From 1c04b7f78248fccd058f9c37af1a07096d8e1cea Mon Sep 17 00:00:00 2001 From: Jon Froehlich Date: Mon, 3 Aug 2026 14:30:13 -0700 Subject: [PATCH 1/2] Measure Stage 2 training cost: 3.5 h/epoch, not 36 (#59, #84) MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit docs/curb_ramp_data_sourcing.md §7 estimated ">=36 h on 16 L40s for one epoch (>=580 GPU-h)" by reading the README's "> 24 hours" as a per-epoch figure. It is ~10x too high: that ">24 h" covers the paper's whole ~12-epoch, preemption- riddled run. The paper run's TensorBoard events survived on a lab scratch volume as the only copy. Rescued here (18 files, SHA256SUMS manifest) and measured: - 1.341 s/step, 16 GPUs x batch 1, p25-p75 spread of 6 ms over 119,902 samples -- a hard I/O bound, ~3% MFU, so cost scales with PANORAMAS and is fixable (pre-resized panos / more dataloader CPU); - one epoch = 9,378 steps = 3.49 h (~56 GPU-h); - at the 500k-record target: ~16,840 steps, 6.27 h/epoch (~100 GPU-h). Stage 2 is an overnight job; Stage 1 generation is the whole "week". Two things the record did not have. The run went ~12 epochs (max step 112,434 = 11.99 x 9,378), and auto-label val loss bottoms at epoch 5 (-12% vs epoch 1) then rises through epoch 11 -- half of #84's epoch curve, already run. But best_model.pth is byte-identical to checkpoints/epoch_1_step_9378.pth, so the released model IS epoch 1 as documented; the 11 further epochs were discarded. The human half of #84 still needs a re-run: the epoch-N weights are gone. Stated gaps: the June code is not in git (squashed Initial Commit, 2025-07-15, already num_epochs=1); sacct shows only 3 of 15 segments because --requeue overwrites the record; the surviving Slurm records show A100s, not the README's L40s (immaterial while I/O-bound). Replication needs no TensorFlow, GPU, or network: python scripts/analysis/stage2_train_cost.py --records 500000 Co-Authored-By: Claude Opus 5 (1M context) --- .gitattributes | 3 + docs/curb_ramp_data_sourcing.md | 17 +- docs/data/rampnet1_stage2_run/SHA256SUMS | 18 ++ ...ents.out.tfevents.1750174762.g3055.43447.0 | Bin 0 -> 117303 bytes ...ents.out.tfevents.1750180844.g3062.14804.0 | Bin 0 -> 88 bytes ...ents.out.tfevents.1750183626.g3050.36559.0 | Bin 0 -> 64510 bytes ...vents.out.tfevents.1750193154.g3050.2751.0 | Bin 0 -> 175156 bytes ...ents.out.tfevents.1750201343.g3044.22332.0 | Bin 0 -> 291957 bytes ...ents.out.tfevents.1750212922.g3044.67324.0 | Bin 0 -> 54034 bytes ...ents.out.tfevents.1750216934.g3050.45169.0 | Bin 0 -> 31894 bytes ...ents.out.tfevents.1750220787.g3050.14431.0 | Bin 0 -> 620604 bytes ...ents.out.tfevents.1750239477.g3050.19797.0 | Bin 0 -> 1039201 bytes ...ents.out.tfevents.1750269421.g3051.72851.0 | Bin 0 -> 79123 bytes ...ents.out.tfevents.1750274992.g3050.52082.0 | Bin 0 -> 261778 bytes ...ents.out.tfevents.1750313942.g3060.84431.0 | Bin 0 -> 1036726 bytes ...ents.out.tfevents.1750342897.g3060.36630.0 | Bin 0 -> 654587 bytes ...ents.out.tfevents.1750364668.g3055.83090.0 | Bin 0 -> 88 bytes ...ents.out.tfevents.1750368998.g3060.43445.0 | Bin 0 -> 88 bytes ...ents.out.tfevents.1750373704.g3060.51918.0 | Bin 0 -> 192697 bytes ...ents.out.tfevents.1750402303.g3075.10163.0 | Bin 0 -> 1110206 bytes ...ents.out.tfevents.1750432003.g3082.25106.0 | Bin 0 -> 849012 bytes docs/stage2_training_cost.md | 128 ++++++++++ scripts/analysis/stage2_train_cost.py | 230 ++++++++++++++++++ 23 files changed, 391 insertions(+), 5 deletions(-) create mode 100644 .gitattributes create mode 100644 docs/data/rampnet1_stage2_run/SHA256SUMS create mode 100644 docs/data/rampnet1_stage2_run/events.out.tfevents.1750174762.g3055.43447.0 create mode 100644 docs/data/rampnet1_stage2_run/events.out.tfevents.1750180844.g3062.14804.0 create mode 100644 docs/data/rampnet1_stage2_run/events.out.tfevents.1750183626.g3050.36559.0 create mode 100644 docs/data/rampnet1_stage2_run/events.out.tfevents.1750193154.g3050.2751.0 create mode 100644 docs/data/rampnet1_stage2_run/events.out.tfevents.1750201343.g3044.22332.0 create mode 100644 docs/data/rampnet1_stage2_run/events.out.tfevents.1750212922.g3044.67324.0 create mode 100644 docs/data/rampnet1_stage2_run/events.out.tfevents.1750216934.g3050.45169.0 create mode 100644 docs/data/rampnet1_stage2_run/events.out.tfevents.1750220787.g3050.14431.0 create mode 100644 docs/data/rampnet1_stage2_run/events.out.tfevents.1750239477.g3050.19797.0 create mode 100644 docs/data/rampnet1_stage2_run/events.out.tfevents.1750269421.g3051.72851.0 create mode 100644 docs/data/rampnet1_stage2_run/events.out.tfevents.1750274992.g3050.52082.0 create mode 100644 docs/data/rampnet1_stage2_run/events.out.tfevents.1750313942.g3060.84431.0 create mode 100644 docs/data/rampnet1_stage2_run/events.out.tfevents.1750342897.g3060.36630.0 create mode 100644 docs/data/rampnet1_stage2_run/events.out.tfevents.1750364668.g3055.83090.0 create mode 100644 docs/data/rampnet1_stage2_run/events.out.tfevents.1750368998.g3060.43445.0 create mode 100644 docs/data/rampnet1_stage2_run/events.out.tfevents.1750373704.g3060.51918.0 create mode 100644 docs/data/rampnet1_stage2_run/events.out.tfevents.1750402303.g3075.10163.0 create mode 100644 docs/data/rampnet1_stage2_run/events.out.tfevents.1750432003.g3082.25106.0 create mode 100644 docs/stage2_training_cost.md create mode 100644 scripts/analysis/stage2_train_cost.py diff --git a/.gitattributes b/.gitattributes new file mode 100644 index 0000000..f440671 --- /dev/null +++ b/.gitattributes @@ -0,0 +1,3 @@ +# The rescued RampNet 1.0 training telemetry is binary: never let autocrlf touch it. +docs/data/rampnet1_stage2_run/events.out.tfevents.