diff --git a/CHANGELOG.md b/CHANGELOG.md index 05a77d4..ef89b7f 100644 --- a/CHANGELOG.md +++ b/CHANGELOG.md @@ -1,6 +1,8 @@ # Changelog ## [Unreleased] +### Added +- `benchmarks/bench_upload_formats.py`: the same tables through a gateway as JSON and as Parquet, interleaved, splitting each fit's time between the hop to tabctx and tabctx's own work. On prod (0.15.0, 12 fits each of 5,000 x 500 and 10,000 x 100 TabICL): Parquet bodies were 8.5 MiB vs 14.0 MiB, the hop's median 1.2 s vs 2.7 s, its worst 42 s vs 113 s, and 1 fit in 12 took over 10 s vs 3 in 12; tabctx's own work was the same. Baselines `prod-bench-0.15.0.json` and `upload-formats-0.15.0.json`. ## [0.15.0] - 2026-10-21 ### Added diff --git a/benchmarks/baselines/prod-bench-0.15.0.json b/benchmarks/baselines/prod-bench-0.15.0.json new file mode 100644 index 0000000..c6199df --- /dev/null +++ b/benchmarks/baselines/prod-bench-0.15.0.json @@ -0,0 +1,20077 @@ +{ + "label": "0.15.0", + "tag": "67d942", + "timestamp": "2026-10-21T14:08:37.573463+00:00", + "base_url": "https://proxy.vectorinstitute.ai/v1", + "stall_s": 5.0, + "latency": { + "tabicl-v2 1000x10": { + "first_fit_s": 0.529, + "repeat_fit_s": 0.488, + "predict_p50_s": 0.131 + }, + "tabicl-v2 10000x10": { + "first_fit_s": 1.223, + "repeat_fit_s": 2.154, + "predict_p50_s": 0.136 + }, + "tabicl-v2 1000x100": { + "first_fit_s": 0.675, + "repeat_fit_s": 0.763, + "predict_p50_s": 0.158 + }, + "tabicl-v2 10000x100": { + "first_fit_s": 56.054, + "repeat_fit_s": 3.608, + "predict_p50_s": 0.15 + }, + "tabicl-v2 1000x500": { + "first_fit_s": 2.306, + 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"read_table": 28.8, + "coalesce_wait": 5.1, + "worker_call": 7406.3, + "wait_host_memory": 0.0, + "wait_device": 6851.1, + "predict_device": 553.9, + "n_predict_chunks": 1, + "n_batched_with": 1 + }, + "server_timing": "gateway;desc=\"gateway\";dur=12.2, upstream;desc=\"round trip to tabctx\";dur=7617.3" + }, + { + "end_s": 1011.61, + "s": 8.071, + "call": "predict tabpfn-3.5", + "timings_ms": { + "read_table": 29.0, + "coalesce_wait": 5.1, + "worker_call": 7850.0, + "wait_host_memory": 0.0, + "wait_device": 7298.6, + "predict_device": 550.4, + "n_predict_chunks": 1, + "n_batched_with": 1 + }, + "server_timing": "gateway;desc=\"gateway\";dur=13.1, upstream;desc=\"round trip to tabctx\";dur=8004.5" + }, + { + "end_s": 1012.16, + "s": 8.388, + "call": "predict tabpfn-3.5", + "timings_ms": { + "read_table": 34.0, + "coalesce_wait": 5.1, + "worker_call": 8178.3, + "wait_host_memory": 0.0, + "wait_device": 7629.2, + "predict_device": 548.2, + "n_predict_chunks": 1, + "n_batched_with": 1 + 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{ + "read_table": 20.5, + "coalesce_wait": 5.1, + "worker_call": 8568.0, + "wait_host_memory": 0.0, + "wait_device": 8016.2, + "predict_device": 550.7, + "n_predict_chunks": 1, + "n_batched_with": 1 + }, + "server_timing": "gateway;desc=\"gateway\";dur=12.1, upstream;desc=\"round trip to tabctx\";dur=8709.2" + }, + { + "end_s": 1014.35, + "s": 8.759, + "call": "predict tabpfn-3.5", + "timings_ms": { + "read_table": 28.9, + "coalesce_wait": 5.1, + "worker_call": 8375.1, + "wait_host_memory": 0.0, + "wait_device": 7823.7, + "predict_device": 550.2, + "n_predict_chunks": 1, + "n_batched_with": 1 + }, + "server_timing": "gateway;desc=\"gateway\";dur=12.0, upstream;desc=\"round trip to tabctx\";dur=8696.9" + } + ] +} diff --git a/benchmarks/baselines/upload-formats-0.15.0.json b/benchmarks/baselines/upload-formats-0.15.0.json new file mode 100644 index 0000000..ce3897d --- /dev/null +++ b/benchmarks/baselines/upload-formats-0.15.0.json @@ -0,0 +1 @@ +{"json": [[5000, 500, 20996993, 119.8058750629425, 119.2266, 5.819], [10000, 100, 8440750, 3.4674301147460938, 3.1988000000000003, 2.1028000000000002], [5000, 500, 20998513, 9.29143214225769, 8.539399999999999, 5.8095], [10000, 100, 8438739, 57.654605865478516, 57.3999, 2.1455], [5000, 500, 21000090, 9.14257287979126, 8.5596, 5.8162], [10000, 100, 8438888, 96.69537305831909, 96.3831, 2.1441999999999997], [5000, 500, 20997107, 9.350135087966919, 8.7999, 5.833399999999999], [10000, 100, 8439134, 3.8683810234069824, 3.6292, 2.1445], [5000, 