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The dataset generation failed
Error code: DatasetGenerationError
Exception: ArrowNotImplementedError
Message: Cannot write struct type 'rename_map' with no child field to Parquet. Consider adding a dummy child field.
Traceback: Traceback (most recent call last):
File "/usr/local/lib/python3.14/site-packages/datasets/builder.py", line 1858, in _prepare_split_single
num_examples, num_bytes = writer.finalize()
~~~~~~~~~~~~~~~^^
File "/usr/local/lib/python3.14/site-packages/datasets/arrow_writer.py", line 781, in finalize
self.write_rows_on_file()
~~~~~~~~~~~~~~~~~~~~~~~^^
File "/usr/local/lib/python3.14/site-packages/datasets/arrow_writer.py", line 663, in write_rows_on_file
self._write_table(table)
~~~~~~~~~~~~~~~~~^^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/arrow_writer.py", line 771, in _write_table
self._build_writer(inferred_schema=pa_table.schema)
~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/arrow_writer.py", line 812, in _build_writer
self.pa_writer = pq.ParquetWriter(
~~~~~~~~~~~~~~~~^
self.stream,
^^^^^^^^^^^^
...<9 lines>...
},
^^
)
^
File "/usr/local/lib/python3.14/site-packages/pyarrow/parquet/core.py", line 1070, in __init__
self.writer = _parquet.ParquetWriter(
~~~~~~~~~~~~~~~~~~~~~~^
sink, schema,
^^^^^^^^^^^^^
...<18 lines>...
store_decimal_as_integer=store_decimal_as_integer,
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
**options)
^^^^^^^^^^
File "pyarrow/_parquet.pyx", line 2363, in pyarrow._parquet.ParquetWriter.__cinit__
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.ArrowNotImplementedError: Cannot write struct type 'rename_map' with no child field to Parquet. Consider adding a dummy child field.
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/parquet_and_info.py", line 1369, in compute_config_parquet_and_info_response
parquet_operations, partial, estimated_dataset_info = stream_convert_to_parquet(
~~~~~~~~~~~~~~~~~~~~~~~~~^
builder, max_dataset_size_bytes=max_dataset_size_bytes
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
)
^
File "/src/services/worker/src/worker/job_runners/config/parquet_and_info.py", line 948, in stream_convert_to_parquet
builder._prepare_split(split_generator=splits_generators[split], file_format="parquet")
~~~~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/builder.py", line 1683, in _prepare_split
for job_id, done, content in self._prepare_split_single(
~~~~~~~~~~~~~~~~~~~~~~~~~~^
gen_kwargs=gen_kwargs, job_id=job_id, **_prepare_split_args
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
):
^
File "/usr/local/lib/python3.14/site-packages/datasets/builder.py", line 1869, in _prepare_split_single
raise DatasetGenerationError("An error occurred while generating the dataset") from e
datasets.exceptions.DatasetGenerationError: An error occurred while generating the datasetNeed help to make the dataset viewer work? Make sure to review how to configure the dataset viewer, and open a discussion for direct support.
dataset dict | env null | policy dict | output_dir string | job_name string | resume bool | seed int64 | cudnn_deterministic bool | num_workers int64 | batch_size int64 | steps int64 | eval_freq int64 | log_freq int64 | tolerance_s float64 | save_checkpoint bool | save_freq int64 | use_policy_training_preset bool | optimizer dict | scheduler null | eval dict | wandb dict | peft null | use_rabc bool | rabc_progress_path null | rabc_kappa float64 | rabc_epsilon float64 | rabc_head_mode string | rename_map dict | checkpoint_path null |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
{
"repo_id": "pai2026/can-full",
"root": "data/lerobot/can-full",
"episodes": [
0,
1,
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8,
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15,
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17,
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19,
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21,
