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The dataset generation failed because of a cast error
Error code: DatasetGenerationCastError
Exception: DatasetGenerationCastError
Message: An error occurred while generating the dataset
All the data files must have the same columns, but at some point there are 1 new columns ({'reward'}) and 3 missing columns ({'index', 'action', 'next'}).
This happened while the json dataset builder was generating data using
hf://datasets/compsciencelab/BricksRL-Datasets/2Wheeler/RunAway/expert_data/next/meta.json (at revision c7b763b99755b944b0db92728f078360f3ef6543)
Please either edit the data files to have matching columns, or separate them into different configurations (see docs at https://hf.co/docs/hub/datasets-manual-configuration#multiple-configurations)
Traceback: Traceback (most recent call last):
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/builder.py", line 2013, in _prepare_split_single
writer.write_table(table)
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/arrow_writer.py", line 585, in write_table
pa_table = table_cast(pa_table, self._schema)
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/table.py", line 2302, in table_cast
return cast_table_to_schema(table, schema)
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/table.py", line 2256, in cast_table_to_schema
raise CastError(
datasets.table.CastError: Couldn't cast
observation: struct<device: string, shape: list<item: int64>, dtype: string, is_nested: bool>
child 0, device: string
child 1, shape: list<item: int64>
child 0, item: int64
child 2, dtype: string
child 3, is_nested: bool
reward: struct<device: string, shape: list<item: int64>, dtype: string, is_nested: bool>
child 0, device: string
child 1, shape: list<item: int64>
child 0, item: int64
child 2, dtype: string
child 3, is_nested: bool
done: struct<device: string, shape: list<item: int64>, dtype: string, is_nested: bool>
child 0, device: string
child 1, shape: list<item: int64>
child 0, item: int64
child 2, dtype: string
child 3, is_nested: bool
distance: struct<device: string, shape: list<item: int64>, dtype: string, is_nested: bool>
child 0, device: string
child 1, shape: list<item: int64>
child 0, item: int64
child 2, dtype: string
child 3, is_nested: bool
terminated: struct<device: string, shape: list<item: int64>, dtype: string, is_nested: bool>
child 0, device: string
child 1, shape: list<item: int64>
child 0, item: int64
child 2, dtype: string
child 3, is_nested: bool
shape: list<item: int64>
child 0, item: int64
device: string
_type: string
to
{'observation': {'device': Value(dtype='string', id=None), 'shape': Sequence(feature=Value(dtype='int64', id=None), length=-1, id=None), 'dtype': Value(dtype='string', id=None), 'is_nested': Value(dtype='bool', id=None)}, 'distance': {'device': Value(dtype='string', id=None), 'shape': Sequence(feature=Value(dtype='int64', id=None), length=-1, id=None), 'dtype': Value(dtype='string', id=None), 'is_nested': Value(dtype='bool', id=None)}, 'done': {'device': Value(dtype='string', id=None), 'shape': Sequence(feature=Value(dtype='int64', id=None), length=-1, id=None), 'dtype': Value(dtype='string', id=None), 'is_nested': Value(dtype='bool', id=None)}, 'terminated': {'device': Value(dtype='string', id=None), 'shape': Sequence(feature=Value(dtype='int64', id=None), length=-1, id=None), 'dtype': Value(dtype='string', id=None), 'is_nested': Value(dtype='bool', id=None)}, 'action': {'device': Value(dtype='string', id=None), 'shape': Sequence(feature=Value(dtype='int64', id=None), length=-1, id=None), 'dtype': Value(dtype='string', id=None), 'is_nested': Value(dtype='bool', id=None)}, 'next': {'type': Value(dtype='string', id=None)}, 'index': {'device': Value(dtype='string', id=None), 'shape': Sequence(feature=Value(dtype='int64', id=None), length=-1, id=None), 'dtype': Value(dtype='string', id=None), 'is_nested': Value(dtype='bool', id=None)}, 'shape': Sequence(feature=Value(dtype='int64', id=None), length=-1, id=None), 'device': Value(dtype='string', id=None), '_type': Value(dtype='string', id=None)}
because column names don't match
During handling of the above exception, another exception occurred:
Traceback (most recent call last):
File "/src/services/worker/src/worker/job_runners/config/parquet_and_info.py", line 1396, 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 1045, in convert_to_parquet
builder.download_and_prepare(
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/builder.py", line 1029, in download_and_prepare
self._download_and_prepare(
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/builder.py", line 1124, 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 1884, 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 2015, in _prepare_split_single
raise DatasetGenerationCastError.from_cast_error(
datasets.exceptions.DatasetGenerationCastError: An error occurred while generating the dataset
All the data files must have the same columns, but at some point there are 1 new columns ({'reward'}) and 3 missing columns ({'index', 'action', 'next'}).
