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
created_at: string
example: string
dataset: struct<url: string, sha256: string, rows: int64>
child 0, url: string
child 1, sha256: string
child 2, rows: int64
seconds: double
bend: struct<first_loss: double, final_loss: double, data_mean: double, data_std: double, sample_mean: dou (... 24 chars omitted)
child 0, first_loss: double
child 1, final_loss: double
child 2, data_mean: double
child 3, data_std: double
child 4, sample_mean: double
child 5, sample_std: double
numpy: struct<first_loss: double, final_loss: double, sample_mean: double, sample_std: double>
child 0, first_loss: double
child 1, final_loss: double
child 2, sample_mean: double
child 3, sample_std: double
repeats: int64
name: string
speed_ratio: double
program: string
schema: string
checksum: double
machine: struct<platform: string, cpu_count: int64, python: string, numpy: string>
child 0, platform: string
child 1, cpu_count: int64
child 2, python: string
child 3, numpy: string
dtype: string
size: list<item: int64>
child 0, item: int64
products: int64
to
{'schema': Value('string'), 'created_at': Value('string'), 'name': Value('string'), 'program': Value('string'), 'size': List(Value('int64')), 'products': Value('int64'), 'dtype': Value('string'), 'repeats': Value('int64'), 'checksum': Value('float64'), 'bend': {'seconds_min': Value('float64'), 'seconds_all': List(Value('float64')), 'gflops': Value('float64')}, 'numpy': {'seconds_min': Value('float64'), 'seconds_all': List(Value('float64')), 'gflops': Value('float64')}, 'speed_ratio': Value('float64'), 'machine': {'platform': Value('string'), 'cpu_count': Value('int64'), 'python': Value('string'), 'numpy': Value('string')}}
because column names don't match
Traceback: Traceback (most recent call last):
File "/src/services/worker/src/worker/utils.py", line 149, 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 129, 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 544, 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 3052, in __iter__
for key, example in ex_iterable:
^^^^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2559, in __iter__
for key, pa_table in self._iter_arrow():
~~~~~~~~~~~~~~~~^^
File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2584, 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 558, in _iter_arrow
for key, pa_table in iterator:
^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 440, 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 361, 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 2381, in table_cast
return cast_table_to_schema(table, schema)
File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 2309, in cast_table_to_schema
raise CastError(
...<3 lines>...
)
datasets.table.CastError: Couldn't cast
created_at: string
example: string
dataset: struct<url: string, sha256: string, rows: int64>
child 0, url: string
child 1, sha256: string
child 2, rows: int64
seconds: double
bend: struct<first_loss: double, final_loss: double, data_mean: double, data_std: double, sample_mean: dou (... 24 chars omitted)
child 0, first_loss: double
child 1, final_loss: double
child 2, data_mean: double
child 3, data_std: double
child 4, sample_mean: double
child 5, sample_std: double
numpy: struct<first_loss: double, final_loss: double, sample_mean: double, sample_std: double>
child 0, first_loss: double
child 1, final_loss: double
child 2, sample_mean: double
child 3, sample_std: double
repeats: int64
name: string
speed_ratio: double
program: string
schema: string
checksum: double
machine: struct<platform: string, cpu_count: int64, python: string, numpy: string>
child 0, platform: string
child 1, cpu_count: int64
child 2, python: string
child 3, numpy: string
dtype: string
size: list<item: int64>
child 0, item: int64
products: int64
to
{'schema': Value('string'), 'created_at': Value('string'), 'name': Value('string'), 'program': Value('string'), 'size': List(Value('int64')), 'products': Value('int64'), 'dtype': Value('string'), 'repeats': Value('int64'), 'checksum': Value('float64'), 'bend': {'seconds_min': Value('float64'), 'seconds_all': List(Value('float64')), 'gflops': Value('float64')}, 'numpy': {'seconds_min': Value('float64'), 'seconds_all': List(Value('float64')), 'gflops': Value('float64')}, 'speed_ratio': Value('float64'), 'machine': {'platform': Value('string'), 'cpu_count': Value('int64'), 'python': Value('string'), 'numpy': Value('string')}}
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.
MLBend — experiment results
The committed result evidence of MLBend,
the experiment layer for machine learning in Bend
(data, splits, metrics, reports and manifests — BendTorch trains, bend-ml
holds the tensors). This repo mirrors results/ from the repository, so the
numbers behind every claim are browsable next to the code.
What is here
RESULTS.md— the corpus table: every task, its dataset's SHA-256, the Bend number, the reference number (NumPy / scikit-learn), the delta, the fetch/run/compute/reference times, and the verdict. Mean ± sample deviation over seeds where a task has several.diffusion-digits/— a DDPM denoiser on the first 1024 rows ofmstz/optdigits:grid.svg(real digits left, Bend samples right),loss.tsv(Bend vs the float32 NumPy twin, step by step),run.json. First loss 8062.69 against the twin's, delta 4.7e-05.diffusion/— the same denoiser on two moons:scatter.svg,loss.svg,loss.tsv,run.json. First loss 152.954, delta -1.5e-05.transformer/— a one-block causal transformer trained with BendTorch.latest/,latest-numpy/,raw/— the report cards (manifests) per task: git commit, Bend version, every package hub hash, machine, seeds, tolerances, pinned dataset bytes, build/run/fetch/reference times.
The evidence rules
- Datasets are pinned by HuggingFace revision and expected SHA-256; the runner hashes the bytes it reads and fails on a mismatch.
- Every gated number has a reference twin computed in float32 in the same
operation order; report-mode numbers are printed but not gated, with the
measured reason in
NOTES.md. make checkin the repository re-derives all of it (90/90 locally).
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