22,
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24,
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28,
29,
... | null | {
"type": "act",
"n_obs_steps": 1,
"input_features": {
"observation.images.agentview": {
"type": "VISUAL",
"shape": [
3,
240,
320
]
},
"observation.images.wrist": {
"type": "VISUAL",
"shape": [
3,
240,
320
]
},
... | out/train/cond2_sync/run | act | false | 1,000 | false | 8 | 16 | 200,000 | 20,000 | 250 | 0.0001 | true | 66,666 | true | {
"type": "adamw",
"lr": 0.00001,
"weight_decay": 0.0001,
"grad_clip_norm": 10,
"betas": [
0.9,
0.999
],
"eps": 1e-8
} | null | {
"n_episodes": 50,
"batch_size": 50,
"use_async_envs": false
} | {
"enable": false,
"disable_artifact": false,
"project": "lerobot",
"entity": null,
"notes": null,
"run_id": null,
"mode": null,
"add_tags": true
} | null | false | null | 0.01 | 0.000001 | sparse | {} | null |
{
"repo_id": "pai2026/lift-mini-d000",
"root": "data/lerobot/lift-mini-d000",
"episodes": null,
"image_transforms": {
"enable": false,
"max_num_transforms": 3,
"random_order": false,
"tfs": {
"brightness": {
"weight": 1,
"type": "ColorJitter",
"kwargs": {
... | null | {
"type": "act",
"n_obs_steps": 1,
"input_features": {
"observation.images.agentview": {
"type": "VISUAL",
"shape": [
3,
96,
96
]
},
"observation.images.wrist": {
"type": "VISUAL",
"shape": [
3,
96,
96
]
},
"ob... | out/mini/lift-mini-d000/run | act | false | 1,000 | false | 4 | 16 | 5,000 | 20,000 | 250 | 0.0001 | true | 5,000 | true | {
"type": "adamw",
"lr": 0.00001,
"weight_decay": 0.0001,
"grad_clip_norm": 10,
"betas": [
0.9,
0.999
],
"eps": 1e-8
} | null | {
"n_episodes": 50,
"batch_size": 50,
"use_async_envs": false
} | {
"enable": false,
"disable_artifact": false,
"project": "lerobot",
"entity": null,
"notes": null,
"run_id": null,
"mode": null,
"add_tags": true
} | null | false | null | 0.01 | 0.000001 | sparse | {} | null |
{
"repo_id": "pai2026/lift-mini-d100",
"root": "data/lerobot/lift-mini-d100",
"episodes": null,
"image_transforms": {
"enable": false,
"max_num_transforms": 3,
"random_order": false,
"tfs": {
"brightness": {
"weight": 1,
"type": "ColorJitter",
"kwargs": {
... | null | {
"type": "act",
"n_obs_steps": 1,
"input_features": {
"observation.images.agentview": {
"type": "VISUAL",
"shape": [
3,
96,
96
]
},
"observation.images.wrist": {
"type": "VISUAL",
"shape": [
3,
96,
96
]
},
"ob... | out/mini/lift-mini-d100/run | act | false | 1,000 | false | 4 | 16 | 5,000 | 20,000 | 250 | 0.0001 | true | 5,000 | true | {
"type": "adamw",
"lr": 0.00001,
"weight_decay": 0.0001,
"grad_clip_norm": 10,
"betas": [
0.9,
0.999
],
"eps": 1e-8
} | null | {
"n_episodes": 50,
"batch_size": 50,
"use_async_envs": false
} | {
"enable": false,
"disable_artifact": false,
"project": "lerobot",
"entity": null,
"notes": null,
"run_id": null,
"mode": null,
"add_tags": true
} | null | false | null | 0.01 | 0.000001 | sparse | {} | null |
{
"repo_id": "pai2026/lift-mini-d100-s4",
"root": "data/lerobot/lift-mini-d100-s4",
"episodes": null,
"image_transforms": {
"enable": false,
"max_num_transforms": 3,
"random_order": false,
"tfs": {
"brightness": {
"weight": 1,
"type": "ColorJitter",
"kwargs": {
... | null | {
"type": "act",
"n_obs_steps": 1,
"input_features": {
"observation.images.agentview": {
"type": "VISUAL",
"shape": [
3,
96,
96
]
},
"observation.images.wrist": {
"type": "VISUAL",
"shape": [
3,
96,
96
]
},
"ob... | out/mini/lift-mini-d100-s4/run | act | false | 1,000 | false | 4 | 16 | 5,000 | 20,000 | 250 | 0.0001 | true | 5,000 | true | {
"type": "adamw",
"lr": 0.00001,
"weight_decay": 0.0001,
"grad_clip_norm": 10,
"betas": [
0.9,
0.999
],
"eps": 1e-8
} | null | {
"n_episodes": 50,
"batch_size": 50,
"use_async_envs": false
} | {
"enable": false,
"disable_artifact": false,
"project": "lerobot",
"entity": null,
"notes": null,
"run_id": null,
"mode": null,
"add_tags": true
} | null | false | null | 0.01 | 0.000001 | sparse | {} | null |
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