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dejavoor
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GPU-Demand

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train

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Cannot load the dataset split (in streaming mode) to extract the first rows.

Error code:   StreamingRowsError
Exception:    CastError
Message:      Couldn't cast
id: int64
timestamp_utc: timestamp[s]
gpu_id: string
display_name: string
vram_gb: double
secure_cloud: bool
stock_status: string
uninterruptable_price: double
available_gpu_counts: null
raw_json: struct<gpu_type: struct<displayName: string, id: string, lowestPrice: struct<availableGpuCounts: nul (... 151 chars omitted)
  child 0, gpu_type: struct<displayName: string, id: string, lowestPrice: struct<availableGpuCounts: null, stockStatus: s (... 56 chars omitted)
      child 0, displayName: string
      child 1, id: string
      child 2, lowestPrice: struct<availableGpuCounts: null, stockStatus: string, uninterruptablePrice: double>
          child 0, availableGpuCounts: null
          child 1, stockStatus: string
          child 2, uninterruptablePrice: double
      child 3, memoryInGb: int64
  child 1, graphql_errors: list<item: null>
      child 0, item: null
  child 2, query_variant: string
  child 3, secure_cloud: bool
cleaned_timestamp_count: int64
expected_rows_per_timestamp: int64
source_json: string
original_timestamp_count: int64
removed_timestamps: list<item: struct<timestamp_utc: timestamp[s], row_count: int64>>
  child 0, item: struct<timestamp_utc: timestamp[s], row_count: int64>
      child 0, timestamp_utc: timestamp[s]
      child 1, row_count: int64
source_report_folder: string
removed_timestamp_count: int64
cleaned_row_count: int64
original_row_count: int64
source_csv: string
to
{'source_report_folder': Value('string'), 'source_csv': Value('string'), 'source_json': Value('string'), 'expected_rows_per_timestamp': Value('int64'), 'original_row_count': Value('int64'), 'cleaned_row_count': Value('int64'), 'original_timestamp_count': Value('int64'), 'cleaned_timestamp_count': Value('int64'), 'removed_timestamp_count': Value('int64'), 'removed_timestamps': List({'timestamp_utc': Value('timestamp[s]'), 'row_count': Value('int64')})}
because column names don't match
Traceback:    Traceback (most recent call last):
                File "/src/services/worker/src/worker/utils.py", line 99, in get_rows_or_raise
                  return get_rows(
                         ^^^^^^^^^
                File "/src/libs/libcommon/src/libcommon/utils.py", line 272, in decorator
                  return func(*args, **kwargs)
                         ^^^^^^^^^^^^^^^^^^^^^
                File "/src/services/worker/src/worker/utils.py", line 77, in get_rows
                  rows_plus_one = list(itertools.islice(ds, rows_max_number + 1))
                                  ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/iterable_dataset.py", line 2690, in __iter__
                  for key, example in ex_iterable:
                                      ^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/iterable_dataset.py", line 2227, in __iter__
                  for key, pa_table in self._iter_arrow():
                                       ^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/iterable_dataset.py", line 2251, in _iter_arrow
                  for key, pa_table in self.ex_iterable._iter_arrow():
                                       ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/iterable_dataset.py", line 494, in _iter_arrow
                  for key, pa_table in iterator:
                                       ^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/iterable_dataset.py", line 384, in _iter_arrow
                  for key, pa_table in self.generate_tables_fn(**gen_kwags):
                                       ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/packaged_modules/json/json.py", line 299, in _generate_tables
                  self._cast_table(pa_table, json_field_paths=json_field_paths),
                  ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/packaged_modules/json/json.py", line 128, in _cast_table
                  pa_table = table_cast(pa_table, self.info.features.arrow_schema)
                             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/table.py", line 2321, in table_cast
                  return cast_table_to_schema(table, schema)
                         ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/table.py", line 2249, in cast_table_to_schema
                  raise CastError(
              datasets.table.CastError: Couldn't cast
              id: int64
              timestamp_utc: timestamp[s]
              gpu_id: string
              display_name: string
              vram_gb: double
              secure_cloud: bool
              stock_status: string
              uninterruptable_price: double
              available_gpu_counts: null
              raw_json: struct<gpu_type: struct<displayName: string, id: string, lowestPrice: struct<availableGpuCounts: nul (... 151 chars omitted)
                child 0, gpu_type: struct<displayName: string, id: string, lowestPrice: struct<availableGpuCounts: null, stockStatus: s (... 56 chars omitted)
                    child 0, displayName: string
                    child 1, id: string
                    child 2, lowestPrice: struct<availableGpuCounts: null, stockStatus: string, uninterruptablePrice: double>
                        child 0, availableGpuCounts: null
                        child 1, stockStatus: string
                        child 2, uninterruptablePrice: double
                    child 3, memoryInGb: int64
                child 1, graphql_errors: list<item: null>
                    child 0, item: null
                child 2, query_variant: string
                child 3, secure_cloud: bool
              cleaned_timestamp_count: int64
              expected_rows_per_timestamp: int64
              source_json: string
              original_timestamp_count: int64
              removed_timestamps: list<item: struct<timestamp_utc: timestamp[s], row_count: int64>>
                child 0, item: struct<timestamp_utc: timestamp[s], row_count: int64>
                    child 0, timestamp_utc: timestamp[s]
                    child 1, row_count: int64
              source_report_folder: string
              removed_timestamp_count: int64
              cleaned_row_count: int64
              original_row_count: int64
              source_csv: string
              to
              {'source_report_folder': Value('string'), 'source_csv': Value('string'), 'source_json': Value('string'), 'expected_rows_per_timestamp': Value('int64'), 'original_row_count': Value('int64'), 'cleaned_row_count': Value('int64'), 'original_timestamp_count': Value('int64'), 'cleaned_timestamp_count': Value('int64'), 'removed_timestamp_count': Value('int64'), 'removed_timestamps': List({'timestamp_utc': Value('timestamp[s]'), 'row_count': Value('int64')})}
              because column names don't match

