The dataset viewer is not available for this split.
Error code: FeaturesError
Exception: ArrowInvalid
Message: JSON parse error: Missing a name for object member. in row 0
Traceback: Traceback (most recent call last):
File "/usr/local/lib/python3.14/site-packages/datasets/packaged_modules/json/json.py", line 324, in _generate_tables
df = pandas_read_json(f)
File "/usr/local/lib/python3.14/site-packages/datasets/packaged_modules/json/json.py", line 38, in pandas_read_json
return pd.read_json(path_or_buf, **kwargs)
~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/pandas/io/json/_json.py", line 815, in read_json
return json_reader.read()
~~~~~~~~~~~~~~~~^^
File "/usr/local/lib/python3.14/site-packages/pandas/io/json/_json.py", line 1014, in read
obj = self._get_object_parser(self.data)
File "/usr/local/lib/python3.14/site-packages/pandas/io/json/_json.py", line 1040, in _get_object_parser
obj = FrameParser(json, **kwargs).parse()
File "/usr/local/lib/python3.14/site-packages/pandas/io/json/_json.py", line 1176, in parse
self._parse()
~~~~~~~~~~~^^
File "/usr/local/lib/python3.14/site-packages/pandas/io/json/_json.py", line 1391, in _parse
self.obj = DataFrame(
~~~~~~~~~^
ujson_loads(json, precise_float=self.precise_float), dtype=None
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
)
^
File "/usr/local/lib/python3.14/site-packages/pandas/core/frame.py", line 782, in __init__
mgr = dict_to_mgr(data, index, columns, dtype=dtype, copy=copy, typ=manager)
File "/usr/local/lib/python3.14/site-packages/pandas/core/internals/construction.py", line 503, in dict_to_mgr
return arrays_to_mgr(arrays, columns, index, dtype=dtype, typ=typ, consolidate=copy)
File "/usr/local/lib/python3.14/site-packages/pandas/core/internals/construction.py", line 114, in arrays_to_mgr
index = _extract_index(arrays)
File "/usr/local/lib/python3.14/site-packages/pandas/core/internals/construction.py", line 677, in _extract_index
raise ValueError("All arrays must be of the same length")
ValueError: All arrays must be of the same length
During handling of the above exception, another exception occurred:
Traceback (most recent call last):
File "/src/services/worker/src/worker/job_runners/split/first_rows.py", line 244, in compute_first_rows_from_streaming_response
iterable_dataset = iterable_dataset._resolve_features()
File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 4408, in _resolve_features
features = _infer_features_from_batch(self.with_format(None)._head())
~~~~~~~~~~~~~~~~~~~~~~~~~~~~^^
File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2679, in _head
return next(iter(self.iter(batch_size=n)))
File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2861, in iter
for key, pa_table in ex_iterable.iter_arrow():
~~~~~~~~~~~~~~~~~~~~~~^^
File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2395, in _iter_arrow
yield from 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 327, in _generate_tables
raise e
File "/usr/local/lib/python3.14/site-packages/datasets/packaged_modules/json/json.py", line 290, in _generate_tables
pa_table = paj.read_json(
io.BytesIO(batch), read_options=paj.ReadOptions(block_size=block_size)
)
File "pyarrow/_json.pyx", line 342, in pyarrow._json.read_json
File "pyarrow/error.pxi", line 155, in pyarrow.lib.pyarrow_internal_check_status
File "pyarrow/error.pxi", line 92, in pyarrow.lib.check_status
raise convert_status(status)
pyarrow.lib.ArrowInvalid: JSON parse error: Missing a name for object member. in row 0Need help to make the dataset viewer work? Make sure to review how to configure the dataset viewer, and open a discussion for direct support.
CSGO training cache
Precomputed feature-only training samples, continuously converted from four source datasets:
peih/csgo-3090-v4-overflow-20260923peih/csgo-4090-v4-20260921peih/csgo-4090-v4-overflow-20260923peih/csgo-dual3090-v4-backup
This dataset is still growing. manifest.json is the current verified snapshot; manifests/vNNNNNN.json contains immutable historical snapshots. Read only listed shards at their pinned revision. New uploads become available after their verification receipt and a manifest update. Do not train from unlisted TAR files.
Format
Schema: csgo_training_cache_features_v1. Each TAR contains ordered pairs <sample_id>.pt and <sample_id>.json. A shard usually holds 60 samples and is approximately 1.49 GB; use the exact size in the manifest.
| Tensor | Shape per sample | dtype |
|---|---|---|
| latent | [1,16,21,60,104] | float32 |
| i2v_y | [1,20,21,60,104] | float32 |
| dense_features | [1,21,32,30,52] | float32 |
| state | [1,21,3,60,104] | float32 |
| interaction | [1,21,134] | float32 |
| camera_poses | [1,21,4,4] | float32 |
| camera_intrinsics | [1,21,4] | float32 |
| text_emb | [1,L,4096] | bfloat16 |
There is no raw seven-channel dense tensor. The Dense VAE features are already encoded. No VAE/T5 encoding is required on the training consumer. Model revisions, weight hashes and preprocessing contract are recorded in the manifest.
Use torch.load(..., weights_only=True). Verify each PT's bytes and SHA256 before deserialization, and verify the complete TAR SHA256 at EOF. A partially consumed shard has not yet passed the consumer's full-TAR check.
Streaming consumer contract
- Read
manifest.jsonat a fixed dataset commit. - Respect train/val splits and
exclude_sample_ids. The split is stable by match, with an expected validation fraction of 2% (not an exact per-snapshot ratio). - Open listed TARs over streaming HTTP at each pinned shard revision. Avoid downloading the entire dataset.
- Interleave shards; assign disjoint shards to different ranks. Sample IDs must not repeat across ranks in the same epoch.
- Keep the manifest fixed throughout an epoch. Refresh only at the next epoch boundary to incorporate newly published samples.
- Checkpoint the manifest, deterministic order/cursor and training state. A restart may reread a TAR prefix from the network but must not train already consumed samples again.
This layout uses a manifest-aware custom TAR reader. Ordinary datasets.load_dataset(streaming=True) is not claimed to implement the exclusion, version refresh and exact-resume protocol automatically.
Publication
Two conversion machines write separate shards/<worker>/... and receipts/<worker>/... paths in this repo. One manifest publisher serializes publication with a parent-commit check. Existing verified shards from the four older cache repos are being copied byte-for-byte; this is not re-encoding.
The full dataset is not yet complete. Manifest totals are the authority for currently published samples. Source discovery and conversion continue while consumers train on a fixed snapshot.
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