Merging 2 learning paths

Author: dominica-of

content/learning-paths/microcontrollers/env-setup-tinyml-on-arm/_index.md

@@ -9,5 +9,7 @@ layout: learningpathall
 ## Module Overview 
 This session delves into TinyML, which applies machine learning to devices with limited resources like microcontrollers. This module 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. 
 
+Additionally, we'll cover the necessary setup on your host machine and target device to facilitate cross-compilation and ensure smooth integration across all devices.
+
 ## Introduction to TinyML
 TinyML represents a significant shift in how we approach 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, such as constrained memory, power, and processing capabilities. TinyML has quickly 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.

content/learning-paths/microcontrollers/env-setup-tinyml-on-arm/_next-steps.md

@@ -1,15 +1,18 @@
 ---
-title: Introduction to TinyML on Arm
+title: Introduction to TinyML on Arm using PyTorch v2.0 and Executorch
 
-minutes_to_complete: 10
+minutes_to_complete: 40
 
-who_is_this_for: This learning module is tailored for developers, engineers, and data scientists who are new to TinyML and interested in exploring its potential for edge AI. If you have an interest in deploying machine learning models on low-power, resource-constrained devices, this course will help you get started.
+who_is_this_for: This learning module is tailored for developers, engineers, and data scientists who are new to TinyML and interested in exploring its potential for edge AI. If you have an interest in deploying machine learning models on low-power, resource-constrained devices, this course will help you get started using PyTorch v2.0 and Executorch on Arm-based platforms.
 
 learning_objectives: 
     - Identify TinyML and how it's different from the AI you might be used to.
     - Understand the benefits of deploying AI models on Arm-based edge devices.
     - Select Arm-based devices for TinyML.
     - Identify real-world use cases demonstrating the impact of TinyML in various industries.
+    - Install and configure a TinyML development environment.
+    - Set up a cross-compilation environment on your host machine.
+    - Perform best practices for ensuring optimal performance on constrained edge devices.
 
 
 prerequisites:
@@ -29,6 +32,14 @@ operatingsystems:
     - Linux
 
 tools_software_languages:
+    - Corstone 300 FVP
+    - Grove - Vision AI Module V2
+    - Arm Virtual Hardware
+    - Python
+    - PyTorch v2.0
+    - Executorch
+    - Arm Compute Library 
+    - GCC
 
 ### FIXED, DO NOT MODIFY
 # ================================================================================

content/learning-paths/microcontrollers/env-setup-tinyml-on-arm/_review.md

@@ -0,0 +1,93 @@
+---
+# User change
+title: "Environment Setup for TinyML Development on Arm"
+
+weight: 6 # 1 is first, 2 is second, etc.
+
+# Do not modify these elements
+layout: "learningpathall"
+---
+## Before you begin 
+
+These instructions have been tested on:
+- A GCP Arm-based Tau T2A Virtual Machine instance Running Ubuntu 22.04 LTS.
+- Host machine with Ubuntu 24.04 on x86_64 architecture.
+
+The host machine is where you will perform most of your development work, especially cross-compiling code for the target Arm devices.
+
+You can use your own Linux host machine or use [Arm Virtual Hardware (AVH)](https://www.arm.com/products/development-tools/simulation/virtual-hardware) for this Learning Path.
+
+The Ubuntu version should be `20.04 or higher`. The `x86_64` architecture must be used because the Corstone-300 FVP is not currently available for the Arm architecture. You will need a Linux desktop to run the FVP because it opens graphical windows for input and output from the software applications. Also, though Executorch supports Windows via WSL, it is limited in resource.
+
+If you want to use Arm Virtual Hardware the [Arm Virtual Hardware install guide](/install-guides/avh#corstone) provides setup instructions.
+
+### Compilers 
+
+The examples can be built with [Arm Compiler for Embedded](https://developer.arm.com/Tools%20and%20Software/Arm%20Compiler%20for%20Embedded) or [Arm GNU Toolchain](https://developer.arm.com/Tools%20and%20Software/GNU%20Toolchain). 
+
+Both compilers are pre-installed in Arm Virtual Hardware.
+
+Alternatively, if you are using Arch Linux or its derivatives, you can use Pacman to install GCC. 
+
+Use the install guides to install the compilers on your computer:
+- [Arm Compiler for Embedded](/install-guides/armclang/)
+- [Arm GNU Toolchain](/install-guides/gcc/arm-gnu)
+- Using Pacman:
+ 
+```console
+pacman -S aarch64-linux-gnu-gcc
+```
+
+### Corstone-300 FVP {#fvp}
+
+To install the Corstone-300 FVP on your computer refer to the [install guide for Arm Ecosystem FVPs](/install-guides/fm_fvp). 
+
+The Corstone-300 FVP is pre-installed in Arm Virtual Hardware. 
+
+
+## Install Executorch
+
+1. Follow the [Setting Up ExecuTorch guide](https://pytorch.org/executorch/stable/getting-started-setup.html ) to install it.
+
+2. Activate the `executorch` virtual environment from the installation guide:
+
+```console
+conda activate executorch
+```
+
+
+## Setup on Grove - Vision AI Module V2 
+Due to its constrained environment, we'll focus on lightweight, optimized tools and models (will be introcuded in the next learning path).
+
+1. Connect the Grove - Vision AI Module V2 to your computer using the USB-C cable. 
+
+![Board connection #center](connect.png)
+
+2. Install Edge Impulse CLI. It will help in data collection and model deployment.
+
+```console
+npm install -g edge-impulse-cli
+```
+
+3. Configure Edge Impulse for the board
+In your terminal, run:
+
+```console
+edge-impulse-daemon
+```
+Follow the prompts to log in.
+
+4. Verify Setup
+Connect to your device
+
+```console
+edge-impulse-run-impulse --api-key YOUR_API_KEY
+```
+
+If successful, you should see data from your Grove - Vision AI Module V2.
+
+5. Install Executorch 
+
+```console
+pip install executorch
+```

content/learning-paths/microcontrollers/env-setup-tinyml-on-arm/example-picture.png

@@ -0,0 +1,18 @@
+---
+title: Benefits of TinyML for Edge Computing on Arm Devices
+weight: 4
+
+### FIXED, DO NOT MODIFY
+layout: learningpathall
+---
+## Troubleshooting
+- If you encounter permission issues, try running the commands with sudo.
+- Ensure your Grove - Vision AI Module V2 is properly connected and recognized by your computer.
+- If Edge Impulse CLI fails to detect your device, try unplugging and replugging the USB cable.
+
+## Best Practices
+- Always cross-compile your code on the host machine to ensure compatibility with the target Arm device.
+- Utilize model quantization techniques to optimize performance on constrained devices like the Grove - Vision AI Module V2.
+- Regularly update your development environment and tools to benefit from the latest improvements in TinyML and edge AI technologies
+
+You've now set up your environment for TinyML development on the Grove - Vision AI Module V2. In the next modules, we'll explore data collection, model training, and deployment using PyTorch v2.0 and Executorch.