OAZ is a C++ library designed to carry out experiments with the Alpha Zero algorithm. It provides a Python front-end, PyOAZ, to run high-level tasks such as orchestrating self-play rounds or updating neural network weights.

• Get Docker

• Run the OAZ cpu-only image, mapping the port to get access to Jupyter from the host:

    $ docker run -it -p 8888:8888 ghcr.io/ameroueh/oaz-cpu bash
  

• From the container, start Jupyter:

    $ jupyter notebook --ip 0.0.0.0 --no-browser
  

• Open http://localhost:8888 from your favourite browser on the host, and open the notebook notebooks/quickstart.ipynb

Clone the repository with

 $ git clone git@github.com:ameroueh/oaz.git

Now initialize the submodules:

$ cd oaz && git submodule update --init

The easiest way of running OAZ is by building the Docker image. This image installs all the required dependencies, and can be used to compile and run the OAZ C++ code, and create install the Python package. Building the image may take a while (several hours) as tensorflow shared libraries are built as part of the process.

 $ cd oaz
 $ docker build . -t oaz

to build the image.

By default, no GPU support is enabled. To enable GPU support for Nvidia cards, provide appropriate parameters to the following build arguments: BASE_IMAGE, TF_NEED_CUDA, TF_CUDA_COMPUTE_CAPABILITIES. For example, to build the image with support for RTX 30xx GPUs, run

 $ cd oaz
 $ docker build --build-arg BASE_IMAGE=nvidia/cuda:11.1-cudnn8-devel-ubuntu18.04 --build-arg TF_NEED_CUDA=1 --build-arg TF_CUDA_COMPUTE_CAPABILITIES=8.6 . -t oaz

This image is tested to work on an Ubuntu 20.10 host with nvidia-docker2 installed. Use the option --gpus=all (or select specific GPU devices) to run the image with GPU support.

The image creates a user oaz with default UID 1000 (this can be overridden) for normal usage not requiring privileged access. You may want to change this default value should you wish to mount a host directory (for example, the OAZ git repository to carry out development work) to the container.

To compile and run the C++ test suite, run

$ cmake . -B build && cd build && make -j$(nproc) && cd test && ctest

from /home/oaz/oaz in the Docker container.

To run the Python tests, run

$ cd pyoaz_tests && pytest .

from /home/oaz/oaz in the Docker container.