From 0d0a080ae949578b75f07ae8c5600b40d3e8c1fd Mon Sep 17 00:00:00 2001 From: Daniel van Strien Date: Wed, 9 Sep 2026 18:35:09 +0100 Subject: [PATCH 1/5] Add reproducible Kraken olmOCR benchmark Jobs --- experiments/kraken-olmocr-bench/README.md | 117 + experiments/kraken-olmocr-bench/bootstrap.sh | 18 + experiments/kraken-olmocr-bench/launch.sh | 47 + .../kraken-olmocr-bench/pyproject.toml | 13 + experiments/kraken-olmocr-bench/report.py | 86 + experiments/kraken-olmocr-bench/runner.py | 404 ++++ .../kraken-olmocr-bench/score-bootstrap.sh | 21 + experiments/kraken-olmocr-bench/score.py | 149 ++ .../scoring/pyproject.toml | 13 + .../kraken-olmocr-bench/scoring/uv.lock | 1976 +++++++++++++++++ experiments/kraken-olmocr-bench/uv.lock | 1467 ++++++++++++ 11 files changed, 4311 insertions(+) create mode 100644 experiments/kraken-olmocr-bench/README.md create mode 100644 experiments/kraken-olmocr-bench/bootstrap.sh create mode 100644 experiments/kraken-olmocr-bench/launch.sh create mode 100644 experiments/kraken-olmocr-bench/pyproject.toml create mode 100644 experiments/kraken-olmocr-bench/report.py create mode 100644 experiments/kraken-olmocr-bench/runner.py create mode 100644 experiments/kraken-olmocr-bench/score-bootstrap.sh create mode 100644 experiments/kraken-olmocr-bench/score.py create mode 100644 experiments/kraken-olmocr-bench/scoring/pyproject.toml create mode 100644 experiments/kraken-olmocr-bench/scoring/uv.lock create mode 100644 experiments/kraken-olmocr-bench/uv.lock diff --git a/experiments/kraken-olmocr-bench/README.md b/experiments/kraken-olmocr-bench/README.md new file mode 100644 index 0000000..f0be0d1 --- /dev/null +++ b/experiments/kraken-olmocr-bench/README.md @@ -0,0 +1,117 @@ +# Kraken on olmOCR-bench + +This experiment runs bundled BLLA segmentation and the PP-OCRv6 medium recognizer +through Kraken's Python API on Hugging Face Jobs, then scores the raw outputs with +the pinned official olmOCR-bench implementation. It includes a 21-page smoke +test (three fixed pages per category) and the full 1,403-page benchmark. + +## Method + +- Pin Kraken to `3e6893a0d5eb06273494bfcdb9d3cac6159e0611`, which includes the GPU + tensor-placement fix. The bundled segmenter's identity follows this pin. +- Pin the model and dataset revisions in `runner.py`; verify recognition weight + bytes against SHA-256 before loading. +- Render PDF pages to RGB at 300 DPI using pypdfium2. No PDF text extraction. +- Use fp32, batch size 32, in-process line extraction, default reading order, + bidi reordering and 16-pixel recognition padding. Torch CPU threads are limited + to four on A10G-small. +- Select smoke pages by a deterministic hash of the page identifier, before + observing predictions. Assign the full manifest to four category-balanced + shards. There is no official modern-only subset. +- The stock arm invokes Kraken's high-level segmentation and recognition methods + for each page. The staged arm configures each model once, runs segmentation + across the manifest, and then recognizes the saved segmentations. It retains + the high-level merge, reading-order and decoding paths. +- Compare every coordinate and ordering field plus raw line-joined text. Random + line/region UUIDs are canonicalized while preserving identity relationships; + image filenames are excluded from geometry comparison. Persist geometry without + discarding any fields, and check its JSON round-trip before recognition. +- Exercise resume with prediction methods replaced by failure sentinels: completed + pages must be loaded without invoking either model again. + +Preparation is implemented with an instance-local guard around +`prepare_for_inference`; a different configuration raises an error. This does not +change network weights, image scaling, thresholds, polygonization or decoding. +Both models currently remain loaded during the staged passes. + +## Run on Jobs + +All runtime checks and tests run on Jobs, including Ruff. The image is pinned by +digest in `launch.sh` and in provenance. The committed `uv.lock` and +`scoring/uv.lock` were resolved and tested on Jobs. Bootstraps prefer these locks +and use `uv sync --locked`. Their fallback, for initial development without a +committed lock, saves a Jobs-generated lock to the Bucket. The compatible pre-1.0 +Hub client is used only for downloads; mounted