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train

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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.min', 'time_stats.send_recv.max', 'time_stats.send_recv.std', 'time_stats.send_recv.median', 'time_stats.send_recv.mean'}) and 5 missing columns ({'time_stats.all_reduce.median', 'time_stats.all_reduce.mean', 'time_stats.all_reduce.min', '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/90568535381115-config-parquet-and-info-project-vajra-dev-staging-8da24ccf/hub/datasets--project-vajra--dev-staging-a100-pairwise-nvlink/snapshots/8306ce4248b02a94343554643ce8db6eb6d6ef64/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.min', 'time_stats.send_recv.max', 'time_stats.send_recv.std', 'time_stats.send_recv.median', 'time_stats.send_recv.mean'}) and 5 missing columns ({'time_stats.all_reduce.median', 'time_stats.all_reduce.mean', 'time_stats.all_reduce.min', '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/90568535381115-config-parquet-and-info-project-vajra-dev-staging-8da24ccf/hub/datasets--project-vajra--dev-staging-a100-pairwise-nvlink/snapshots/8306ce4248b02a94343554643ce8db6eb6d6ef64/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	0.007	0.038	0.017333	0.007	0.014614	2	2,048	all_reduce	2	4
1	0.024	0.047	0.038667	0.045	0.010403	2	10,240	all_reduce	2	4
2	0.049	0.062	0.056667	0.059	0.005558	2	18,432	all_reduce	2	4
3	0.06	0.071	0.064333	0.062	0.004784	2	26,624	all_reduce	2	4
4	0.023	0.063	0.049	0.061	0.018403	2	34,816	all_reduce	2	4
5	0.012	0.054	0.04	0.054	0.019799	2	43,008	all_reduce	2	4
6	0.043	0.059	0.049667	0.047	0.006799	2	51,200	all_reduce	2	4
7	0.016	0.063	0.036333	0.03	0.019703	2	59,392	all_reduce	2	4
8	0.058	0.06	0.059333	0.06	0.000943	2	67,584	all_reduce	2	4
9	0.051	0.062	0.057333	0.059	0.004643	2	75,776	all_reduce	2	4
10	0.051	0.067	0.060667	0.064	0.006944	2	83,968	all_reduce	2	4
11	0.01	0.01	0.01	0.01	0	2	92,160	all_reduce	2	4
12	0.01	0.055	0.025	0.01	0.021213	2	100,352	all_reduce	2	4
13	0.01	0.068	0.029333	0.01	0.027341	2	108,544	all_reduce	2	4
14	0.01	0.039	0.025	0.026	0.01186	2	116,736	all_reduce	2	4
15	0.019	0.05	0.035333	0.037	0.01271	2	124,928	all_reduce	2	4
16	0.031	0.057	0.046667	0.052	0.011264	2	133,120	all_reduce	2	4
17	0.011	0.011	0.011	0.011	0	2	141,312	all_reduce	2	4
18	0.033	0.062	0.043	0.034	0.013441	2	149,504	all_reduce	2	4
19	0.044	0.07	0.053333	0.046	0.011813	2	157,696	all_reduce	2	4
20	0.042	0.061	0.050333	0.048	0.00793	2	165,888	all_reduce	2	4
21	0.018	0.062	0.042333	0.047	0.018264	2	174,080	all_reduce	2	4
22	0.019	0.063	0.047667	0.061	0.020287	2	182,272	all_reduce	2	4
23	0.019	0.064	0.046667	0.057	0.019771	2	190,464	all_reduce	2	4
24	0.059	0.064	0.061667	0.062	0.002055	2	198,656	all_reduce	2	4
25	0.048	0.073	0.061333	0.063	0.010274	2	206,848	all_reduce	2	4
26	0.037	0.049	0.044667	0.048	0.005437	2	215,040	all_reduce	2	4
27	0.011	0.069	0.043667	0.051	0.02424	2	223,232	all_reduce	2	4
28	0.037	0.066	0.051	0.05	0.01186	2	231,424	all_reduce	2	4
29	0.011	0.025	0.019667	0.023	0.006182	2	239,616	all_reduce	2	4
30	0.04	0.065	0.049	0.042	0.011343	2	247,808	all_reduce	2	4
31	0.011	0.054	0.039	0.052	0.019816	2	256,000	all_reduce	2	4
32	0.011	0.034	0.019333	0.013	0.010403	2	264,192	all_reduce	2	4
33	0.043	0.066	0.056	0.059	0.009626	2	272,384	all_reduce	2	4
34	0.043	0.064	0.053333	0.053	0.008576	2	280,576	all_reduce	2	4
35	0.011	0.043	0.027333	0.028	0.013072	2	288,768	all_reduce	2	4
36	0.048	0.064	0.058333	0.063	0.007318	2	296,960	all_reduce	2	4
37	0.062	0.066	0.063667	0.063	0.0017	2	305,152	all_reduce	2	4
38	0.024	0.061	0.046	0.053	0.015895	2	313,344	all_reduce	2	4
39	0.012	0.062	0.040667	0.048	0.021061	2	321,536	all_reduce	2	4
40	0.055	0.062	0.059333	0.061	0.003091	2	329,728	all_reduce	2	4
