Add devcontainer and update docs

.devcontainer/README.md

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+# Dev Container Configuration
+
+This directory contains the development container configuration for the diffusion model workshop.
+
+## What gets installed
+
+When a user opens this repository in GitHub Codespaces, the following will be automatically set up:
+
+### Base Environment
+- Python 3.11 (Debian Bullseye)
+- Git and GitHub CLI
+
+### Python Dependencies
+- **Workshop dependencies**: Installed from `pyproject.toml`
+  - Diffusers, transformers, scikit-learn and other ML libraries
+  - PyTorch CPU version (for lighter resource usage in Codespaces)
+- **Documentation dependencies**: Also installed from `pyproject.toml`
+  - MkDocs and related tools for building documentation
+
+### VS Code Extensions
+- Python development: Python, Pylance, Black formatter, Ruff linter
+- Jupyter notebooks: Full Jupyter support with renderers and keybindings
+- General development: JSON, YAML, and Markdown support
+
+### Port Forwarding
+- **Port 8888**: Jupyter notebook server
+
+## Usage
+
+Users simply need to:
+1. Fork this repository
+2. Open in GitHub Codespaces
+3. Wait for the automatic setup to complete
+4. Start working with the workshop materials
+
+The environment will be ready to use with all dependencies installed and VS Code properly configured for Python and Jupyter development. 

.devcontainer/devcontainer.json

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+{
+	"name": "Diffusion Models Workshop",
+	"image": "mcr.microsoft.com/devcontainers/python:1-3.11-bullseye",
+
+	"features": {
+		"ghcr.io/devcontainers/features/git:1": {},
+		"ghcr.io/devcontainers/features/github-cli:1": {}
+	},
+
+	"customizations": {
+		"vscode": {
+			"extensions": [
+				"ms-python.python",
+				"ms-python.vscode-pylance",
+				"ms-toolsai.jupyter",
+				"ms-toolsai.jupyter-keymap",
+				"ms-toolsai.jupyter-renderers",
+				"ms-python.black-formatter",
+				"charliermarsh.ruff",
+				"ms-vscode.vscode-json",
+				"redhat.vscode-yaml",
+				"ms-vscode.vscode-markdown"
+			],
+			"settings": {
+				"python.defaultInterpreterPath": "/usr/local/bin/python",
+				"python.linting.enabled": true,
+				"python.linting.pylintEnabled": false,
+				"python.linting.flake8Enabled": false,
+				"python.formatting.provider": "black",
+				"jupyter.askForKernelRestart": false,
+				"files.associations": {
+					"*.ipynb": "jupyter-notebook"
+				}
+			}
+		}
+	},
+
+	"postCreateCommand": "./.devcontainer/uv_setup.sh && ./.devcontainer/torch_setup.sh",
+
+	"remoteUser": "vscode",
+
+	"forwardPorts": [8888],
+
+	"portsAttributes": {
+		"8888": {
+			"label": "Jupyter",
+			"onAutoForward": "notify"
+		}
+	}
+}

.devcontainer/torch_setup.sh

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+#!/usr/bin/env bash
+set -euxo pipefail
+
+# Install torch into venv (CPU build for codespaces)
+uv pip install torch torchvision --index-url https://download.pytorch.org/whl/cpu

.devcontainer/uv_setup.sh

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+#!/usr/bin/env bash
+set -euxo pipefail
+
+# Install uv
+curl -LsSf https://astral.sh/uv/install.sh | sh
+
+# Install runtime deps into .venv
+uv sync
+
+# Activate venv
+echo 'source .venv/bin/activate' >> ~/.bashrc

docs/setting-up/codespaces.md

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+# Codespaces
+
+For this workshop, you can work entirely within Github Codespaces.
+
+!!! warning
+
+    GitHub Codespaces only provides CPU resources, which means training and inference of the models will run much slower. If you have access to a GPU on your local machine, we recommend running the workshop locally instead and installing PyTorch manually by following the instructions in [Running Locally](./running-locally.md).
+
+First create a new repository on Github, and then create a codespace from that repository. 
+
+Navigate to the LLM workshop repo (click the GitHub symbol in the top right of this page). Switch to the `Handson` branch, and download the content as a zip file. Upload this to your new repo. Open the repo as a Codespace. There is no need to create a virtual environment, since you're already in a containerized environment anyway with a version of Python 3.12.
+
+You don’t need to create or manage a virtual environment yourself. The devcontainer will automatically install `uv`, create and activate a `.venv`, and install all required dependencies when your Codespace is built.

mkdocs.yml

@@ -7,9 +7,11 @@ nav:
     - The Workshop: Home/workshop.md
     - License: Home/LICENSE.md
     - About us: Home/about.md
+  - Setting up:
+    - Codespaces: setting-up/codespaces.md
+    - Running Locally: setting-up/running-locally.md
   - Introduction:
     - Introduction: Introduction/Introduction.md
-    - Running Locally: Introduction/running-locally.md
   - Physics: Physics/Physics.md
   - Building Blocks:
     - BuildingBlocks/index.md