, and wait until the job succeeds.
+### Uninstall the control plane components 
+```bash
+helm uninstall lens -n lens
+```
+### Uninstall the data plane components if installed as OKE daemon set
 
-This will remove:
-- The OKE cluster and all nodes
-- The VCN and networking components
-- All OCI GPU Scanner application components
-- Associated storage and IAM policies (if created)
+```bash
+helm uninstall lens -n lens
+```
+### Uninstall the data plane components if it was installed as system services (per GPU node)
+
+```bash
+cd /home/ubuntu/$(hostname)/oci-lens-plugin/
+./uninstall 
+cd ..
+rm -rf *
+cd ..
+rmdir  $(hostname)
+
+```
 
-Once the stack is destroyed, your tenancy will be free of any OCI GPU Scanner-related resources.
+Once the stack is destroyed, your OKE cluster will be free of any OCI GPU Scanner-related resources.

GETTING_STARTED_HELM_DEPLOY.md

@@ -1,4 +1,4 @@
-# Getting Started with OCI GPU Scanner Quickstart
+# Getting started with OCI GPU Scanner quickstart using resource manager
 
 **❗❗Important: The instructions below are for creating a new standalone deployment. To install OCI GPU Scanner on an existing OKE cluster, please refer to the [Install OCI GPU Scanner to an Existing OKE Cluster](GETTING_STARTED_HELM_DEPLOY.md)**
 

GETTING_STARTED_RM_DEPLOY.md

@@ -86,6 +86,33 @@ eth0 presence check: Checks if the eth0 network interface is present
 
 Additional checks are performed based on GPU type (AMD or NVIDIA), such as XGMI, NVLINK, and fabric manager monitoring.
 
+## Architecture
+
+The Helm chart deploys the following components:
+
+1. **Frontend (Portal)**
+   - React/Node.js application
+   - Served on port 3000
+   - Service for internal/external access
+
+2. **Backend (Control Plane)**
+   - Django application
+   - Served on port 5000 (container), 80 (service)
+   - External access via LoadBalancer service
+   - Connects to Postgres
+   - Configured with Prometheus Push gateway and Grafana URLs
+
+3. **Postgres Database**
+   - Managed via StatefulSet/Deployment
+   - Persistent storage via PVC
+   - Service for backend connectivity
+
+4. **ConfigMaps and Secrets**
+   - All environment variables and sensitive data are managed via ConfigMaps and Kubernetes Secrets
+
+Sample deployment stamp.
+
+![deployment architecture](/media/scanner_architecture.png "architecture snapshot")
 
 ## Dashboards & Monitoring
 After deployment, you will have access to Grafana, Prometheus, and Portal endpoints for data interaction. See example screenshots below:
@@ -140,7 +167,8 @@ The below list of features are being prioritized. If you would like a new featur
 ## Limitations
 
 1. Only Ubuntu Linux OS based GPU node monitoring is supported.
-2. Control plane components only work with x86 CPU nodes  
+2. Control plane components only work with x86 CPU nodes.
+3. Active health checks do not run as low priority jobs hence running a active health check will disrupt any existing GPU workloads active on that node.   
 
 ## Support & Contact