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Runpod GPU Weekly Snapshots: Current: 2026-05-24 to 2026-05-30

This package contains raw and cleaned exports of Runpod GPU snapshot data collected each week. This dataset will be updated once a week on Sundays.

Folder structure

raw/runpod_gpu_snapshots_2026-05-24_2026-05-30.csv
raw/runpod_gpu_snapshots_2026-05-24_2026-05-30.json
cleaned/runpod_gpu_snapshots_2026-05-24_2026-05-30_cleaned.csv
cleaned/runpod_gpu_snapshots_2026-05-24_2026-05-30_cleaned.json
cleaned/cleaning_summary.json
cleaned/removed_timestamps.txt

Coverage

Raw export: 14,802 rows across 159 timestamps
Cleaned export: 12,408 rows across 132 complete timestamps
Expected complete snapshot size: 94 rows per timestamp
Removed incomplete timestamps: 27

Schema

Each row contains:

id
timestamp_utc
gpu_id
display_name
vram_gb
secure_cloud
stock_status
uninterruptable_price
available_gpu_counts
raw_json

Cleaning method

The cleaned split keeps only complete timestamps with the full expected 94 rows. Incomplete timestamps were removed when the source API returned partial snapshots, including:

timestamps with 92 rows instead of 94
timestamps with 47 rows that contained only one secure_cloud half of the snapshot

See cleaned/cleaning_summary.json and cleaned/removed_timestamps.txt for the exact timestamps removed.

Recommended use

Use cleaned/ for analysis, benchmarking, and downstream tabular work.
Use raw/ for auditability and reproducibility.

Caveats

The cleaned split is higher quality than the raw split, but it is not a perfectly continuous week because incomplete timestamps were removed.
The missing rows appear to come from incomplete upstream Runpod API responses rather than local file corruption.

Downloads last month: 23

Total file size:

28.5 MB