Prod update

.github/pull_request_template.md

@@ -2,7 +2,7 @@
 Before submitting a pull request for a new Learning Path, please review [Create a Learning Path](https://learn.arm.com//learning-paths/cross-platform/_example-learning-path/)
 - [ ] I have reviewed Create a Learning Path
 
-Please do not include any confidential information in your contribution. This includes confidential microarchitecture details and unannounced product information. No AI tool can be used to generate either content or code when creating a learning path or install guide.
+Please do not include any confidential information in your contribution. This includes confidential microarchitecture details and unannounced product information. 
 
 - [ ] I have checked my contribution for confidential information
 

.github/workflows/deploy.yml

@@ -26,7 +26,7 @@ env:
 jobs:
   build_and_deploy:
     # The type of runner that the job will run on
-    runs-on: ubuntu-latest
+    runs-on: ubuntu-24.04-arm
     permissions:
       id-token: write
       contents: read
@@ -59,7 +59,7 @@ jobs:
         run: |
           hugo --minify
           cp learn-image-sitemap.xml public/learn-image-sitemap.xml
-          bin/pagefind --site "public"
+          bin/pagefind.aarch64 --site "public"
         env:
           HUGO_LLM_API: ${{ secrets.HUGO_LLM_API }}
           HUGO_RAG_API: ${{ secrets.HUGO_RAG_API }}

.wordlist.txt

@@ -3558,4 +3558,17 @@ threadCount
 threadNum
 useAPL
 vvenc
-workspaces
+workspaces
+ETDump
+ETRecord
+FAISS
+IVI
+PDFs
+Powertrain
+SpinTheCubeInGDI
+TaaS
+cloudsdk
+highcpu
+proj
+sln
+uploader

README.md

@@ -12,7 +12,7 @@ The Learning Paths created here are maintained by Arm and the Arm software devel
 
 All contributions are welcome as long as they relate to software development for the Arm architecture. 
   * Write a Learning Path (or improve existing content)
-    * Fork this repo and submit pull requests; follow the step by step instructions in [Create a Learning Path](https://learn.arm.com//learning-paths/cross-platform/_example-learning-path/) on the website.
+    * Fork this repo and submit pull requests; follow the step by step instructions in [Create a Learning Path](https://learn.arm.com/learning-paths/cross-platform/_example-learning-path/) on the website.
   * Ideas for a new Learning Path
     * Create a new GitHub idea under the [Discussions](https://github.com/ArmDeveloperEcosystem/arm-learning-paths/discussions) area in this GitHub repo.
   * Log a code issue (or other general issues)

content/install-guides/acfl.md

@@ -142,33 +142,87 @@ install takes place **after** ACfL, you will no longer be able to fully
 uninstall ACfL.
 {{% /notice %}}
 
-## Download and install using System Packages - Ubuntu Linux
+## Download and install using System Packages
+
+### Ubuntu Linux 20.04 and 22.04
 
 Arm Compiler for Linux is available to install with the Ubuntu system package manager `apt` command.
 
-### Setup the ACfL package repository:
+#### Set up the ACfL package repository
 
 Add the ACfL `apt` package repository to your Ubuntu 20.04 or 22.04 system:
 
 ```bash { target="ubuntu:latest" }
 sudo apt update
-sudo apt install -y curl
-source /etc/os-release
+sudo apt install -y curl environment-modules python3 libc6-dev
+. /etc/os-release
 curl "https://developer.arm.com/packages/ACfL%3A${NAME}-${VERSION_ID/%.*/}/${VERSION_CODENAME}/Release.key" | sudo tee /etc/apt/trusted.gpg.d/developer-arm-com.asc
 echo "deb https://developer.arm.com/packages/ACfL%3A${NAME}-${VERSION_ID/%.*/}/${VERSION_CODENAME}/ ./" | sudo tee /etc/apt/sources.list.d/developer-arm-com.list
 sudo apt update
 ```
 
 The ACfL Ubuntu package repository is now ready to use.
 
