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Browse files- README.md +8 -8
- croissant.json +25 -25
- synthetic_distributional/summary.parquet +3 -0
README.md
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data_files:
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- split: summary
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path: trace_replay/summary.parquet
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-
- config_name:
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data_files:
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- split: summary
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-
path:
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- config_name: kernels_labeled
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data_files:
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- split: train
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- name: summary
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num_examples: 2902
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num_bytes: 640182
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-
- config_name:
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features:
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- name: run_id
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dtype: string
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17 profiles: `chat-medium`, `chat-multiturn-long`, `chat-multiturn-medium`, `chat-multiturn-short`, `chat-short`, `chat-singleturn`, `coding-singleturn`, `decode-heavy`, `osworld-multiturn-long`, `osworld-multiturn-medium`, `osworld-multiturn-short`, `prefill-heavy`, `random-1k`, `swebench-multiturn-medium`, `swebench-multiturn-short`, `terminalbench-multiturn-medium`, `terminalbench-multiturn-short`
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-
###
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ISL/OSL sampled from lognormal fits to real workload statistics. 42 combinations, 6 profiles, 7 concurrency levels.
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### Concurrency filtering
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Concurrency is controlled by a fixed-size connection pool. Trace replay uses levels {1, 5, 10, 20, 40, 80};
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-
Early runs used a session-pool size smaller than the declared concurrency (`num_sessions=100` for trace replay, `num_sessions=10` for
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Configurations where fewer than 75% of requests completed successfully are excluded.
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| Config | Rows |
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|--------|------|
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| trace_replay | 2,902 |
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-
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| **Total** | **3,147** |
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## Coverage
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from datasets import load_dataset
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ds = load_dataset("agent-perf-bench/AgentPerfBench", "trace_replay")
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# or "
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```
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## Benchmark methodology
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data_files:
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- split: summary
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path: trace_replay/summary.parquet
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- config_name: synthetic_distributional
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data_files:
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- split: summary
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path: synthetic_distributional/summary.parquet
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- config_name: kernels_labeled
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data_files:
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- split: train
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- name: summary
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num_examples: 2902
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num_bytes: 640182
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+
- config_name: synthetic_distributional
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features:
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- name: run_id
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dtype: string
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17 profiles: `chat-medium`, `chat-multiturn-long`, `chat-multiturn-medium`, `chat-multiturn-short`, `chat-short`, `chat-singleturn`, `coding-singleturn`, `decode-heavy`, `osworld-multiturn-long`, `osworld-multiturn-medium`, `osworld-multiturn-short`, `prefill-heavy`, `random-1k`, `swebench-multiturn-medium`, `swebench-multiturn-short`, `terminalbench-multiturn-medium`, `terminalbench-multiturn-short`
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### synthetic_distributional (245 rows)
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ISL/OSL sampled from lognormal fits to real workload statistics. 42 combinations, 6 profiles, 7 concurrency levels.
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### Concurrency filtering
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Concurrency is controlled by a fixed-size connection pool. Trace replay uses levels {1, 5, 10, 20, 40, 80}; synthetic_distributional uses {1, 5, 10, 40, 80, 200, 320}.
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+
Early runs used a session-pool size smaller than the declared concurrency (`num_sessions=100` for trace replay, `num_sessions=10` for synthetic_distributional), capping actual load below the nominal value. Rows where declared concurrency exceeded the session pool were dropped.
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Configurations where fewer than 75% of requests completed successfully are excluded.
