Collection of tools to assist in the development of the Leelenstein nets

All you should need to run the server is a collection of games in the .gz format that you wish to score, as well as python3.7. Then, install requirements.txt globally or in a virtualenv using pip install -r requirements.txt

If you're pro python, just set up a virtualenv and pip install the requirements in there. Client uses asyncio features that are new as of python3.7, so python3.7 is a requirement

git clone git@github.com:kmorrison/leelenscorer.git
cd leelenscorer/
pip install -r requirements.txt
python3 multi_client.py \
  --clients-per-gpu=3 \
  --engine-path=<path to lc0> \
  --weights-path=<path to LS 11.1 network> \
  --host=173.67.18.127 \
  --port=8889 \
  --backend=cudnn-fp16 \
  --num-nodes=128 \
  --minibatchsize=16 \
  --client-name=RAFmb16N128 \
  --chunk-size=5

If you like docker instead, the client code should be all ready to go in the kmorrison64/leelenscorer dockerhub repo. If you're using vast.ai, just switch the container to the latest version at that repo and you're half-way there.

The server can be run like python game_server.py --input-folder=<example folder> --output-folder=<different folder>

The client can be run in two forms, either the single client which will pull games from the server and give to one engine instance for scoring, or the multi-client.py which uses nvidia-smi to detect the number of available GPUs and attempts to use them all.

python multi_client.py --clients-per-gpu=1 --chunk-size=10 --engine-path=<where to engine> --weights-path=<where to weights> --host=<route to server> --port=<server port>

python rescore_client.py --chunk-size=10 --engine-path=<where to engine> --weights-path=<where to weights> --host=<route to server> --port=<server port>

#To just parrot back the input files and not score anything, use --dry-run option
python rescore_client.py --dry-run=True --chunk-size=10 --engine-path=<where to engine> --weights-path=<where to weights> --host=<route to server> --port=<server port>

python3 multi_client.py --host=localhost --port=8888 --backend=cudnn-fp16 --clients-per-gpu=5 --engine-path=/root/binaries/lc0 --weights-path=/root/binaries/ls-n11-1.pb.gz --chunk-size=10

• the client-server protocol is custom (seemed like a good idea at the time :P) and uses four newlines as a separator between messages. If any files you're transmitting have b'\n\n\n\n' in them, we're gonna have a bad time
• if a client fails to score a set of games it is handed for some reason, there is no mechanism for requeueing them and handing them to another client.
• when running locally via docker you will have to set --network="host" as an arg to docker run, and pass --host="host.docker.internal" to your client script