Bucket I/O does not depend on its +newer Bucket APIs. + +```sh +experiment=experiments/kraken-olmocr-bench +bucket=YOUR_NAME/YOUR_BUCKET +hf buckets create "$bucket" --private +sh "$experiment/launch.sh" smoke "$bucket" smoke-v1 +# Replace these uppercase Job ID placeholders with the IDs printed by launch.sh. +hf jobs wait SMOKE_JOB_ID +sh "$experiment/launch.sh" score-smoke "$bucket" smoke-v1 +hf jobs wait SMOKE_SCORER_JOB_ID +sh "$experiment/launch.sh" full "$bucket" full-v1 +hf jobs wait SHARD_0_JOB_ID SHARD_1_JOB_ID SHARD_2_JOB_ID SHARD_3_JOB_ID +sh "$experiment/launch.sh" score-full "$bucket" full-v1 +hf jobs wait FULL_SCORER_JOB_ID +``` + +Each phase is asynchronous. Require a `COMPLETED` Job state, passing smoke checks +and zero scorer candidate errors before moving on; waiting alone is not evidence +of success. The full entrypoint uses the same `runner.py` with `--mode full +--shard 0` through `3`. Give each new configuration a new run identifier; resume +only identical code and settings. The smoke's `--verify-only` option compares +previously saved outputs and tests resumption without repeating predictions. + +Defaults cap the GPU smoke at 50 minutes, each full shard at three hours, and each +CPU scoring Job at one hour. These are per-Job limits. Account for all attempts +when applying a cumulative budget; do not blindly relaunch all shards after one +fails. Inspect `errors.json` and resume only the incomplete shard with the same +run identifier and inference settings. Read retries are bounded at 30 seconds for +mounted-Bucket visibility delays; missing/corrupt artifacts remain hard failures. + +## Artifacts and interpretation + +The Bucket stores the manifest, environment/package versions, input hashes, +geometry, raw candidate Markdown, line predictions, stage timings, errors and +fixed-sample overlays. Candidate names follow the official scorer convention, +`{pdf_stem}_pg{page}_repeat1.md`. Empty text is valid if segmentation found no +lines; exceptions are separately reported and make the run fail. + +`summary.json` records inference-stage totals and elapsed time. The first page +also has a CPU profile, so its runtime includes profiling overhead. Stock runs +before staged in this smoke: cold-start and cache effects mean the result cannot +isolate the benefit of model preparation or support a precise speedup claim. +Use Job running durations, including setup and failed attempts, for compute cost. + +`score.py` uses official scorer revision +`f7cfe4c22098b154c76b6ec950d1c0a464eecf8d` in `allenai/olmocr`, including baseline +tests and its category-aware bootstrap. It checks complete candidate coverage, +raw-text identity and PDF hashes before calling the official `main()` function. +Thin observers capture its returned test results and confidence interval without +replacing scoring code. The official overall is the mean of per-JSONL category +scores, including the automatic baseline category; it is not the micro-average. +Both are exported, with raw test results and the complete official console output. +NumPy/Python seeds are fixed at 20260909 for 1,000 bootstrap samples. + +Scoring artifacts live under `scores/RUN_ID/`: `summary.json`, +`test-results.jsonl`, `coverage.json`, `candidate-errors.json`, +`official-output.txt`, a PNG/SVG category chart, and bounded diagnostic examples +with original page, line overlay, prediction and test explanation. Diagnostic +selection happens after scoring and never changes the scored set. Full math/table +tests assess structured output that plain line text does not provide. Smoke +scores validate execution and should not be presented as benchmark estimates. + +To retrieve private artifacts after a successful run: + +```sh +hf buckets sync "hf://buckets/$bucket/scores/full-v1" ./kraken-results +``` + +The recognizer contains approximately 15.92 million parameters; the complete +pipeline also includes a segmenter. Its training card mentions historical, +contemporary and born-digital material without proportions, so results should +not be framed as proof of transfer from exclusively or mostly historical data. diff --git a/experiments/kraken-olmocr-bench/bootstrap.sh b/experiments/kraken-olmocr-bench/bootstrap.sh new file mode 100644 index 0000000..424789e --- /dev/null +++ b/experiments/kraken-olmocr-bench/bootstrap.sh @@ -0,0 +1,18 @@ +#!