Datasets:

alirezaaminzadeh
/

clustercast-sample-workloads

Modalities:
Dataset Preview
Split (2)
train
The full dataset viewer is not available (click to read why). Only showing a preview of the rows.
The dataset generation failed
Error code:   DatasetGenerationError
Exception:    CastError
Message:      Couldn't cast
benchmarks: list<item: struct<scenario: string, stress: string, policy: string, algorithm: string, gpu_utilizati (... 178 chars omitted)
  child 0, item: struct<scenario: string, stress: string, policy: string, algorithm: string, gpu_utilization_pct: dou (... 166 chars omitted)
      child 0, scenario: string
      child 1, stress: string
      child 2, policy: string
      child 3, algorithm: string
      child 4, gpu_utilization_pct: double
      child 5, avg_queue_time_min: double
      child 6, deadline_miss_rate_pct: double
      child 7, fairness_index: double
      child 8, fragmentation_index: double
      child 9, preemption_count: int64
      child 10, solve_time_sec: double
solver_metadata: struct<optimization: list<item: string>, simulation: string>
  child 0, optimization: list<item: string>
      child 0, item: string
  child 1, simulation: string
optimization: struct<policy: string, algorithm: string, placements: list<item: struct<job_id: string, node_id: str (... 2295 chars omitted)
  child 0, policy: string
  child 1, algorithm: string
  child 2, placements: list<item: struct<job_id: string, node_id: string, node_name: string, start_min: int64, end_min: int (... 166 chars omitted)
      child 0, item: struct<job_id: string, node_id: string, node_name: string, start_min: int64, end_min: int64, gpu_all (... 154 chars omitted)
          child 0, job_id: string
          child 1, node_id: string
          child 2, node_name: string
          child 3, start_min: i
...
el: string, start: int64, end: int64, gpu: int64, preempted: bool>
              child 0, job_id: string
              child 1, label: string
              child 2, start: int64
              child 3, end: int64
              child 4, gpu: int64
              child 5, preempted: bool
      child 11, node-009: list<item: struct<job_id: string, label: string, start: int64, end: int64, gpu: int64, preempted: bo (... 4 chars omitted)
          child 0, item: struct<job_id: string, label: string, start: int64, end: int64, gpu: int64, preempted: bool>
              child 0, job_id: string
              child 1, label: string
              child 2, start: int64
              child 3, end: int64
              child 4, gpu: int64
              child 5, preempted: bool
  child 5, simulation: struct<method: string, horizon_min: int64, gpu_utilization_mean: double, queue_time_p95_min: double, (... 69 chars omitted)
      child 0, method: string
      child 1, horizon_min: int64
      child 2, gpu_utilization_mean: double
      child 3, queue_time_p95_min: double
      child 4, sla_compliance_pct: double
      child 5, energy_kwh: double
      child 6, replications: int64
  child 6, notes: list<item: string>
      child 0, item: string
summary: struct<nodes: int64, jobs: int64, total_gpus: int64, horizon_min: int64>
  child 0, nodes: int64
  child 1, jobs: int64
  child 2, total_gpus: int64
  child 3, horizon_min: int64
scenario: string
policy: string
stress: string
scenario_label: string
to
{'scenario': Value('string'), 'stress': Value('string'), 'policy': Value('string'), 'scenario_label': Value('string'), 'summary': {'nodes': Value('int64'), 'jobs': Value('int64'), 'total_gpus': Value('int64'), 'horizon_min': Value('int64')}, 'optimization': {'policy': Value('string'), 'algorithm': Value('string'), 'placements': List({'job_id': Value('string'), 'node_id': Value('string'), 'node_name': Value('string'), 'start_min': Value('int64'), 'end_min': Value('int64'), 'gpu_allocated': Value('int64'), 'preempted': Value('bool'), 'gpu_shared': Value('bool'), 'data_transfer_gb': Value('float64'), 'data_transfer_cost': Value('float64'), 'checkpoint_overhead_min': Value('int64'), 'rationale': Value('string')}), 'metrics': {'gpu_utilization_pct': Value('float64'), 'avg_queue_time_min': Value('float64'), 'max_queue_time_min': Value('int64'), 'job_slowdown_mean': Value('float64'), 'deadline_misses': Value('int64'), 'deadline_miss_rate_pct': Value('float64'), 'energy_kwh': Value('float64'), 'fragmentation_index': Value('float64'), 'preemption_count': Value('int64'), 'fairness_index': Value('float64'), 'cost_per_completed_job': Value('float64'), 'total_cost': Value('float64'), 'jobs_completed': Value('int64'), 'jobs_scheduled': Value('int64'), 'nodes_idle_shutdown': Value('int64'), 'solve_time_sec': Value('float64'), 'status': Value('string')}, 'node_timelines': {'node-004': List({'job_id': Value('string'), 'label': Value('string'), 'start': Value('int64'), 'end': Value('int64'), '
...