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| Config | Rows |
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|--------|------|
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| trace_replay | 2,902 |
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| synthetic_distributional | 245 |
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| **Total** | **3,147** |
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## Coverage
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from datasets import load_dataset
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ds = load_dataset("agent-perf-bench/AgentPerfBench", "trace_replay")
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# or "synthetic_distributional", "kernels_labeled"
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```
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## Benchmark methodology
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croissant.json
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},
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"@type": "sc:Dataset",
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"name": "AgentPerfBench",
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-
"description": "LLM inference benchmark dataset measuring serving performance (TTFT, TPOT, throughput) across 9 models, 4 GPU platforms, 2 serving engines under agentic and chat workloads. Three configurations: trace_replay,
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"url": "https://huggingface.co/datasets/agent-perf-bench/AgentPerfBench",
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"license": "https://spdx.org/licenses/Apache-2.0.html",
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"conformsTo": "http://mlcommons.org/croissant/1.1",
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},
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{
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"@type": "cr:FileObject",
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"@id": "distributional-summary-parquet",
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-
"name": "
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-
"contentUrl": "https://huggingface.co/datasets/agent-perf-bench/AgentPerfBench/resolve/main/
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"encodingFormat": "application/x-parquet",
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"sha256": "67d63072831143e929e0b6d6464744756f783b53ac30fed0232bc01c601676ec"
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},
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},
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{
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"@type": "cr:RecordSet",
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"@id": "distributional-summary",
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"name": "Distributional Summary",
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-
"description": "One row per benchmark configuration from distributional runs (statistical sampling from lognormal fits to recorded workload statistics).",
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"field": [
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{
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"@type": "cr:Field",
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-
"@id": "distributional-summary/run_id",
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"name": "run_id",
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"dataType": "sc:Text",
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"source": {
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"fileObject": {
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-
"@id": "distributional-summary-parquet"
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},
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"extract": {
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"column": "run_id"
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},
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{
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"@type": "cr:Field",
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"@id": "distributional-summary/model",
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"name": "model",
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"dataType": "sc:Text",
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"source": {
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"fileObject": {
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-
"@id": "distributional-summary-parquet"
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},
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"extract": {
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"column": "model"
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},
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{
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"@type": "cr:Field",
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-
"@id": "distributional-summary/hardware",
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"name": "hardware",
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"dataType": "sc:Text",
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"source": {
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"fileObject": {
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-
"@id": "distributional-summary-parquet"
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},
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"extract": {
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"column": "hardware"
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},
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{
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"@type": "cr:Field",
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"@id": "distributional-summary/engine",
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"name": "engine",
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"dataType": "sc:Text",
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"source": {
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"fileObject": {
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-
"@id": "distributional-summary-parquet"
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},
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"extract": {
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"column": "engine"
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},
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{
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"@type": "cr:Field",
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-
"@id": "distributional-summary/profile",
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"name": "profile",
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"dataType": "sc:Text",
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"source": {
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"fileObject": {
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-
"@id": "distributional-summary-parquet"
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},
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"extract": {
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"column": "profile"
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},
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{
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"@type": "cr:Field",
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-
"@id": "distributional-summary/concurrency",
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"name": "concurrency",
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"dataType": "sc:Integer",
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"source": {
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"fileObject": {
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-
"@id": "distributional-summary-parquet"
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},
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"extract": {
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"column": "concurrency"
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},
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{
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"@type": "cr:Field",
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-
"@id": "distributional-summary/request_throughput",
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"name": "request_throughput",
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"dataType": "sc:Float",
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"source": {
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"fileObject": {
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-
"@id": "distributional-summary-parquet"
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},
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"extract": {
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"column": "request_throughput"
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},
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{
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"@type": "cr:Field",
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-
"@id": "distributional-summary/median_ttft_ms",
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"name": "median_ttft_ms",
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"dataType": "sc:Float",
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"source": {
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"fileObject": {
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-
"@id": "distributional-summary-parquet"
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},
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"extract": {
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"column": "median_ttft_ms"
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},
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{
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"@type": "cr:Field",
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-
"@id": "distributional-summary/median_tpot_ms",
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"name": "median_tpot_ms",
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"dataType": "sc:Float",
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"source": {
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"fileObject": {
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-
"@id": "distributional-summary-parquet"
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},
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"extract": {