* binary +docs/data/rampnet1_stage2_run/SHA256SUMS text eol=lf diff --git a/docs/curb_ramp_data_sourcing.md b/docs/curb_ramp_data_sourcing.md index 0289546..4fb7607 100644 --- a/docs/curb_ramp_data_sourcing.md +++ b/docs/curb_ramp_data_sourcing.md @@ -769,17 +769,24 @@ illustrative rather than recommended). | + four cities | ~422,000 | ~325,000 | ~227,500 | ~14,200 | | Growth | +52% | **1.52×** | | | -- **Stage 2: ≳36 h on 16 L40s** for one epoch (≳580 GPU-h) — the paper trained 1 epoch on 16 L40s, - batch 1 per GPU (VRAM-bound), and the README says it *"will take a very long time (> 24 hours)"*. - **>24 h is a floor, not a measurement**; no exact wall-clock is recorded anywhere. +- **Stage 2: 3.49 h on 16 GPUs for one epoch (~56 GPU-h)** — **measured** 2026-08-03 from the paper + run's own TensorBoard events at 1.341 s/step, 9,378 steps/epoch. Scaled to this scenario + (~14,200 steps): **~5.3 h/epoch**. Full method, the 500k projection, and the caveats are in + [`stage2_training_cost.md`](stage2_training_cost.md). + > **Correction.** This bullet previously read *"≳36 h on 16 L40s for one epoch (≳580 GPU-h)"*, + > extrapolated from the README's *"> 24 hours"*. That was **~10× too high**: the ">24 h" covers + > the paper's *whole ~12-epoch, preemption-riddled run* (44.7 h active / 74.6 h calendar), not one + > epoch. Stage 2 is an overnight job; **the epoch count matters ~12× more than the corpus size**. - **Stage 1 generation is the long pole and is entirely unmeasured** — ~111k new panoramas at 32 tiles each ≈ **3.5M tile requests** against Google's undocumented endpoints, fetched 26 panoramas at a time. `run_download_dataset.slurm` allocates 100 h. Rate limiting is the dominant risk. - **The crop model needs no retrain** — reusing it is also the cleaner experiment, since only the data changes. -Order of magnitude: **about a week of wall-clock**, wide error bars on the Stage 1 half. Add #84's -epoch curve and multiply. +Order of magnitude: **about a week of wall-clock**, wide error bars on the Stage 1 half — and now +that Stage 2 is measured, that week is **essentially all Stage 1**. #84's epoch curve is the one +multiplier that still moves the Stage 2 half (×5 at 1.0's auto-val optimum, ×12 at what the paper +run actually did). ## 8. 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z>K;S>wC3wV#I|IPH54`3NwJ=(vdpMqK3>wcXDF!(Bi!AJ7_Bc{4YZl|b8TZ#=@-^Qfe#=9eCHgGvGJBp1;4dN1`mzN^8zx*gLdy1`jSIf+| zUBF))qpAR44ixh@sOQWhFXP3^=)!bhjueX?JcTWYbjMq^2lIabbE23^mMe=gKePf} zTjksFyF^VoQ_M!}D9bKOIgi++J1c;>Q0(eQ388mf=RU+bo?8HOrP$f^F#zZ%$eirp`n$1B@AJVNZ4`6*z&6pJ1CoO@__pdGPuyKe&XquANt zZ+ztTiw209Y{~@YPq99g+04D#0RMvhcirE>0w{LdGePjaS_dDbJzk}-Th!zYiiNzs zEOba+t%U}8ew8k;K#G~gU+14*QNjC@KL?xv7DTZduf#Qu=`X#A(%$w70~SoN9*GLh z-Z-HRG2LHTz(Oddd1EY#TyzA_f)f9B0=r4E?97*7Dlo4+I3uc|IQS|YP!w>yG1ekzskZ@3Rm&>ZOy-1z;07a{k^k>ZtJ$3II(IBT(cLBRgu}ZzGZ1eI>_=&UgjgpC|$#9DOHcen7rPl?a zw5F(a!0u73xv-YK7+j-{*!Jt^f!(K=+%Xw`jO@{8h`9&e0rr4mPtE+9!>A+FntUk0@5oJ>g8(mDQp_emlJZ z*kg)?*xlyly=(VD%*gZtut 24 hours)"* as a per-epoch figure; the ">24 hours" in fact describes the **whole ~12-epoch, +preemption-riddled run**. + +Everything below is reproduced by one committed command: + +```bash +python scripts/analysis/stage2_train_cost.py --records 500000 +``` + +## The measurement + +| | | +| :--- | ---: | +| Median step time (rank 0) | **1.341 s** (p25 1.339 / p75 1.345, n = 119,902) | +| Global batch | 16 panoramas (16 GPUs x batch 1) | +| Throughput | **11.93 panos/s** | +| Steps per epoch | **9,378** (150,063 train panos / 16) | +| **Wall-clock per epoch** | **3.49 h** (+ ~0.3 h validation) | +| **GPU-hours per epoch** | **~56** | + +The interquartile band is 6 ms wide across 119,902 samples, which is the signature of a hard +**I/O bound**, not a compute bound: ~7.7 TFLOPs of forward+backward per panorama against an A100 +is roughly **3% MFU**. The cost is 8.4 MP JPEG decode + resize on ~3 CPU cores per rank +(`--cpus-per-task=12`, 4 ranks per node, `num_workers=4`). Two consequences: + +- **cost scales with panoramas, not with label count** — which is what makes the projection below + meaningful, and what makes "number of labels" the wrong unit for a training-time question; +- **it is fixable**. Pre-resized panoramas, more dataloader CPU, or a shard format that avoids + per-file decode could cut it several-fold. Nothing here is a floor imposed by the model. + +## The run was ~12 epochs, and the released model is epoch 1 + +Both statements are true and the repo previously recorded only the second. + +Validation scalars land on exact multiples of 9,378, so the epoch length is confirmed +independently of the step-rate arithmetic. The run reached step 112,434 = **11.99 epochs** before +being cancelled, over **44.7 h of active compute** and **74.6 h of calendar time** across 15 +preemptions on the `ckpt-all` scavenger partition — a **x1.67 overhead**, each restart rewinding to +the last 1000-step checkpoint. + +| epoch | 1 | 2 | 3 | 4 | **5** | 6 | 7 | 8 | 9 | 10 | 11 | +| :--- | ---: | ---: | ---: | ---: | ---: | ---: | ---: | ---: | ---: | ---: | ---: | +| auto-label val loss | .000520 | .000478 | .000463 | .000466 | **.000458** | .000468 | .000470 | .000473 | .000484 | .000487 | .000487 | + +**Auto-label validation loss bottoms at epoch 5 (-12% against epoch 1), then rises monotonically +through epoch 11.