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