tmax-1.1
Collection
Apptainer SIF pools for tmax-1.1 environments, with source task dataset links and unified download manifests. • 7 items • Updated
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
published: int64
total: int64
covered_tasks: int64
task_count: int64
seconds: double
image: string
sif_sha256: string
revision: string
task_id: string
job: string
source_status: string
key: string
host: string
runtime_smoke_ok: bool
commit: string
sif_bytes: int64
inspect_ok: bool
hf_path: string
method: string
repo: string
image_id: string
status: string
sif_name: string
to
{'key': Value('string'), 'repo': Value('string'), 'revision': Value('string'), 'image': Value('string'), 'sif_name': Value('string'), 'image_id': Value('string'), 'task_id': Value('string'), 'task_count': Value('int64'), 'source_status': Value('string'), 'method': Value('string'), 'sif_sha256': Value('string'), 'sif_bytes': Value('int64'), 'hf_path': Value('string'), 'inspect_ok': Value('bool'), 'runtime_smoke_ok': Value('bool'), 'host': Value('string'), 'job': Value('string'), 'seconds': Value('float64'), 'commit': Value('string'), 'status': 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
published: int64
total: int64
covered_tasks: int64
task_count: int64
seconds: double
image: string
sif_sha256: string
revision: string
task_id: string
job: string
source_status: string
key: string
host: string
runtime_smoke_ok: bool
commit: string
sif_bytes: int64
inspect_ok: bool
hf_path: string
method: string
repo: string
image_id: string
status: string
sif_name: string
to
{'key': Value('string'), 'repo': Value('string'), 'revision': Value('string'), 'image': Value('string'), 'sif_name': Value('string'), 'image_id': Value('string'), 'task_id': Value('string'), 'task_count': Value('int64'), 'source_status': Value('string'), 'method': Value('string'), 'sif_sha256': Value('string'), 'sif_bytes': Value('int64'), 'hf_path': Value('string'), 'inspect_ok': Value('bool'), 'runtime_smoke_ok': Value('bool'), 'host': Value('string'), 'job': Value('string'), 'seconds': Value('float64'), 'commit': Value('string'), 'status': Value('string')}
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Apptainer pool for hamishivi/agent-task-openthoughts-agent-rl-5k
This repository hosts tmax-compatible SIF images and a unified download manifest. Training data and task archives are in hamishivi/agent-task-openthoughts-agent-rl-5k. The manifest includes earlier images hosted under hamishivi and new images hosted under TMaxxx; the downloader selects the correct repository and immutable commit for each image.
Apptainer images
The pool currently contains 1 / 1 verified Apptainer SIF images. Shared environments are stored once. Each published SIF passed apptainer inspect, a contained shell smoke check, and remote SHA256 verification. These checks do not constitute a full task evaluation.
Download apptainer/download_apptainer.py and run with Python 3.11+ and huggingface_hub installed:
python download_apptainer.py --repo TMaxxx/agent-task-openthoughts-agent-rl-5k --output ./sifs --allow-partial
export SWERL_APPTAINER_SIF_DIR="$PWD/sifs"
Omit --allow-partial to require a complete pool. Filenames follow tmax-private/geomean_mask image resolution. Task archives and the training split remain necessary. apptainer/manifest.jsonl records source references, SIF checksums, publication commits, and build provenance. Pending images cannot run from this pool yet. Indexes refresh periodically while builds are in progress.
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