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https://github.com/LeelaChessZero/lc0

The rewritten engine, originally for tensorflow. Now all other backends have been ported here.
https://github.com/LeelaChessZero/lc0

Last synced: 2 months ago
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The rewritten engine, originally for tensorflow. Now all other backends have been ported here.

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README 

        
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# Lc0



Lc0 is a UCI-compliant chess engine designed to play chess via neural network, specifically those of the [LeelaChessZero project](https://lczero.org).



## Downloading source



Lc0 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 `release/version` branch (for example `release/0.30`), which is equivalent to using the latest version tag.



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:



```shell

git clone -b release/0.30 --recurse-submodules https://github.com/LeelaChessZero/lc0.git

```



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

```shell

git fetch --all

git branch --all

git checkout -t remotes/origin/release/0.30

```



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

 * Download the [.zip](https://api.github.com/repos/LeelaChessZero/lc0/zipball/release/0.30) file ([.tar.gz](https://api.github.com/repos/LeelaChessZero/lc0/tarball/release/0.30) archive is also available)

 * Extract

 * Download https://github.com/LeelaChessZero/lczero-common/archive/master.zip (also available as [.tar.gz](https://github.com/LeelaChessZero/lczero-common/archive/master.tar.gz))

 * Move the second archive into the first archive's `libs/lczero-common/` folder and extract

 * The final form should look like `/libs/lczero-common/proto/`



Having successfully acquired Lc0 via either of these methods, proceed to the build section below and follow the instructions for your OS.



## Building and running Lc0



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



Aside from the git submodule, lc0 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 lc0 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, lc0 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.



### Linux



#### Generic



1. Install backend:

    - If you want to use NVidia graphics cards Install [CUDA](https://developer.nvidia.com/cuda-zone) and [cuDNN](https://developer.nvidia.com/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 `lc0/`

4. Run `./build.sh`

5. `lc0` will be in `lc0/build/release/` directory

6. Unzip a [neural network](https://lczero.org/play/networks/bestnets/) 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



#### Note on installing CUDA on Ubuntu



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 ("LeelaZero"

if you have none), primary industry segment ("Other"/none) and which development areas you are interested

in ("Deep Learning").



#### Ubuntu 18.04



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.



#### Ubuntu 16.04



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.



#### openSUSE (all versions)



Instructions, packages and tools for building on openSUSE are at [openSUSE_install.md](openSUSE_install.md)



#### Docker



Use https://github.com/vochicong/lc0-docker

to run latest releases of lc0 and the client inside a Docker container.



### Windows



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](https://developer.nvidia.com/cuda-zone)

2. Install [cuDNN](https://developer.nvidia.com/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.



### Mac



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)



### Raspberry Pi



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



1. Install OpenBLAS



```shell

git clone https://github.com/xianyi/OpenBLAS.git

cd OpenBLAS/

make

sudo make PREFIX=/usr install

cd ..

```



2. Install Meson



```shell

pip install meson

pip install ninja

```



3. Install compiler and standard libraries



```shell

sudo apt install clang-6.0 libstdc++-8-dev

```



4. Clone lc0 and compile



```shell

git clone https://github.com/LeelaChessZero/lc0.git

cd lc0

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



Python bindings can be built and installed as follows.



```shell

pip install --user git+https://github.com/LeelaChessZero/lc0.git

```



This will build the package `lczero-bindings` and install it to your Python user install directory.

All the `lc0` functionality related to position evaluation is now available in the module `lczero.backends`.

An example interactive session can be found [here](https://github.com/LeelaChessZero/lc0/pull/1261#issuecomment-622951248).



## License



Leela Chess 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.



Leela Chess 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 Leela Chess.  If not, see .



### Additional permission under GNU GPL version 3 section 7



_The source files of Lc0 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.