DL261-Assignment

CO3133 course project checkpoints, group DeepDive

Selected runs for the three course assignments. Weights are never committed to git; this repository is the only place they live.

<assignment>/runs/<run_id>/
  checkpoint.pt      weights, selected by the checkpoint rule in config.yaml
  config.yaml        the resolved configuration that produced them
  environment.json   commit hash, library versions, hardware, thread count
  history.json       one record per epoch
  summary.json       parameters, best epoch, training time, selected metric
  checksums.json     SHA-256 of every file above

Reconstructing a checkpoint

Clone the repository at the commit recorded in environment.json, then run the model configuration named in config.yaml with the seed it records:

cd assignments
python -m venv .venv && source .venv/bin/activate
pip install -e .

cd assignment-1
python -m src.data --config configs/base.yaml
python -m src.train --config configs/<model>.yaml --set seed=<seed>

The dataset split is fixed and committed, so the run reproduces the reported accuracy and macro-F1 on any machine. Wall-clock times depend on the hardware recorded in environment.json.