move devcontainer (#113)

.1st_setup/README.md

@@ -3,7 +3,7 @@
 
 This document lists different approaches for setting up your machine and using the code in this repository. I recommend browsing through the different sections from top to bottom and then deciding which approach best suits your needs.
 
-<br>
+&nbsp;
 
 ## Quickstart
 
@@ -13,7 +13,7 @@ If you already have a Python installation on your machine, the quickest way to g
 pip install -r requirements.txt
 ```
 
-<br>
+&nbsp;
 
 ## Setting up Python
 
@@ -25,7 +25,7 @@ If you don't have Python set up on your machine yet, I have written about my per
 
 
 
-<br>
+&nbsp;
 
 ## Using Google Colab
 
@@ -44,13 +44,19 @@ You can optionally run the code on a GPU by changing the *Runtime* as illustrate
 <img src="./figures/3.webp" alt="3" width="700">
 
 
-<br>
+&nbsp;
 
 ## Using DevContainers
 
 Alternatively, If you prefer a development setup that isolates a project's dependencies and configurations, using Docker is a highly effective solution. This approach eliminates the need to manually install software packages and libraries and ensures a consistent development environment. You can find more instructions for setting up Docker and using a DevContainer here in [../appendix-A/04_optional-docker-environment](../appendix-A/04_optional-docker-environment).
 
-<br>
+&nbsp;
+
+## VSCode extensions
+
+If you are using Visual Studio Code (VSCode) as your primary code editor, you can find recommended extensions in the `.vscode` subfolder. To install these, open the `extensions.json` file in VSCode and click the "Install" button in the pop-up menu on the lower right.
+
+&nbsp;
 
 ## Questions?
 

appendix-A/04_optional-docker-environment/.devcontainer/README.md

@@ -1,3 +1,3 @@
 # Optional Docker Environment
 
-This is an optional Docker environment for those users who prefer Docker. For more instructions, see the *Docker Environment Setup Guide* in [../appendix-A/04_optional-docker-environment](../appendix-A/04_optional-docker-environment).
+This is an optional Docker environment for those users who prefer Docker. For more instructions, see the *Docker Environment Setup Guide* in [appendix-A/04_optional-docker-environment](../).

appendix-A/04_optional-docker-environment/README.md

@@ -27,10 +27,16 @@ git clone https://github.com/rasbt/LLMs-from-scratch.git
 cd LLMs-from-scratch
 ```
 
-2. In Docker Desktop, make sure that ***desktop-linux* builder** is running and will be used to build the Docker container (see *Docker Desktop* -> *Change settings* -> *Builders* -> *desktop-linux* -> *...* -> *Use*)
+2. Move the `.devcontainer` file to the main `LLMs-from-scratch` project directory.
 
-3. If you have a [CUDA-supported GPU](https://developer.nvidia.com/cuda-gpus), you can speed up the training and inference:
-    
+```bash
+mv appendix-A/04_optional-docker-environment/.devcontainer ./
+```
+
+3. In Docker Desktop, make sure that ***desktop-linux* builder** is running and will be used to build the Docker container (see *Docker Desktop* -> *Change settings* -> *Builders* -> *desktop-linux* -> *...* -> *Use*)
+
+4. If you have a [CUDA-supported GPU](https://developer.nvidia.com/cuda-gpus), you can speed up the training and inference:
+   
     3.1  Install **NVIDIA Container Toolkit** as described [here](https://docs.nvidia.com/datacenter/cloud-native/container-toolkit/latest/install-guide.html#installing-with-apt). NVIDIA Container Toolkit is supported as written [here](https://docs.nvidia.com/cuda/wsl-user-guide/index.html#nvidia-compute-software-support-on-wsl-2). 
 
     3.2 Add *nvidia* as runtime in Docker Engine daemon config (see *Docker Desktop* -> *Change settings* -> *Docker Engine*). Add these lines to your config:
@@ -61,15 +67,15 @@ cd LLMs-from-scratch
     ```
     and restart Docker Desktop.
 
-4. Type `code .` in the terminal to open the project in VS Code. Alternatively, you can launch VS Code and select the project to open from the UI.
+5. Type `code .` in the terminal to open the project in VS Code. Alternatively, you can launch VS Code and select the project to open from the UI.
 
-5. Install the **Remote Development** extension from the VS Code *Extensions* menu on the left-hand side.
+6. Install the **Remote Development** extension from the VS Code *Extensions* menu on the left-hand side.
 
-6. Open the DevContainer. 
+7. Open the DevContainer. 
 
 Since the `.devcontainer` folder is present in the main `LLMs-from-scratch` directory (folders starting with `.` may be invisible in your OS depending on your settings), VS Code should automatically detect it and ask whether you would like to open the project in a devcontainer. If it doesn't, simply press `Ctrl + Shift + P` to open the command palette and start typing `dev containers` to see a list of all DevContainer-specific options.
 
-7. Select **Reopen in Container**.
+8. Select **Reopen in Container**.
 
 Docker will now begin the process of building the Docker image specified in the `.devcontainer` configuration if it hasn't been built before, or pull the image if it's available from a registry. 
 
@@ -80,7 +86,7 @@ Once completed, VS Code will automatically connect to the container and reopen t
 > [!WARNING]
 > If you are encountering an error during the build process, this is likely because your machine does not support NVIDIA container toolkit because your machine doesn't have a compatible GPU. In this case, edit the `devcontainer.json` file to remove the `"runArgs": ["--runtime=nvidia", "--gpus=all"],` line and run the "Reopen Dev Container" procedure again.
 
-8. Finished. 
+9. Finished. 
 
 Once the image has been pulled and built, you should have your project mounted inside the container with all the packages installed, ready for development.