This happened while the json dataset builder was generating data using
hf://datasets/compsciencelab/BricksRL-Datasets/2Wheeler/RunAway/expert_data/next/meta.json (at revision c7b763b99755b944b0db92728f078360f3ef6543)
Please either edit the data files to have matching columns, or separate them into different configurations (see docs at https://hf.co/docs/hub/datasets-manual-configuration#multiple-configurations)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.
observation
dict | distance
dict | done
dict | terminated
dict | action
dict | next
dict | index
dict | shape
sequence | device
string | _type
string | reward
dict |
|---|---|---|---|---|---|---|---|---|---|---|
{
"device": "cpu",
"shape": [
1987,
5
],
"dtype": "torch.float32",
"is_nested": false
} | {
"device": "cpu",
"shape": [
1987,
1
],
"dtype": "torch.float32",
"is_nested": false
} | {
"device": "cpu",
"shape": [
1987,
1
],
"dtype": "torch.bool",
"is_nested": false
} | {
"device": "cpu",
"shape": [
1987,
1
],
"dtype": "torch.bool",
"is_nested": false
} | {
"device": "cpu",
"shape": [
1987,
1
],
"dtype": "torch.float32",
"is_nested": false
} | {
"type": "TensorDict"
} | {
"device": "cpu",
"shape": [
1987
],
"dtype": "torch.int64",
"is_nested": false
} | [
1987
] | cpu | <class 'tensordict._td.TensorDict'> | null |
{
"device": "cpu",
"shape": [
1987,
5
],
"dtype": "torch.float32",
"is_nested": false
} | {
"device": "cpu",
"shape": [
1987,
1
],
"dtype": "torch.float32",
"is_nested": false
} | {
"device": "cpu",
"shape": [
1987,
1
],
"dtype": "torch.bool",
"is_nested": false
} | {
"device": "cpu",
"shape": [
1987,
1
],
"dtype": "torch.bool",
"is_nested": false
} | null | null | null | [
1987
] | cpu | <class 'tensordict._td.TensorDict'> | {
"device": "cpu",
"shape": [
1987,
1
],
"dtype": "torch.float32",
"is_nested": false
} |
{
"device": "cpu",
"shape": [
1612,
5
],
"dtype": "torch.float32",
"is_nested": false
} | {
"device": "cpu",
"shape": [
1612,
1
],
"dtype": "torch.float32",
"is_nested": false
} | {
"device": "cpu",
"shape": [
1612,
1
],
"dtype": "torch.bool",
"is_nested": false
} | {
"device": "cpu",
"shape": [
1612,
1
],
"dtype": "torch.bool",
"is_nested": false
} | {
"device": "cpu",
"shape": [
1612,
1
],
"dtype": "torch.float32",
"is_nested": false
} | {
"type": "TensorDict"
} | {
"device": "cpu",
"shape": [
1612
],
"dtype": "torch.int64",
"is_nested": false
} | [
1612
] | cpu | <class 'tensordict._td.TensorDict'> | null |
{
"device": "cpu",
"shape": [
1612,
5
],
"dtype": "torch.float32",
"is_nested": false
} | {
"device": "cpu",
"shape": [
1612,
1
],
"dtype": "torch.float32",
"is_nested": false
} | {
"device": "cpu",
"shape": [
1612,
1
],
"dtype": "torch.bool",
"is_nested": false
} | {
"device": "cpu",
"shape": [
1612,
1
],
"dtype": "torch.bool",
"is_nested": false
} | null | null | null | [
1612
] | cpu | <class 'tensordict._td.TensorDict'> | {
"device": "cpu",
"shape": [
1612,
1
],
"dtype": "torch.float32",
