Dataset Preview

Split (1)

train

The full dataset viewer is not available (click to read why). Only showing a preview of the rows.

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

This happened while the csv dataset builder was generating data using

/tmp/hf-datasets-cache/medium/datasets/15934734091035-config-parquet-and-info-project-vajra-dev-staging-a6b010d1/hub/datasets--project-vajra--dev-staging-l40-pcie/snapshots/39f89c336454c9c18d4ab7e22c47eb9c0274f32c/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 1831, in _prepare_split_single
                  writer.write_table(table)
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/arrow_writer.py", line 644, 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 2272, in table_cast
                  return cast_table_to_schema(table, schema)
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/table.py", line 2218, 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('int64'), 'time_stats.all_reduce.min': Value('float64'), 'time_stats.all_reduce.max': Value('float64'), 'time_stats.all_reduce.mean': Value('float64'), 'time_stats.all_reduce.median': Value('float64'), 'time_stats.all_reduce.std': Value('float64'), 'rank': Value('int64'), 'num_workers': Value('int64'), 'size': Value('int64'), 'collective': Value('string'), 'devices_per_node': Value('int64'), 'max_devices_per_node': Value('int64')}
              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 1456, 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 1055, in convert_to_parquet
                  builder.download_and_prepare(
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/builder.py", line 894, in download_and_prepare
                  self._download_and_prepare(
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/builder.py", line 970, 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 1702, 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 1833, 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.std', 'time_stats.send_recv.median', 'time_stats.send_recv.mean', 'time_stats.send_recv.max'}) and 5 missing columns ({'time_stats.all_reduce.mean', 'time_stats.all_reduce.min', 'time_stats.all_reduce.median', 'time_stats.all_reduce.std', 'time_stats.all_reduce.max'}).
              
              This happened while the csv dataset builder was generating data using
              
              /tmp/hf-datasets-cache/medium/datasets/15934734091035-config-parquet-and-info-project-vajra-dev-staging-a6b010d1/hub/datasets--project-vajra--dev-staging-l40-pcie/snapshots/39f89c336454c9c18d4ab7e22c47eb9c0274f32c/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)

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.

