Merge pull request #270 from chaitanya1731/readme-patch

documentation: Updated Readme Formatting

Author: uMartinXu

e2e/inference/README.md

@@ -1,4 +1,6 @@
-# Overview
+# Intel AI Inference End-to-End Solution
+
+## Overview
 Intel AI inference end-to-end solution with RHOCP is based on the Intel® Data Center GPU Flex Series provisioning, Intel® OpenVINO™, and [Red Hat OpenShift AI](https://www.redhat.com/en/technologies/cloud-computing/openshift/openshift-ai) (RHOAI) on RHOCP. There are two AI inference modes verified with Intel® Xeon® processors and Intel Data Center GPU Flex Series with RHOCP.
 * Interactive mode – RHOAI provides OpenVINO based Jupyter Notebooks for users to interactively debug the inference applications or [optimize the models](https://docs.openvino.ai/2023.0/openvino_docs_MO_DG_Deep_Learning_Model_Optimizer_DevGuide.html) on RHOCP using data center GPU cards or Intel Xeon processors.
 * Deployment mode – [OpenVINO Model Sever](https://github.com/openvinotoolkit/model_server) (OVMS) can be used to deploy the inference workloads in data center and edge computing environments on RHOCP.  
@@ -26,6 +28,7 @@ The OpenVINO operator is published at [Red Hat Ecosystem Catalog](https://catalo
 ### Install using CLI (To be added)
 ### Install using Web Console
 Follow this [link](https://github.com/openvinotoolkit/operator/blob/v1.1.0/docs/operator_installation.md#operator-instalation)  to install the operator via the web console. 
+
 ## Work with Interactive Mode
 To enable the interactive mode, the OpenVINO notebook CR needs to be created and integrated with RHOAI.  
 1.	Click on the `create Notebook` option from the web console and follow these [steps](https://github.com/openvinotoolkit/operator/blob/main/docs/notebook_in_rhods.md#integration-with-openshift-data-science-and-open-data-hub) to create the notebook CR.
@@ -62,4 +65,4 @@ Follow the [link](https://github.com/openvinotoolkit/operator/blob/main/docs/not
 
 
 ## See Also 
-[GPU accelerated demo with OpenVINO](https://www.youtube.com/watch?v=3fTz_k4JT2A)
+[GPU accelerated demo with OpenVINO](https://www.youtube.com/watch?v=3fTz_k4JT2A)

kmmo/README.md

@@ -1,29 +1,29 @@
 # Setting up Out of Tree Drivers
 
-# Introduction
+## Introduction
 [Kernel module management (KMM) operator](https://github.com/rh-ecosystem-edge/kernel-module-management) manages the deployment and lifecycle of out-of-tree kernel modules on RHOCP.
 
 In this release, KMM operator is used to manage and deploy the Intel® Data Center GPU driver container image on the RHOCP cluster.
 
 Intel data center GPU driver container images are released from [Intel Data Center GPU Driver for OpenShift Project](https://github.com/intel/intel-data-center-gpu-driver-for-openshift/tree/main/release#intel-data-center-gpu-driver-container-images-for-openshift-release).
 
-# KMM operator working mode
+## KMM operator working mode
 - **Pre-build mode** - This is the default and recommended mode. KMM Operator uses [this pre-built and certified Intel Data Center GPU driver container image](https://catalog.redhat.com/software/containers/intel/intel-data-center-gpu-driver-container/6495ee55c8b2461e35fb8264), which is published on the Red Hat Ecosystem Catalog to provision Intel Data Center GPUs on a RHOCP cluster.
 - **On-premises build mode** - Users can optionally build and deploy their own driver container images on-premises through the KMM operator.
 
-# Prerequisites
+## Prerequisites
 - Provisioned RHOCP cluster. Follow steps [here](/README.md#provisioning-rhocp-cluster).
 - Setup node feature discovery. Follow steps [here](/nfd/README.md).
 
-# Install KMM operator
+## Install KMM operator
 Follow the installation guide below to install the KMM operator via CLI or web console. 
 - [Install from CLI](https://docs.openshift.com/container-platform/4.14/hardware_enablement/kmm-kernel-module-management.html#kmm-install-using-cli_kernel-module-management-operator)
 - [Install from web console](https://docs.openshift.com/container-platform/4.14/hardware_enablement/kmm-kernel-module-management.html#kmm-install-using-web-console_kernel-module-management-operator)
 
-# Canary deployment with KMM
+## Canary deployment with KMM
 Canary deployment is enabled by default to deploy the driver container image only on specific node(s) to ensure the initial deployment succeeds prior to rollout to all the eligible nodes in the cluster. This safety mechanism can reduce risk and prevent a deployment from adversely affecting the entire cluster.
 
-# Set alternative firmware path at runtime with KMM
+## Set alternative firmware path at runtime with KMM
 Follow the steps below to set the alternative firmware path at runtime.
 