), 'node-012': List({'job_id': Value('string'), 'label': Value('string'), 'start': Value('int64'), 'end': Value('int64'), 'gpu': Value('int64'), 'preempted': Value('bool')}), 'node-006': List({'job_id': Value('string'), 'label': Value('string'), 'start': Value('int64'), 'end': Value('int64'), 'gpu': Value('int64'), 'preempted': Value('bool')}), 'node-011': List({'job_id': Value('string'), 'label': Value('string'), 'start': Value('int64'), 'end': Value('int64'), 'gpu': Value('int64'), 'preempted': Value('bool')}), 'node-001': List({'job_id': Value('string'), 'label': Value('string'), 'start': Value('int64'), 'end': Value('int64'), 'gpu': Value('int64'), 'preempted': Value('bool')}), 'node-002': List({'job_id': Value('string'), 'label': Value('string'), 'start': Value('int64'), 'end': Value('int64'), 'gpu': Value('int64'), 'preempted': Value('bool')}), 'node-003': List({'job_id': Value('string'), 'label': Value('string'), 'start': Value('int64'), 'end': Value('int64'), 'gpu': Value('int64'), 'preempted': Value('bool')}), 'node-009': List({'job_id': Value('string'), 'label': Value('string'), 'start': Value('int64'), 'end': Value('int64'), 'gpu': Value('int64'), 'preempted': Value('bool')})}, 'simulation': {'method': Value('string'), 'horizon_min': Value('int64'), 'gpu_utilization_mean': Value('float64'), 'queue_time_p95_min': Value('float64'), 'sla_compliance_pct': Value('float64'), 'energy_kwh': Value('float64'), 'replications': Value('int64')}, 'notes': List(Value('string'))}}
because column names don't match
Traceback:    Traceback (most recent call last):
                File "/usr/local/lib/python3.14/site-packages/datasets/builder.py", line 1816, in _prepare_split_single
                  for key, table in generator:
                                    ^^^^^^^^^
                File "/src/services/worker/src/worker/job_runners/config/parquet_and_info.py", line 613, in wrapped
                  for item in generator(*args, **kwargs):
                              ~~~~~~~~~^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.14/site-packages/datasets/packaged_modules/json/json.py", line 343, in _generate_tables
                  self._cast_table(pa_table, json_field_paths=json_field_paths),
                  ~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.14/site-packages/datasets/packaged_modules/json/json.py", line 132, in _cast_table
                  pa_table = table_cast(pa_table, self.info.features.arrow_schema)
                File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 2369, in table_cast
                  return cast_table_to_schema(table, schema)
                File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 2297, in cast_table_to_schema
                  raise CastError(
                  ...<3 lines>...
                  )
              datasets.table.CastError: Couldn't cast
              benchmarks: list<item: struct<scenario: string, stress: string, policy: string, algorithm: string, gpu_utilizati (... 178 chars omitted)
                child 0, item: struct<scenario: string, stress: string, policy: string, algorithm: string, gpu_utilization_pct: dou (... 166 chars omitted)
                    child 0, scenario: string
                    child 1, stress: string
                    child 2, policy: string
                    child 3, algorithm: string
                    child 4, gpu_utilization_pct: double
                    child 5, avg_queue_time_min: double
                    child 6, deadline_miss_rate_pct: double
                    child 7, fairness_index: double
                    child 8, fragmentation_index: double
                    child 9, preemption_count: int64
                    child 10, solve_time_sec: double
              solver_metadata: struct<optimization: list<item: string>, simulation: string>
                child 0, optimization: list<item: string>
                    child 0, item: string
                child 1, simulation: string
              optimization: struct<policy: string, algorithm: string, placements: list<item: struct<job_id: string, node_id: str (... 2295 chars omitted)
                child 0, policy: string
                child 1, algorithm: string
                child 2, placements: list<item: struct<job_id: string, node_id: string, node_name: string, start_min: int64, end_min: int (... 166 chars omitted)
                    child 0, item: struct<job_id: string, node_id: string, node_name: string, start_min: int64, end_min: int64, gpu_all (... 154 chars omitted)
                        child 0, job_id: string
                        child 1, node_id: string
                        child 2, node_name: string
                        child 3, start_min: i
              ...
              el: string, start: int64, end: int64, gpu: int64, preempted: bool>
                            child 0, job_id: string
                            child 1, label: string
