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A collection of tools for local and distributed tuning of chess engines.
Bayesian optimization <https://github.com/kiudee/bayes-skopt>_.In order to be able to start the tuning, first create a python environment (at least Python 3.7) and install chess-tuning-tools by typing::
pip install chess-tuning-tools
Furthermore, you need to have cutechess-cli <https://github.com/cutechess/cutechess>_
in the path. The tuner will use it to run matches.
To execute the local tuner, simply run::
tune local -c tuning_config.json
Take a look at the usage instructions_ and the example configurations_ to
learn how to set up the tuning_config.json file.
Installation on Windows ^^^^^^^^^^^^^^^^^^^^^^^
To get chess-tuning-tools to work on Windows, the easiest way is to install
the Miniconda <https://docs.conda.io/en/latest/miniconda.html>_ distribution.
Then, create a new environment and install chess-tuning-tools::
conda create -n myenv python=3.9 scikit-learn=0.23 activate myenv pip install chess-tuning-tools
.. _example configurations: https://github.com/kiudee/chess-tuning-tools/tree/master/examples .. _usage instructions: https://chess-tuning-tools.readthedocs.io/en/latest/usage.html
FAQs
A collection of tools for local and distributed tuning of chess engines.
We found that chess-tuning-tools demonstrated a healthy version release cadence and project activity because the last version was released less than a year ago. It has 1 open source maintainer collaborating on the project.
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