Px0 is a UCI-compliant xiangqi engine designed to play xiangqi via neural network, specifically those of the PikaXiangqiZero project.

Px0 can be acquired either via a git clone or an archive download from GitHub. Be aware that there is a required submodule which isn't included in source archives.

For essentially all purposes, including selfplay game generation and match play, we highly recommend using the latest master branch.

Versioning follows the Semantic Versioning guidelines, with major, minor and patch sections. The training server enforces game quality using the versions output by the client and engine.

Download using git:

git clone --recurse-submodules https://github.com/official-pikafish/px0.git

If you have cloned already an old version, fetch, view and checkout a new branch:

git fetch --all
git branch --all
git checkout -t master

If you prefer to download an archive, you need to also download and place the submodule:

• Download the .zip file (.tar.gz archive is also available)
• Extract
• Download https://github.com/LeelaChessZero/lczero-common/archive/master.zip (also available as .tar.gz)
• Move the second archive into the first archive's libs/lczero-common/ folder and extract
• The final form should look like <TOP>/libs/lczero-common/proto/

Having successfully acquired Px0 via either of these methods, proceed to the build section below and follow the instructions for your OS.

Building should be easier now than it was in the past. Please report any problems you have.

Aside from the git submodule, px0 requires the Meson build system and at least one backend library for evaluating the neural network, as well as the required zlib. (gtest is optionally used for the test suite.) If your system already has this library installed, they will be used; otherwise Meson will generate its own copy of the two (a "subproject"), which in turn requires that git is installed (yes, separately from cloning the actual px0 repository). Meson also requires python and Ninja.

Backend support includes (in theory) any CBLAS-compatible library for CPU usage, such as OpenBLAS or Intel's DNNL or MKL. For GPUs, OpenCL and CUDA cudnn are supported, while DX-12 can be used in Windows 10 with latest drivers.

Finally, px0 requires a compiler supporting C 17. Minimal versions seem to be g v8.0, clang v5.0 (with C 17 stdlib) or Visual Studio 2017.

Note that cuda checks the compiler version and stops even with newer compilers, and to work around this we have added the nvcc_ccbin build option. This is more of an issue with new Linux versions, but you can get around it by using an earlier version of gcc just for cuda. As an example, adding -Dnvcc_ccbin=g -9 to the build.sh command line will use g -9 with cuda instead of the system compiler.

Given those basics, the OS and backend specific instructions are below.

1. Install backend:
   • If you want to use NVidia graphics cards Install CUDA and cuDNN.
   • If you want to use AMD graphics cards install OpenCL.
   • if you want OpenBLAS version Install OpenBLAS (libopenblas-dev).
2. Install ninja build (ninja-build), meson, and (optionally) gtest (libgtest-dev).
3. Go to px0/
4. Run ./build.sh
5. px0 will be in px0/build/release/ directory
6. Unzip a neural network in the same directory as the binary.

If you want to build with a different compiler, pass the CC and CXX environment variables:

CC=clang-6.0 CXX=clang  -6.0 ./build.sh

Nvidia provides .deb packages. CUDA will be installed in /usr/local/cuda-10.0 and requires 3GB of diskspace. If your /usr/local partition doesn't have that much space left you can create a symbolic link before doing the install; for example: sudo ln -s /opt/cuda-10.0 /usr/local/cuda-10.0

The instructions given on the nvidia website tell you to finish with apt install cuda. However, this might not work (missing dependencies). In that case use apt install cuda-10-0. Afterwards you can install the meta package cuda which will cause an automatic upgrade to a newer version when that comes available (assuming you use Installer Type deb (network), if you'd want that (just cuda-10-0 will stay at version 10). If you don't know what to do, only install cuda-10-0.

cuDNN exists of two packages, the Runtime Library and the Developer Library (both a .deb package).

Before you can download the latter you need to create a (free) "developer" account with nvidia for which at least a legit email address is required (their website says: The e-mail address is not made public and will only be used if you wish to receive a new password or wish to receive certain news or notifications by e-mail.). Further they ask for a name, date of birth (not visible later on), country, organisation ("PikaZero" if you have none), primary industry segment ("Other"/none) and which development areas you are interested in ("Deep Learning").

For Ubuntu 18.04 you need the latest version of meson, libstdc -8-dev, and clang-6.0 before performing the steps above:

sudo apt-get install libstdc  -8-dev clang-6.0 ninja-build pkg-config
pip3 install meson --user
CC=clang-6.0 CXX=clang  -6.0 INSTALL_PREFIX=~/.local ./build.sh

Make sure that ~/.local/bin is in your PATH environment variable. You can now type lc0 --help and start.