                            child 2, start: int64
                            child 3, end: int64
                            child 4, gpu: int64
                            child 5, preempted: bool
                    child 11, node-009: list<item: struct<job_id: string, label: string, start: int64, end: int64, gpu: int64, preempted: bo (... 4 chars omitted)
                        child 0, item: struct<job_id: string, label: string, start: int64, end: int64, gpu: int64, preempted: bool>
                            child 0, job_id: string
                            child 1, label: string
                            child 2, start: int64
                            child 3, end: int64
                            child 4, gpu: int64
                            child 5, preempted: bool
                child 5, simulation: struct<method: string, horizon_min: int64, gpu_utilization_mean: double, queue_time_p95_min: double, (... 69 chars omitted)
                    child 0, method: string
                    child 1, horizon_min: int64
                    child 2, gpu_utilization_mean: double
                    child 3, queue_time_p95_min: double
                    child 4, sla_compliance_pct: double
                    child 5, energy_kwh: double
                    child 6, replications: int64
                child 6, notes: list<item: string>
                    child 0, item: string
              summary: struct<nodes: int64, jobs: int64, total_gpus: int64, horizon_min: int64>
                child 0, nodes: int64
                child 1, jobs: int64
                child 2, total_gpus: int64
                child 3, horizon_min: int64
              scenario: string
              policy: string
              stress: string
              scenario_label: string
              to
              {'scenario': Value('string'), 'stress': Value('string'), 'policy': Value('string'), 'scenario_label': Value('string'), 'summary': {'nodes': Value('int64'), 'jobs': Value('int64'), 'total_gpus': Value('int64'), 'horizon_min': Value('int64')}, 'optimization': {'policy': Value('string'), 'algorithm': Value('string'), 'placements': List({'job_id': Value('string'), 'node_id': Value('string'), 'node_name': Value('string'), 'start_min': Value('int64'), 'end_min': Value('int64'), 'gpu_allocated': Value('int64'), 'preempted': Value('bool'), 'gpu_shared': Value('bool'), 'data_transfer_gb': Value('float64'), 'data_transfer_cost': Value('float64'), 'checkpoint_overhead_min': Value('int64'), 'rationale': Value('string')}), 'metrics': {'gpu_utilization_pct': Value('float64'), 'avg_queue_time_min': Value('float64'), 'max_queue_time_min': Value('int64'), 'job_slowdown_mean': Value('float64'), 'deadline_misses': Value('int64'), 'deadline_miss_rate_pct': Value('float64'), 'energy_kwh': Value('float64'), 'fragmentation_index': Value('float64'), 'preemption_count': Value('int64'), 'fairness_index': Value('float64'), 'cost_per_completed_job': Value('float64'), 'total_cost': Value('float64'), 'jobs_completed': Value('int64'), 'jobs_scheduled': Value('int64'), 'nodes_idle_shutdown': Value('int64'), 'solve_time_sec': Value('float64'), 'status': Value('string')}, 'node_timelines': {'node-004': List({'job_id': Value('string'), 'label': Value('string'), 'start': Value('int64'), 'end': Value('int64'), '
              ...
              ), 'node-012': List({'job_id': Value('string'), 'label': Value('string'), 'start': Value('int64'), 'end': Value('int64'), 'gpu': Value('int64'), 'preempted': Value('bool')}), 'node-006': List({'job_id': Value('string'), 'label': Value('string'), 'start': Value('int64'), 'end': Value('int64'), 'gpu': Value('int64'), 'preempted': Value('bool')}), 'node-011': List({'job_id': Value('string'), 'label': Value('string'), 'start': Value('int64'), 'end': Value('int64'), 'gpu': Value('int64'), 'preempted': Value('bool')}), 'node-001': List({'job_id': Value('string'), 'label': Value('string'), 'start': Value('int64'), 'end': Value('int64'), 'gpu': Value('int64'), 'preempted': Value('bool')}), 'node-002': List({'job_id': Value('string'), 'label': Value('string'), 'start': Value('int64'), 'end': Value('int64'), 'gpu': Value('int64'), 'preempted': Value('bool')}), 'node-003': List({'job_id': Value('string'), 'label': Value('string'), 'start': Value('int64'), 'end': Value('int64'), 'gpu': Value('int64'), 'preempted': Value('bool')}), 'node-009': List({'job_id': Value('string'), 'label': Value('string'), 'start': Value('int64'), 'end': Value('int64'), 'gpu': Value('int64'), 'preempted': Value('bool')})}, 'simulation': {'method': Value('string'), 'horizon_min': Value('int64'), 'gpu_utilization_mean': Value('float64'), 'queue_time_p95_min': Value('float64'), 'sla_compliance_pct': Value('float64'), 'energy_kwh': Value('float64'), 'replications': Value('int64')}, 'notes': List(Value('string'))}}
              because column names don't match
              
