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Python Source Code Overview

This is a summary of some of the most notable python files here. Some of the less important or minor scripts are left out, but this covers the most notable ones:

Main Training Loop

These are the critical scripts needed for training neural nets.

  • tfrecordio.py - Used by shuffle.py, helpers for the TFRecord format for shuffling.
  • shuffle.py - Shuffles the data for neural net training and writes it into TFRecords, used by selfplay/shuffle.sh.
  • model.py - Implementation of the neural net and loss functions in Tensorflow 1.15.
    • Also contains implementations of the input features, which are NOT used during training because all of it has been computed by the C++ already. But they should work and allong with board.py and data.py should allow the ability to apply the raw neural net to board positions and SGF files from the python code.
  • modelconfigs.py - Specific channel and block configurations for different-sized nets. These are codes that you can supply to train.py as an argument to pick the net size.
  • train.py - Trains the neural net using the shuffled data and saves it to a SavedModel periodically, used by selfplay/train.sh.
  • export_model.py - Exports the trained neural net SavedModel to KataGo's .bin.gz format. Used by selfplay/export_model_for_selfplay.sh.

And:

  • selfplay/*.sh - A variety of bash scripts used in selfplay training that wrap the above python scripts with logic to loop repeatedly, to pipe output to files, to daemonize and disown so as to keep running when a given shell session ends, and to move stuff around between directories to hook everything up.

Auxiliary scripts

These are some scripts on the side that might be useful for debugging or experimentation. These are more or less NOT used in training, because normally the C++ selfplay code does all of the work in implementing the board, writing out the input features and data produced from selfplay games, etc. These scripts are a bit more likely than the ones above to suffer from code rot and to be stale, since they aren't used a lot.

  • board.py - Implementation of Go board, analogous to ../cpp/game/board.{cpp,h}. NOT used in training.
  • data.py - Loads SGF files.
  • test.py - Runs a model on some shuffled TFRecord data without doing gradient updates - instead just reports the stats.
  • visualize.py - Slightly sandboxy script, lets you dump out the raw weight tensors from a model.

And:

  • play.py - A working very primitive GTP engine that just uses the raw neural net to make moves. Also implements a ton of GTP extensions to draw colored heatmaps in GoGUI. This or test.py are good places to look if you're interested in how to use the raw Tensorflow weights for direct inference in python.

Distributed backend

These are some backend scripts for the distributed training. You should never need them, unless somehow you are running your own full website (https://github.com/katago/katago-server) on Docker or Kubernetes.

  • upload_model.py
  • upload_poses.py

Genboard

These are a separate set of scripts that have nothing to do with any of the above training scripts, or even with KataGo in general. They use Pytorch to train a neural net that can "fill in" the rest of a board with plausible stone configurations given part of the board. See lightvector#347 for a little bit of info on usage, including a pretrained neural net.

  • genboard_common.py
  • genboard_run.py
  • genboard_train.py