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Chess Embedding Experiments

Cheat Sheet

  • Generate training positions

    python -m src.run.generate_positions

  • Train a neural network

    python -m src.run.train

  • Evaluate a trained network by starting the notebook: src/run/evaluate.ipynb

Tools

  • Start ML Flow UI, in correct python venv

    mlflow ui

  • Export dependencies to requirements.txt

    poetry export > requirements.txt

Notes

Milvus

  • could only get milvus 2.3.1 to work, so use that for now
  • but had to downgrade python to 3.9, because of compatibility issues
  • and only works with recent tensorflow version, so it's incompatible with aws sage maker
    • maybe I need to build a different toolchain for different python versions

TODOs

  • Document findings of up to current model training
  • Write to db from .npy files
    • write tokenized positions with some metadata and id
    • write embeddings generated from a model
  • Write to db from .pgn file
    • maybe some refactoring is needed
  • make embeddings better for search
    • document approaches
    • make a plan