Datasets:
Update data with fresh R2 pull (5,743 rows) and add retention table to dataset card
Browse files- README.md +10 -3
- croissant.json +1 -1
- distributional/summary.parquet +2 -2
README.md
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@@ -37,13 +37,13 @@ LLM inference benchmark dataset measuring serving performance (TTFT, TPOT, ITL,
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This dataset provides two benchmark configurations reflecting distinct data collection methodologies:
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### trace_replay
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Requests replay exact ISL/OSL sequences from recorded agent sessions (SWE-Bench, TerminalBench, OSWorld, ShareGPT). Input distributions are empirically grounded in real tool-use patterns, capturing realistic burstiness and turn-depth correlations.
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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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### distributional
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Requests sample ISL/OSL from parameterized statistical distributions (e.g., lognormal) fitted to real workload statistics. Shorter to run than full trace replays, enabling faster characterization of profile-level serving behavior across the hardware matrix. MSE validation confirms distributional runs reproduce the latency and throughput characteristics of their trace-replay counterparts.
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### Concurrency filtering
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## Coverage
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This dataset provides two benchmark configurations reflecting distinct data collection methodologies:
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### trace_replay (3,147 rows)
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Requests replay exact ISL/OSL sequences from recorded agent sessions (SWE-Bench, TerminalBench, OSWorld, ShareGPT). Input distributions are empirically grounded in real tool-use patterns, capturing realistic burstiness and turn-depth correlations.
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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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### distributional (245 rows)
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Requests sample ISL/OSL from parameterized statistical distributions (e.g., lognormal) fitted to real workload statistics. Shorter to run than full trace replays, enabling faster characterization of profile-level serving behavior across the hardware matrix. MSE validation confirms distributional runs reproduce the latency and throughput characteristics of their trace-replay counterparts.
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### Concurrency filtering
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The benchmark harness capped actual concurrent connections at the session pool size. Rows where declared concurrency exceeds the pool size never achieved the intended load and have been excluded.
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| Scope | Raw rows | After filter | Removed | Filter rule |
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|-------|----------|-------------|---------|-------------|
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| trace_replay (archive) | 4,814 | 3,147 | 1,667 | C > 100 (session pool = 100) |
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| distributional (current) | 843 | 159 | 684 | C > 10 (session pool = 10) |
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| distributional (fixed) | 86 | 86 | 0 | No cap — bug fixed |
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| **Total** | **5,743** | **3,392** | **2,351** | **Retention: 59.1%** |
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## Coverage
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croissant.json
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"name": "distributional/summary.parquet",
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"contentUrl": "https://huggingface.co/datasets/lynae-1219/AgentPerfBench/resolve/main/distributional/summary.parquet",
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"encodingFormat": "application/x-parquet",
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"sha256": "
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},
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{
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"@type": "cr:FileObject",
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"name": "distributional/summary.parquet",
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"contentUrl": "https://huggingface.co/datasets/lynae-1219/AgentPerfBench/resolve/main/distributional/summary.parquet",
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"encodingFormat": "application/x-parquet",
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},
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{
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"@type": "cr:FileObject",
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distributional/summary.parquet
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oid sha256:
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size
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version https://git-lfs.github.com/spec/v1
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size 70836
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