ZacharyZhang-NY
e80883cfc9
Both reports were produced by a harness that had just changed, so their stamps named a commit without that code in it. Re-running them on an idle machine puts the stamp and the source back in sync. Recall did not move: all seven leg configurations reproduce to the third decimal, which is the part of the number anyone should act on. Latency moved within its usual load band, and the encoder probe came in slower this time, so the picture index's build cost is now quoted as a band rather than a single remembered figure.
74 lines
2.1 KiB
JSON
74 lines
2.1 KiB
JSON
{
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"commit": "71733d86d6d27d1f1ebb75205d5e0599f253f791",
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"database": "bench/runs/dense_scale/data/ely_eye.sqlite",
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"atoms": 165973,
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"memories_with_words": 153857,
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"vectors": 153857,
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"pending": 0,
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"dim": 384,
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"model": "sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2",
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"copy_seconds": 4.653337935000309,
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"build_seconds": 1444.851663221998,
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"texts_per_second": 106.48636390596745,
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"repair_seconds": 1.2708722250099527,
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"writes": 50,
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"write_latency": {
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"queries": 50,
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"mean": 0.5132235406010295,
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"p50": 0.50813193799695,
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"p95": 0.5353954541016719,
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"max": 0.5569016869994812
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},
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"probes": 300,
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"probe_modalities": [
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"code",
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"image",
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"pdf",
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"ui_screenshot",
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"video"
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],
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"latency": {
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"queries": 300,
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"mean": 0.07290171435301697,
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"p50": 0.07302105800044956,
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"p95": 0.07570205154406721,
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"max": 0.08561979999649338
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},
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"encode_latency": {
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"queries": 300,
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"mean": 0.012841759066456385,
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"p50": 0.011289245005173143,
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"p95": 0.024048260039126034,
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"max": 0.02614720400015358
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},
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"correctness": {
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"probes": 40,
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"top_k": 10,
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"tie_aware_agreement": 0.9975,
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"note": "a returned atom counts when its true cosine is at least the k-th best, so ties cannot fail the check"
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},
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"bytes_per_vector": 1729.2666307025354,
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"vector_bytes": 266059776,
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"bytes_method": "dbstat page sizes over the vec0 shadow tables",
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"extrapolation": {
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"to": 1000000,
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"basis": 153857,
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"assumption": "sqlite-vec 0.1.9 scans every vector, so latency and bytes grow linearly with the vector count; every row here is scaled by 1000000 / 153857 vectors, and the model, the machine and the thread budget stay the same",
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"p50_seconds": 0.47460341746199103,
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"p95_seconds": 0.49202864701682225,
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"write_p50_seconds": 3.3026247619344584,
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"gibibytes": 1.6105050507025191,
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"build_seconds": 9390.873754343305
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},
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"machine": {
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"system": "Linux",
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"release": "6.18.40.1-microsoft-standard-WSL2",
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"cpus": 32,
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"threads_per_query": 4,
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"load_average_1_5_15": [
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3.53,
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4.63,
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4.85
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]
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}
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} |