Merge branch 'ArmDeveloperEcosystem:main' into buildkite

Author: madeline-underwood

.wordlist.txt

@@ -5039,4 +5039,19 @@ vesion
 wwwrun
 xdebug
 zoneIdentifier
-zypper
+zypper
+keyspace
+Keyspace
+CQL
+cqlsh
+keyspaces
+CQLSH
+SAI
+SSTables
+Trie
+UCS
+memtables
+cassandra
+Cassandra's
+Cassandra
+CircleCI

content/learning-paths/mobile-graphics-and-gaming/nss-unreal/1-install-plugin.md

@@ -35,7 +35,8 @@ Before you begin, download the required plugins and dependencies. These two repo
 
 [**Neural Super Sampling Unreal Engine Plugin** → GitHub Repository](https://github.com/arm/neural-graphics-for-unreal)
 
-Download the latest release package and extract it on your Windows machine.
+Download the latest release package and extract it on your Windows machine. Use the folder corresponding to your Unreal version.
+
 
 ### 2. Download the runtime for ML Extensions for Vulkan
 [**Unreal NNE Runtime RDG for ML Extensions for Vulkan** → GitHub Repository](https://github.com/arm/ml-extensions-for-vulkan-unreal-plugin).

content/learning-paths/mobile-graphics-and-gaming/nss-unreal/2-emulation-layer.md

@@ -65,17 +65,30 @@ By default, Unreal uses DirectX. Instead, you need to choose Vulkan as the defau
 
 ![Project Settings with Vulkan selected as Default RHI under Targeted RHIs#center](./images/targeted_rhis.png "Figure 4: Set Vulkan as the default RHI.")
 
+## Create the Plugins directory
 
-## Add and enable the plugins
+Open your project directory in Windows explorer, and create a new folder called `Plugins`.
 
-1. Open your project directory in Windows explorer, and create a new folder called `Plugins`.
-2. Copy the downloaded and extracted `.zips` into the new directory:
+Enabling the plugin will look slightly different depending on what Unreal version you are using. Follow the steps corresponding to your setup.
+
+## For Unreal 5.5
+
+1. Copy the downloaded and extracted `.zip` into the new `Plugins` directory:
+   - `UE5.5`
    - `NNERuntimeRDGMLExtensionsForVulkan`
-   - `NSS`
-3. Re-open Unreal Engine. When prompted, confirm plugin integration.
-4. Rebuild your project in Visual Studio from source.
-5. Verify the installation by opening the Plugins view in Unreal Engine, and making sure the checkbox is selected for both `NSS` and `NNERuntimeRDGMLExtensionsForVulkan` as shown. Restart Unreal Engine if prompted.
+2. Re-open Unreal Engine. When prompted, confirm plugin integration.
+3. Rebuild your project in Visual Studio from source.
+4. Verify the installation by opening the Plugins view in Unreal Engine, and making sure the checkbox is selected for both `NSS` and `NNERuntimeRDGMLExtensionsForVulkan` as shown. Restart Unreal Engine if prompted.
 
 ![Unreal Engine plugins window showing NSS and NNERuntimeRDGMLExtensionsForVulkan enabled#center](./images/verify_plugin_enabled.png "Figure 5: Verify plugin installation in Unreal Engine.")
 
-With the emulation layers and plugins configured, you're ready to run Neural Super Sampling in Unreal Engine. Continue to the next section to test the integration.
+
+## For Unreal 5.4
+
+1. Copy the downloaded and extracted `.zip` into the new `Plugins` directory:
+   - `UE5.4`
+2. Re-open Unreal Engine. When prompted, confirm plugin integration.
+3. Rebuild your project in Visual Studio from source.
+4. Verify the installation by opening the Plugins view in Unreal Engine, and making sure the checkbox is selected for `NSS`. Restart Unreal Engine if prompted.
+
+With the emulation layers and plugins configured, you're ready to run Neural Super Sampling in Unreal Engine. Continue to the next section to test the integration.

content/learning-paths/mobile-graphics-and-gaming/nss-unreal/_index.md

@@ -15,7 +15,7 @@ learning_objectives:
 
 prerequisites:
     - Windows 11
-    - Unreal Engine 5.5 (Templates and Feature Pack enabled)
+    - Unreal Engine 5.4 or 5.5 (Templates and Feature Pack enabled)
     - Visual Studio 2022 (with Desktop Development with C++ and .NET desktop build tools)
 
 

content/learning-paths/servers-and-cloud-computing/cassandra-on-gcp/benchmnarking.md

@@ -1,15 +1,15 @@
 ---
-title: Cassendra Benchmarking
+title: Cassandra Benchmarking
 weight: 6
 
 ### FIXED, DO NOT MODIFY
 layout: learningpathall
 ---
 
-## Cassendra Benchmarking by Cassendra-Stress
+## Cassandra Benchmarking by Cassandra-Stress
 Cassandra benchmarking can be performed using the built-in `cassandra-stress` tool, which helps measure database performance under different workloads such as write, read, and mixed operations.
 
-### Steps for Cassendra Benchmarking with Cassendra-Stress
+### Steps for Cassandra Benchmarking with Cassandra-Stress
 **Verify cassandra-stress Installation:**
 
 Cassandra comes with a built-in tool called **cassandra-stress** that is used for testing performance. It is usually located in the `tools/bin/` folder of your Cassandra installation. 
@@ -356,7 +356,7 @@ Results from the earlier run on the `c4a-standard-4` (4 vCPU, 16 GB memory) Arm6
 | Total GC Time               | 0.0 s                | 0.0 s                |
 | Total Operation Time        | 0:00:00              | 0:00:02              |
 
-### Cassendra performance benchmarking notes
+### Cassandra performance benchmarking notes
 When examining the benchmark results, you will notice that on the Google Axion C4A Arm-based instances:
 
 - The write operations achieved a high throughput of **10,690 op/s**, while read operations reached **4,962 op/s** on the `c4a-standard-4` Arm64 VM.  

content/learning-paths/servers-and-cloud-computing/cassandra-on-gcp/installation.md

@@ -74,4 +74,4 @@ You should see an output similar to:
 ```output
 5.0.5
 ```
-Cassadra's installation is complete. You can now proceed with the baseline testing.
+Cassandra's installation is complete. You can now proceed with the baseline testing.

content/learning-paths/servers-and-cloud-computing/php-on-gcp/_index.md

@@ -4,7 +4,7 @@ title:  Deploy PHP on Google Cloud C4A Arm-based Axion VMs
 
 minutes_to_complete: 30
 
-who_is_this_for: This is an introductory topic for developers migrating Hypertext Preprocesso (PHP) workloads from x86_64 to Arm-based servers, specifically on Google Cloud C4A virtual machines (VM) built on Axion processors.
+who_is_this_for: This is an introductory topic for developers migrating Hypertext Preprocessor (PHP) workloads from x86_64 to Arm-based servers, specifically on Google Cloud C4A virtual machines (VM) built on Axion processors.
 
 learning_objectives:
   - Provision a SUSE Linux Enterprise Server (SLES) virtual machine on a Google Cloud C4A Arm-based Axion virtual machine