** So at 1 epoch the model is *not* converged — but the available headroom is +small and it reverses. + +Nevertheless `best_model.pth` is **byte-identical** (`cmp`) to `checkpoints/epoch_1_step_9378.pth`, +copied back by hand on 2025-06-21 13:02 over the val-best file the training script had saved +automatically. **The released RampNet model is the epoch-1 checkpoint**, exactly as the README +describes. What the README omits is that 11 further epochs were run and discarded. + +This is **half of issue #84's epoch curve, already run**: the auto-label half exists above at zero +cost. The half that is still missing is the **human** one — evaluating epochs 1/3/5/8 against +`manual_gold`. If auto-label val loss keeps improving while human F1 does not, that is the Stage 1 +label ceiling, and it is the whole question #84 was filed to answer. **The epoch-N weights no +longer exist** (a 2025-07-11 cleanup left only epoch 1), so that half needs a re-run. + +## Projection to a 500,000-record corpus + +**"500k" counts government ramp records, not labels.** RampNet 1.0 is 278,544 records -> +214,376 panoramas carrying **849,895 point labels** (~3 views per ramp, ~4 labels per panorama). +500k records is **1.80x** the records and would imply ~1.5M labels. + +| | RampNet 1.0 | at 500k records | +| :--- | ---: | ---: | +| Government ramp records | 278,544 | 500,000 (**1.80x**) | +| Panoramas (x0.770 per record) | 214,376 | ~384,800 | +| Train split (70.0%) | 150,063 | ~269,400 | +| Steps per epoch (/16 GPUs) | 9,378 | ~16,840 | +| **Wall-clock per epoch** | 3.49 h | **6.27 h** (~100 GPU-h) | + +| Schedule | Compute (16 GPUs) | Calendar on preemptible `ckpt` | +| :--- | ---: | ---: | +| 1 epoch (the released recipe) | **6.3 h** | ~10.5 h | +| 5 epochs (1.0's auto-val optimum) | 31.4 h | ~2.2 d | +| 12 epochs (what 1.0 actually ran) | 75.2 h | ~5.2 d | + +**Stage 2 retraining is an overnight job.** The epoch count is worth ~12x what the data doubling +is worth, so the schedule decision dominates the corpus decision for wall-clock purposes. + +**Stage 1 generation remains the long pole and is still unmeasured**: ~170k *new* panoramas at 32 +tiles each is ~5.5M tile requests against Google's undocumented endpoints, rate-limit bound, with +100 h allocated in `run_download_dataset.slurm`, plus ~800 GB of storage. §7's "about a week of +wall-clock" stands — but it is essentially all Stage 1. The crop model needs no retrain. + +Two caveats on the projection: the 0.770 panoramas/record ratio and the 70.0% train share are +RampNet 1.0's and will shift with city density (denser, NYC-like inventories put more ramps in each +panorama); and at a fixed 1 epoch, 1.8x the data silently becomes a 1.8x longer schedule, which is +a change in training regime, not only in cost. + +## Provenance, and what is not replicable + +The inputs are **18 TensorBoard event files, committed at `docs/data/rampnet1_stage2_run/`** with a +`SHA256SUMS` manifest. They were rescued 2026-08-03 from +`/gscratch/makelab/jsomeara/RampNet/stage_two/runs/experiment_1` on the UW Hyak cluster, where they +were the only copy. `scripts/analysis/stage2_train_cost.py` parses the TFRecord and protobuf +framing directly, so replication needs **no TensorFlow, no TensorBoard, no GPU and no network**: + +```bash +python scripts/analysis/stage2_train_cost.py # the measurement +python scripts/analysis/stage2_train_cost.py --records 500000 # the projection +python scripts/analysis/stage2_train_cost.py --verify # re-check SHA256SUMS +``` + +Stated gaps, deliberately: + +- **The code that produced this run is not in git.** The public history begins with a squashed + "Initial Commit" dated 2025-07-15, a month after the run, already carrying `num_epochs = 1`. The + run-time value was >= 12 and is unrecoverable. +- **The Slurm record is incomplete.** `sacct -u jsomeara -S 2025-06-16 -E 2025-06-22` shows only + three `train_curb_ramp_detector` jobs of 2-3 h each, because `--requeue` overwrites the accounting + record on each requeue; the event files are the only evidence of the other 12 segments. (The + event-file hostnames — g3050, g3060, g3082 — are what tie the logs to those 4-node jobs.) +- **The hardware claim is looser than the README's.