-### Install ACfL
+#### Install ACfL
 
 Download and install Arm Compiler for Linux with:
 
 ```bash { target="ubuntu:latest" }
 sudo apt install acfl
 ```
 
+### Amazon Linux 2023
+
+Arm Compiler for Linux is available to install with either the `dnf` or `yum` system package manager.
+
+#### Install ACfL from the Amazon Linux 2023 package repository
+
+Install ACfL and prerequisites from the Amazon Linux 2023 `rpm` package repository with `dnf`:
+
+```bash
+sudo dnf update
+sudo dnf install 'dnf-command(config-manager)' procps psmisc make environment-modules
+sudo dnf config-manager --add-repo https://developer.arm.com/packages/ACfL%3AAmazonLinux-2023/latest/ACfL%3AAmazonLinux-2023.repo
+sudo dnf install acfl
+```
+
+Or using the equivalent `yum` commands:
+
+```bash
+sudo yum update
+sudo yum install 'dnf-command(config-manager)' procps psmisc make environment-modules
+sudo yum config-manager --add-repo https://developer.arm.com/packages/ACfL%3AAmazonLinux-2023/latest/ACfL%3AAmazonLinux-2023.repo
+sudo yum install acfl
+```
+
+The ACfL tools are now ready to use.
+
+### Red Hat Enterprise Linux (RHEL) 9 
+
+Arm Compiler for Linux is available to install with either the `dnf` or `yum` system package manager.
+
+#### Install ACfL from the RHEL 9 package repository
+
+Install ACfL and prerequisites from the RHEL 9 `rpm` package repository with `dnf`:
+
+```bash
+sudo dnf update
+sudo dnf install 'dnf-command(config-manager)' procps psmisc make environment-modules
+sudo dnf config-manager --add-repo https://developer.arm.com/packages/ACfL%3ARHEL-9/standard/ACfL%3ARHEL-9.repo
+sudo dnf install acfl
+```
+
+Or using the equivalent `yum` commands:
+
+```bash
+sudo yum update
+sudo yum install 'dnf-command(config-manager)' procps psmisc make environment-modules
+sudo yum config-manager --add-repo https://developer.arm.com/packages/ACfL%3ARHEL-9/standard/ACfL%3ARHEL-9.repo
+sudo yum install acfl
+```
+
+The ACfL tools are now ready to use.
+
 ### Set up environment
 
 Arm Compiler for Linux uses environment modules to dynamically modify your user environment. Refer to the [Environment Modules documentation](https://lmod.readthedocs.io/en/latest/#id) for more information.
@@ -178,17 +232,17 @@ Set up the environment, for example, in your `.bashrc` and add module files.
 #### Ubuntu Linux:
 
 ```bash { target="ubuntu:latest" }
-echo "source /usr/share/modules/init/bash" >> ~/.bashrc
+echo ". /usr/share/modules/init/bash" >> ~/.bashrc
 echo "module use /opt/arm/modulefiles" >> ~/.bashrc
-source ~/.bashrc
+. ~/.bashrc
 ```
 
-#### Red Hat Linux:
+#### Red Hat or Amazon Linux:
 
 ```bash { target="fedora:latest" }
-echo "source /usr/share/Modules/init/bash" >> ~/.bashrc
+echo ". /usr/share/Modules/init/bash" >> ~/.bashrc
 echo "module use /opt/arm/modulefiles" >> ~/.bashrc
-source ~/.bashrc
+. ~/.bashrc
 ```
 
 To list available modules:
@@ -217,7 +271,7 @@ Arm Compiler for Linux is available with the [Spack](https://spack.io/) package
 
 See the [Arm Compiler for Linux and Arm PL now available in Spack](https://community.arm.com/arm-community-blogs/b/high-performance-computing-blog/posts/arm-compiler-for-linux-and-arm-pl-now-available-in-spack) blog for full details.
 
-### Setup Spack
+### Set up Spack
 
 Clone the Spack repository and add `bin` directory to the path:
 
@@ -248,7 +302,7 @@ If you wish to install just the Arm Performance Libraries, use:
 spack install armpl-gcc
 ```
 
-### Setup environment
+### Set up environment
 Use the commands below to set up the environment:
 
 ```console

content/install-guides/cmake.md

@@ -34,7 +34,7 @@ This article provides quick instructions to install CMake for Arm Linux distribu
 
 ### How do I download and install CMake for Windows on Arm?
 
-Confirm you are using a Windows on Arm device such as Windows Dev Kit 2023 or a laptop such as Lenovo ThinkPad X13s or Surface Pro 9 with 5G.
+Confirm you are using a Windows on Arm device such as the Lenovo ThinkPad X13s or Surface Pro 9 with 5G.
 
 ### How do I download and install CMake for Arm Linux distributions?
 

content/install-guides/gcloud.md

@@ -11,7 +11,7 @@ minutes_to_complete: 5
 author_primary: Jason Andrews
 multi_install: false
 multitool_install_part: false
-official_docs: https://cloud.google.com/sdk/docs/install-sdk
+official_docs: https://cloud.google.com/sdk/docs/install-sdk#deb
 test_images:
 - ubuntu:latest
 test_maintenance: false
@@ -44,7 +44,9 @@ aarch64
 
 If you see a different result, you are not using an Arm computer running 64-bit Linux.
 
