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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 5 new columns ({'time_stats.send_recv.max', 'time_stats.send_recv.mean', 'time_stats.send_recv.min', 'time_stats.send_recv.median', 'time_stats.send_recv.std'}) and 5 missing columns ({'time_stats.all_reduce.median', 'time_stats.all_reduce.min', 'time_stats.all_reduce.mean', 'time_stats.all_reduce.max', 'time_stats.all_reduce.std'}).

This happened while the csv dataset builder was generating data using

/tmp/hf-datasets-cache/medium/datasets/19933959863243-config-parquet-and-info-project-vajra-dev-staging-411dde33/hub/datasets--project-vajra--dev-staging-h100-pairwise-nvlink/snapshots/25002fa9b57e3d8b266b255341469308bee8d237/send_recv.csv.xz

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 1870, in _prepare_split_single
                  writer.write_table(table)
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/arrow_writer.py", line 622, 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 2292, in table_cast
                  return cast_table_to_schema(table, schema)
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/table.py", line 2240, in cast_table_to_schema
                  raise CastError(
              datasets.table.CastError: Couldn't cast
              Unnamed: 0: int64
              time_stats.send_recv.min: double
              time_stats.send_recv.max: double
              time_stats.send_recv.mean: double
              time_stats.send_recv.median: double
              time_stats.send_recv.std: double
              rank: int64
              num_workers: int64
              size: int64
              collective: string
              devices_per_node: int64
              max_devices_per_node: int64
              -- schema metadata --
              pandas: '{"index_columns": [{"kind": "range", "name": null, "start": 0, "' + 1847
              to
              {'Unnamed: 0': Value(dtype='int64', id=None), 'time_stats.all_reduce.min': Value(dtype='float64', id=None), 'time_stats.all_reduce.max': Value(dtype='float64', id=None), 'time_stats.all_reduce.mean': Value(dtype='float64', id=None), 'time_stats.all_reduce.median': Value(dtype='float64', id=None), 'time_stats.all_reduce.std': Value(dtype='float64', id=None), 'rank': Value(dtype='int64', id=None), 'num_workers': Value(dtype='int64', id=None), 'size': Value(dtype='int64', id=None), 'collective': Value(dtype='string', id=None), 'devices_per_node': Value(dtype='int64', id=None), 'max_devices_per_node': Value(dtype='int64', 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 1420, 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 1052, in convert_to_parquet
                  builder.download_and_prepare(
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/builder.py", line 924, in download_and_prepare
                  self._download_and_prepare(
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/builder.py", line 1000, 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 1741, 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 1872, 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 5 new columns ({'time_stats.send_recv.max', 'time_stats.send_recv.mean', 'time_stats.send_recv.min', 'time_stats.send_recv.median', 'time_stats.send_recv.std'}) and 5 missing columns ({'time_stats.all_reduce.median', 'time_stats.all_reduce.min', 'time_stats.all_reduce.mean', 'time_stats.all_reduce.max', 'time_stats.all_reduce.std'}).
              
              This happened while the csv dataset builder was generating data using
              
              /tmp/hf-datasets-cache/medium/datasets/19933959863243-config-parquet-and-info-project-vajra-dev-staging-411dde33/hub/datasets--project-vajra--dev-staging-h100-pairwise-nvlink/snapshots/25002fa9b57e3d8b266b255341469308bee8d237/send_recv.csv.xz
              
              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)