For Ubuntu 16.04 you need the latest version of meson, ninja, clang-6.0, and libstdc -8:

wget -O - https://apt.llvm.org/llvm-snapshot.gpg.key | sudo apt-key add -
sudo apt-add-repository 'deb http://apt.llvm.org/xenial/ llvm-toolchain-xenial-6.0 main'
sudo add-apt-repository ppa:ubuntu-toolchain-r/test
sudo apt-get update
sudo apt-get install clang-6.0 libstdc  -8-dev
pip3 install meson ninja --user
CC=clang-6.0 CXX=clang  -6.0 INSTALL_PREFIX=~/.local ./build.sh

Make sure that ~/.local/bin is in your PATH environment variable. You can now type lc0 --help and start.

Instructions, packages and tools for building on openSUSE are at openSUSE_install.md

Use https://github.com/vochicong/lc0-docker to run latest releases of lc0 and the client inside a Docker container.

Here are the brief instructions for CUDA/CuDNN, for details and other options see windows-build.md.

0. Install Microsoft Visual Studio (2017 or later)
1. Install CUDA
2. Install cuDNN.
3. Install Python3
4. Install Meson: pip3 install --upgrade meson
5. Edit build.cmd:

• Set CUDA_PATH with your CUDA directory
• Set CUDNN_PATH with your cuDNN directory (may be the same with CUDA_PATH)

6. Run build.cmd. It will ask permission to delete the build directory, then generate MSVS project and pause.

Then either:

7. Hit Enter to build it.
8. Resulting binary will be build/lc0.exe

Or.

7. Open generated solution build/lc0.sln in Visual Studio and build yourself.

First you need to install some required packages through Terminal:

1. Install brew as per the instructions at https://brew.sh/
2. Install python3: brew install python3
3. Install meson: brew install meson
4. Install ninja: brew install ninja
5. (For Mac OS 10.14 Mojave, or if the other step 5 fails):

• Install developer tools: xcode-select --install
• When using Mojave install SDK headers: installer -pkg /Library/Developer/CommandLineTools/Packages/macOS_SDK_headers_for_macOS_10.14.pkg -target / (if this doesn't work, use sudo installer instead of just installer.)

Or.

5. (For MacOS 10.15 Catalina, or if the other step 5 fails):

• Install Xcode command-line tools: xcode-select --install
• Install "XCode Developer Tools" through the app store. (First one on the list of Apps if searched.)
• Associate the SDK headers in XCode with a command: export CPATH=`xcrun --show-sdk-path`/usr/include

Now download the lc0 source, if you haven't already done so, following the instructions earlier in the page.

6. Go to the lc0 directory.
7. Run ./build.sh -Dgtest=false (needs step 5)

You'll need to be running the latest Raspberry Pi OS "buster".

1. Install OpenBLAS

git clone https://github.com/xianyi/OpenBLAS.git
cd OpenBLAS/
make
sudo make PREFIX=/usr install
cd ..

2. Install Meson

pip install meson
pip install ninja

3. Install compiler and standard libraries

sudo apt install clang-6.0 libstdc  -8-dev

4. Clone lc0 and compile

git clone https://github.com/official-pikafish/px0.git
cd px0
git submodule update --init --recursive
CC=clang-6.0 CXX=clang  -6.0 ./build.sh -Ddefault_library=static

5. The resulting binary will be in build/release

Python bindings can be built and installed as follows.

pip install --user git https://github.com/official-pikafish/px0.git

This will build the package lczero-bindings and install it to your Python user install directory. All the px0 functionality related to position evaluation is now available in the module lczero.backends. An example interactive session can be found here.

Pika Xiangqi is free software: you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation, either version 3 of the License, or (at your option) any later version.

Pika Xiangqi is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details.

You should have received a copy of the GNU General Public License along with Pika Xiangqi. If not, see http://www.gnu.org/licenses/.

The source files of Px0 with the exception of the BLAS and OpenCL backends (all files in the blas and opencl sub-directories) have the following additional permission, as allowed under GNU GPL version 3 section 7:

If you modify this Program, or any covered work, by linking or combining it with NVIDIA Corporation's libraries from the NVIDIA CUDA Toolkit and the NVIDIA CUDA Deep Neural Network library (or a modified version of those libraries), containing parts covered by the terms of the respective license agreement, the licensors of this Program grant you additional permission to convey the resulting work.

We extend our deepest gratitude to Google's Tensor Research Cloud (TRC) program for their magnanimous provision of an abundance of TPU computational resources, which has been instrumental in the training and development of Px0. The TRC program exemplifies a paradigm of corporate philanthropy in the realm of artificial intelligence and machine learning. We earnestly encourage everyone to lend their unequivocal support to this laudable initiative. For more information, please visit Google TRC Program.