** README says 16x L40s; the surviving Slurm + records show those incarnations holding **A100s**, under `--constraint='l40s|l40|a40|a100'`. + Because the run is I/O-bound, GPU type barely moves the step rate — consistent with the observed + 1.34-1.53 s/step across segments on mixed hardware. +- **Per-epoch weights are gone**, so the human half of the epoch curve cannot be recovered from + this run; it needs a re-run (#84). diff --git a/scripts/analysis/stage2_train_cost.py b/scripts/analysis/stage2_train_cost.py new file mode 100644 index 0000000..d7a2886 --- /dev/null +++ b/scripts/analysis/stage2_train_cost.py @@ -0,0 +1,230 @@ +"""What did RampNet 1.0's Stage 2 training actually cost? 3.5 h/epoch on 16 GPUs — not 36. + +`docs/curb_ramp_data_sourcing.md` §7 estimated "≳36 h on 16 L40s for one epoch +(≳580 GPU-h)" by extrapolating the README's ">24 hours". That extrapolation is +~10x too high: the ">24 hours" describes the *whole* multi-epoch, preemption- +riddled run, not one epoch. + +This script measures it instead, from the paper run's own TensorBoard event +files (committed at docs/data/rampnet1_stage2_run/, rescued 2026-08-03 from +/gscratch/makelab/jsomeara/RampNet/stage_two/runs/experiment_1). It parses the +TFRecord + protobuf framing directly, so it needs no TensorFlow, no +TensorBoard, and no network: + + - 1.341 s/step median (rank 0), 16 GPUs x batch 1 = 16 panos/step; + - one epoch = 9,378 steps exactly (150,063 train panos / 16), confirmed by + validation scalars landing on exact multiples of 9,378; + - so one epoch = 3.49 h wall-clock, ~56 GPU-h; + - the run itself did ~12 epochs (max step 112,434 = 11.99 x 9,378) over + 44.7 h of active compute and 74.6 h of calendar time across 15 preemptions + (x1.67 overhead on the `ckpt-all` scavenger partition); + - auto-label val loss bottoms at epoch 5 (0.000458) vs epoch 1 (0.000520), + then rises through epoch 11 — half of #84's epoch curve, already run. + +The released model is nonetheless epoch 1: best_model.pth is byte-identical +(`cmp`) to checkpoints/epoch_1_step_9378.pth, copied back by hand on +2025-06-21. So the documented "1 epoch" recipe describes the released artifact +correctly; it just omits that 11 further epochs were run and discarded. + +Because the step time is I/O-bound (~3% MFU: 8.4 MP JPEG decode + resize on +~3 cores per rank), it scales with *panoramas*, not with label count -- which +is what makes the --records projection below meaningful. + + python scripts/analysis/stage2_train_cost.py + python scripts/analysis/stage2_train_cost.py --records 500000 + python scripts/analysis/stage2_train_cost.py --verify # sha256 manifest +""" +import argparse +import glob +import hashlib +import os +import statistics +import struct + +REPO = os.path.dirname(os.path.dirname(os.path.dirname(os.path.abspath(__file__)))) +DEFAULT_EVENTS = os.path.join(REPO, "docs", "data", "rampnet1_stage2_run") + +# RampNet 1.0 ground truth, from README.md's dataset table and docs/curb_ramp_data_sourcing.md §7. +RAMPNET1_RECORDS = 278_544 # government ramp records (NYC + Portland + Bend) +RAMPNET1_PANOS = 214_376 # panoramas generated from them +RAMPNET1_LABELS = 849_895 # point labels on those panoramas +RAMPNET1_TRAIN_PANOS = 150_063 # train split +WORLD_SIZE = 16 # 4 nodes x 4 GPUs, run_train.slurm +MAX_PLAUSIBLE_STEP_GAP_S = 600 # drop gaps that span a preemption, not a step + + +def _varint(buf, j): + val = shift = 0 + while j < len(buf): + byte = buf[j] + j += 1 + val |= (byte & 0x7F) << shift + shift += 7 + if not byte & 0x80: + break + return val, j + + +def _fields(buf): + """Yield (field_number, wire_type, payload) for one protobuf message.""" + j = 0 + while j < len(buf): + key, j = _varint(buf, j) + fno, wire = key >> 3, key & 7 + if wire == 0: + val, j = _varint(buf, j) + yield fno, wire, val + elif wire == 1: + yield fno, wire, buf[j:j + 8] + j += 8 + elif wire == 2: + ln, j = _varint(buf, j) + yield fno, wire, buf[j:j + ln] + j += ln + elif wire == 5: + yield fno, wire, buf[j:j + 4] + j += 4 + else: + return + + +def _records(path): + """Yield each Event payload from a TFRecord-framed tfevents file.""" + with open(path, "rb") as handle: + buf = handle.read() + i, n = 0, len(buf) + while i + 12 <= n: + (length,) = struct.unpack_from(" n: + break # truncated tail: the job was killed mid-write + yield buf[i:i + length] + i += length + 4 # payload + masked crc32 + + +def read_scalars(path): + """Return [(wall_time, step, tag, value)] for every scalar summary in a file.""" + out = [] + for payload in _records(path): + wall = summary = None + step = 0 + for fno, wire, val in _fields(payload): + if fno == 1 and wire == 1: + (wall,) = struct.unpack("7,} " + f"val {loss:.6f}{mark}") + + print(f"\n== per-epoch cost, {args.train_panos:,} train panos / {args.world_size} GPUs ==") + epoch_h = steps_per_epoch * s_per_step / 3600 + print(f" {steps_per_epoch:,} steps x {s_per_step:.3f} s = {hms(epoch_h)} " + f"({epoch_h * args.world_size:.0f} GPU-h)") + + if args.records: + ppr = RAMPNET1_PANOS / RAMPNET1_RECORDS + train_share = args.train_panos / RAMPNET1_PANOS + panos = args.records * ppr + train_panos = panos * train_share + steps = train_panos / args.world_size + per_epoch_h = steps * s_per_step / 3600 + print(f"\n== projection: {args.records:,} records " + f"({args.records / RAMPNET1_RECORDS:.2f}x RampNet 1.0) ==") + print(f" panoramas : {panos:>12,.0f} (x{ppr:.3f} per record, 1.0's ratio)") + print(f" train split : {train_panos:>12,.0f} ({train_share:.1%}, 1.0's share)") + print(f" steps/epoch : {steps:>12,.0f}") + print(f" labels : {args.records * RAMPNET1_LABELS / RAMPNET1_RECORDS:>12,.0f} " + f"(1.0 has {RAMPNET1_LABELS:,} -- 'records' and 'labels' differ ~3x)") + print(f" {'epochs':>8} {'compute':>12} {'GPU-h':>8} {'calendar on ckpt':>18}") + for n in args.epochs: + total = per_epoch_h * n + print(f" {n:>8} {hms(total):>12} {total * args.world_size:>8.0f} " + f"{hms(total * calendar_h / active_h):>18}") + + +if __name__ == "__main__": + main() From f63fc01a8a1c3dd3bb68f7a2b9ae7b081d05581a Mon Sep 17 00:00:00 2001 From: Jon Froehlich Date: Mon, 3 Aug 2026 15:34:01 -0700 Subject: [PATCH 2/2] Measure Stage 1 generation too (#59, #18): 97.91% yield, all loss is fetch failure MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit Second half of the §7 correction. The bullet called Stage 1 "the long pole and entirely unmeasured" -- it is the long pole, and it is now measured, from the paper run's own logs rescued off the same scratch volume as the Stage 2 events. Yield: 219,170 intended -> 214,599 written (97.91%). Of the 4,571 never written, 4,570 are exactly the panoramas Google refused to serve; one is unexplained. download_dataset.py marks an index done only after both the .jpg and .json are written and re-reads progress.txt on restart, so it is a clean completion ledger and fetch failure is the whole loss mechanism. The disk-quota wall cost nothing. The last log ends in 11,438 "[Errno 122] Disk quota exceeded" errors, which reads like a truncated dataset; all 11,438 line indices appear in progress.txt, i.e. every one completed on a later pass. Worth recording because the surface impression is alarming and wrong -- and because the run did hit a storage wall at ~214k panos, which a 2.0-scale corpus doubles. Timing, from sacct and explicitly LOWER BOUNDS (--requeue overwrites the record, and the run was finished off in interactive jobs whose stdout never reached the log -- progress.txt was modified two days after the last Slurm log write): run_download_dataset.slurm >=49.1 h / 26 jobs => <=4,370 panos/h run_generate_meta.slurm >=13.5 h / 7 jobs run_generate_negatives.slurm >=1.5 h / 2 jobs So a full 385k-pano rebuild is >=88 h and the incremental path >=39 h: §7's "about a week" is confirmed as the right order of magnitude, now with a measured basis, and it is essentially all Stage 1. Also rescued: the evaluation output behind the paper's Stage 1 94.0% precision. It records the 119 ignored redundant points the README says have never been re-measured, so counting them as FP gives precision 0.9403 -> 0.9121 (-2.8 pts), recall unaffected. That is the redundant-detection half of #18 only -- the matching-rule half still needs a re-run -- so 0.9121 is an upper bound on the corrected number, and it says the Stage 1 correction is the same size as the Stage 2 one, not an order larger. Open: progress.txt records 214,599 written but the published dataset has 214,376, a gap of 223 (0.10%) that nothing in the rescued evidence explains. Not done here: location_data/ (71 MB) and street_data/ (801 MB) are the PAPER-ERA source inventories, also rescued to gscratch but too large to commit. Publishing them is the largest remaining Stage 1 replication win, and §9 is the reason it matters -- today's endpoints have drifted (Bend +8.7%). Co-Authored-By: Claude Opus 5 (1M context) --- .gitattributes | 1 + docs/curb_ramp_data_sourcing.md | 11 +- docs/data/rampnet1_stage1_run/SHA256SUMS | 4 + .../rampnet1_stage1_run/missing_panos.csv | 4572 + docs/data/rampnet1_stage1_run/progress.txt | 214599 +++++++++++++++ .../stage1_evaluation_results.txt | 21 + docs/stage1_generation_cost.md | 147 + scripts/analysis/stage1_yield.py | 136 + 8 files changed, 219488 insertions(+), 3 deletions(-) create mode 100644 docs/data/rampnet1_stage1_run/SHA256SUMS create mode 100644 docs/data/rampnet1_stage1_run/missing_panos.csv create mode 100644 docs/data/rampnet1_stage1_run/progress.txt create mode 100644 docs/data/rampnet1_stage1_run/stage1_evaluation_results.txt create mode 100644 docs/stage1_generation_cost.md create mode 100644 scripts/analysis/stage1_yield.py diff --git a/.gitattributes b/.gitattributes index f440671..d74787c 100644 --- a/.gitattributes +++ b/.gitattributes @@ -1,3 +1,4 @@ # The rescued RampNet 1.0 training telemetry is binary: never let autocrlf touch it. docs/data/rampnet1_stage2_run/events.out.tfevents.* binary docs/data/rampnet1_stage2_run/SHA256SUMS text eol=lf +docs/data/rampnet1_stage1_run/SHA256SUMS text eol=lf diff --git a/docs/curb_ramp_data_sourcing.md b/docs/curb_ramp_data_sourcing.md index 4fb7607..a4119cd 100644 --- a/docs/curb_ramp_data_sourcing.md +++ b/docs/curb_ramp_data_sourcing.md @@ -777,9 +777,14 @@ illustrative rather than recommended). > extrapolated from the README's *"> 24 hours"*. That was **~10× too high**: the ">24 h" covers > the paper's *whole ~12-epoch, preemption-riddled run* (44.7 h active / 74.6 h calendar), not one > epoch. Stage 2 is an overnight job; **the epoch count matters ~12× more than the corpus size**. -- **Stage 1 generation is the long pole and is entirely unmeasured** — ~111k new panoramas at 32 - tiles each ≈ **3.5M tile requests** against Google's undocumented endpoints, fetched 26 panoramas - at a time. `run_download_dataset.slurm` allocates 100 h. Rate limiting is the dominant risk. +- **Stage 1 generation is the long pole — and is now measured at ≤4,370 panoramas/h** (≥49.1 h for + the paper's 214,599, a lower bound: `--requeue` hides incarnations and the run was finished + interactively). ~111k new panoramas at 32 tiles each ≈ **3.5M tile requests** against Google's + undocumented endpoints ⇒ **≥25 h**; a full 385k-panorama rebuild is **≥88 h**. Rate limiting + remains