Proverbot9001

A bot for proving.

You can find the paper and talk video at our website.

Prerequisites

MacOS

1. Check your python version with python --version in the terminal. If your version is older than Python 3.7, or the python command isn't found, install python through the python website.
2. Make sure pip, the python package manager, is available, by running in your terminal: python -m ensurepip.
3. Install Homebrew from their website.
4. Install wget, git, opam, rustup, GNU awk, and graphviz through Homebrew: brew install wget git opam rustup-init gawk graphviz && rustup-init

Linux

1. Check your python version with python --version in the terminal. If your version is older than Python 3.7, or the python command isn't found, install python through your package manager.
   1. On Ubuntu, that's:

   sudo apt install python3 python3-dev python3-pip
   

2. Make sure pip, the python package manager, is available, by running in your terminal: python -m ensurepip.
3. Install git, opam, rustup, and graphviz using your package manager.
   1. On Ubuntu, that's:

   sudo apt install software-properties-common
   sudo add-apt-repository ppa:avsm/ppa
   sudo apt update
   sudo apt install git opam rustup graphviz libgraphviz-dev
   

Windows

Windows support is more experimental, but you can try installing prereqs through:

https://gitforwindows.org/

https://fdopen.github.io/opam-repository-mingw/installation/

https://graphviz.gitlab.io/_pages/Download/Download_windows.html

https://www.python.org/downloads/windows/

or use Windows Subsystem for Linux

Getting Started

The first thing you'll need to do is clone the repository (download the code).

Cloning the repository (downloading the project)

I recommend that you do this over ssh. To clone github projects using git@ urls (over ssh), you'll need to follow the instructions on this github page. Then, open a terminal (windows users can use "Bash on Ubuntu on Windows"), move to the directory you want to work in, and run:

git clone [email protected]:agrarpan/proverbot9001-plugin.git
git clone [email protected]:UCSD-PL/proverbot9001.git

Alternatively, you can clone over https without setting up your github keys, with this command:

git clone https://github.com/UCSD-PL/proverbot9001.git

That should give you a new folder called proverbot9001-plugin with all the code inside. Move into this new directory:

cd proverbot9001-plugin

Create a python virtual environment

Next, you'll need to create a python virtual environment to work in. This is a good idea in general, but is also required for maturin to work properly.

python -m venv proverbot-env

Whenever you want to work with Proverbot from a new shell, you'll have to first activate this environment:

source proverbot-env/bin/activate

Also do that now.

Install python and rust dependencies

On MacOS, you'll need to install pygraphviz differently, so do that now: pip install --global-option=build_ext --global-option="-I/usr/local/include/" --global-option="-L/usr/local/lib/" pygraphviz

Then, no matter your platform, run this command to install the dependencies:

make setup

this step will take a while, and might involve having to type y a few times.

If this step fails, and part of the error message near the bottom says:

pygraphviz/graphviz_wrap.c:2711:10: fatal error: graphviz/cgraph.h: No such file or directory
 2711 | #include "graphviz/cgraph.h"
      |          ^~~~~~~~~~~~~~~~~~~
compilation terminated.
error: command 'x86_64-linux-gnu-gcc' failed with exit status 1

then python needs help finding your graphviz installation. Check out this github issue: pygraphviz/pygraphviz#155, and possibly this one: pypa/setuptools#2740 .

Download the pre-trained weights

Running Proverbot9001 requires training on existing proof files. Training takes a while, and usually you need some pretty advanced hardware. So to quickly get started, we'll download pre-trained weights instead:

make download-weights

Now you can run Proverbot9001:

make search-report

Which will generate some html in the reports directory.

You should be able to check out the results by opening a web browser and navigating to the reports directory in the project.

Once you've done that, you might like to try training from scratch with make scrape and make train, or writing your own predictor.