Dataset Viewer
The dataset viewer is not available for this split.
Cannot load the dataset split (in streaming mode) to extract the first rows.
Error code: StreamingRowsError
Exception: CastError
Message: Couldn't cast
d: struct<block05: list<item: struct<shape: list<item: int64>, min: double, max: double, true_max: doub (... 2351 chars omitted)
child 0, block05: list<item: struct<shape: list<item: int64>, min: double, max: double, true_max: double, clipped: boo (... 33 chars omitted)
child 0, item: struct<shape: list<item: int64>, min: double, max: double, true_max: double, clipped: bool, levels: (... 21 chars omitted)
child 0, shape: list<item: int64>
child 0, item: int64
child 1, min: double
child 2, max: double
child 3, true_max: double
child 4, clipped: bool
child 5, levels: int64
child 6, bytes: string
child 1, block06: list<item: struct<shape: list<item: int64>, min: double, max: double, true_max: double, clipped: boo (... 33 chars omitted)
child 0, item: struct<shape: list<item: int64>, min: double, max: double, true_max: double, clipped: bool, levels: (... 21 chars omitted)
child 0, shape: list<item: int64>
child 0, item: int64
child 1, min: double
child 2, max: double
child 3, true_max: double
child 4, clipped: bool
child 5, levels: int64
child 6, bytes: string
child 2, block07: list<item: struct<shape: list<item: int64>, min: double, max: double, true_max: double, clipped: boo (... 33 chars omitted)
child 0, item: struct<shape: list<item: int64>, min: double, max: double, true_max: double,
...
vels: (... 21 chars omitted)
child 0, shape: list<item: int64>
child 0, item: int64
child 1, min: double
child 2, max: double
child 3, true_max: double
child 4, clipped: bool
child 5, levels: int64
child 6, bytes: string
thumbs: null
classes: null
classNames: null
checkpoints: list<item: struct<step: int64, path: string>>
child 0, item: struct<step: int64, path: string>
child 0, step: int64
child 1, path: string
training_config: struct<seed: int64, batch_size: int64, learning_rate: double, momentum: double, weight_decay: double (... 1 chars omitted)
child 0, seed: int64
child 1, batch_size: int64
child 2, learning_rate: double
child 3, momentum: double
child 4, weight_decay: double
task: string
schema_version: int64
total_steps: int64
metrics: list<item: struct<step: int64, train_loss: double, train_accuracy: double, test_loss: double, test_a (... 17 chars omitted)
child 0, item: struct<step: int64, train_loss: double, train_accuracy: double, test_loss: double, test_accuracy: do (... 5 chars omitted)
child 0, step: int64
child 1, train_loss: double
child 2, train_accuracy: double
child 3, test_loss: double
child 4, test_accuracy: double
model_config: struct<input_dim: int64, width: int64, blocks: int64, classes: int64, activation: string>
child 0, input_dim: int64
child 1, width: int64
child 2, blocks: int64
child 3, classes: int64
child 4, activation: string
to
{'schema_version': Value('int64'), 'task': Value('string'), 'total_steps': Value('int64'), 'model_config': {'input_dim': Value('int64'), 'width': Value('int64'), 'blocks': Value('int64'), 'classes': Value('int64'), 'activation': Value('string')}, 'checkpoints': List({'step': Value('int64'), 'path': Value('string')}), 'metrics': List({'step': Value('int64'), 'train_loss': Value('float64'), 'train_accuracy': Value('float64'), 'test_loss': Value('float64'), 'test_accuracy': Value('float64')}), 'training_config': {'seed': Value('int64'), 'batch_size': Value('int64'), 'learning_rate': Value('float64'), 'momentum': Value('float64'), 'weight_decay': Value('float64')}}
because column names don't match
Traceback: Traceback (most recent call last):
File "/src/services/worker/src/worker/utils.py", line 147, in get_rows_or_raise
return get_rows(
dataset=dataset,
...<4 lines>...
