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The dataset generation failed
Error code: DatasetGenerationError
Exception: ArrowNotImplementedError
Message: Cannot write struct type 'value' with no child field to Parquet. Consider adding a dummy child field.
Traceback: Traceback (most recent call last):
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/builder.py", line 1871, in _prepare_split_single
writer.write_table(table)
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/arrow_writer.py", line 641, in write_table
self._build_writer(inferred_schema=pa_table.schema)
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/arrow_writer.py", line 456, in _build_writer
self.pa_writer = self._WRITER_CLASS(self.stream, schema)
File "/src/services/worker/.venv/lib/python3.9/site-packages/pyarrow/parquet/core.py", line 1010, in __init__
self.writer = _parquet.ParquetWriter(
File "pyarrow/_parquet.pyx", line 2157, in pyarrow._parquet.ParquetWriter.__cinit__
File "pyarrow/error.pxi", line 154, in pyarrow.lib.pyarrow_internal_check_status
File "pyarrow/error.pxi", line 91, in pyarrow.lib.check_status
pyarrow.lib.ArrowNotImplementedError: Cannot write struct type 'value' with no child field to Parquet. Consider adding a dummy child field.
During handling of the above exception, another exception occurred:
Traceback (most recent call last):
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/builder.py", line 1887, in _prepare_split_single
num_examples, num_bytes = writer.finalize()
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/arrow_writer.py", line 660, in finalize
self._build_writer(self.schema)
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/arrow_writer.py", line 456, in _build_writer
self.pa_writer = self._WRITER_CLASS(self.stream, schema)
File "/src/services/worker/.venv/lib/python3.9/site-packages/pyarrow/parquet/core.py", line 1010, in __init__
self.writer = _parquet.ParquetWriter(
File "pyarrow/_parquet.pyx", line 2157, in pyarrow._parquet.ParquetWriter.__cinit__
File "pyarrow/error.pxi", line 154, in pyarrow.lib.pyarrow_internal_check_status
File "pyarrow/error.pxi", line 91, in pyarrow.lib.check_status
pyarrow.lib.ArrowNotImplementedError: Cannot write struct type 'value' 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 1433, in compute_config_parquet_and_info_response
parquet_operations = convert_to_parquet(builder)
File "/src/services/worker/src/worker/job_runners/config/parquet_and_info.py", line 1050, in convert_to_parquet
builder.download_and_prepare(
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/builder.py", line 925, in download_and_prepare
self._download_and_prepare(
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/builder.py", line 1001, in _download_and_prepare
self._prepare_split(split_generator, **prepare_split_kwargs)
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/builder.py", line 1742, in _prepare_split
for job_id, done, content in self._prepare_split_single(
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/builder.py", line 1898, 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.
rows dict | cols dict | levels dict | obstacle_prob dict | goods_prob dict | shelves dict | aisles dict | obstacles dict | goods dict | vacant_area dict | person dict | drones dict | forklifts dict | tasks dict |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
{
"value": 6,
"description": "The number of rows in the warehouse"
} | {
"value": 6,
"description": "Number of columns in the warehouse"
} | {
"value": 3,
"description": "The number of shelve levels in the warehouse"
} | {
"value": 0,
"description": "Probability of obstacle generation"
} | {
"value": 0,
"description": "Probability of goods generation"
} | {
"description": "The information of all shelves in the warehouse, with the key being shelf ID and the value being detailed shelf information, including ID, capacity, coordinates, type, floor height, and cargo",
"value": {
"0": {
"id": 0,
"capacity": 1,
"coord": [
1,
1,
... | {
"description": "The information of all channels in the warehouse is divided into internal channels and peripheral channels",
"value": {
"internal": [
[
4,
3
],
[
2,
3
],
[
3,
3
],
[
1,
3
]
],
... | {
"description": "Information on all obstacles in the warehouse, with the key being obstacle ID and the value being detailed obstacle information, including ID, category, and coordinates",
"value": {
"0": {
"id": "0",
"category": "good",
"position": [
4,
14,
0
]
... | {
"description": "The information of all goods in the warehouse, with the key being the goods ID and the value being the detailed information of the goods, including category, name, ID, and coordinates",
"value": {}
} | {
"value": [
[
1,
4
],
[
2,
4
],
[
3,
4
],
[
4,
4
]
],
"description": "Coordinate list of each cell in the vacant area of the warehouse"
} | {
"description": "The information of all people in the warehouse, with the key being the person ID and the value being the person detailed information, including ID, name, and face matching degree",
"value": {
"0": {
"name": "Jack",
"position": [
8,
12
],
"face_match": 0.... | {
"description": "The information of all drones in the warehouse, with the key being the drone ID and the value being the detailed information of the drone, including ID, coordinates, and mounted devices",
"value": {
"1": {
"id": 1,
"coord": [
0,
0,
0
],
"mount": ... | {
"description": "The information of all forklifts in the warehouse, with the key being the forklift ID and the value being the detailed information of the forklift, including ID and coordinates. The third dimension of the forklift coordinates is the height of the fork",
"value": {
"1": {
"id": 1,
"... | {
"value": {
"1": {
"task": "The obstacle good at [4, 14, 0] is in the warehouse, recommend to move it to somewhere else like [2, 13] or anywhere else in the vacant_area",
"related_device": "forklift",
"task_completion_criteria": "The obstacle good at [4, 14, 0] is in the warehouse, has moved to... |
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