41	0.044	0.069	0.058333	0.062	0.01053	2	337,920	all_reduce	2	4
42	0.025	0.065	0.049667	0.059	0.017613	2	346,112	all_reduce	2	4
43	0.03	0.061	0.048667	0.055	0.013425	2	354,304	all_reduce	2	4
44	0.037	0.062	0.052667	0.059	0.011146	2	362,496	all_reduce	2	4
45	0.012	0.064	0.045667	0.061	0.023837	2	370,688	all_reduce	2	4
46	0.039	0.051	0.046667	0.05	0.005437	2	378,880	all_reduce	2	4
47	0.036	0.066	0.054667	0.062	0.0133	2	387,072	all_reduce	2	4
48	0.013	0.023	0.016333	0.013	0.004714	2	395,264	all_reduce	2	4
49	0.063	0.065	0.063667	0.063	0.000943	2	403,456	all_reduce	2	4
50	0.061	0.068	0.065	0.066	0.002944	2	411,648	all_reduce	2	4
51	0.013	0.064	0.046333	0.062	0.023584	2	419,840	all_reduce	2	4
52	0.062	0.065	0.063	0.062	0.001414	2	428,032	all_reduce	2	4
53	0.013	0.064	0.046667	0.063	0.023809	2	436,224	all_reduce	2	4
54	0.053	0.062	0.058333	0.06	0.003859	2	444,416	all_reduce	2	4
55	0.036	0.072	0.057333	0.064	0.015434	2	452,608	all_reduce	2	4
56	0.044	0.062	0.054333	0.057	0.007587	2	460,800	all_reduce	2	4
57	0.047	0.063	0.056333	0.059	0.006799	2	468,992	all_reduce	2	4
58	0.066	0.092	0.075333	0.068	0.011813	2	477,184	all_reduce	2	4
59	0.04	0.054	0.048333	0.051	0.006018	2	485,376	all_reduce	2	4
60	0.015	0.063	0.045333	0.058	0.021546	2	493,568	all_reduce	2	4
61	0.06	0.064	0.062	0.062	0.001633	2	501,760	all_reduce	2	4
62	0.015	0.062	0.046	0.061	0.021924	2	509,952	all_reduce	2	4
63	0.057	0.067	0.062	0.062	0.004082	2	518,144	all_reduce	2	4
64	0.027	0.07	0.05	0.053	0.017682	2	526,336	all_reduce	2	4
65	0.054	0.062	0.059333	0.062	0.003771	2	534,528	all_reduce	2	4
66	0.053	0.07	0.062	0.063	0.006976	2	542,720	all_reduce	2	4
67	0.015	0.042	0.024	0.015	0.012728	2	550,912	all_reduce	2	4
68	0.015	0.062	0.038667	0.039	0.019189	2	559,104	all_reduce	2	4
69	0.015	0.07	0.049667	0.064	0.024635	2	567,296	all_reduce	2	4
70	0.057	0.064	0.059333	0.057	0.0033	2	575,488	all_reduce	2	4
71	0.016	0.056	0.032667	0.026	0.016997	2	583,680	all_reduce	2	4
72	0.016	0.045	0.032333	0.036	0.01212	2	591,872	all_reduce	2	4
73	0.062	0.066	0.064667	0.066	0.001886	2	600,064	all_reduce	2	4
74	0.059	0.072	0.067333	0.071	0.005907	2	608,256	all_reduce	2	4
75	0.059	0.066	0.063	0.064	0.002944	2	616,448	all_reduce	2	4
76	0.034	0.067	0.053667	0.06	0.014197	2	624,640	all_reduce	2	4
77	0.065	0.078	0.069667	0.066	0.005907	2	632,832	all_reduce	2	4
78	0.063	0.065	0.064	0.064	0.000816	2	641,024	all_reduce	2	4
79	0.035	0.064	0.053667	0.062	0.013225	2	649,216	all_reduce	2	4
80	0.057	0.07	0.064667	0.067	0.005558	2	657,408	all_reduce	2	4
81	0.038	0.071	0.057667	0.064	0.014197	2	665,600	all_reduce	2	4
82	0.033	0.059	0.05	0.058	0.012028	2	673,792	all_reduce	2	4
83	0.033	0.069	0.052667	0.056	0.014885	2	681,984	all_reduce	2	4
84	0.059	0.075	0.065333	0.062	0.006944	2	690,176	all_reduce	2	4
85	0.033	0.033	0.033	0.033	0	2	698,368	all_reduce	2	4
86	0.033	0.055	0.040667	0.034	0.010143	2	706,560	all_reduce	2	4
87	0.033	0.07	0.057333	0.069	0.017211	2	714,752	all_reduce	2	4
88	0.033	0.055	0.043667	0.043	0.008994	2	722,944	all_reduce	2	4
89	0.053	0.07	0.064	0.069	0.007789	2	731,136	all_reduce	2	4
90	0.033	0.047	0.037667	0.033	0.0066	2	739,328	all_reduce	2	4
91	0.043	0.054	0.050333	0.054	0.005185	2	747,520	all_reduce	2	4
92	0.033	0.041	0.036	0.034	0.003559	2	755,712	all_reduce	2	4
93	0.047	0.062	0.057	0.062	0.007071	2	763,904	all_reduce	2	4
94	0.066	0.075	0.069	0.066	0.004243	2	772,096	all_reduce	2	4
95	0.033	0.064	0.051333	0.057	0.013275	2	780,288	all_reduce	2	4
96	0.033	0.06	0.042	0.033	0.012728	2	788,480	all_reduce	2	4
97	0.052	0.068	0.062667	0.068	0.007542	2	796,672	all_reduce	2	4
98	0.061	0.068	0.063333	0.061	0.0033	2	804,864	all_reduce	2	4
99	0.041	0.063	0.055	0.061	0.009933	2	813,056	all_reduce	2	4

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