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Datasets:
ashman1705
/
LLMScheduling

Tasks:
Text Generation
Languages:
English
Size:
10K<n<100K
Tags:
Synthetic
conversations
scheduling
benchmarking
License:
Dataset card Files
xet
 Community
Dataset Viewer
Duplicate
The dataset viewer is not available for this split.
Cannot load the dataset split (in streaming mode) to extract the first rows.
Error code:   StreamingRowsError
Exception:    CastError
Message:      Couldn't cast
case_id: string
category: string
description: string
arrival_mode: string
metadata: struct<requests_per_case: int64, stress_focus: string>
  child 0, requests_per_case: int64
  child 1, stress_focus: string
requests: list<item: struct<request_id: int64, prompt: string, input_tokens: int64, output_tokens: int64, arri (... 168 chars omitted)
  child 0, item: struct<request_id: int64, prompt: string, input_tokens: int64, output_tokens: int64, arrival_time: d (... 156 chars omitted)
      child 0, request_id: int64
      child 1, prompt: string
      child 2, input_tokens: int64
      child 3, output_tokens: int64
      child 4, arrival_time: double
      child 5, priority_class: string
      child 6, service_tier: string
      child 7, metadata: struct<source_request_id: int64, source_conversation_index: int64, source_pair_index: int64>
          child 0, source_request_id: int64
          child 1, source_conversation_index: int64
          child 2, source_pair_index: int64
generation_seed: int64
tokenizer_name: string
dataset_name: string
split: string
tokenizer_backend: string
cases_per_category: int64
source_record_count: int64
categories: list<item: struct<name: string, description: string, arrival_mode: string, requests_per_case: int64, (... 117 chars omitted)
  child 0, item: struct<name: string, description: string, arrival_mode: string, requests_per_case: int64, sampling_p (... 105 chars omitted)
      child 0, name: string
      child 1, description: string
      child 2, arrival_mode: string
      child 3, requests_per_case: int64
      child 4, sampling_policy: string
      child 5, priority_mode: string
      child 6, class_weights: struct<short: double, medium: double, long: double>
          child 0, short: double
          child 1, medium: double
          child 2, long: double
to
{'dataset_name': Value('string'), 'split': Value('string'), 'tokenizer_backend': Value('string'), 'tokenizer_name': Value('string'), 'source_record_count': Value('int64'), 'cases_per_category': Value('int64'), 'categories': List({'name': Value('string'), 'description': Value('string'), 'arrival_mode': Value('string'), 'requests_per_case': Value('int64'), 'sampling_policy': Value('string'), 'priority_mode': Value('string'), 'class_weights': {'short': Value('float64'), 'medium': Value('float64'), 'long': Value('float64')}}), 'generation_seed': 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 295, 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
              case_id: string
              category: string
              description: string
              arrival_mode: string
              metadata: struct<requests_per_case: int64, stress_focus: string>
                child 0, requests_per_case: int64
                child 1, stress_focus: string
              requests: list<item: struct<request_id: int64, prompt: string, input_tokens: int64, output_tokens: int64, arri (... 168 chars omitted)
                child 0, item: struct<request_id: int64, prompt: string, input_tokens: int64, output_tokens: int64, arrival_time: d (... 156 chars omitted)
                    child 0, request_id: int64
                    child 1, prompt: string
                    child 2, input_tokens: int64
                    child 3, output_tokens: int64
                    child 4, arrival_time: double
                    child 5, priority_class: string
                    child 6, service_tier: string
                    child 7, metadata: struct<source_request_id: int64, source_conversation_index: int64, source_pair_index: int64>
                        child 0, source_request_id: int64
                        child 1, source_conversation_index: int64
                        child 2, source_pair_index: int64
              generation_seed: int64
              tokenizer_name: string
              dataset_name: string
              split: string
              tokenizer_backend: string
              cases_per_category: int64
              source_record_count: int64
              categories: list<item: struct<name: string, description: string, arrival_mode: string, requests_per_case: int64, (... 117 chars omitted)
                child 0, item: struct<name: string, description: string, arrival_mode: string, requests_per_case: int64, sampling_p (... 105 chars omitted)
                    child 0, name: string
                    child 1, description: string
                    child 2, arrival_mode: string
                    child 3, requests_per_case: int64
                    child 4, sampling_policy: string
                    child 5, priority_mode: string
                    child 6, class_weights: struct<short: double, medium: double, long: double>
                        child 0, short: double
                        child 1, medium: double
                        child 2, long: double
              to
              {'dataset_name': Value('string'), 'split': Value('string'), 'tokenizer_backend': Value('string'), 'tokenizer_name': Value('string'), 'source_record_count': Value('int64'), 'cases_per_category': Value('int64'), 'categories': List({'name': Value('string'), 'description': Value('string'), 'arrival_mode': Value('string'), 'requests_per_case': Value('int64'), 'sampling_policy': Value('string'), 'priority_mode': Value('string'), 'class_weights': {'short': Value('float64'), 'medium': Value('float64'), 'long': Value('float64')}}), 'generation_seed': Value('int64')}
              because column names don't match

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ShareGPT Workload Cases

This dataset packages workload-generation artifacts derived from Aeala/ShareGPT_Vicuna_unfiltered. It includes the manifest describing how cases were generated and per-category case files intended for scheduler and systems benchmarking.

Contents

  • manifest.json: dataset generation metadata and category definitions.
  • cases/: 176 generated case files across 22 workload categories.

Generation Metadata

  • Split: train
  • Cases per category: 8
  • Tokenizer: gpt-4 via tiktoken
  • Generation seed: 7

Category Preview

  • steady_short: Steady Poisson arrivals dominated by short prompts. (32 requests per case)
  • steady_mixed: Steady mixed load with balanced request lengths. (64 requests per case)
  • bursty_chat: Bursty conversational traffic with mostly short and medium prompts. (96 requests per case)
  • bursty_long: Burst-heavy load with a larger share of long prompts. (72 requests per case)
  • flash_crowd: Large clustered traffic spikes for stress testing scheduler fairness and queueing. (192 requests per case)
  • uniform_workload: Uniform request sizes and steady arrivals for baseline throughput and scheduler overhead. (64 requests per case)
  • low_load: Low-arrival workload with little contention to approximate ideal latency. (32 requests per case)
  • short_long_mix: Majority short requests with a minority of long requests to expose head-of-line blocking. (80 requests per case)
  • ...and 14 more categories.

Notes

  • Each category folder also contains a category.json file with category-level metadata.
  • Source records are intentionally excluded from this Hub upload.
  • The uploaded layout mirrors the local directory structure for reproducibility.
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