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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 22 new columns ({'path_loader_kwargs', 'version', 'rollout_fn', 'qf_kwargs', 'snapshot_gap', 'snapshot_mode', 'alg_class', 'policy_kwargs', 'algorithm_kwargs', 'IQN', 'trainer_cls', 'algorithm', 'replay_buffer_size', 'trainer_kwargs', 'env_id', 'pipeline', 'seed', 'replay_buffer_class', 'qf_class', 'policy_class', 'd4rl', 'normalize_env'}) and 9 missing columns ({'hopper-medium-expert-v2', 'hopper-medium-v2', 'halfcheetah-medium-v2', 'halfcheetah-medium-replay-v2', 'walker2d-medium-expert-v2', 'halfcheetah-medium-expert-v2', 'hopper-medium-replay-v2', 'walker2d-medium-replay-v2', 'walker2d-medium-v2'}).
This happened while the json dataset builder was generating data using
hf://datasets/ezipe/cfpi/mg-12-no-normalize/antmaze-large-diverse-v0/0/variant.json (at revision aa0e3614ecef0eaa16cd220df9459d5344bb3064)
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 2011, 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
IQN: bool
alg_class: struct<$class: string>
child 0, $class: string
algorithm: string
algorithm_kwargs: struct<batch_size: int64, max_path_length: int64, num_epochs: int64, num_eval_steps_per_epoch: int64, num_trains_per_train_loop: int64, start_epoch: int64>
child 0, batch_size: int64
child 1, max_path_length: int64
child 2, num_epochs: int64
child 3, num_eval_steps_per_epoch: int64
child 4, num_trains_per_train_loop: int64
child 5, start_epoch: int64
d4rl: bool
env_id: string
normalize_env: bool
path_loader_kwargs: struct<>
pipeline: struct<$class: string>
child 0, $class: string
policy_class: struct<$class: string>
child 0, $class: string
policy_kwargs: struct<hidden_sizes: list<item: int64>, num_gaussians: int64>
child 0, hidden_sizes: list<item: int64>
child 0, item: int64
child 1, num_gaussians: int64
qf_class: struct<$class: string>
child 0, $class: string
qf_kwargs: struct<hidden_sizes: list<item: int64>>
child 0, hidden_sizes: list<item: int64>
child 0, item: int64
replay_buffer_class: struct<$class: string>
child 0, $class: string
replay_buffer_size: int64
rollout_fn: struct<$function: string>
child 0, $function: string
seed: int64
snapshot_gap: int64
snapshot_mode: string
trainer_cls: struct<$class: string>
child 0, $class: string
trainer_kwargs: struct<discount: double, policy_lr: double, qf_lr: double, reward_scale: int64, soft_target_tau: double, target_update_period: int64>
child 0, discount: double
child 1, policy_lr: double
child 2, qf_lr: double
child 3, reward_scale: int64
child 4, soft_target_tau: double
child 5, target_update_period: int64
version: string
to
{'halfcheetah-medium-expert-v2': Sequence(feature=Value(dtype='float64', id=None), length=-1, id=None), 'walker2d-medium-expert-v2': Sequence(feature=Value(dtype='float64', id=None), length=-1, id=None), 'hopper-medium-expert-v2': Sequence(feature=Value(dtype='float64', id=None), length=-1, id=None), 'halfcheetah-medium-v2': Sequence(feature=Value(dtype='float64', id=None), length=-1, id=None), 'walker2d-medium-v2': Sequence(feature=Value(dtype='float64', id=None), length=-1, id=None), 'hopper-medium-v2': Sequence(feature=Value(dtype='float64', id=None), length=-1, id=None), 'halfcheetah-medium-replay-v2': Sequence(feature=Value(dtype='float64', id=None), length=-1, id=None), 'walker2d-medium-replay-v2': Sequence(feature=Value(dtype='float64', id=None), length=-1, id=None), 'hopper-medium-replay-v2': Sequence(feature=Value(dtype='float64', id=None), length=-1, 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 1316, in compute_config_parquet_and_info_response
