Datasets:
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
id: string
kind: string
provider: string
product: string
name: string
summary: string
source: string
path: string
category: string
tags: list<item: string>
child 0, item: string
related: list<item: null>
child 0, item: null
method: string
hostname: string
protocol: string
authentication: string
operationId: string
specs: struct<instanceId: string, gpus: int64, gpuRam: string, vcpus: int64, ram: string, storage: string, (... 191 chars omitted)
child 0, instanceId: string
child 1, gpus: int64
child 2, gpuRam: string
child 3, vcpus: int64
child 4, ram: string
child 5, storage: string
child 6, architecture: string
child 7, category: string
child 8, gpu: string
child 9, connectivity: string
child 10, cpu: string
child 11, nic: string
child 12, defaultDriver: string
child 13, compatibleDrivers: list<item: string>
child 0, item: string
child 14, regions: list<item: string>
child 0, item: string
typeName: string
environment_variable: bool
reconstruct: string
files: struct<datasets.json: struct<kind: string, count: int64, format: string>, documents-1.json.gz: struc (... 939 chars omitted)
child 0, datasets.json: struct<kind: string, count: int64, format: string>
child 0, kind: string
child 1, count: int64
child 2, format: string
child 1, documents-1.json.gz: struct<kind: string, count: int64, format: string, part: int64, of: int64>
child 0, kind: string
child 1, count: int64
child 2, format: string
...
, hardwares.json: struct<kind: string, count: int64, format: string>
child 0, kind: string
child 1, count: int64
child 2, format: string
child 6, models.json: struct<kind: string, count: int64, format: string>
child 0, kind: string
child 1, count: int64
child 2, format: string
child 7, schemas.json: struct<kind: string, count: int64, format: string>
child 0, kind: string
child 1, count: int64
child 2, format: string
child 8, sitemaps.json.gz: struct<kind: string, count: int64, format: string>
child 0, kind: string
child 1, count: int64
child 2, format: string
child 9, tools.json: struct<kind: string, count: int64, format: string>
child 0, kind: string
child 1, count: int64
child 2, format: string
child 10, variables.json: struct<kind: string, count: int64, format: string>
child 0, kind: string
child 1, count: int64
child 2, format: string
child 11, coreweave.json.gz: struct<provider: string, count: int64, format: string>
child 0, provider: string
child 1, count: int64
child 2, format: string
child 12, financialfilings.json.gz: struct<provider: string, count: int64, format: string>
child 0, provider: string
child 1, count: int64
child 2, format: string
child 13, nvidia.json.gz: struct<provider: string, count: int64, format: string>
child 0, provider: string
child 1, count: int64
child 2, format: string
to
{'files': {'datasets.json': {'kind': Value('string'), 'count': Value('int64'), 'format': Value('string')}, 'documents-1.json.gz': {'kind': Value('string'), 'count': Value('int64'), 'format': Value('string'), 'part': Value('int64'), 'of': Value('int64')}, 'documents-2.json.gz': {'kind': Value('string'), 'count': Value('int64'), 'format': Value('string'), 'part': Value('int64'), 'of': Value('int64')}, 'endpoints.json.gz': {'kind': Value('string'), 'count': Value('int64'), 'format': Value('string')}, 'functions.json.gz': {'kind': Value('string'), 'count': Value('int64'), 'format': Value('string')}, 'hardwares.json': {'kind': Value('string'), 'count': Value('int64'), 'format': Value('string')}, 'models.json': {'kind': Value('string'), 'count': Value('int64'), 'format': Value('string')}, 'schemas.json': {'kind': Value('string'), 'count': Value('int64'), 'format': Value('string')}, 'sitemaps.json.gz': {'kind': Value('string'), 'count': Value('int64'), 'format': Value('string')}, 'tools.json': {'kind': Value('string'), 'count': Value('int64'), 'format': Value('string')}, 'variables.json': {'kind': Value('string'), 'count': Value('int64'), 'format': Value('string')}, 'coreweave.json.gz': {'provider': Value('string'), 'count': Value('int64'), 'format': Value('string')}, 'financialfilings.json.gz': {'provider': Value('string'), 'count': Value('int64'), 'format': Value('string')}, 'nvidia.json.gz': {'provider': Value('string'), 'count': Value('int64'), 'format': Value('string')}}, 'reconstruct': Value('string')}
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
id: string
kind: string
provider: string
product: string
name: string
summary: string
source: string
path: string
category: string
tags: list<item: string>
child 0, item: string
related: list<item: null>
child 0, item: null
method: string
hostname: string
protocol: string
authentication: string
operationId: string
specs: struct<instanceId: string, gpus: int64, gpuRam: string, vcpus: int64, ram: string, storage: string, (... 191 chars omitted)
child 0, instanceId: string
child 1, gpus: int64
child 2, gpuRam: string
child 3, vcpus: int64
child 4, ram: string
child 5, storage: string
child 6, architecture: string
child 7, category: string
child 8, gpu: string
child 9, connectivity: string
child 10, cpu: string
child 11, nic: string
child 12, defaultDriver: string
child 13, compatibleDrivers: list<item: string>
child 0, item: string
child 14, regions: list<item: string>
child 0, item: string
typeName: string
environment_variable: bool
reconstruct: string
files: struct<datasets.json: struct<kind: string, count: int64, format: string>, documents-1.json.gz: struc (... 939 chars omitted)
child 0, datasets.json: struct<kind: string, count: int64, format: string>
child 0, kind: string
child 1, count: int64
child 2, format: string
child 1, documents-1.json.gz: struct<kind: string, count: int64, format: string, part: int64, of: int64>
child 0, kind: string
child 1, count: int64
child 2, format: string
...