500, 20996931, 8.964529991149902, 8.4201, 5.8328], [10000, 100, 8438861, 3.690462112426758, 3.4435, 2.2672], [5000, 500, 20998565, 8.993377208709717, 8.4707, 5.8933], [10000, 100, 8438533, 3.898621082305908, 3.6381, 2.4614000000000003]], "parquet": [[5000, 500, 12823871, 7.7264978885650635, 7.4955, 5.8472], [10000, 100, 5003832, 2.8273868560791016, 2.6939, 2.0948], [5000, 500, 12823805, 7.705421209335327, 7.4798, 5.7523], [10000, 100, 5003693, 44.643141984939575, 44.505, 2.1267], [5000, 500, 12823275, 7.398684978485107, 7.1949, 5.7483], [10000, 100, 5004402, 3.03066086769104, 2.8676999999999997, 2.1422], [5000, 500, 12825915, 7.195647954940796, 6.985399999999999, 5.754899999999999], [10000, 100, 5004461, 3.2773349285125732, 3.151, 2.128], [5000, 500, 12822958, 7.261019945144653, 7.0492, 5.8436], [10000, 100, 5003859, 2.9874889850616455, 2.8461, 2.3122], [5000, 500, 12824351, 7.126675844192505, 6.9263, 5.7143999999999995], [10000, 100, 5003264, 3.1192901134490967, 2.9824, 2.4323]]} diff --git a/benchmarks/bench_upload_formats.py b/benchmarks/bench_upload_formats.py new file mode 100644 index 0000000..601aebb --- /dev/null +++ b/benchmarks/bench_upload_formats.py @@ -0,0 +1,102 @@ +"""The same training tables sent through a gateway as JSON rows and as a +Parquet file (multipart), interleaved, to see whether the request's size +decides the time it spends in transit. + +For each fit it prints the total time, the time in the hop to tabctx +(the gateway's Server-Timing `upstream`, less tabctx's own `worker_call`) +and tabctx's own work. Values are rounded to 4 decimals, as prod_bench's +tables are, so the JSON stays under the gateway's 30 MiB limit. + + set -a; . ./.env; set +a + OUT=baselines/upload-formats.json uv run python benchmarks/bench_upload_formats.py +""" + +import json +import os +import pathlib +import re +import time +import urllib.request + +import numpy as np + +from tabctx.client import _Multipart +from tabctx.table_files import columns, to_parquet + +KEY = os.environ["VP_API_KEY"] +BASE = "https://proxy.vectorinstitute.ai" + + +def server_timing(headers): + st = {} + for part in (headers.get("Server-Timing") or "").split(","): + m = re.match(r"\s*(\w+).*dur=([\d.]+)", part) + if m: + st[m.group(1)] = float(m.group(2)) / 1000 + return st + + +def send(path, data, ctype): + req = urllib.request.Request( + BASE + path, data, {"Authorization": f"Bearer {KEY}", "Content-Type": ctype} + ) + t = time.time() + with urllib.request.urlopen(req, timeout=600) as r: + body = json.loads(r.read()) + st = server_timing(r.headers) + tm = body.get("timings_ms") or {} + return time.time() - t, st.get("upstream", 0), tm.get("worker_call", 0) / 1000 + + +rng = np.random.default_rng(0) +results = {"json": [], "parquet": []} +for rnd in range(6): + for n, f in ((5000, 500), (10000, 100)): + X = np.round(rng.normal(size=(n, f)), 4).astype(np.float32) + y = (X[:, 0] > 0).astype(int) + for fmt in ("json", "parquet") if rnd % 2 == 0 else ("parquet", "json"): + ds = f"jvp-{fmt}-{rnd}-{n}x{f}" + if fmt == "json": + data = json.dumps( + { + "model": "tabicl-v2", + "dataset_id": ds, + "train_X": np.round(X.astype(np.float64), 4).tolist(), + "train_y": y.tolist(), + } + ).encode() + ctype = "application/json" + else: + pq = to_parquet(columns(X), ("label", y)) + form = _Multipart( + {"model": "tabicl-v2", "dataset_id": ds, "target": "label"}, pq + ) + data = b"".join(form.chunks()) + ctype = form.content_type + total, up, work = send("/v1/tabctx/fit", data, ctype) + results[fmt].append((n, f, len(data), total, up, work)) + print( + f"{fmt:7s} {n}x{f} {len(data) / 2**20:5.1f} MiB total {total:6.1f}s hop {up - work:6.1f}s tabctx {work:5.1f}s", + flush=True, + ) +pathlib.Path(os.environ["OUT"]).write_text(json.dumps(results)) +for fmt, rows in results.items(): + hops = sorted(r[4] - r[5] for r in rows) + tot = sorted(r[3] for r in rows) + print( + fmt, + "MiB", + round(np.mean([r[2] for r in rows]) / 2**20, 1), + "hop p50", + round(hops[len(hops) // 2], 1), + "max", + round(hops[-1], 1), + "| total p50", + round(tot[len(tot) // 2], 1), + "max", + round(tot[-1], 1), + "| over 10s:", + sum(t > 10 for t in tot), + "/", + len(tot), + )