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	0.058	0.068	0.064667	0.068	0.004714	2	2,048	all_reduce	2	8
1	0.028	0.029	0.028333	0.028	0.000471	2	10,240	all_reduce	2	8
2	0.006	0.03	0.014	0.006	0.011314	2	18,432	all_reduce	2	8
3	0.024	0.029	0.027	0.028	0.00216	2	26,624	all_reduce	2	8
4	0.028	0.029	0.028667	0.029	0.000471	2	34,816	all_reduce	2	8
5	0.018	0.03	0.022	0.018	0.005657	2	43,008	all_reduce	2	8
6	0.018	0.029	0.021667	0.018	0.005185	2	51,200	all_reduce	2	8
7	0.028	0.029	0.028667	0.029	0.000471	2	59,392	all_reduce	2	8
8	0.028	0.03	0.029333	0.03	0.000943	2	67,584	all_reduce	2	8
9	0.02	0.029	0.026	0.029	0.004243	2	75,776	all_reduce	2	8
10	0.02	0.032	0.024	0.02	0.005657	2	83,968	all_reduce	2	8
11	0.027	0.032	0.029333	0.029	0.002055	2	92,160	all_reduce	2	8
12	0.029	0.029	0.029	0.029	0	2	100,352	all_reduce	2	8
13	0.021	0.03	0.024	0.021	0.004243	2	108,544	all_reduce	2	8
14	0.029	0.032	0.030333	0.03	0.001247	2	116,736	all_reduce	2	8
15	0.022	0.031	0.027333	0.029	0.003859	2	124,928	all_reduce	2	8
16	0.028	0.032	0.03	0.03	0.001633	2	133,120	all_reduce	2	8
17	0.023	0.03	0.026	0.025	0.002944	2	141,312	all_reduce	2	8
18	0.025	0.03	0.028	0.029	0.00216	2	149,504	all_reduce	2	8
19	0.029	0.03	0.029333	0.029	0.000471	2	157,696	all_reduce	2	8
20	0.029	0.03	0.029667	0.03	0.000471	2	165,888	all_reduce	2	8
21	0.029	0.032	0.03	0.029	0.001414	2	174,080	all_reduce	2	8
22	0.029	0.031	0.03	0.03	0.000816	2	182,272	all_reduce	2	8
23	0.029	0.031	0.029667	0.029	0.000943	2	190,464	all_reduce	2	8
24	0.029	0.03	0.029333	0.029	0.000471	2	198,656	all_reduce	2	8
25	0.026	0.03	0.028333	0.029	0.0017	2	206,848	all_reduce	2	8
26	0.029	0.029	0.029	0.029	0	2	215,040	all_reduce	2	8
27	0.029	0.03	0.029333	0.029	0.000471	2	223,232	all_reduce	2	8
28	0.026	0.03	0.028667	0.03	0.001886	2	231,424	all_reduce	2	8
29	0.029	0.032	0.030667	0.031	0.001247	2	239,616	all_reduce	2	8
30	0.029	0.032	0.030333	0.03	0.001247	2	247,808	all_reduce	2	8
31	0.027	0.032	0.028667	0.027	0.002357	2	256,000	all_reduce	2	8
32	0.029	0.031	0.029667	0.029	0.000943	2	264,192	all_reduce	2	8
33	0.029	0.03	0.029333	0.029	0.000471	2	272,384	all_reduce	2	8
34	0.03	0.03	0.03	0.03	0	2	280,576	all_reduce	2	8
35	0.03	0.03	0.03	0.03	0	2	288,768	all_reduce	2	8
36	0.031	0.031	0.031	0.031	0	2	296,960	all_reduce	2	8
37	0.031	0.031	0.031	0.031	0	2	305,152	all_reduce	2	8
38	0.031	0.033	0.031667	0.031	0.000943	2	313,344	all_reduce	2	8
39	0.031	0.032	0.031667	0.032	0.000471	2	321,536	all_reduce	2	8
40	0.032	0.032	0.032	0.032	0	2	329,728	all_reduce	2	8
41	0.032	0.032	0.032	0.032	0	2	337,920	all_reduce	2	8
42	0.032	0.032	0.032	0.032	0	2	346,112	all_reduce	2	8
43	0.032	0.032	0.032	0.032	0	2	354,304	all_reduce	2	8
44	0.032	0.033	0.032667	0.033	0.000471	2	362,496	all_reduce	2	8
45	0.033	0.033	0.033	0.033	0	2	370,688	all_reduce	2	8
46	0.033	0.033	0.033	0.033	0	2	378,880	all_reduce	2	8
47	0.033	0.033	0.033	0.033	0	2	387,072	all_reduce	2	8
48	0.033	0.034	0.033667	0.034	0.000471	2	395,264	all_reduce	2	8
49	0.034	0.034	0.034	0.034	0	2	403,456	all_reduce	2	8
50	0.034	0.034	0.034	0.034	0	2	411,648	all_reduce	2	8