-## How do I download and install for Ubuntu on Arm?
+## How do I download and install gcloud for Ubuntu on Arm?
+
+### Install gcloud using the package manager 
 
 The easiest way to install `gcloud` for Ubuntu on Arm is to use the package manager.
 
@@ -62,13 +64,64 @@ curl https://packages.cloud.google.com/apt/doc/apt-key.gpg | sudo apt-key add -
 sudo apt-get update && sudo apt-get install google-cloud-cli -y
 ```
 
-Confirm the executable is available.
+### Install gcloud using the archive file
+
+If you cannot use the package manager or you get a Python version error such as the one below you can use the archive file.
+
+```output
+The following packages have unmet dependencies:
+ google-cloud-cli : Depends: python3 (< 3.12) but 3.12.3-0ubuntu2 is to be installed
+```
+
+Download the archive file and extract the contents:
+
+```bash { target="ubuntu:latest" }
+wget https://dl.google.com/dl/cloudsdk/channels/rapid/downloads/google-cloud-cli-linux-arm.tar.gz
+sudo tar -xzf google-cloud-cli-linux-arm.tar.gz -C /opt
+```
+
+Run the installer:
+
+```bash { target="ubuntu:latest" }
+cd /opt/google-cloud-sdk
+sudo ./install.sh -q
+```
+
+{{% notice Note %}}
+You can change the installation directory from `/opt` to a location of your choice. 
+{{% /notice %}}
+
+Add the installation directory to your search path. The installer will print the path to a script you can source to add `gcloud` to your search path.
+
+```output
+==> Source [/opt/google-cloud-sdk/completion.bash.inc] in your profile to enable shell command completion for gcloud.
+==> Source [/opt/google-cloud-sdk/path.bash.inc] in your profile to add the Google Cloud SDK command line tools to your $PATH.
+
+For more information on how to get started, please visit:
+  https://cloud.google.com/sdk/docs/quickstarts
+```
+
+Source the file to include `gcloud` in your search path:
+
+```bash { target="ubuntu:latest" }
+source /opt/google-cloud-sdk/path.bash.inc
+```
+
+Alternatively, you can add the `bin` directory to your path by adding the line below to your `$HOME/.bashrc` file.
+
+```console
+export PATH="/opt/google-cloud-sdk/bin:$PATH"
+```
+
+## Test gcloud
+
+Confirm the executable is available and print the version:
 
 ```bash { target="ubuntu:latest" }
 gcloud -v
 ```
 
-The output should be similar to:
+The output is similar to:
 
 ```output
 Google Cloud SDK 418.0.0

content/install-guides/windows-perf-vs-extension.md

@@ -41,13 +41,13 @@ The WindowsPerf GUI extension is composed of several key features, each designed
 - **Output Logging**: All commands executed through the GUI are logged, ensuring transparency and supporting performance analysis.
 - **Sampling UI**: Customize your sampling experience by selecting events, setting frequency and duration, choosing programs for sampling, and comprehensively analyzing results. See screenshot below.
 
-![Sampling preview #center](../_images/wperf-vs-extension-sampling-preview.png "Sampling settings UI Overview")
+![Sampling preview #center](/install_guides/_images/wperf-vs-extension-sampling-preview.png "Sampling settings UI Overview")
 
 
 - **Counting Settings UI**: Build a `wperf stat` command from scratch using the configuration interface, then view the output in the IDE or open it with Windows Performance Analyzer (WPA). See screenshot below.
 
 
-![Counting preview #center](../_images/wperf-vs-extension-counting-preview.png "Counting settings UI Overview")
+![Counting preview #center](/install_guides/_images/wperf-vs-extension-counting-preview.png "Counting settings UI Overview")
 
 ## Before you begin
 
@@ -69,7 +69,7 @@ To install the WindowsPerf Visual Studio Extension from Visual Studio:
 4. Click on the search bar (Ctrl+L) and type `WindowsPerf`.
 5. Click on the **Install** button and restart Visual Studio.
 