 1. Update KMM operator `ConfigMap` to set `worker.setFirmwareClassPath` to `/var/lib/firmware`
@@ -38,7 +38,7 @@ $ oc get pods -n openshift-kmm | grep -i "kmm-operator-controller-" | awk '{prin
 
 For more details, see [link.](https://openshift-kmm.netlify.app/documentation/firmwares/#setting-the-kernels-firmware-search-path)
 
-# Deploy Intel Data Center GPU Driver with pre-build mode
+## Deploy Intel Data Center GPU Driver with pre-build mode
 Follow the steps below to deploy the driver container image with pre-build mode.
 1.	Find all nodes with an Intel Data Center GPU card using the following command:
 ``` 
@@ -65,7 +65,7 @@ $ oc apply -f https://raw.githubusercontent.com/intel/intel-technology-enabling-
 intel.feature.node.kubernetes.io/dgpu-canary: 'true'
 ```
 
-# Verification
+## Verification
 To verify that the drivers have been loaded, follow the steps below:
 1.	List the nodes labeled with `kmm.node.kubernetes.io/openshift-kmm.intel-dgpu.ready` using the command shown below:
 ```
@@ -99,4 +99,4 @@ The label shown above indicates that the KMM operator has successfully deployed
     ```
     c. Run dmesg to ensure there are no errors in the kernel message log.
 
-# See Also
+## See Also

machine_configuration/README.md

@@ -1,6 +1,6 @@
 # Setting up Machine Configuration
 
-# Introduction
+## Introduction
 Machine configuration operation is used to configure [Red Hat Enterprise Linux CoreOS (RHCOS)](https://docs.openshift.com/container-platform/4.14/architecture/architecture-rhcos.html) on each node in a RHOCP cluster.
 
 [Machine config operator](https://github.com/openshift/machine-config-operator) (MCO) is provided by Red Hat to manage the operating system and machine configuration. In this project through the MCO, cluster administrators can configure and update the kernel to provision Intel Hardware features on the worker nodes.
@@ -15,11 +15,11 @@ If the configuration cannot be set as the default setting, we recommend using so
 
 Any contribution in this area is welcome. 
 
-# Prerequisites 
+## Prerequisites 
 - Provisioned RHOCP cluster. Follow steps [here](/README.md#provisioning-rhocp-cluster).
 - Setup node feature discovery (NFD). Follow steps [here](/nfd/README.md).
 
-# Machine Configuration for Provisioning Intel® QAT
+## Machine Configuration for Provisioning Intel® QAT
 
 * Turn on `intel_iommu` kernel parameter and load `vfio_pci` at boot for QAT provisioning
 
@@ -42,5 +42,5 @@ $ lsmod | grep vfio_pci
 ```
 Ensure that `vfio_pci` driver is present.
 
-# See Also
+## See Also
 - [Red Hat OpenShift Container Platform Day-2 operations](https://www.ibm.com/cloud/architecture/content/course/red-hat-openshift-container-platform-day-2-ops/)

nfd/README.md

@@ -1,15 +1,15 @@
 # Setting up Node Feature Discovery
 [Node Feature Discovery (NFD) Operator](https://docs.openshift.com/container-platform/4.14/hardware_enablement/psap-node-feature-discovery-operator.html) manages the deployment and lifecycle of the NFD add-on to detect hardware features and system configuration, such as PCI cards, kernel, operating system version, etc.
 
-# Prerequisites
+## Prerequisites
 - Provisioned RHOCP cluster. Follow steps [here](/README.md#provisioning-rhocp-cluster).
 
-# Install NFD Operator
+## Install NFD Operator
 Follow the guide below to install the NFD operator using CLI or web console. 
 - [Install from the CLI](https://docs.openshift.com/container-platform/4.14/hardware_enablement/psap-node-feature-discovery-operator.html#install-operator-cli_node-feature-discovery-operator)
 - [Install from the web console](https://docs.openshift.com/container-platform/4.14/hardware_enablement/psap-node-feature-discovery-operator.html#install-operator-web-console_node-feature-discovery-operator)
 
-# Configure NFD Operator
+## Configure NFD Operator
 Note: As RHOCP cluster administrator, you might need to merge the NFD operator config from the following Custom Resources (CRs) with other NFD operator configs that are already applied on your cluster.  
 
 1. Create `NodeFeatureDiscovery` CR instance.
@@ -22,7 +22,7 @@ $ oc apply -f https://raw.githubusercontent.com/intel/intel-technology-enabling-
 $ oc apply -f https://raw.githubusercontent.com/intel/intel-technology-enabling-for-openshift/main/nfd/node-feature-rules-openshift.yaml
 ```
 
-# Verification 
+## Verification 
 Use the command shown below to verify whether the nodes are labeled properly by NFD:
 ```
 $ oc describe node node_name | grep intel.feature.node.kubernetes.io
@@ -33,11 +33,11 @@ intel.feature.node.kubernetes.io/dgpu-canary=true
 intel.feature.node.kubernetes.io/gpu=true
 ```
 
-# Labels Table
+## Labels Table
 | Label | Intel hardware feature | 
 | ----- | ---------------------- |
 | `intel.feature.node.kubernetes.io/gpu=true` | Intel® Data Center GPU Flex Series or Intel® Data Center GPU Max Series | 
 | `intel.feature.node.kubernetes.io/sgx=true` | Intel® SGX | 
 | `intel.feature.node.kubernetes.io/qat=true` | Intel® QAT | 
 
-# See Also
+## See Also

one_click/README.md

@@ -5,8 +5,9 @@ Red Hat [Ansible](https://www.ansible.com/) and Operator technologies are used f
 
 The referenced Ansible playbooks here can be used by the cluster administrators to customize their own playbooks.
 
->[!NOTE]
-> It is recommended to start from [Get started](/README.md#getting-started) to get familiar with the installation and configuration of the general operator before composing the first playbook.
+```{note}   
+It is recommended to start from [Get started](/README.md#getting-started) to get familiar with the installation and configuration of the general operator before composing the first playbook.
+```
 
 ## Reference Playbook – Intel Data Center GPU Provisioning