The dataset viewer is not available for this subset.
Exception: SplitsNotFoundError
Message: The split names could not be parsed from the dataset config.
Traceback: Traceback (most recent call last):
File "/usr/local/lib/python3.14/site-packages/datasets/inspect.py", line 286, in get_dataset_config_info
for split_generator in builder._split_generators(
~~~~~~~~~~~~~~~~~~~~~~~~~^
StreamingDownloadManager(base_path=builder.base_path, download_config=download_config)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
)
^
File "/usr/local/lib/python3.14/site-packages/datasets/packaged_modules/webdataset/webdataset.py", line 81, in _split_generators
first_examples = list(islice(pipeline, self.NUM_EXAMPLES_FOR_FEATURES_INFERENCE))
File "/usr/local/lib/python3.14/site-packages/datasets/packaged_modules/webdataset/webdataset.py", line 32, in _get_pipeline_from_tar
fs: fsspec.AbstractFileSystem = fsspec.filesystem("memory")
~~~~~~~~~~~~~~~~~^^^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/fsspec/registry.py", line 302, in filesystem
cls = get_filesystem_class(protocol)
File "/usr/local/lib/python3.14/site-packages/fsspec/registry.py", line 239, in get_filesystem_class
raise ValueError(f"Protocol not known: {protocol}")
ValueError: Protocol not known: memory
The above exception was the direct cause of the following exception:
Traceback (most recent call last):
File "/src/services/worker/src/worker/job_runners/config/split_names.py", line 66, in compute_split_names_from_streaming_response
for split in get_dataset_split_names(
~~~~~~~~~~~~~~~~~~~~~~~^
path=dataset,
^^^^^^^^^^^^^
config_name=config,
^^^^^^^^^^^^^^^^^^^
token=hf_token,
^^^^^^^^^^^^^^^
)
^
File "/usr/local/lib/python3.14/site-packages/datasets/inspect.py", line 340, in get_dataset_split_names
info = get_dataset_config_info(
path,
...<6 lines>...
**config_kwargs,
)
File "/usr/local/lib/python3.14/site-packages/datasets/inspect.py", line 291, in get_dataset_config_info
raise SplitsNotFoundError("The split names could not be parsed from the dataset config.") from err
datasets.inspect.SplitsNotFoundError: The split names could not be parsed from the dataset config.Need help to make the dataset viewer work? Make sure to review how to configure the dataset viewer, and open a discussion for direct support.
RoboDojo Assets — verified transport shards
This public dataset is a byte-preserving transport repack of Assets/ from
RoboDojo-Benchmark/RoboDojo, pinned at source commit
aada55a7a58b520a2004b9db02e884144b6e83e7. It reduces 15,335 source files to 11 independently downloadable and
restorable tar.zst shards. No payload file was changed.
这是 RoboDojo Assets/ 的公开传输分片：固定源 commit、减少小文件数量、每片可独立下载/重试，
完整复原后会逐文件做 SHA-256 校验。
Contents
- Source files: 15,335
- Source logical bytes: 41,268,637,776
- Source revision:
aada55a7a58b520a2004b9db02e884144b6e83e7 - Archive format: GNU tar + Zstandard level 3
- Every archive is below 4,000,000,000 bytes
| shard | files | input bytes | archive bytes | SHA-256 prefix |
|---|---|---|---|---|
data/assets-000.tar.zst |
2,023 | 3,758,095,437 | 3,538,570,683 | a0846895771da2ac… |
data/assets-001.tar.zst |
143 | 3,758,074,568 | 3,749,841,773 | 711b3a5bcee029cd… |
data/assets-002.tar.zst |
263 | 3,758,095,164 | 3,175,194,388 | b8360b8f0bf21af0… |
data/assets-003.tar.zst |
491 | 3,758,080,869 | 3,488,084,115 | 9b184cbe5ccc249e… |
data/assets-004.tar.zst |
143 | 3,758,095,598 | 3,672,499,179 | 442e12ad6c7820aa… |
data/assets-005.tar.zst |
472 | 3,758,095,587 | 3,385,701,591 | 1e5b0fdf922b09e7… |
data/assets-006.tar.zst |
467 | 3,758,084,191 | 3,491,925,802 | 1469148d3284b53b… |
data/assets-007.tar.zst |
8,488 | 3,758,059,501 | 3,311,607,868 | 29d4b73bf90de4d3… |
data/assets-008.tar.zst |
517 | 3,758,096,082 | 3,433,188,150 | 045b896487b1484c… |
data/assets-009.tar.zst |
592 | 3,758,062,850 | 3,570,379,301 | 0a7aa894670f58e9… |
data/assets-010.tar.zst |
1,736 | 3,687,797,929 | 3,459,212,794 | 7e4d65469a712e59… |
SHA256SUMS verifies the 11 transport archives. FILES.sha256 verifies every restored
source file. MANIFEST.json records exact counts, sizes, paths, and full hashes.
Download with low external concurrency
Install the current Hugging Face CLI, then choose one or two workers. Pin --revision
to a full commit SHA in production.
hf download didfd/robodojo-assets-packed \
--repo-type dataset \
--revision <FULL_PACKED_REPO_COMMIT_SHA> \
--local-dir /path/to/robodojo-assets-packed \
--max-workers 2
If the company importer accepts file URLs only, import the 11 files under data/ plus
MANIFEST.json, SHA256SUMS, FILES.sha256, lists/, and restore.py. With concurrency
1, only one ~3.7 GB object is active at a time; a failed transfer only retries that shard.
Restore and verify
Python 3.9+, zstd, and Linux with /proc/self/fd plus O_NOFOLLOW support are required.
The restore tool validates all metadata, archive hashes, and structured tar headers before
creating the output. It rejects links, sparse files, devices, FIFOs, non-canonical paths,
duplicates, and cross-shard path conflicts. Each shard is extracted into an isolated private
staging directory; only a fully hashed tree is atomically published as Assets/.
python3 restore.py \
--repo-dir /path/to/robodojo-assets-packed \
--output-dir /path/to/restored \
--workers 4 \
--hash-workers 16
Success ends with RESTORE VERIFIED; the restored root is
/path/to/restored/Assets/. If Assets/ already exists, --force builds and verifies a
fresh tree first, atomically replaces the old tree, and preserves the old tree as
Assets.backup.<random-id>; it does not perform an in-place overwrite. Atomic replacement
requires filesystem support for Linux RENAME_EXCHANGE; if unavailable, --force fails
without modifying the existing tree.
Provenance and license
The source dataset declares Apache-2.0. This repository changes packaging only and keeps the same declared license; users remain responsible for following the upstream dataset's terms and notices.
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