| # Run the noisy-latent Sudoku trainer |
|
|
| Data lives on Hugging Face: [Avra98/Sudoku_superposition](https://huggingface.co/Avra98/Sudoku_superposition). |
| This folder is the trainer (`train/`). Do not regenerate masks or instances unless you changed a generation flag. |
|
|
| ## 1. Download the 11 required files |
|
|
| ```bash |
| pip install -U "huggingface_hub[hf_xet]" |
| |
| huggingface-cli download Avra98/Sudoku_superposition \ |
| --include "data/train_sudoku_puzzles.npy" \ |
| --include "data/test_sudoku_puzzles.npy" \ |
| --include "data/train_cand_masks.npy" \ |
| --include "data/test_cand_masks.npy" \ |
| --include "data/train_assignments.npy" \ |
| --include "data/train_starts.npy" \ |
| --include "data/train_counts.npy" \ |
| --include "data/test_assignments.npy" \ |
| --include "data/test_starts.npy" \ |
| --include "data/test_counts.npy" \ |
| --include "data/test_bigq300.npz" \ |
| --include "code/*" \ |
| --local-dir ./Sudoku_superposition |
| ``` |
|
|
| Skip `*_index.npy`, `*_meta.npy`, `*_solution.npy`. |
|
|
| ## 2. Install trainer deps |
|
|
| ```bash |
| pip install jax flax optax ml_collections clu absl-py tensorflow wandb |
| ``` |
|
|
| GPU JAX must match your CUDA. CPU will start but is too slow for the full run. |
|
|
| ## 3. Run |
|
|
| From `Sudoku_superposition/` (HF layout: `data/` + `code/`): |
|
|
| ```bash |
| bash code/run_experiment.sh |
| ``` |
|
|
| From this GitHub tree (`sudoku-wavecurriculum/`), after the HF `data/` folder exists: |
|
|
| ```bash |
| SUDOKU_DATA_DIR=/path/to/Sudoku_superposition/data bash run_experiment.sh |
| ``` |
|
|
| The script checks that every required file exists, then runs: |
|
|
| ```text |
| python -m train.main --workdir=../runs/noise --exp_name=noise |
| ``` |
|
|
| ## If you rebuilt instances |
|
|
| Keep the premade masks in `data/`. Point only the instance dir at the new folder |
| (must contain `{train,test}_{assignments,starts,counts}.npy`): |
|
|
| ```bash |
| SUDOKU_INSTANCE_DIR=/path/to/datasets_superposition bash code/run_experiment.sh |
| ``` |
|
|
| If that folder sits at `Sudoku_superposition/datasets_superposition/` and is complete, the script picks it automatically. |
|
|
| ## Optional env knobs |
|
|
| | var | default | meaning | |
| |---|---|---| |
| | `SUDOKU_LATENT_NOISE` | `1` | noisy latents on | |
| | `SUDOKU_MC_SAMPLES` | `8` | Monte-Carlo draws | |
| | `SUDOKU_LATENT_SLOTS` | `12` | must match the 12-stage masks | |
| | `SUDOKU_AUX_WEIGHT` | `0` | candidate-head off (instance CE) | |
| | `SUDOKU_START_STAGE` | `1` | set `4` to skip early curriculum | |
| | `SUDOKU_MINIBATCH` | `64` | global batch; must divide GPU count | |
| | `WORKDIR` | `runs/noise` | checkpoints + logs | |
|
|
| Do not run `staged_candidate_gen.py` or `build_superposition_dataset.py` to start training. Those files are already in `data/`. |
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|
