Update outdated LPs

- Don't use apt docker.io for installation
- Don't use vLLM abbreviation
- Fix PaddlePaddle repo paths
- Update ExecuTorch installation method
- Add Mac installation for Ansible
- Add Arm ASR Fab store link

Author: annietllnd

content/install-guides/ansible.md

@@ -19,29 +19,36 @@ weight: 1
 
 Ansible is an open source, command-line automation used to configure systems and deploy software.
 
-Ansible command-line tools can be installed on a variety of Linux distributions.
+Ansible command-line tools can be installed on a variety of Linux and Unix distributions.
 
 [General installation information](https://docs.ansible.com/ansible/latest/installation_guide/installation_distros.html) is available which covers all supported operating systems, but it doesn't talk about Arm-based hosts.
 
 ## What should I do before I start installing the Ansible command line tools?
 
-This article provides a quick solution to install the Ansible command line tools, such as `ansible-playbook` for Ubuntu on Arm.
+This article provides a quick solution to install the Ansible command line tools, such as `ansible-playbook` for macOS and Ubuntu running Arm.
 
 Confirm you are using an Arm machine by running:
 
 ```bash
 uname -m
 ```
 
-The output should be:
+The output on Ubuntu should be:
 
 ```output
 aarch64
 ```
 
+And for macOS:
+
+```output
+arm64
+```
+
+
 If you see a different result, you are not using an Arm-based machine running 64-bit Linux.
 
-## How do I download and install Ansible for Ubuntu on Arm?
+## Install Ansible for Ubuntu on Arm
 
 The easiest way to install the latest version of Ansible for Ubuntu on Arm is to use the PPA (Personal Package Archive).
 
@@ -54,6 +61,16 @@ sudo add-apt-repository --yes --update ppa:ansible/ansible
 sudo apt install ansible -y
 ```
 
+## Install Ansible for macOS
+
+You can use the `brew` package manager to install on `arm64`:
+
+```console
+brew install ansible
+```
+
+## Confirm installation
+
 Confirm the Ansible command line tools are installed by running:
 
 ```bash

content/learning-paths/embedded-and-microcontrollers/avh_ppocr/end-to-end_workflow.md

@@ -38,10 +38,10 @@ sudo bash scripts/config_tvm.sh
 Now you can navigate to the text recognition example directory.
 
 ```bash
-cd ./ocr/text_recognition/
+cd ./OCR-example/Text-recognition-example-m85/
 ```
 
-In this directory, there is a script named [run_demo.sh](https://github.com/ArmDeveloperEcosystem/Paddle-examples-for-AVH/blob/main/OCR-example/run_demo.sh) that automates the entire process described in the End-to-end workflow diagram.
+In this directory, there is a script named [run_demo.sh](https://github.com/ArmDeveloperEcosystem/Paddle-examples-for-AVH/blob/main/OCR-example/Text-recognition-example-m85/run_demo.sh) that automates the entire process described in the End-to-end workflow diagram.
 
 Update the FVP executable name in the `run_demo.sh` script. The `VHT_Platform` should match what is installed in the system. The executable starts with either `VHT_Corstone_SSE` or `FVP_Corstone_SSE`. Check which one is available in the `$PATH` by typing it out and using the Tab key to autocomplete. Then, using a code editor of your choice or `vim`, you can assign the correct executable:
 

content/learning-paths/embedded-and-microcontrollers/jetson_object_detection/_index.md

@@ -5,12 +5,12 @@ minutes_to_complete: 60
 
 who_is_this_for: This is an introductory topic for developers interested in integrating object detection into their applications.
 
-learning_objectives: 
+learning_objectives:
     - Set up a Jetson Orin Nano with a MIPI CSI-2 camera for object detection
     - Detect objects from both live video and image files
 
 prerequisites:
-    - A Jetson Orin Nano (https://developer.nvidia.com/embedded/learn/jetson-orin-nano-devkit-user-guide/index.html)
+    - A [Jetson Orin Nano](https://developer.nvidia.com/embedded/learn/jetson-orin-nano-devkit-user-guide/index.html)
     - A microSD card (64GB UHS-1 or larger is recommended)
     - A MIPI CSI-2 camera, with a 22 pin connector on at least one end
 

content/learning-paths/embedded-and-microcontrollers/rpi-llama3/_index.md

@@ -6,7 +6,7 @@ minutes_to_complete: 60
 who_is_this_for: This is an introductory topic for anyone interested in running the Llama 3 model on a Raspberry Pi 5, and learning about techniques for running large language models (LLMs) in an embedded environment.
 