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"column": "median_tpot_ms"
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},
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"@type": "sc:Dataset",
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"name": "AgentPerfBench",
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"description": "LLM inference benchmark dataset measuring serving performance (TTFT, TPOT, throughput) across 9 models, 4 GPU platforms, 2 serving engines under agentic and chat workloads. Three configurations: trace_replay, synthetic_distributional, and kernels_labeled.",
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"url": "https://huggingface.co/datasets/agent-perf-bench/AgentPerfBench",
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"license": "https://spdx.org/licenses/Apache-2.0.html",
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"conformsTo": "http://mlcommons.org/croissant/1.1",
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},
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{
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"@type": "cr:FileObject",
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"@id": "synthetic-distributional-summary-parquet",
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"name": "synthetic_distributional/summary.parquet",
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"contentUrl": "https://huggingface.co/datasets/agent-perf-bench/AgentPerfBench/resolve/main/synthetic_distributional/summary.parquet",
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"encodingFormat": "application/x-parquet",
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"sha256": "67d63072831143e929e0b6d6464744756f783b53ac30fed0232bc01c601676ec"
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},
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},
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{
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"@type": "cr:RecordSet",
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"@id": "synthetic-distributional-summary",
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"name": "Synthetic Distributional Summary",
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"description": "One row per benchmark configuration from synthetic distributional runs (statistical sampling from lognormal fits to recorded workload statistics).",
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"field": [
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{
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"@type": "cr:Field",
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"@id": "synthetic-distributional-summary/run_id",
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"name": "run_id",
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"dataType": "sc:Text",
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"source": {
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"fileObject": {
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"@id": "synthetic-distributional-summary-parquet"
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},
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"extract": {
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"column": "run_id"
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},
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{
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"@type": "cr:Field",
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"@id": "synthetic-distributional-summary/model",
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"name": "model",
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"dataType": "sc:Text",
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"source": {
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"fileObject": {
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"@id": "synthetic-distributional-summary-parquet"
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},
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"extract": {
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"column": "model"
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},
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{
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"@type": "cr:Field",
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"@id": "synthetic-distributional-summary/hardware",
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"name": "hardware",
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"dataType": "sc:Text",
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"source": {
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"fileObject": {
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"@id": "synthetic-distributional-summary-parquet"
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},
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"extract": {
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"column": "hardware"
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},
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{
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"@type": "cr:Field",
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"@id": "synthetic-distributional-summary/engine",
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"name": "engine",
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"dataType": "sc:Text",
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"source": {
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"fileObject": {
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"@id": "synthetic-distributional-summary-parquet"
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},
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"extract": {
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"column": "engine"
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},
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{
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"@type": "cr:Field",
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"@id": "synthetic-distributional-summary/profile",
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"name": "profile",
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"dataType": "sc:Text",
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"source": {
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"fileObject": {
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"@id": "synthetic-distributional-summary-parquet"
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},
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"extract": {
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"column": "profile"
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},
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{
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"@type": "cr:Field",
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"@id": "synthetic-distributional-summary/concurrency",
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"name": "concurrency",
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"dataType": "sc:Integer",
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"source": {
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"fileObject": {
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"@id": "synthetic-distributional-summary-parquet"
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},
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"extract": {
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"column": "concurrency"
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},
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{
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"@type": "cr:Field",
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"@id": "synthetic-distributional-summary/request_throughput",
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"name": "request_throughput",
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"dataType": "sc:Float",
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"source": {
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"fileObject": {
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"@id": "synthetic-distributional-summary-parquet"
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},
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"extract": {
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"column": "request_throughput"
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},
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{
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"@type": "cr:Field",
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"@id": "synthetic-distributional-summary/median_ttft_ms",
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"name": "median_ttft_ms",
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"dataType": "sc:Float",
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"source": {
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"fileObject": {
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"@id": "synthetic-distributional-summary-parquet"
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},
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"extract": {
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"column": "median_ttft_ms"
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},
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{
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"@type": "cr:Field",
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"@id": "synthetic-distributional-summary/median_tpot_ms",
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"name": "median_tpot_ms",
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"dataType": "sc:Float",
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"source": {
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"fileObject": {
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"@id": "synthetic-distributional-summary-parquet"
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},
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"extract": {
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"column": "median_tpot_ms"
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synthetic_distributional/summary.parquet
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version https://git-lfs.github.com/spec/v1
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oid sha256:67d63072831143e929e0b6d6464744756f783b53ac30fed0232bc01c601676ec
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size 68747
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