"is_nested": false
} |
{
"device": "cpu",
"shape": [
5000,
6
],
"dtype": "torch.float32",
"is_nested": false
} | null | {
"device": "cpu",
"shape": [
5000,
1
],
"dtype": "torch.bool",
"is_nested": false
} | {
"device": "cpu",
"shape": [
5000,
1
],
"dtype": "torch.bool",
"is_nested": false
} | {
"device": "cpu",
"shape": [
5000,
2
],
"dtype": "torch.float32",
"is_nested": false
} | {
"type": "TensorDict"
} | {
"device": "cpu",
"shape": [
5000
],
"dtype": "torch.int64",
"is_nested": false
} | [
5000
] | cpu | <class 'tensordict._td.TensorDict'> | null |
{
"device": "cpu",
"shape": [
5000,
6
],
"dtype": "torch.float32",
"is_nested": false
} | null | {
"device": "cpu",
"shape": [
5000,
1
],
"dtype": "torch.bool",
"is_nested": false
} | {
"device": "cpu",
"shape": [
5000,
1
],
"dtype": "torch.bool",
"is_nested": false
} | null | null | null | [
5000
] | cpu | <class 'tensordict._td.TensorDict'> | {
"device": "cpu",
"shape": [
5000,
1
],
"dtype": "torch.float32",
"is_nested": false
} |
{
"device": "cpu",
"shape": [
5000,
6
],
"dtype": "torch.float32",
"is_nested": false
} | null | {
"device": "cpu",
"shape": [
5000,
1
],
"dtype": "torch.bool",
"is_nested": false
} | {
"device": "cpu",
"shape": [
5000,
1
],
"dtype": "torch.bool",
"is_nested": false
} | {
"device": "cpu",
"shape": [
5000,
2
],
"dtype": "torch.float32",
"is_nested": false
} | {
"type": "TensorDict"
} | {
"device": "cpu",
"shape": [
5000
],
"dtype": "torch.int64",
"is_nested": false
} | [
5000
] | cpu | <class 'tensordict._td.TensorDict'> | null |
{
"device": "cpu",
"shape": [
5000,
6
],
"dtype": "torch.float32",
"is_nested": false
} | null | {
"device": "cpu",
"shape": [
5000,
1
],
"dtype": "torch.bool",
"is_nested": false
} | {
"device": "cpu",
"shape": [
5000,
1
],
"dtype": "torch.bool",
"is_nested": false
} | null | null | null | [
5000
] | cpu | <class 'tensordict._td.TensorDict'> | {
"device": "cpu",
"shape": [
5000,
1
],
"dtype": "torch.float32",
"is_nested": false
} |
{
"device": "cpu",
"shape": [
1297,
4
],
"dtype": "torch.float32",
"is_nested": false
} | null | {
"device": "cpu",
"shape": [
1297,
1
],
"dtype": "torch.bool",
"is_nested": false
} | {
"device": "cpu",
"shape": [
1297,
1
],
"dtype": "torch.bool",
"is_nested": false
} | {
"device": "cpu",
"shape": [
1297,
4
],
"dtype": "torch.float32",
"is_nested": false
} | {
"type": "TensorDict"
} | {
"device": "cpu",
"shape": [
1297
],
"dtype": "torch.int64",
"is_nested": false
} | [
1297
] | cpu | <class 'tensordict._td.TensorDict'> | null |
{
"device": "cpu",
"shape": [
1297,
4
],
"dtype": "torch.float32",
"is_nested": false
} | null | {
"device": "cpu",
"shape": [
1297,
1
],
"dtype": "torch.bool",
"is_nested": false
} | {
"device": "cpu",
"shape": [
1297,
1
],
"dtype": "torch.bool",
"is_nested": false
} | null | null | null | [
1297
] | cpu | <class 'tensordict._td.TensorDict'> | {
"device": "cpu",
"shape": [
1297,
1
],
"dtype": "torch.float32",
"is_nested": false
} |