the dominant risk, and **storage is a real prerequisite** — the paper run hit a disk-quota + wall at ~214k panoramas (it recovered, losing nothing). Yield was **97.91%**, the shortfall being + almost exactly the panoramas Google refused to serve. Full method and caveats: + [`stage1_generation_cost.md`](stage1_generation_cost.md). - **The crop model needs no retrain** — reusing it is also the cleaner experiment, since only the data changes. diff --git a/docs/data/rampnet1_stage1_run/SHA256SUMS b/docs/data/rampnet1_stage1_run/SHA256SUMS new file mode 100644 index 0000000..70be2d2 --- /dev/null +++ b/docs/data/rampnet1_stage1_run/SHA256SUMS @@ -0,0 +1,4 @@ +59b9a64c06232a7d24a7008cd25a3f1fd901fb1275a2a920051b4d4b59bc28aa *progress.txt +d0956c263b90aaae9bcaccb5a06ac5799d3e21e14d1da1a9c28d252b52c86a6d *download_dataset.out +9e9f5897a69596f731d11b9b83199313573ba46b503143a9ae52629980809da9 *missing_panos.csv +19f74ca3114a63e56a98e8c6bf06d71561414279171bbfb58c4d6105bacfb308 *stage1_evaluation_results.txt diff --git a/docs/data/rampnet1_stage1_run/missing_panos.csv b/docs/data/rampnet1_stage1_run/missing_panos.csv new file mode 100644 index 0000000..a6fb9ac --- /dev/null +++ b/docs/data/rampnet1_stage1_run/missing_panos.csv @@ -0,0 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threshold (normalized): 0.022 +------------------------------------------------------------- +Number of common images processed: 1000 +Total Ground Truth points (from manual labels in common images): 3919 +Total 'Predicted' points from test split labels (in common images): 3972 + -> 'Predicted' points considered (TP+FP): 3853 + -> 'Predicted' points ignored (matched already-claimed GT): 119 +------------------------------------------------------------- +True Positives (TP): 3623 +False Positives (FP): 230 +False Negatives (FN): 296 +------------------------------------------------------------- +Precision (TP / (TP + FP)): 0.9403 +Recall (TP / Total GT): 0.9245 +------------------------------------------------------------- diff --git a/docs/stage1_generation_cost.md b/docs/stage1_generation_cost.md new file mode 100644 index 0000000..7b4e059 --- /dev/null +++ b/docs/stage1_generation_cost.md @@ -0,0 +1,147 @@ +# What Stage 1 generation cost, and what it lost + +**97.91% yield, and the entire 2.09% loss is panoramas Google refused to serve.** Measured +2026-08-03 from the paper run's own logs, which survived on a lab scratch volume and are now +committed at `docs/data/rampnet1_stage1_run/`. + +[`curb_ramp_data_sourcing.md` §7](curb_ramp_data_sourcing.md#7-retrain-cost) calls Stage 1 "the long +pole and **entirely unmeasured**". It is the long pole — that part holds, and is now the *only* +reason a retrain takes days rather than hours (see [`stage2_training_cost.md`](stage2_training_cost.md)). +It is no longer unmeasured. + +```bash +python scripts/analysis/stage1_yield.py +``` + +## Yield + +| | | +| :--- | ---: | +| Intended (`finaldataset.jsonl` lines) | 219,170 | +| Written (`progress.txt`, unique) | **214,599** | +| Never written | 4,571 (**2.09%**) | +| ...of which Google refused to serve | **4,570 of 4,571 (99.98%)** | +| ...unexplained | 1 | + +`download_dataset.py` appends an index to `progress.txt` only after **both** the `.jpg` and the +`.json` are written (`mark_done(idx)` is the last statement of `process_line`), and re-reads that +file on restart, so it is a clean completion ledger with no duplicates. A fetch failure returns +early without marking, so it is simply retried on the next pass — which is why the residual is +almost exactly the set of panos that never became fetchable. + +**Fetch failure is the whole loss mechanism.** For planning purposes that is the useful form: +budget ~2% shrinkage between "records that project onto a panorama" and "panoramas you actually +get", and expect it to be irreducible by retrying. + +### The disk-quota scare cost nothing + +The last Slurm incarnation's log ends in a wall of `[Errno 122] Disk quota exceeded` — 11,438 of +them — which reads like a truncated dataset. It was not: + +| | | +| :--- | ---: | +| Quota-failed line indices in the log | 11,438 | +| ...still missing from `progress.txt` | **0** | + +Every one completed on a later pass. Worth recording because the surface impression is alarming and +wrong, and because **the run did hit a storage wall at ~214k panoramas**. A 2.0-scale corpus is +roughly double that; storage headroom is a real prerequisite, not an afterthought. + +### An unexplained 223 + +`progress.txt` records 214,599 written, and the published dataset has **214,376** — a gap of **223 +panoramas (0.10%)**, unaccounted for. It plausibly arises downstream in `split_dataset.py` or at +publication time, but nothing in the rescued evidence establishes that, so it is recorded as open +rather than explained. + +## Timing — lower bounds + +From Slurm accounting (`sacct -u jsomeara -S 2025-03-01 -E 2025-07-01`), summed across every +incarnation: + +| Step | Wall-clock | Jobs | +| :--- | ---: | ---: | +| `run_download_dataset.slurm` (fetch + crop-model inference) | **≥49.1 h** | 26 | +| `run_generate_meta.slurm` | ≥13.5 h | 7 | +| `run_generate_negatives.slurm` | ≥1.5 h | 2 | +| *(separate: `download_data.slurm`, the crop-model training data)* | ≥45.3 h | 7 | + +**These are lower bounds, for two reasons**: `--requeue` overwrites the accounting record, so +requeued incarnations are invisible (the same artefact that hid the Stage 2 run); and the download +was finished off in `interactive` jobs on 2025-06-16/17 whose output never reached +`download_dataset.out`. The evidence for the second is direct — `progress.txt` was modified +**2025-06-17**, two days after the last Slurm log write on 2025-06-15, and Stage 2 training began +2025-06-17 08:39, hours after the last panorama landed. + +So: **≤4,370 panoramas/hour** end-to-end, including crop-model inference on every panorama, against +Google's undocumented tile endpoints at 32 tiles each. + +### What that implies for a rebuild + +At the 500k-record target (~385k panoramas, see `stage2_training_cost.md`): + +| | Panoramas | At ≤4,370/h | +| :--- | ---: | ---: | +| Incremental (new panoramas only) | ~170,000 | **≥39 h** | +| Full regeneration from scratch | ~385,000 | **≥88 h (≥3.7 d)** | + +Add `generate_dataset_meta` at ~1.8x its measured 13.5 h (~24 h). **§7's "about a week of +wall-clock" is confirmed as the right order of magnitude for a full rebuild** — and now with a +measured basis rather than an allocation guess. The incremental path is ~2.5-3 days. + +Two caveats. The rate is a *lower bound on duration* built from a *lower bound on time*, so treat +≥39 h as optimistic. And rate limiting remains the dominant risk: nothing here establishes that +Google's endpoints will sustain this rate in 2026, only that they did in 2025. + +## A rescued artefact: the paper's Stage 1 accuracy, and half of #18's correction + +`docs/data/rampnet1_stage1_run/stage1_evaluation_results.txt` is the output that produced the +paper's Stage 1 dataset-agreement numbers — precision **0.9403**, recall **0.9245** on the +1,000-panorama gold set, which the README quotes as 94.0%. + +It also records the quantity the README says has **not** been re-measured: **119 predicted points +"ignored (matched already-claimed GT)"** out of 3,972. Counting those as false positives — the +redundant-detection half of the [#18](https://github.com/ProjectSidewalk/RampNet/issues/18) +correction — is arithmetic on the committed file: + +| Stage 1 dataset agreement | As published | Redundant points counted as FP | +| :--- | ---: | ---: | +| Precision | 0.9403 | **0.9121** (−2.8 pts) | +| Recall | 0.9245 | 0.9245 (unaffected) | + +**This is not the corrected Stage 1 number.** The README is explicit that *two* changes move it: +redundant points counted as FP, **and** matching through `rampnet/metrics.py` (nearest unclaimed +ground truth) rather than first-in-list order. Only the first is computable from this artefact; the +second still requires re-running `stage_one/dataset_evaluation/evaluate.py`. 0.9121 is therefore an +**upper bound** on the corrected precision, and a useful one: it says the Stage 1 correction is of +the same size as the Stage 2 one (−1.0 precision), not an order larger. + +## Provenance and replication + +Committed evidence in `docs/data/rampnet1_stage1_run/` (2.6 MB, `SHA256SUMS` alongside): + +| File | What it is | +| :--- | :--- | +| `progress.txt` | 214,599 completed line indices — the completion ledger | +| `download_dataset.out` | the final Slurm incarnation's log (fetch failures + quota errors) | +| `missing_panos.csv` | the 4,571 never-written `(line_index, pano_id)` pairs, derived | +| `stage1_evaluation_results.txt` | the paper's Stage 1 gold-set evaluation output | + +Rescued from `/gscratch/makelab/jsomeara/RampNet/stage_one/dataset_generation/`, where they were +the only copy; the full 19 GB tree is now mirrored to +`/gscratch/makelab/jonf/rescue_jsomeara_rampnet/`. + +Stated gaps: + +- **`finaldataset.jsonl` (64 MB, 219,170 lines) is not committed** — it is the manifest the run + consumed, and it lives in the rescued tree above. `stage1_yield.py` reproduces every number + without it, because `missing_panos.csv` is committed; pass `--finaldataset` to regenerate that + CSV from the manifest and assert it identical. +- **`download_dataset.out` is only the last incarnation.** `#SBATCH --output=download_dataset.out` + carries no `%j`, so each of the 26 jobs overwrote its predecessor. Per-run failure taxonomy for + the earlier passes is gone. +- **The source inventories are also in the rescued tree but not committed** — `location_data/` + (nyc.csv, portland.geojson, bend.geojson; 71 MB) and `street_data/` (801 MB). These are the + **paper-era** files, which matters because §9 measures drift against today's endpoints (Bend + +8.7%). Publishing them is the single largest remaining replication win for Stage 1 and is not + done here. diff --git a/scripts/analysis/stage1_yield.py b/scripts/analysis/stage1_yield.py new file mode 100644 index 0000000..3fb20a4 --- /dev/null +++ b/scripts/analysis/stage1_yield.py @@ -0,0 +1,136 @@ +"""What did Stage 1 generation cost, and what did it lose? 97.91% yield, all of it Google's fault. + +`docs/curb_ramp_data_sourcing.md` §7 calls Stage 1 generation "the long pole and +entirely unmeasured". The paper run's logs survived on a lab scratch volume, so +it is measurable now. Two results, from the committed evidence in +docs/data/rampnet1_stage1_run/: + + - **Yield: 214,599 of 219,170 intended panoramas were written (97.91%).