The dataset viewer is not available because its heuristics could not detect any supported data files. You can try uploading some data files, or configuring the data files location manually.
Cells, developmental: trained trajectories and measured parts
The artifacts produced by the measurement code at https://github.com/bgradowhite/Cells_Developmental, mirrored so a collaborator starts from the same base without retraining or re-measuring.
Only this project's own artifacts are here. The KataGo checkpoints, the
Pythia/GPT-2/Gemma weights and the image corpora are public elsewhere, are
hash-pinned in that repository's configs/inputs/, and are fetched from their
own sources; a second, staler copy of them would help nobody.
| path | what it is | size |
|---|---|---|
paper_mlp/ |
MNIST MLP training trajectories, plain and res, one step_*.npz per logged step |
73 MB |
mnist_dense/ |
the dense-sampled MNIST trajectory | 62 MB |
parts/ |
the measured parts the pages are built from, one tar.gz per group; unpacked they are 12,032 JSONs, one per panel, named for what each measured |
240 MB |
pages/ |
the built pages and their frames, so the results can be read without rebuilding | 119 MB |
Using it
Clone the code repository, then:
python scripts/fetch_artifacts.py # checkpoints, verified per file
python scripts/fetch_artifacts.py --with-parts # and the parts, verified and unpacked
python scripts/build_pages.py # from whatever parts are present
configs/inputs/shared_artifacts.yaml in that repository is the identity of
these files: it records the size and SHA-256 of every checkpoint and of every
parts archive. The parts are archived rather than loose because this hub
refuses a directory holding more than ten thousand files and parts/mnist
holds eleven thousand; on disk they stay one file per panel, which is what
lets a rerun replace exactly its own file. The fetch script refuses to keep a file
whose digest disagrees, so a corrupted or substituted download fails there
rather than silently changing a measurement later.
Provenance
The trajectories were trained by scripts/train_paper_mlp.py and
scripts/train_mnist_dense.py; the parts were measured by the build_*_sweeps
scripts, some of them on rented GPUs. What each part measured, and under which
of the nine method conventions, is recorded in docs/conventions.md and in the
part's own metadata -- not in this file, which would go stale.
- Downloads last month
- 817