Update TinyML Intro LP (WIP)

Author: annietllnd

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

@@ -18,8 +18,7 @@ learning_objectives:
 
 
 prerequisites:
-    - Basic knowledge of machine learning concepts. ?
-    - Understanding of IoT and embedded systems. ?
+    - 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).
 

content/learning-paths/microcontrollers/introduction-to-tinyml-on-arm/build-model-8.md

@@ -8,7 +8,8 @@ weight: 7 # 1 is first, 2 is second, etc.
 layout: "learningpathall"
 ---
 
-? Where is this executed? Context?
+TODO Where is this executed? Context? Make sure the steps works out-of-the-box
+
 With our Environment ready, you can create a simple program to test the setup.
 
 This example defines a small feedforward neural network for a classification task. The model consists of 2 linear layers with ReLU activation in between.

content/learning-paths/microcontrollers/introduction-to-tinyml-on-arm/env-setup-5.md

@@ -10,16 +10,17 @@ layout: "learningpathall"
 
 In this section, you will prepare a development environment to compile the model.
 
-## Before you begin ? remove ?
+## Before you begin
 
+TODO remove this section?
 
-The instructions are for Ubuntu 22.04 or newer. ?
+
+The instructions are for Ubuntu 22.04 or newer.
 
 {{% notice Note %}}
 Note that the Corstone-300 FVP is not available for the Arm architecture so your host machine needs to x86_64.
 {{% /notice %}}
 
-^?
 
 The instructions have been tested on:
 - Arm-based cloud instances running Ubuntu 22.04.
@@ -32,45 +33,17 @@ Python3 is required and comes installed with Ubuntu, but some additional package
 
 ```bash
 sudo apt update
-sudo apt install python-is-python3 python3-dev gcc g++ make cmake clang -y
+sudo apt install python-is-python3 python3-dev python3-venv gcc g++ make -y
 ```
 
-```
-???
-sudo update-alternatives --install /usr/bin/cc cc /usr/bin/clang 100
-sudo update-alternatives --install /usr/bin/c++ c++ /usr/bin/clang++ 100
-sudo update-alternatives --set cc /usr/bin/clang
-sudo update-alternatives --set c++ /usr/bin/clang++
-```
 ## Create a virtual environment
 
-Create a Python virtual environment using Miniconda.
-
-For Arm Linux:
+Create a Python virtual environment using `python venv`.
 
 ```console
-curl -O https://repo.anaconda.com/miniconda/Miniconda3-latest-Linux-aarch64.sh
-sh ./Miniconda3-latest-Linux-aarch64.sh -b
-eval "$($HOME/miniconda3/bin/conda shell.bash hook)"
-conda --version
+python3 -m venv $HOME/executorch
+source $HOME/executorch/bin/activate
 ```
-
-For x86_64 Linux:
-
-```console
-curl -O https://repo.anaconda.com/miniconda/Miniconda3-latest-Linux-x86_64.sh
-sh ./Miniconda3-latest-Linux-x86_64.sh -b
-eval "$($HOME/miniconda3/bin/conda shell.bash hook)"
-conda --version
-```
-
-Activate the Python virtual environment:
-
-```bash
-conda create -yn executorch python=3.12.7
-conda activate executorch
-```
-
 The prompt of your terminal now has (executorch) as a prefix to indicate the virtual environment is active.
 
 
@@ -88,8 +61,8 @@ Run a few commands to set up the ExecuTorch internal dependencies.
 ```bash
 git submodule sync
 git submodule update --init
-??? pip install buck
 
+pip install buck
 ./install_requirements.sh
 ```
 

content/learning-paths/microcontrollers/introduction-to-tinyml-on-arm/env-setup-6-FVP.md

@@ -15,7 +15,7 @@ Navigate to the Arm examples directory in the ExecuTorch repository.
 cd $HOME/executorch/examples/arm
 ./setup.sh --i-agree-to-the-contained-eula
 ```
-?
+
 ```bash
 export FVP_PATH=${pwd}/ethos-u-scratch/FVP-corstone300/models/Linux64_GCC-9.3
 export PATH=$FVP_PATH:$PATH
@@ -26,6 +26,6 @@ Test that the setup was successful by running the `run.sh` script.
 ./run.sh
 ```
 
-You will see a number of examples run on the FVP.
-## Next Steps
-1. Go to [Build a Simple PyTorch Model](/learning-paths/microcontrollers/introduction-to-tinyml-on-arm/build-model-8/) to test your environment setup.
+TODO verify that this works with the simple_nn.py
+
+You will see a number of examples run on the FVP. This means you can proceed to the next section to test your environment setup.

content/learning-paths/microcontrollers/introduction-to-tinyml-on-arm/troubleshooting-6.md

@@ -5,6 +5,9 @@ weight: 8
 ### FIXED, DO NOT MODIFY
 layout: learningpathall
 ---
+
+TODO can these be incorporated in the LP?
+
 ## 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.