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Unnamed: 0 int64	time_stats.all_reduce.min float64	time_stats.all_reduce.max float64	time_stats.all_reduce.mean float64	time_stats.all_reduce.median float64	time_stats.all_reduce.std float64	num_workers int64	size int64	collective string	devices_per_node int64	max_devices_per_node int64
0	0.028	0.028	0.028	0.028	0	2	2,048	all_reduce	2	4
1	0.028	0.033	0.029667	0.028	0.002357	2	10,240	all_reduce	2	4
2	0.007	0.007	0.007	0.007	0	2	18,432	all_reduce	2	4
3	0.007	0.007	0.007	0.007	0	2	26,624	all_reduce	2	4
4	0.028	0.029	0.028333	0.028	0.000471	2	34,816	all_reduce	2	4
5	0.007	0.007	0.007	0.007	0	2	43,008	all_reduce	2	4
6	0.007	0.007	0.007	0.007	0	2	51,200	all_reduce	2	4
7	0.007	0.007	0.007	0.007	0	2	59,392	all_reduce	2	4
8	0.007	0.029	0.021667	0.029	0.010371	2	67,584	all_reduce	2	4
9	0.021	0.031	0.027	0.029	0.00432	2	75,776	all_reduce	2	4
10	0.012	0.018	0.015667	0.017	0.002625	2	83,968	all_reduce	2	4
11	0.007	0.008	0.007333	0.007	0.000471	2	92,160	all_reduce	2	4
12	0.007	0.029	0.014333	0.007	0.010371	2	100,352	all_reduce	2	4
13	0.007	0.007	0.007	0.007	0	2	108,544	all_reduce	2	4
14	0.007	0.013	0.010667	0.012	0.002625	2	116,736	all_reduce	2	4
15	0.026	0.029	0.028	0.029	0.001414	2	124,928	all_reduce	2	4
16	0.007	0.03	0.018333	0.018	0.009393	2	133,120	all_reduce	2	4
17	0.03	0.031	0.030333	0.03	0.000471	2	141,312	all_reduce	2	4
18	0.008	0.026	0.014	0.008	0.008485	2	149,504	all_reduce	2	4
19	0.008	0.01	0.008667	0.008	0.000943	2	157,696	all_reduce	2	4
20	0.008	0.008	0.008	0.008	0	2	165,888	all_reduce	2	4
21	0.008	0.008	0.008	0.008	0	2	174,080	all_reduce	2	4
22	0.008	0.029	0.020333	0.024	0.008957	2	182,272	all_reduce	2	4
23	0.008	0.008	0.008	0.008	0	2	190,464	all_reduce	2	4
24	0.008	0.03	0.022667	0.03	0.010371	2	198,656	all_reduce	2	4
25	0.018	0.031	0.026667	0.031	0.006128	2	206,848	all_reduce	2	4
26	0.009	0.031	0.023333	0.03	0.010143	2	215,040	all_reduce	2	4
27	0.008	0.01	0.008667	0.008	0.000943	2	223,232	all_reduce	2	4
28	0.008	0.008	0.008	0.008	0	2	231,424	all_reduce	2	4
29	0.03	0.031	0.030333	0.03	0.000471	2	239,616	all_reduce	2	4
30	0.008	0.024	0.018	0.022	0.007118	2	247,808	all_reduce	2	4
31	0.016	0.031	0.022	0.019	0.006481	2	256,000	all_reduce	2	4
32	0.03	0.031	0.030667	0.031	0.000471	2	264,192	all_reduce	2	4
33	0.01	0.013	0.011	0.01	0.001414	2	272,384	all_reduce	2	4
34	0.03	0.03	0.03	0.03	0	2	280,576	all_reduce	2	4
35	0.01	0.01	0.01	0.01	0	2	288,768	all_reduce	2	4
36	0.01	0.01	0.01	0.01	0	2	296,960	all_reduce	2	4
37	0.014	0.032	0.024333	0.027	0.007587	2	305,152	all_reduce	2	4
38	0.024	0.029	0.026667	0.027	0.002055	2	313,344	all_reduce	2	4
39	0.01	0.013	0.011	0.01	0.001414	2	321,536	all_reduce	2	4
40	0.01	0.03	0.023333	0.03	0.009428	2	329,728	all_reduce	2	4
41	0.01	0.031	0.024	0.031	0.009899	2	337,920	all_reduce	2	4
42	0.01	0.01	0.01	0.01	0	2	346,112	all_reduce	2	4
43	0.029	0.032	0.030667	0.031	0.001247	2	354,304	all_reduce	2	4
44	0.016	0.033	0.026333	0.03	0.007409	2	362,496	all_reduce	2	4
45	0.01	0.01	0.01	0.01	0	2	370,688	all_reduce	2	4
46	0.03	0.031	0.030333	0.03	0.000471	2	378,880	all_reduce	2	4
47	0.026	0.03	0.028333	0.029	0.0017	2	387,072	all_reduce	2	4