column_names=column_names,
)
File "/src/libs/libcommon/src/libcommon/utils.py", line 272, in decorator
return func(*args, **kwargs)
File "/src/services/worker/src/worker/utils.py", line 127, in get_rows
rows_plus_one = list(itertools.islice(safe_iter(ds, dataset=dataset), rows_max_number + 1))
File "/src/services/worker/src/worker/utils.py", line 483, in safe_iter
yield from ds.decode(False) if ds.features else ds
File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2840, in __iter__
for key, example in ex_iterable:
^^^^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2373, in __iter__
for key, pa_table in self._iter_arrow():
~~~~~~~~~~~~~~~~^^
File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2398, in _iter_arrow
for key, pa_table in self.ex_iterable._iter_arrow():
~~~~~~~~~~~~~~~~~~~~~~~~~~~~^^
File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 536, in _iter_arrow
for key, pa_table in iterator:
^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 419, in _iter_arrow
for key, pa_table in self.generate_tables_fn(**gen_kwags):
~~~~~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/packaged_modules/json/json.py", line 343, in _generate_tables
self._cast_table(pa_table, json_field_paths=json_field_paths),
~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/packaged_modules/json/json.py", line 132, in _cast_table
pa_table = table_cast(pa_table, self.info.features.arrow_schema)
File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 2378, in table_cast
return cast_table_to_schema(table, schema)
File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 2306, in cast_table_to_schema
raise CastError(
...<3 lines>...
)
datasets.table.CastError: Couldn't cast
d: struct<block05: list<item: struct<shape: list<item: int64>, min: double, max: double, true_max: doub (... 2351 chars omitted)
child 0, block05: list<item: struct<shape: list<item: int64>, min: double, max: double, true_max: double, clipped: boo (... 33 chars omitted)
child 0, item: struct<shape: list<item: int64>, min: double, max: double, true_max: double, clipped: bool, levels: (... 21 chars omitted)
child 0, shape: list<item: int64>
child 0, item: int64
child 1, min: double
child 2, max: double
child 3, true_max: double
child 4, clipped: bool
child 5, levels: int64
child 6, bytes: string
child 1, block06: list<item: struct<shape: list<item: int64>, min: double, max: double, true_max: double, clipped: boo (... 33 chars omitted)
child 0, item: struct<shape: list<item: int64>, min: double, max: double, true_max: double, clipped: bool, levels: (... 21 chars omitted)
child 0, shape: list<item: int64>
child 0, item: int64
child 1, min: double
child 2, max: double
child 3, true_max: double
child 4, clipped: bool
child 5, levels: int64
child 6, bytes: string
child 2, block07: list<item: struct<shape: list<item: int64>, min: double, max: double, true_max: double, clipped: boo (... 33 chars omitted)
child 0, item: struct<shape: list<item: int64>, min: double, max: double, true_max: double,
...
vels: (... 21 chars omitted)
child 0, shape: list<item: int64>
child 0, item: int64
child 1, min: double
child 2, max: double
child 3, true_max: double
child 4, clipped: bool
child 5, levels: int64
child 6, bytes: string
thumbs: null
classes: null
classNames: null
checkpoints: list<item: struct<step: int64, path: string>>
child 0, item: struct<step: int64, path: string>
child 0, step: int64
child 1, path: string
training_config: struct<seed: int64, batch_size: int64, learning_rate: double, momentum: double, weight_decay: double (... 1 chars omitted)
child 0, seed: int64
child 1, batch_size: int64
child 2, learning_rate: double
child 3, momentum: double
child 4, weight_decay: double
task: string
schema_version: int64
total_steps: int64
metrics: list<item: struct<step: int64, train_loss: double, train_accuracy: double, test_loss: double, test_a (... 17 chars omitted)
child 0, item: struct<step: int64, train_loss: double, train_accuracy: double, test_loss: double, test_accuracy: do (... 5 chars omitted)
child 0, step: int64
child 1, train_loss: double
child 2, train_accuracy: double
child 3, test_loss: double
child 4, test_accuracy: double
model_config: struct<input_dim: int64, width: int64, blocks: int64, classes: int64, activation: string>
child 0, input_dim: int64
child 1, width: int64
child 2, blocks: int64
child 3, classes: int64
child 4, activation: string
to
{'schema_version': Value('int64'), 'task': Value('string'), 'total_steps': Value('int64'), 'model_config': {'input_dim': Value('int64'), 'width': Value('int64'), 'blocks': Value('int64'), 'classes': Value('int64'), 'activation': Value('string')}, 'checkpoints': List({'step': Value('int64'), 'path': Value('string')}), 'metrics': List({'step': Value('int64'), 'train_loss': Value('float64'), 'train_accuracy': Value('float64'), 'test_loss': Value('float64'), 'test_accuracy': Value('float64')}), 'training_config': {'seed': Value('int64'), 'batch_size': Value('int64'), 'learning_rate': Value('float64'), 'momentum': Value('float64'), 'weight_decay': Value('float64')}}
because column names don't matchNeed help to make the dataset viewer work? Make sure to review how to configure the dataset viewer, and open a discussion for direct support.
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.
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