 learning_objectives:
-    - Use Docker to run Raspberry Pi OS on an Arm Linux server. 
+    - Use Docker to run Raspberry Pi OS on an Arm Linux server.
     - Compile a Large Language Model (LLM) using ExecuTorch.
     - Deploy the Llama 3 model on an edge device.
     - Describe how to run Llama 3 on a Raspberry Pi 5 using ExecuTorch.
@@ -31,7 +31,7 @@ tools_software_languages:
     - LLM
     - GenAI
     - Raspberry Pi
-    
+
 
 
 further_reading:
@@ -47,6 +47,10 @@ further_reading:
         title: ExecuTorch Examples
         link: https://github.com/pytorch/executorch/blob/main/examples/README.md
         type: website
+    - resource:
+        title: Run Llama3 8B on a Raspberry Pi 5 with ExecuTorch
+        link: https://dev-discuss.pytorch.org/t/run-llama3-8b-on-a-raspberry-pi-5-with-executorch/2048
+        type: website
 
 
 

content/learning-paths/embedded-and-microcontrollers/rpi-llama3/executorch.md

@@ -35,7 +35,7 @@ conda activate executorch-venv
 
 ## Install Clang
 
-Install Clang, which is required to build ExecuTorch: 
+Install Clang, which is required to build ExecuTorch:
 
 ```bash
 sudo apt install clang -y
@@ -52,7 +52,7 @@ sudo update-alternatives --set c++ /usr/bin/clang++
 
 ## Clone ExecuTorch and install the required dependencies
 
-Continue in your Python virtual environment, and run the commands below to download the ExecuTorch repository and install the required packages. 
+Continue in your Python virtual environment, and run the commands below to download the ExecuTorch repository and install the required packages.
 
 After cloning the repository, the project's submodules are updated, and two scripts install additional dependencies.
 
@@ -61,13 +61,8 @@ git clone https://github.com/pytorch/executorch.git
 cd executorch
 git submodule sync
 git submodule update --init
-./install_requirements.sh --pybind xnnpack
+./install_executorch.sh
 ./examples/models/llama2/install_requirements.sh
 ```
 
-{{% notice Note %}}
-You can safely ignore the following error on failing to import lm_eval running the install_requirements.sh scripts:
-`Failed to import examples.models due to lm_eval conflict`
-{{% /notice %}}
-
 When these scripts finish successfully, ExecuTorch is all set up. That means it's time to dive into the world of Llama models!

content/learning-paths/mobile-graphics-and-gaming/Build-Llama3-Chat-Android-App-Using-Executorch-And-XNNPACK/2-executorch-setup.md

