parser

This project includes a parser for processing FE (Foundation Exam) exam PDFs and a development environment setup using Dev Containers.

Running the Scraper

To run the scraper, use the following command:

python parser/scrape.py

Generated file exam_paths.json provides path information for each stored exam file.

Running the Parser

To run the parser, use the following command:

python parser/parse.py <path to FE pdf>

After running the parser, open document.json to view the parsed questions.

Development Environment Setup

This project uses Dev Containers to provide a consistent development environment. There are two configurations available: a base setup and a CUDA-enabled setup.

Base Dev Container

The base dev container is defined in .devcontainer/devcontainer.json and includes the following features:

• Python 3.12 with tools like flake8, autopep8, black, yapf, mypy, pydocstyle, pycodestyle, bandit, pipenv, virtualenv, pytest, pylint, poetry, and ruff.
• Git LFS for handling large files.

VSCode Extensions

The base dev container also includes the following VSCode extensions:

• Conventional Commits
• Ruff
• Jupyter
• Python
• Python Indent

CUDA Dev Container

The CUDA dev container is defined in .devcontainer/cuda/devcontainer.json and includes all the features of the base container, plus:

• NVIDIA CUDA with support for CUDA version 12.2+.

If you don't know what version of CUDA you have, you can check by running nvidia-smi. If nothing shows up, you need to install CUDA.

Host Requirements

• A CUDA-enabled Nvidia GPU.
• Nvidia Driver. You can install it by following the instructions here.
• The NVIDIA Container Toolkit. You can install it by following the instructions here.
• The CUDA Toolkit. You can install it by following the instructions here.

If you are running WSL2, checkout this and this.

Using the Dev Containers

To use the dev containers, follow these steps:

1. Install Prerequisites:

   • Ensure you have Docker installed on your machine.
   • Install Visual Studio Code (VSCode) and the Remote - Containers extension.

2. Open the Project in VSCode:

   • Launch VSCode and open the project folder.

3. Select a Dev Container Configuration:

   • Click on the green icon in the bottom-left corner of VSCode (or press F1 and type Dev Containers: Open Folder in Container...).
   • Choose the desired dev container configuration from the list. You can select either the base setup or the CUDA-enabled setup (if you have an NVIDIA GPU).

4. Start the Development Environment:

   • VSCode will build and start the selected dev container. This may take a few minutes the first time as it downloads necessary images and sets up the environment.

5. Switching Between Dev Containers:

   • To switch between the base and CUDA dev containers, repeat step 3 and select the other configuration.
   • VSCode will rebuild and restart the environment with the new configuration.

Python Virtual Environment

In VSCode, open any python file and at the bottom right of your screen you should see the python environment being used, see screenshot below:

On your side, click the button that is in the place of where it says "3.12.6" on my screenshot. Select the python virtual environment that includes the word .venv.

See below for an example (may not be exactly the same):

Common Issues

Permission denied

If you get a permission denied error, you will need to update the owner of the file to the user in the container.

From within the container, run the following command:

sudo chown -R vscode:vscode /workspace/parser