{
"device": "cpu",
"shape": [
10000,
4
],
"dtype": "torch.float32",
"is_nested": false
} | null | {
"device": "cpu",
"shape": [
10000,
1
],
"dtype": "torch.bool",
"is_nested": false
} | {
"device": "cpu",
"shape": [
10000,
1
],
"dtype": "torch.bool",
"is_nested": false
} | {
"device": "cpu",
"shape": [
10000,
4
],
"dtype": "torch.float32",
"is_nested": false
} | {
"type": "TensorDict"
} | {
"device": "cpu",
"shape": [
10000
],
"dtype": "torch.int64",
"is_nested": false
} | [
10000
] | cpu | <class 'tensordict._td.TensorDict'> | null |
{
"device": "cpu",
"shape": [
10000,
4
],
"dtype": "torch.float32",
"is_nested": false
} | null | {
"device": "cpu",
"shape": [
10000,
1
],
"dtype": "torch.bool",
"is_nested": false
} | {
"device": "cpu",
"shape": [
10000,
1
],
"dtype": "torch.bool",
"is_nested": false
} | null | null | null | [
10000
] | cpu | <class 'tensordict._td.TensorDict'> | {
"device": "cpu",
"shape": [
10000,
1
],
"dtype": "torch.float32",
"is_nested": false
} |
{
"device": "cpu",
"shape": [
9244,
7
],
"dtype": "torch.float32",
"is_nested": false
} | null | {
"device": "cpu",
"shape": [
9244,
1
],
"dtype": "torch.bool",
"is_nested": false
} | {
"device": "cpu",
"shape": [
9244,
1
],
"dtype": "torch.bool",
"is_nested": false
} | {
"device": "cpu",
"shape": [
9244,
4
],
"dtype": "torch.float32",
"is_nested": false
} | {
"type": "TensorDict"
} | {
"device": "cpu",
"shape": [
9244
],
"dtype": "torch.int64",
"is_nested": false
} | [
9244
] | cpu | <class 'tensordict._td.TensorDict'> | null |
{
"device": "cpu",
"shape": [
9244,
7
],
"dtype": "torch.float32",
"is_nested": false
} | null | {
"device": "cpu",
"shape": [
9244,
1
],
"dtype": "torch.bool",
"is_nested": false
} | {
"device": "cpu",
"shape": [
9244,
1
],
"dtype": "torch.bool",
"is_nested": false
} | null | null | null | [
9244
] | cpu | <class 'tensordict._td.TensorDict'> | {
"device": "cpu",
"shape": [
9244,
1
],
"dtype": "torch.float32",
"is_nested": false
} |
{
"device": "cpu",
"shape": [
10000,
7
],
"dtype": "torch.float32",
"is_nested": false
} | null | {
"device": "cpu",
"shape": [
10000,
1
],
"dtype": "torch.bool",
"is_nested": false
} | {
"device": "cpu",
"shape": [
10000,
1
],
"dtype": "torch.bool",
"is_nested": false
} | {
"device": "cpu",
"shape": [
10000,
4
],
"dtype": "torch.float32",
"is_nested": false
} | {
"type": "TensorDict"
} | {
"device": "cpu",
"shape": [
10000
],
"dtype": "torch.int64",
"is_nested": false
} | [
10000
] | cpu | <class 'tensordict._td.TensorDict'> | null |
{
"device": "cpu",
"shape": [
10000,
7
],
"dtype": "torch.float32",
"is_nested": false
} | null | {
"device": "cpu",
"shape": [
10000,
1
],
"dtype": "torch.bool",
"is_nested": false
} | {
"device": "cpu",
"shape": [
10000,
1
],
"dtype": "torch.bool",
"is_nested": false
} | null | null | null | [
10000
] | cpu | <class 'tensordict._td.TensorDict'> | {
"device": "cpu",
"shape": [
10000,
1
],
"dtype": "torch.float32",
"is_nested": false
} |