51	0.034	0.034	0.034	0.034	0	2	419,840	all_reduce	2	8
52	0.035	0.035	0.035	0.035	0	2	428,032	all_reduce	2	8
53	0.035	0.036	0.035667	0.036	0.000471	2	436,224	all_reduce	2	8
54	0.035	0.036	0.035333	0.035	0.000471	2	444,416	all_reduce	2	8
55	0.035	0.035	0.035	0.035	0	2	452,608	all_reduce	2	8
56	0.036	0.036	0.036	0.036	0	2	460,800	all_reduce	2	8
57	0.036	0.037	0.036667	0.037	0.000471	2	468,992	all_reduce	2	8
58	0.037	0.037	0.037	0.037	0	2	477,184	all_reduce	2	8
59	0.036	0.037	0.036667	0.037	0.000471	2	485,376	all_reduce	2	8
60	0.037	0.038	0.037333	0.037	0.000471	2	493,568	all_reduce	2	8
61	0.038	0.038	0.038	0.038	0	2	501,760	all_reduce	2	8
62	0.038	0.039	0.038333	0.038	0.000471	2	509,952	all_reduce	2	8
63	0.038	0.038	0.038	0.038	0	2	518,144	all_reduce	2	8
64	0.038	0.04	0.039	0.039	0.000816	2	526,336	all_reduce	2	8
65	0.039	0.039	0.039	0.039	0	2	534,528	all_reduce	2	8
66	0.039	0.04	0.039333	0.039	0.000471	2	542,720	all_reduce	2	8
67	0.039	0.04	0.039667	0.04	0.000471	2	550,912	all_reduce	2	8
68	0.04	0.04	0.04	0.04	0	2	559,104	all_reduce	2	8
69	0.04	0.041	0.040667	0.041	0.000471	2	567,296	all_reduce	2	8
70	0.04	0.041	0.040667	0.041	0.000471	2	575,488	all_reduce	2	8
71	0.041	0.041	0.041	0.041	0	2	583,680	all_reduce	2	8
72	0.041	0.042	0.041333	0.041	0.000471	2	591,872	all_reduce	2	8
73	0.041	0.042	0.041667	0.042	0.000471	2	600,064	all_reduce	2	8
74	0.042	0.042	0.042	0.042	0	2	608,256	all_reduce	2	8
75	0.042	0.057	0.047333	0.043	0.006848	2	616,448	all_reduce	2	8
76	0.042	0.043	0.042667	0.043	0.000471	2	624,640	all_reduce	2	8
77	0.043	0.043	0.043	0.043	0	2	632,832	all_reduce	2	8
78	0.043	0.044	0.043333	0.043	0.000471	2	641,024	all_reduce	2	8
79	0.043	0.045	0.044	0.044	0.000816	2	649,216	all_reduce	2	8
80	0.045	0.045	0.045	0.045	0	2	657,408	all_reduce	2	8
81	0.045	0.046	0.045667	0.046	0.000471	2	665,600	all_reduce	2	8
82	0.045	0.046	0.045333	0.045	0.000471	2	673,792	all_reduce	2	8
83	0.044	0.047	0.045667	0.046	0.001247	2	681,984	all_reduce	2	8
84	0.045	0.046	0.045333	0.045	0.000471	2	690,176	all_reduce	2	8
85	0.046	0.047	0.046333	0.046	0.000471	2	698,368	all_reduce	2	8
86	0.046	0.047	0.046667	0.047	0.000471	2	706,560	all_reduce	2	8
87	0.047	0.049	0.047667	0.047	0.000943	2	714,752	all_reduce	2	8
88	0.046	0.048	0.046667	0.046	0.000943	2	722,944	all_reduce	2	8
89	0.046	0.048	0.047	0.047	0.000816	2	731,136	all_reduce	2	8
90	0.049	0.05	0.049333	0.049	0.000471	2	739,328	all_reduce	2	8
91	0.049	0.049	0.049	0.049	0	2	747,520	all_reduce	2	8
92	0.048	0.05	0.049333	0.05	0.000943	2	755,712	all_reduce	2	8
93	0.049	0.05	0.049667	0.05	0.000471	2	763,904	all_reduce	2	8
94	0.049	0.051	0.05	0.05	0.000816	2	772,096	all_reduce	2	8
95	0.049	0.05	0.049667	0.05	0.000471	2	780,288	all_reduce	2	8
96	0.048	0.052	0.049667	0.049	0.0017	2	788,480	all_reduce	2	8
97	0.051	0.053	0.052333	0.053	0.000943	2	796,672	all_reduce	2	8
98	0.05	0.053	0.051667	0.052	0.001247	2	804,864	all_reduce	2	8
99	0.05	0.052	0.051333	0.052	0.000943	2	813,056	all_reduce	2	8

End of preview.

No dataset card yet

Downloads last month: 4

Total file size:

71.9 kB