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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/0.26`), 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:

```
git clone -b release/0.26 --recurse-submodules https://github.com/LeelaChessZero/lc0.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 remotes/origin/release/0.26
```


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/0.26) file ([.tar.gz](https://api.github.com/repos/LeelaChessZero/lc0/tarball/release/0.26) 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 `<TOP>/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, where we recommend to install `g++-7` and add `-Dnvcc_ccbin=g++-7` to the `build.sh` command.

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

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 raspbian "buster".

1. Install OpenBLAS

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

2. Install Meson

```
pip3 install meson
pip3 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/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

## 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 <http://www.gnu.org/licenses/>.

### 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.

