Stockfish NNUE trainer

A containerized linux environment for training Stockfish NNUE with nnue-pytorch.

The container image is Ubuntu 22.04 LTS with Cuda, Torch, and all the necessary dependencies for running nnue-pytorch installed.

Installing on Ubuntu 20.04 hosts

To use this, make sure Nvidia drivers, Docker, and the Nvidia container toolkit are installed.

See server_setup.sh for commands to install dependencies from a clean Ubuntu server environment, should be run with sudo rights. This will enable docker as a non-root user, as described here https://docs.docker.com/engine/install/linux-postinstall/.

Afterwards, use these scripts to prepare and run the container image.

./docker_build.sh   # builds an image with a working nnue-pytorch environment
./docker_run.sh     # access the command-line within the container

The following files will be copied into the container image:

requirements.txt
yaml_easy_train.py
.bash_profile
misc/utils.sh
misc/get_native_properties.sh
easy-train.sh
fetch-nnue.sh

Running the container

A few directories are mounted into the container to share data between the host and container.

training-data
easy-train-data
config

/dev/shm
/mnt

There are multiple ways to get your training data into the container:

• /dev/shm is a shared memory directory where files can temporarily be stored for fast access.
• /mnt typically for mounting a large storage volume, like an external hard drive.
• training-data and easy-train-data can also be used to store training data, though their primary purpose is binpack scripts and the directory structure for nnue-pytorch, respectively.
• /media is a directory that can be used to mount external storage devices as well.

Download training data

See training data files in this July 2022 PR: official-stockfish/Stockfish#4100 or https://www.kaggle.com/linrock/datasets.

Info on how the training data is generated is in the nnue-pytorch wiki.

Use this interleave_binpacks.py script to mix downloaded .binpack files together to prepare training data.

Download large data files from Google Drive via cmd-line

• Go to OAuth 2.0 Playground
• In the "Input your own scopes" text box, enter in:
  • https://www.googleapis.com/auth/drive.readonly
• Click Authorize APIs and then click "Exchange authorization code for tokens"
• Copy the access token

curl -H "Authorization: Bearer <access_token>" \
  "https://www.googleapis.com/drive/v3/files/<file_id>?alt=media" \
  -o output.binpack

Download datasets from kaggle

• Install kaggle

  pipx install kaggle
  

• Go to https://www.kaggle.com/<USER_NAME>/account
• Create a New API Token in the "API" section
• place your downloaded file at ~/.kaggle/kaggle.json
• kaggle datasets download linrock/<DATASET_NAME>