              The above exception was the direct cause of the following exception:
              
              Traceback (most recent call last):
                File "/src/services/worker/src/worker/job_runners/config/parquet_and_info.py", line 1369, in compute_config_parquet_and_info_response
                  parquet_operations, partial, estimated_dataset_info = stream_convert_to_parquet(
                                                                        ~~~~~~~~~~~~~~~~~~~~~~~~~^
                      builder, max_dataset_size_bytes=max_dataset_size_bytes
                      ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                  )
                  ^
                File "/src/services/worker/src/worker/job_runners/config/parquet_and_info.py", line 948, in stream_convert_to_parquet
                  builder._prepare_split(split_generator=splits_generators[split], file_format="parquet")
                  ~~~~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.14/site-packages/datasets/builder.py", line 1683, in _prepare_split
                  for job_id, done, content in self._prepare_split_single(
                                               ~~~~~~~~~~~~~~~~~~~~~~~~~~^
                      gen_kwargs=gen_kwargs, job_id=job_id, **_prepare_split_args
                      ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                  ):
                  ^
                File "/usr/local/lib/python3.14/site-packages/datasets/builder.py", line 1869, in _prepare_split_single
                  raise DatasetGenerationError("An error occurred while generating the dataset") from e
              datasets.exceptions.DatasetGenerationError: An error occurred while generating the dataset
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.
scenario string	stress string	policy string	scenario_label string	summary dict	optimization dict
ai_training	baseline	max_gpu_utilization	AI Training Cluster	{ "nodes": 12, "jobs": 28, "total_gpus": 32, "horizon_min": 1381 }	{ "policy": "max_gpu_utilization", "algorithm": "first_fit_decreasing", "placements": [ { "job_id": "job-0004", "node_id": "node-004", "node_name": "A100-worker-4", "start_min": 81, "end_min": 514, "gpu_allocated": 8, "preempted": false, "gpu_shared": false, ...
YAML Metadata Warning:empty or missing yaml metadata in repo card
Check out the documentation for more information.
ClusterCast Sample Workloads

Synthetic GPU cluster scenarios for benchmarking schedulers.
Samples

File	Scenario	Stress
`sample_ai_training.json`	AI Training Cluster	Baseline
`sample_inference_serving.json`	Inference Serving	GPU Shortage
`sample_render_farm.json`	Render Farm	Training Burst
`sample_scientific_compute.json`	Scientific HPC	Deadline Pressure
`sample_cloud_provider.json`	Cloud Provider	Network Congestion
Each sample includes cluster nodes, workload jobs, and optimization results.
Downloads last month: 37
Total file size:
210 kB
Space using alirezaaminzadeh/clustercast-sample-workloads 1

Collection including alirezaaminzadeh/clustercast-sample-workloads

Aria AI Operations Research Portfolio

Collection
Enterprise OR, optimization, and decomposition demos by Aria AI • 136 items • Updated 5 days ago