move devcontainer (#113)

appendix-A/04_optional-docker-environment/.devcontainer/Dockerfile

@@ -0,0 +1,11 @@
+FROM pytorch/pytorch:2.0.1-cuda11.7-cudnn8-runtime
+
+RUN apt-get update && \
+    apt-get upgrade -y && \
+    apt-get install -y rsync && \
+    apt-get install -y git && \
+    rm -rf /var/lib/apt/lists/*
+
+COPY requirements.txt requirements.txt
+
+RUN pip install --no-cache-dir -r requirements.txt

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

@@ -0,0 +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/.devcontainer/devcontainer.json

@@ -0,0 +1,19 @@
+{
+  "name": "LLMs From Scratch",
+  "build": {
+    "context": "..",
+    "dockerfile": "Dockerfile"
+  },
+  "runArgs": ["--runtime=nvidia", "--gpus=all"],
+  "customizations": {
+    "vscode": {
+      "extensions": [
+        "ms-python.python",
+        "ms-azuretools.vscode-docker",
+        "ms-toolsai.jupyter",
+        "yahyabatulu.vscode-markdown-alert",
+        "tomoki1207.pdf"
+      ]
+    }
+  }
+}

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.