48	0.011	0.014	0.012	0.011	0.001414	2	395,264	all_reduce	2	4
49	0.011	0.022	0.014667	0.011	0.005185	2	403,456	all_reduce	2	4
50	0.011	0.013	0.011667	0.011	0.000943	2	411,648	all_reduce	2	4
51	0.016	0.031	0.025667	0.03	0.006848	2	419,840	all_reduce	2	4
52	0.011	0.011	0.011	0.011	0	2	428,032	all_reduce	2	4
53	0.03	0.032	0.030667	0.03	0.000943	2	436,224	all_reduce	2	4
54	0.03	0.031	0.030333	0.03	0.000471	2	444,416	all_reduce	2	4
55	0.011	0.024	0.017333	0.017	0.005312	2	452,608	all_reduce	2	4
56	0.011	0.031	0.024	0.03	0.009201	2	460,800	all_reduce	2	4
57	0.011	0.03	0.023667	0.03	0.008957	2	468,992	all_reduce	2	4
58	0.011	0.012	0.011333	0.011	0.000471	2	477,184	all_reduce	2	4
59	0.012	0.025	0.017667	0.016	0.005437	2	485,376	all_reduce	2	4
60	0.011	0.03	0.023667	0.03	0.008957	2	493,568	all_reduce	2	4
61	0.029	0.031	0.030333	0.031	0.000943	2	501,760	all_reduce	2	4
62	0.012	0.03	0.018333	0.013	0.00826	2	509,952	all_reduce	2	4
63	0.011	0.02	0.015667	0.016	0.003682	2	518,144	all_reduce	2	4
64	0.013	0.028	0.018	0.013	0.007071	2	526,336	all_reduce	2	4
65	0.025	0.03	0.028	0.029	0.00216	2	534,528	all_reduce	2	4
66	0.013	0.013	0.013	0.013	0	2	542,720	all_reduce	2	4
67	0.03	0.031	0.030667	0.031	0.000471	2	550,912	all_reduce	2	4
68	0.013	0.029	0.018333	0.013	0.007542	2	559,104	all_reduce	2	4
69	0.013	0.013	0.013	0.013	0	2	567,296	all_reduce	2	4
70	0.013	0.013	0.013	0.013	0	2	575,488	all_reduce	2	4
71	0.021	0.03	0.027	0.03	0.004243	2	583,680	all_reduce	2	4
72	0.03	0.03	0.03	0.03	0	2	591,872	all_reduce	2	4
73	0.02	0.03	0.025	0.025	0.004082	2	600,064	all_reduce	2	4
74	0.029	0.031	0.03	0.03	0.000816	2	608,256	all_reduce	2	4
75	0.03	0.031	0.030667	0.031	0.000471	2	616,448	all_reduce	2	4
76	0.02	0.029	0.023	0.02	0.004243	2	624,640	all_reduce	2	4
77	0.029	0.031	0.03	0.03	0.000816	2	632,832	all_reduce	2	4
78	0.02	0.03	0.026333	0.029	0.004497	2	641,024	all_reduce	2	4
79	0.02	0.02	0.02	0.02	0	2	649,216	all_reduce	2	4
80	0.02	0.03	0.023333	0.02	0.004714	2	657,408	all_reduce	2	4
81	0.02	0.03	0.023333	0.02	0.004714	2	665,600	all_reduce	2	4
82	0.02	0.02	0.02	0.02	0	2	673,792	all_reduce	2	4
83	0.02	0.02	0.02	0.02	0	2	681,984	all_reduce	2	4
84	0.03	0.032	0.031	0.031	0.000816	2	690,176	all_reduce	2	4
85	0.024	0.03	0.026667	0.026	0.002494	2	698,368	all_reduce	2	4
86	0.02	0.02	0.02	0.02	0	2	706,560	all_reduce	2	4
87	0.029	0.031	0.03	0.03	0.000816	2	714,752	all_reduce	2	4
88	0.026	0.031	0.028	0.027	0.00216	2	722,944	all_reduce	2	4
89	0.027	0.03	0.029	0.03	0.001414	2	731,136	all_reduce	2	4
90	0.021	0.021	0.021	0.021	0	2	739,328	all_reduce	2	4
91	0.021	0.031	0.027333	0.03	0.004497	2	747,520	all_reduce	2	4
92	0.03	0.03	0.03	0.03	0	2	755,712	all_reduce	2	4
93	0.02	0.02	0.02	0.02	0	2	763,904	all_reduce	2	4
94	0.029	0.031	0.030333	0.031	0.000943	2	772,096	all_reduce	2	4
95	0.02	0.021	0.020333	0.02	0.000471	2	780,288	all_reduce	2	4
96	0.021	0.03	0.027	0.03	0.004243	2	788,480	all_reduce	2	4
97	0.021	0.021	0.021	0.021	0	2	796,672	all_reduce	2	4
98	0.021	0.031	0.024333	0.021	0.004714	2	804,864	all_reduce	2	4
99	0.03	0.039	0.033	0.03	0.004243	2	813,056	all_reduce	2	4

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