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