parquet_operations, partial = stream_convert_to_parquet(
File "/src/services/worker/src/worker/job_runners/config/parquet_and_info.py", line 909, in stream_convert_to_parquet
builder._prepare_split(
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/builder.py", line 1882, 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 2013, 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 22 new columns ({'path_loader_kwargs', 'version', 'rollout_fn', 'qf_kwargs', 'snapshot_gap', 'snapshot_mode', 'alg_class', 'policy_kwargs', 'algorithm_kwargs', 'IQN', 'trainer_cls', 'algorithm', 'replay_buffer_size', 'trainer_kwargs', 'env_id', 'pipeline', 'seed', 'replay_buffer_class', 'qf_class', 'policy_class', 'd4rl', 'normalize_env'}) and 9 missing columns ({'hopper-medium-expert-v2', 'hopper-medium-v2', 'halfcheetah-medium-v2', 'halfcheetah-medium-replay-v2', 'walker2d-medium-expert-v2', 'halfcheetah-medium-expert-v2', 'hopper-medium-replay-v2', 'walker2d-medium-replay-v2', 'walker2d-medium-v2'}).
This happened while the json dataset builder was generating data using
hf://datasets/ezipe/cfpi/mg-12-no-normalize/antmaze-large-diverse-v0/0/variant.json (at revision aa0e3614ecef0eaa16cd220df9459d5344bb3064)
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.
halfcheetah-medium-expert-v2 sequence | walker2d-medium-expert-v2 sequence | hopper-medium-expert-v2 sequence | halfcheetah-medium-v2 sequence | walker2d-medium-v2 sequence | hopper-medium-v2 sequence | halfcheetah-medium-replay-v2 sequence | walker2d-medium-replay-v2 sequence | hopper-medium-replay-v2 sequence | IQN bool | alg_class dict | algorithm string | algorithm_kwargs dict | d4rl bool | env_id string | normalize_env bool | path_loader_kwargs dict | pipeline dict | policy_class dict | policy_kwargs dict | qf_class dict | qf_kwargs dict | replay_buffer_class dict | replay_buffer_size int64 | rollout_fn dict | seed int64 | snapshot_gap int64 | snapshot_mode string | trainer_cls dict | trainer_kwargs dict | version string |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
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97.4371700812476,
79.5575002339242,
28.878944227571584,
95.60386322851883,
99.52736553921949,
95.20685214928376,
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29.56881496110... | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null |
[
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100.0425... | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null |
[87.30306269296739,83.90261457453286,91.48420279931702,39.2658890207759,68.86289493133997,53.9299889(...TRUNCATED) | [112.17411126603854,111.34176461748058,111.2587001072742,111.36333743927548,111.48705293767726,111.7(...TRUNCATED) | [30.048938275912707,53.175952471155355,32.20424345261726,48.496383694682564,31.67357506996693,50.857(...TRUNCATED) | [52.276845947115355,52.975048026850814,52.895708347326476,53.67493465401918,53.85489064796719,53.990(...TRUNCATED) | [39.98948096723127,90.61949911040583,92.48141291957036,89.95622971886937,92.12456668613244,94.361120(...TRUNCATED) | [58.31284684045912,103.06270733947129,97.11260300878449,102.41251929601982,77.26483495577384,85.5076(...TRUNCATED) | [43.37495474452912,40.88942347180704,42.958169736265276,43.51197226433958,41.65088424464633,46.13404(...TRUNCATED) | [45.903140940159496,65.42085028462024,97.67104310921818,62.77635189221667,93.25430134794186,11.27543(...TRUNCATED) | [102.53208195935086,101.57468595209582,102.60641562600863,101.49279996832313,101.95494183066039,102.(...TRUNCATED) | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null |