-![WindowsPerf install page #center](../_images/wperf-vs-extension-install-page.png)
+![WindowsPerf install page #center](/install_guides/_images/wperf-vs-extension-install-page.png)
 
 ### Installation from GitHub
 

content/learning-paths/embedded-and-microcontrollers/introduction-to-tinyml-on-arm/Overview-1.md

@@ -6,12 +6,12 @@ weight: 2
 layout: learningpathall
 ---
 
-This Learning Path is about TinyML. It serves as a starting point for learning how cutting-edge AI technologies may be put on even the smallest of devices, making Edge AI more accessible and efficient. You will learn how to setup on your host machine and target device to facilitate compilation and ensure smooth integration across all devices.
+This Learning Path is about TinyML. It serves as a starting point for learning how cutting-edge AI technologies may be used on even the smallest devices, making Edge AI more accessible and efficient. You will learn how to set up your host machine and target device to facilitate compilation and ensure smooth integration across devices.
 
 In this section, you get an overview of the domain with real-life use-cases and available devices.
 
 ## Overview
-TinyML represents a significant shift in machine learning deployment. Unlike traditional machine learning, which typically depends on cloud-based servers or high-powered hardware, TinyML is tailored to function on devices with limited resources, constrained memory, low power, and less processing capabilities. TinyML has gained popularity because it enables AI applications to operate in real-time, directly on the device, with minimal latency, enhanced privacy, and the ability to work offline. This shift opens up new possibilities for creating smarter and more efficient embedded systems.
+TinyML represents a significant shift in machine learning deployment. Unlike traditional machine learning, which typically depends on cloud-based servers or high-performance hardware, TinyML is tailored to function on devices with limited resources, constrained memory, low power, and less processing capabilities. TinyML has gained popularity because it enables AI applications to operate in real-time, directly on the device, with minimal latency, enhanced privacy, and the ability to work offline. This shift opens up new possibilities for creating smarter and more efficient embedded systems.
 
 ### Benefits and applications
 
@@ -42,7 +42,7 @@ TinyML is being deployed across multiple industries, enhancing everyday experien
 
 ### Examples of Arm-based devices
 
-There are many Arm-based off-the-shelf devices you can use for TinyML projects. Some of them are listed below, but the list is not exhaustive.
+There are many Arm-based devices you can use for TinyML projects. Some of them are listed below, but the list is not exhaustive.
 
 #### Raspberry Pi 4 and 5
 
@@ -64,6 +64,6 @@ The Arduino Nano, equipped with a suite of sensors, supports TinyML and is ideal
 
 In addition to hardware, there are software platforms that can help you build TinyML applications.
 
-Edge Impulse platform offers a suite of tools for developers to build and deploy TinyML applications on Arm-based devices. It supports devices like Raspberry Pi, Arduino, and STMicroelectronics boards.
+Edge Impulse offers a suite of tools for developers to build and deploy TinyML applications on Arm-based devices. It supports devices like Raspberry Pi, Arduino, and STMicroelectronics boards.
 
 Now that you have an overview of the subject, move on to the next section where you will set up an environment on your host machine.

content/learning-paths/embedded-and-microcontrollers/introduction-to-tinyml-on-arm/_index.md

@@ -14,13 +14,13 @@ learning_objectives:
     - Understand the benefits of deploying AI models on Arm-based edge devices.
     - Select Arm-based devices for TinyML.
     - Install and configure a TinyML development environment.
-    - Perform best practices for ensuring optimal performance on constrained edge devices.
+    - Apply best practices for ensuring optimal performance on constrained edge devices.
 
 
 prerequisites:
     - Basic knowledge of machine learning concepts.
     - A Linux host machine or VM running Ubuntu 22.04 or higher.
-    - A [Grove Vision AI Module](https://wiki.seeedstudio.com/Grove-Vision-AI-Module/) **or** an Arm license to run the Corstone-300 Fixed Virtual Platform (FVP).
+    - A [Grove Vision AI Module](https://wiki.seeedstudio.com/Grove-Vision-AI-Module/) or an Arm license to run the Corstone-300 Fixed Virtual Platform (FVP).
 
 
 author_primary: Dominica Abena O. Amanfo

content/learning-paths/embedded-and-microcontrollers/introduction-to-tinyml-on-arm/_next-steps.md

@@ -9,6 +9,14 @@ further_reading:
         title: TinyML Brings AI to Smallest Arm Devices 
         link: https://newsroom.arm.com/blog/tinyml
         type: blog
+    - resource:
+        title: Arm Compiler for Embedded
+        link: https://developer.arm.com/Tools%20and%20Software/Arm%20Compiler%20for%20Embedded
+        type: documentation
+    - resource:
+        title: Arm GNU Toolchain
+        link: https://developer.arm.com/Tools%20and%20Software/GNU%20Toolchain
+        type: documentation