** + 4,571 were never written, and **4,570 of those 4,571 are exactly the panos + Google refused to serve** ("Failed to fetch panorama for pano_id ..."). One + is unexplained. Fetch failure is the entire loss mechanism. + + - **The disk-quota wall cost nothing.** The final Slurm incarnation logged + 11,438 `[Errno 122] Disk quota exceeded` errors, which looks like a + truncated dataset -- but every one of those 11,438 line indices appears in + progress.txt, i.e. all were completed on a later pass. Quota was an + operational incident, not data loss. (`download_dataset.py` marks an index + done only after both the .jpg and .json are written, and re-reads + progress.txt on restart, so a quota-failed index is simply retried.) + +Timing is from Slurm accounting (`sacct -u jsomeara -S 2025-03-01 -E 2025-07-01`) +and is a **lower bound**: `--requeue` overwrites the accounting record, and the +run was finished off in interactive jobs whose stdout never reached the log. + + run_download_dataset.slurm >=49.1 h across 26 jobs (fetch + crop-model inference) + run_generate_meta.slurm >=13.5 h across 7 jobs + run_generate_negatives.slurm >=1.5 h across 2 jobs + +So <=4,370 panoramas/hour, against Google's undocumented endpoints at 32 tiles +each. See docs/stage1_generation_cost.md for what that implies for a rebuild. + +The 219,170-line finaldataset.jsonl is NOT committed (64 MB); it lives on Hyak +at /gscratch/makelab/jonf/rescue_jsomeara_rampnet/RampNet/stage_one/ +dataset_generation/. Everything below reproduces without it, because +missing_panos.csv is committed. Pass --finaldataset to regenerate that CSV from +the manifest and prove it identical. + + python scripts/analysis/stage1_yield.py + python scripts/analysis/stage1_yield.py --finaldataset /path/to/finaldataset.jsonl +""" +import argparse +import csv +import json +import os + +REPO = os.path.dirname(os.path.dirname(os.path.dirname(os.path.abspath(__file__)))) +DEFAULT_DIR = os.path.join(REPO, "docs", "data", "rampnet1_stage1_run") + + +def load_progress(path): + """Completed line indices. download_dataset.py appends one per written pano.""" + done = set() + dupes = 0 + with open(path) as handle: + for line in handle: + line = line.strip() + if line.isdigit(): + idx = int(line) + dupes += idx in done + done.add(idx) + return done, dupes + + +def load_log(path): + """Unique pano_ids Google refused, and line indices that hit the quota wall.""" + failed, quota = set(), set() + with open(path, errors="replace") as handle: + for line in handle: + if line.startswith("Failed to fetch panorama for pano_id "): + failed.add(line.strip().split()[-1]) + elif "Disk quota exceeded" in line and "line index" in line: + try: + quota.add(int(line.split("line index")[1].split(":")[0])) + except (IndexError, ValueError): + pass + return failed, quota + + +def main(): + ap = argparse.ArgumentParser(description=__doc__.split("\n")[0]) + ap.add_argument("--evidence-dir", default=DEFAULT_DIR, + help="committed Stage 1 evidence (default: docs/data/rampnet1_stage1_run)") + ap.add_argument("--finaldataset", default=None, + help="optional 64 MB finaldataset.jsonl: regenerate missing_panos.csv " + "from it and verify it matches the committed copy") + args = ap.parse_args() + + done, dupes = load_progress(os.path.join(args.evidence_dir, "progress.txt")) + failed, quota = load_log(os.path.join(args.evidence_dir, "download_dataset.out")) + + with open(os.path.join(args.evidence_dir, "missing_panos.csv")) as handle: + missing = [(int(r["line_index"]), r["pano_id"]) for r in csv.DictReader(handle)] + missing_ids = {pid for _, pid in missing if pid} + + intended = len(done) + len(missing) + print("== yield ==") + print(f" intended (finaldataset.jsonl lines) : {intended:,}") + print(f" written (progress.txt, unique) : {len(done):,} " + f"(duplicate lines: {dupes})") + print(f" never written : {len(missing):,} " + f"({len(missing) / intended:.2%})") + print(f" yield : {len(done) / intended:.2%}") + + print("\n== why they are missing ==") + hit = len(missing_ids & failed) + print(f" unique 'Failed to fetch' in last log: {len(failed):,}") + print(f" never-written that Google refused : {hit:,} / {len(missing):,} " + f"({hit / len(missing):.2%})") + print(f" unexplained : {len(missing) - hit:,}") + + print("\n== the disk-quota scare ==") + still = quota - done + print(f" quota-failed line indices in log : {len(quota):,}") + print(f" ...still missing : {len(still):,}") + print(" => quota cost nothing; every quota-failed index completed on a later pass" + if not still else f" => {len(still):,} genuinely lost to quota") + + if args.finaldataset: + regen = [] + with open(args.finaldataset) as handle: + for i, line in enumerate(handle): + if i not in done: + try: + regen.append((i, json.loads(line)["pano_id"])) + except (ValueError, KeyError): + regen.append((i, "")) + same = regen == missing + print(f"\n== regenerated from {os.path.basename(args.finaldataset)} ==") + print(f" lines: {i + 1:,} missing: {len(regen):,} " + f"matches committed CSV: {'YES' if same else 'NO'}") + if not same: + raise SystemExit("regenerated missing-pano list does NOT match the committed CSV") + + +if __name__ == "__main__": + main()

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