[92.59230539206611,98.66960701242188,21.31549616869439,23.149664629842917,92.83809102279403,51.49905(...TRUNCATED) | [109.85714643825753,109.41736133352381,110.6115533695409,110.90682577667783,110.39542385274291,110.6(...TRUNCATED) | [53.65253577454692,31.864293785814095,46.78213218481977,54.39869529809776,100.89191197840164,54.4901(...TRUNCATED) | [54.47976113824349,54.48048696668665,54.61948342622234,55.30659876443453,54.98377470810855,54.231915(...TRUNCATED) | [93.08800622285015,93.3750761294721,91.42471826225194,91.50390857819373,94.94606736961921,9.57770698(...TRUNCATED) | [51.53304579099745,102.61770023937693,95.39730718341477,69.88321330325469,86.55574750330642,101.0972(...TRUNCATED) | [44.05061253685321,41.54296516522307,45.73120385017107,41.82201603402944,44.481127514800455,44.64455(...TRUNCATED) | [95.16458286729592,93.01870696465849,4.321601197663494,7.7303201510475485,91.99808695280815,81.74950(...TRUNCATED) | [100.04672008495044,100.8703492329317,100.76625892754436,101.39919817898515,101.06976571914346,101.7(...TRUNCATED) | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null |
null | null | null | null | null | null | null | null | null | true | {
"$class": "rlkit.torch.torch_rl_algorithm.OfflineTorchBatchRLAlgorithm"
} | mg-behavior-cloning | {"batch_size":256,"max_path_length":1000,"num_epochs":0,"num_eval_steps_per_epoch":1000,"num_trains_(...TRUNCATED) | true | antmaze-large-diverse-v0 | false | {} | {"$class":"rlkit.launchers.pipeline.<Pipeline offline_bc_pipeline>:\nbc_sanity,\noffline_init,\ncrea(...TRUNCATED) | {
"$class": "rlkit.policies.gaussian_policy.TanhGaussianMixturePolicy"
} | {
"hidden_sizes": [
256,
256,
256
],
"num_gaussians": 12
} | {
"$class": "rlkit.torch.networks.mlp.ConcatMlp"
} | {
"hidden_sizes": [
1024,
1024
]
} | {
"$class": "rlkit.data_management.env_replay_buffer.EnvReplayBuffer"
} | 2,000,000 | {
"$function": "rlkit.samplers.rollout_functions.rollout"
} | 0 | 100 | gap_and_last | {
"$class": "rlkit.torch.algorithms.bc.BCTrainer"
} | {"discount":0.99,"policy_lr":0.0001,"qf_lr":0.0001,"reward_scale":1,"soft_target_tau":0.005,"target_(...TRUNCATED) | 12-gaussian-3-layers-all-data-iclr-rebuttal |
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null | null | null | null | null | null | null | null | null | true | {
"$class": "rlkit.torch.torch_rl_algorithm.OfflineTorchBatchRLAlgorithm"
} | mg-behavior-cloning | {"batch_size":256,"max_path_length":1000,"num_epochs":0,"num_eval_steps_per_epoch":1000,"num_trains_(...TRUNCATED) | true | antmaze-large-diverse-v0 | false | {} | {"$class":"rlkit.launchers.pipeline.<Pipeline offline_bc_pipeline>:\nbc_sanity,\noffline_init,\ncrea(...TRUNCATED) | {
"$class": "rlkit.policies.gaussian_policy.TanhGaussianMixturePolicy"
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} | {
"$class": "rlkit.torch.networks.mlp.ConcatMlp"
} | {
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} | {
"$class": "rlkit.data_management.env_replay_buffer.EnvReplayBuffer"
} | 2,000,000 | {
"$function": "rlkit.samplers.rollout_functions.rollout"
} | 1 | 100 | gap_and_last | {
"$class": "rlkit.torch.algorithms.bc.BCTrainer"
} | {"discount":0.99,"policy_lr":0.0001,"qf_lr":0.0001,"reward_scale":1,"soft_target_tau":0.005,"target_(...TRUNCATED) | 12-gaussian-3-layers-all-data-iclr-rebuttal |
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