@@ -41,8 +41,7 @@ git clone https://github.com/pytorch/executorch.git
 cd executorch
 git submodule sync
 git submodule update --init
-./install_requirements.sh
-./install_requirements.sh --pybind xnnpack
+./install_executorch.sh
 ./examples/models/llama/install_requirements.sh
 ```
 

content/learning-paths/mobile-graphics-and-gaming/get-started-with-arm-asr/01-what-is-arm-asr.md

@@ -8,11 +8,11 @@ layout: learningpathall
 
 ## Introduction
 
-[Arm® Accuracy Super Resolution™ (Arm ASR)](https://www.arm.com/developer-hub/mobile-graphics-and-gaming/arm-accuracy-super-resolution) is a mobile-optimized temporal upscaling technique derived from [AMD's Fidelity Super Resolution 2 v2.2.2](https://github.com/GPUOpen-LibrariesAndSDKs/FidelityFX-SDK/blob/main/docs/techniques/super-resolution-temporal.md). 
+[Arm® Accuracy Super Resolution™ (Arm ASR)](https://www.arm.com/developer-hub/mobile-graphics-and-gaming/arm-accuracy-super-resolution) is a mobile-optimized temporal upscaling technique derived from [AMD's Fidelity Super Resolution 2 v2.2.2](https://github.com/GPUOpen-LibrariesAndSDKs/FidelityFX-SDK/blob/main/docs/techniques/super-resolution-temporal.md).
 
 Arm ASR extends this technology with optimizations suited to the resource-constrained environment of mobile gaming.
 
-Arm ASR is currently available as an easy-to-integrate plug-in for Unreal Engine versions 5.3, 5.4, and 5.5, with a Unity plugin coming soon. It is also available as a generic library that you can integrate into other engines. 
+Arm ASR is currently available as an easy-to-integrate plug-in for Unreal Engine versions 5.3, 5.4, and 5.5, with a Unity plugin coming soon. It is also available as a generic library that you can integrate into other engines.
 
 Using ASR, you can improve frames per second (FPS), enhance visual quality, and prevent thermal throttling for smoother and longer gameplay.
 
@@ -35,7 +35,7 @@ You have control over a range of different settings, including:
 
 ## Overview of Arm ASR
 
-The [Arm ASR Experience Kit](https://github.com/arm/accuracy-super-resolution) provides resources to help you evaluate and effectively utilize this technology. 
+The [Arm ASR Experience Kit](https://github.com/arm/accuracy-super-resolution) provides resources to help you evaluate and effectively utilize this technology.
 
 It includes:
 
@@ -47,7 +47,9 @@ It includes:
 
 The Arm ASR plugin for Unreal Engine 5 integrates into your project within minutes. Once installed, simply enable temporal upscaling, and the plugin automatically handles frame upscaling.
 
-See [Using Arm ASR in Unreal Engine](../02-ue).
+The plugin for Unreal Engine is available in the [Fab store](https://www.fab.com/listings/9a75a41d-6fad-44c3-995f-646f62cd2512).
+
+To set it up from source, proceed to [Using Arm ASR in Unreal Engine](../02-ue).
 
 ## Custom Engine Usage
 

content/learning-paths/servers-and-cloud-computing/mariadb/docker_deployment.md

@@ -8,22 +8,22 @@ weight: 6 # 1 is first, 2 is second, etc.
 layout: "learningpathall"
 ---
 
-##  Install MariaDB in a Docker container 
+##  Install MariaDB in a Docker container
 
-You can deploy [MariaDB](https://mariadb.org/) in a Docker container using Ansible. 
+You can deploy [MariaDB](https://mariadb.org/) in a Docker container using Ansible.
 
 ## Before you begin
 
 For this section you will need a computer which has [Ansible](/install-guides/ansible/) installed. You can use the same SSH key pair. You also need a cloud instance or VM, or a physical machine with Ubuntu installed, running and ready to deploy MariaDB.
- 
- 
+
+
 ## Deploy a MariaDB container using Ansible
 
 Docker is an open platform for developing, shipping, and running applications. Docker enables you to separate your applications from your infrastructure so you can deliver software quickly. If you are new to Docker, consider reviewing [Learn how to use Docker](/learning-paths/cross-platform/docker/).
 
 To run Ansible, you can use an Ansible playbook. The playbook uses the `community.docker` collection to deploy MariaDB in a container.
 
-The playbook maps the container port to the host port, which is `3306`. 
+The playbook maps the container port to the host port, which is `3306`.
 
 1. Use a text editor to add the contents below to a new file named `playbook.yml`.
 
@@ -37,8 +37,8 @@ The playbook maps the container port to the host port, which is `3306`.
       shell: |
              apt-get update -y
              apt-get -y install mariadb-client
-             apt-get install docker.io -y
-             usermod -aG docker ubuntu
+             curl -fsSL get.docker.com -o get-docker.sh && sh get-docker.sh
+             usermod -aG docker ubuntu ; newgrp docker
              apt-get -y install python3-pip
              pip3 install PyMySQL
              pip3 install docker
@@ -70,9 +70,9 @@ The playbook maps the container port to the host port, which is `3306`.
 
 ```
 
-2. Edit `playbook.yml` to use your values 
+2. Edit `playbook.yml` to use your values
 
-Replace **{{your_mariadb_password}}** with your own password. 
+Replace **{{your_mariadb_password}}** with your own password.
 
 Also, replace **{{dockerhub_uname}}** and **{{dockerhub_pass}}** with your [Docker Hub](https://hub.docker.com/) credentials.
 
@@ -81,7 +81,7 @@ Also, replace **{{dockerhub_uname}}** and **{{dockerhub_pass}}** with your [Dock
 [all]
 ansible-target1 ansible_connection=ssh ansible_host={{public_ip of VM where MariaDB to be deployed}} ansible_user={{user_of VM where MariaDB to be deployed}}
 ```
-4. Edit `inventory.txt` to use your values 
+4. Edit `inventory.txt` to use your values
 
 Replace **{{public_ip of VM where MariaDB to be deployed}}** and **{{user_of VM where MariaDB to be deployed}}** with your own values.
 
@@ -95,9 +95,9 @@ Replace **{{public_ip of VM where MariaDB to be deployed}}** and **{{user_of VM
 ansible-playbook playbook.yaml -i {your_inventory_file_location}
 ```
 
-2. Answer `yes` when prompted for the SSH connection. 
+2. Answer `yes` when prompted for the SSH connection.
 
-Deployment may take a few minutes. 
+Deployment may take a few minutes.
 