, hardwares.json: struct<kind: string, count: int64, format: string>
child 0, kind: string
child 1, count: int64
child 2, format: string
child 6, models.json: struct<kind: string, count: int64, format: string>
child 0, kind: string
child 1, count: int64
child 2, format: string
child 7, schemas.json: struct<kind: string, count: int64, format: string>
child 0, kind: string
child 1, count: int64
child 2, format: string
child 8, sitemaps.json.gz: struct<kind: string, count: int64, format: string>
child 0, kind: string
child 1, count: int64
child 2, format: string
child 9, tools.json: struct<kind: string, count: int64, format: string>
child 0, kind: string
child 1, count: int64
child 2, format: string
child 10, variables.json: struct<kind: string, count: int64, format: string>
child 0, kind: string
child 1, count: int64
child 2, format: string
child 11, coreweave.json.gz: struct<provider: string, count: int64, format: string>
child 0, provider: string
child 1, count: int64
child 2, format: string
child 12, financialfilings.json.gz: struct<provider: string, count: int64, format: string>
child 0, provider: string
child 1, count: int64
child 2, format: string
child 13, nvidia.json.gz: struct<provider: string, count: int64, format: string>
child 0, provider: string
child 1, count: int64
child 2, format: string
to
{'files': {'datasets.json': {'kind': Value('string'), 'count': Value('int64'), 'format': Value('string')}, 'documents-1.json.gz': {'kind': Value('string'), 'count': Value('int64'), 'format': Value('string'), 'part': Value('int64'), 'of': Value('int64')}, 'documents-2.json.gz': {'kind': Value('string'), 'count': Value('int64'), 'format': Value('string'), 'part': Value('int64'), 'of': Value('int64')}, 'endpoints.json.gz': {'kind': Value('string'), 'count': Value('int64'), 'format': Value('string')}, 'functions.json.gz': {'kind': Value('string'), 'count': Value('int64'), 'format': Value('string')}, 'hardwares.json': {'kind': Value('string'), 'count': Value('int64'), 'format': Value('string')}, 'models.json': {'kind': Value('string'), 'count': Value('int64'), 'format': Value('string')}, 'schemas.json': {'kind': Value('string'), 'count': Value('int64'), 'format': Value('string')}, 'sitemaps.json.gz': {'kind': Value('string'), 'count': Value('int64'), 'format': Value('string')}, 'tools.json': {'kind': Value('string'), 'count': Value('int64'), 'format': Value('string')}, 'variables.json': {'kind': Value('string'), 'count': Value('int64'), 'format': Value('string')}, 'coreweave.json.gz': {'provider': Value('string'), 'count': Value('int64'), 'format': Value('string')}, 'financialfilings.json.gz': {'provider': Value('string'), 'count': Value('int64'), 'format': Value('string')}, 'nvidia.json.gz': {'provider': Value('string'), 'count': Value('int64'), 'format': Value('string')}}, 'reconstruct': 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.
Omnipay — Lattice AI Infrastructure Archive
Machine-readable documentation index extracted from the Lattice archive (nvidia-infinate.grok.me), covering NVIDIA, CoreWeave, and FinancialFilings — endpoints, CUDA symbols, instance types, models, taxonomies, and schemas.
Contents
- 3,072 records across three providers:
- NVIDIA — 1,444
- CoreWeave — 1,485
- FinancialFilings — 143
- 10 record kinds: documents (2,274), sitemaps (219), endpoints (187), functions (141), schemas (97), tools (66), models (29), variables (28), hardware (17), datasets (14).
Files
| File | Description |
|---|---|
corpus.json |
Canonical full dump — all entities, relationships (edges), counts, seed URLs, and upstream indexes. |
manifest.json |
Schema documentation — common fields plus kind-specific fields, and the provider/kind file map. |
edges.json |
Relationship edges between entities (e.g. hardware "runs-on" links). |
Entity model
Every entity carries: id, kind, provider, product, name, summary,
source, path, tags, related — plus kind-specific fields documented in
manifest.json (e.g. signature/returnType on functions, method/hostname/
authentication on endpoints, specs on hardware).
Integration
This dataset is integrated with the Klawpay Purchasing object-store bucket:
- Bucket:
hf://buckets/Klawpay/Purchasing - Integration pointer:
omnipay_lattice_integration.jsonin that bucket references this dataset.
Upstream indexes crawled
- Downloads last month
- 120