# Show HN: Training a sudoku solver from scratch on Jetson Nano > Source: > Published: 2026-09-14 12:54:58+00:00 **Looped MLP-Mixer** sudoku solver with an outer commit loop, inner mixer iterations, and a learned halt head, trained from scratch on [sapientinc/sudoku-extreme](https://huggingface.co/datasets/sapientinc/sudoku-extreme). Install with `uv sync`, download the dataset (~798 MB), train, and open the local viz server. Minimal path: ``` uv sync uv run download-dataset uv run train \ --min-rating 0 --max-rating 0 \ --max-samples 100 --epochs 30 \ --dim 512 --num-blocks 2 \ --inner-iters 5 --train-max-outer-iters 10 \ --eval-max-outer-iters 10 \ --train-batch-size 8 --batches-per-epoch 100 uv run python -m http.server 8000 ``` Open [http://localhost:8000/viz/](http://localhost:8000/viz/). Serve from the **repo root**. Shared MLP-Mixer stack reused across an outer commit loop: each outer step applies the previous prediction, runs inner mixer iterations on encoded grid state, and updates detached cell memory. A halt head learns when the grid matches the solution. Training uses parallel puzzle slots with optional augmentations and partial ground-truth reveal. Entry points: `download-dataset`, `train`, `eval`, `resume`. Eval supports test-time restarts and one-dimensional compute sweeps (inner steps, outer commits, tries). PyTorch profiler hooks are available on `train`. Each run writes `history.json`, checkpoints, and per-puzzle trajectory JSON under `runs//`. The viz page charts train/val metrics and plays back outer-commit trajectories. Train and validation metrics per epoch: loss, cell/puzzle accuracy, halt rate, and accuracy by rating group. Trajectory player for one puzzle: model input and output at each outer commit until halt or max steps. Docker setup for training and eval on NVIDIA Jetson (JetPack). Source is bind-mounted; rebuild only when dependencies change. MIT License.