 The output should be similar to:
 
@@ -128,5 +128,5 @@ ansible-target1            : ok=3    changed=4    unreachable=0    failed=0    s
 
 ## Connect to Database using your local machine
 
-You can use the instructions from the previous topic to [connect to the database](/learning-paths/servers-and-cloud-computing/mariadb/ec2_deployment#connect-to-database-from-local-machine) and confirm the Docker container deployment is working. 
+You can use the instructions from the previous topic to [connect to the database](/learning-paths/servers-and-cloud-computing/mariadb/ec2_deployment#connect-to-database-from-local-machine) and confirm the Docker container deployment is working.
 

content/learning-paths/servers-and-cloud-computing/vLLM/_index.md

@@ -1,16 +1,16 @@
 ---
-title: Build and Run a Virtual Large Language Model (vLLM) on Arm Servers
+title: Build and Run vLLM on Arm Servers
 
 minutes_to_complete: 45
 
-who_is_this_for: This is an introductory topic for software developers and AI engineers interested in learning how to use a vLLM (Virtual Large Language Model) on Arm servers.
+who_is_this_for: This is an introductory topic for software developers and AI engineers interested in learning how to use the vLLM library on Arm servers.
 
 learning_objectives:
-    - Build a vLLM from source on an Arm server.
+    - Build vLLM from source on an Arm server.
     - Download a Qwen LLM from Hugging Face.
-    - Run local batch inference using a vLLM.
-    - Create and interact with an OpenAI-compatible server provided by a vLLM on your Arm server.
-    
+    - Run local batch inference using vLLM.
+    - Create and interact with an OpenAI-compatible server provided by vLLM on your Arm server.
+
 prerequisites:
     - An [Arm-based instance](/learning-paths/servers-and-cloud-computing/csp/) from a cloud service provider, or a local Arm Linux computer with at least 8 CPUs and 16 GB RAM.
 

content/learning-paths/servers-and-cloud-computing/vLLM/vllm-setup.md

@@ -12,9 +12,9 @@ To follow the instructions for this Learning Path, you will need an Arm server r
 
 ## What is vLLM?
 
-[vLLM](https://github.com/vllm-project/vllm) stands for Virtual Large Language Model, and is a fast and easy-to-use library for inference and model serving. 
+[vLLM](https://github.com/vllm-project/vllm) stands for Virtual Large Language Model, and is a fast and easy-to-use library for inference and model serving.
 
-You can use vLLM in batch mode, or by running an OpenAI-compatible server. 
+You can use vLLM in batch mode, or by running an OpenAI-compatible server.
 
 In this Learning Path, you will learn how to build vLLM from source and run inference on an Arm-based server, highlighting its effectiveness.
 
@@ -23,8 +23,8 @@ In this Learning Path, you will learn how to build vLLM from source and run infe
 First, ensure your system is up-to-date and install the required tools and libraries:
 
 ```bash
-sudo apt-get update -y 
-sudo apt-get install -y curl ccache git wget vim numactl gcc-12 g++-12 python3 python3-pip python3-venv python-is-python3 libtcmalloc-minimal4 libnuma-dev ffmpeg libsm6 libxext6 libgl1 libssl-dev pkg-config 
+sudo apt-get update -y
+sudo apt-get install -y curl ccache git wget vim numactl gcc-12 g++-12 python3 python3-pip python3-venv python-is-python3 libtcmalloc-minimal4 libnuma-dev ffmpeg libsm6 libxext6 libgl1 libssl-dev pkg-config
 ```
 
 Set the default GCC to version 12:
@@ -58,7 +58,7 @@ python -m venv env
 source env/bin/activate
 ```
 
-Your command-line prompt is prefixed by `(env)`, which indicates that you are in the Python virtual environment. 
+Your command-line prompt is prefixed by `(env)`, which indicates that you are in the Python virtual environment.
 
 Now update Pip and install Python packages:
 
@@ -67,7 +67,7 @@ pip install --upgrade pip
 pip install py-cpuinfo
 ```
 
-### How do I download vLLM and build it? 
+### How do I download vLLM and build it?
 
 First, clone the vLLM repository from GitHub:
 
@@ -78,9 +78,9 @@ git checkout 72ff3a968682e6a3f7620ab59f2baf5e8eb2777b
 ```
 
 {{% notice Note %}}
-The Git checkout specifies a specific hash known to work for this example. 
+The Git checkout specifies a specific hash known to work for this example.
 
-Omit this command to use the latest code on the main branch. 
+Omit this command to use the latest code on the main branch.
 {{% /notice %}}
 
 Install the Python packages for vLLM: