If you don't want to manually install all the of the software dependencies locally you can instead do everything inside of singularity container. To build the container, you can use the recipe singularity in the repository, and just do (tested using singularity version 2.5):

./container.img # will give you a shell into the container environment

To do.

Currently in order to build the solver on a fresh opam switch I needed the following packages (anecdotal data from Arch x64, assuming you have opam):

opam update                 # Seriously, do that one
opam switch 4.06.1+flambda  # caml.inria.fr/pub/docs/manual-ocaml/flambda.html
eval `opam config env`      # *sight*
opam install ppx_jane core re2 yojson vg cairo2 camlimages menhir ocaml-protoc zmq

Now try to run make in the root folder, it should build several ocaml binaries.

Get Rust (e.g. curl https://sh.rustup.rs -sSf | sh according to https://www.rust-lang.org/)

Now running make in the rust_compressor folder should install the right packages and build the binary.

Run:

git submodule update --recursive --init

from within the main project directory. You might need a recent version of git; 2.7.4 worked.

This should install all of the Python packages that you need. Not all of these are needed for any particular domain, but all of these are required by at least one domain.

pip install dill
pip install sexpdata
pip install Box2D-kengz
pip install graphviz
pip install pygame
pip install pycairo
pip install cairocffi
pip install psutil
conda install protobuf
pip install pypng
conda install pyzmq

If for some reason you want to run something in pypy, install it from:

https://github.com/squeaky-pl/portable-pypy#portable-pypy-distribution-for-linux

Be sure to add pypy3 to the path. Really though you should try to use the rust compressor and the ocaml solver. You will have to (annoyingly) install parallel libraries on the pypy side even if you have them installed on the Python side:

pypy3 -m ensurepip
pypy3 -m pip install --user vmprof
pypy3 -m pip install --user dill
pypy3 -m pip install --user psutil

The protonet-networks folder contains some modifications over a big chunk of code from this repository, here is the attribution information :

Code for the NIPS 2017 paper Prototypical Networks for Few-shot Learning

If you use that part of the code, please cite their paper paper, and check out what they did:

@inproceedings{snell2017prototypical,
  title={Prototypical Networks for Few-shot Learning},
  author={Snell, Jake and Swersky, Kevin and Zemel, Richard},
  booktitle={Advances in Neural Information Processing Systems},
  year={2017}
}

MIT License

Copyright (c) 2017 Jake Snell

Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions:

The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software.

THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.