/bin/sh +set -eu +mkdir -p /tmp/kraken-bench +cp /input/*.py /input/pyproject.toml /tmp/kraken-bench/ +cd /tmp/kraken-bench +if [ -f /input/uv.lock ]; then + cp /input/uv.lock uv.lock +elif [ -f /bucket/environment/uv.lock ]; then + cp /bucket/environment/uv.lock uv.lock +else + uv lock + mkdir -p /bucket/environment + cp uv.lock /bucket/environment/uv.lock + cp pyproject.toml /bucket/environment/pyproject.toml +fi +uv sync --locked +uv run --locked ruff check runner.py +uv run --locked python runner.py "$@" diff --git a/experiments/kraken-olmocr-bench/launch.sh b/experiments/kraken-olmocr-bench/launch.sh new file mode 100644 index 0000000..09958a4 --- /dev/null +++ b/experiments/kraken-olmocr-bench/launch.sh @@ -0,0 +1,47 @@ +#!/bin/sh +# Submit the reproducible experiment. Inference and all runtime checks run on Jobs. +set -eu +if [ "$#" -ne 3 ]; then + echo "Usage: sh launch.sh smoke|full|score-smoke|score-full NAMESPACE/BUCKET RUN_ID" >&2 + exit 2 +fi +phase=$1 +bucket=$2 +run_id=$3 +source_dir=$(CDPATH= cd -- "$(dirname -- "$0")" && pwd) +image='ghcr.io/astral-sh/uv@sha256:85d4cb1afa769a7338e095b927bee941cf5ec92266c7424b3f6c0f2748567248' + +submit() { + flavor=$1 + timeout=$2 + name=$3 + shift 3 + hf jobs run --detach --name "$name" --label experiment=kraken-olmbench \ + --label "run=$run_id" --label "phase=$phase" \ + --flavor "$flavor" --timeout "$timeout" --secrets HF_TOKEN \ + --env PYTHONUNBUFFERED=1 --env OMP_NUM_THREADS=4 --env OPENBLAS_NUM_THREADS=4 \ + --volume "$source_dir:/input:ro" \ + --volume "hf://buckets/$bucket:/bucket:rw" "$image" "$@" +} + +case "$phase" in + smoke) + submit a10g-small 50m "kraken-$run_id-smoke" \ + sh /input/bootstrap.sh --mode smoke --run-id "$run_id" + ;; + full) + for shard in 0 1 2 3; do + submit a10g-small 3h "kraken-$run_id-shard-$shard" \ + sh /input/bootstrap.sh --mode full --run-id "$run_id" --shard "$shard" + done + ;; + score-smoke|score-full) + mode=${phase#score-} + submit cpu-upgrade 1h "kraken-$run_id-score" \ + sh /input/score-bootstrap.sh --mode "$mode" --run-id "$run_id" + ;; + *) + echo "Unknown phase: $phase" >&2 + exit 2 + ;; +esac diff --git a/experiments/kraken-olmocr-bench/pyproject.toml b/experiments/kraken-olmocr-bench/pyproject.toml new file mode 100644 index 0000000..e3ce8d9 --- /dev/null +++ b/experiments/kraken-olmocr-bench/pyproject.toml @@ -0,0 +1,13 @@ +[project] +name = "kraken-olmocr-bench" +version = "0.1.0" +requires-python = ">=3.12,<3.13" +dependencies = [ + "kraken @ git+https://github.com/mittagessen/kraken@3e6893a0d5eb06273494bfcdb9d3cac6159e0611", + "huggingface-hub>=0.34,<1.0", + "pypdfium2>=4.30", + "ruff==0.15.6", +] + +[tool.ruff] +line-length = 100 diff --git a/experiments/kraken-olmocr-bench/report.py b/experiments/kraken-olmocr-bench/report.py new file mode 100644 index 0000000..462568c --- /dev/null +++ b/experiments/kraken-olmocr-bench/report.py @@ -0,0 +1,86 @@ +"""Build a category chart and bounded diagnostic examples from saved official results.""" + +import argparse +import json +from pathlib import Path + +from runner import key, overlay, read_json, render, write_json + + +def main(): + import matplotlib + + matplotlib.use("Agg") + import matplotlib.pyplot as plt + + parser = argparse.ArgumentParser() + parser.add_argument("--bucket", type=Path, default=Path("/bucket")) + parser.add_argument("--run-id", required=True) + parser.add_argument("--mode", choices=("smoke", "full"), required=True) + args = parser.parse_args() + root = args.bucket / "scores" / args.run_id + summary = read_json(root / "summary.json") + names = [ + "old_scans", "multi_column", "long_tiny_text", "headers_footers", + "table_tests", "old_scans_math", "arxiv_math", "baseline", + ] + labels = [ + "Old scans", "Multiple columns", "Long / tiny text", "Headers / footers", + "Tables", "Old scans: math", "ArXiv math", "Baseline checks", + ] + scores = [ + summary["categories"][n if n == "baseline" else f"{n}.jsonl"]["score"] * 100 + for n in names + ] + fig, ax = plt.subplots(figsize=(9, 5.5), layout="constrained") + bars = ax.barh(labels, scores, color="#31688e", height=0.65) + ax.invert_yaxis() + ax.set_xlim(0, 108) + ax.set_xticks([0, 25, 50, 75, 100], ["0%", "25%", "50%", "75%", "100%"]) + ax.bar_label(bars, labels=[f"{v:.1f}%" for v in scores], padding=5) + ax.set_xlabel("Official test pass rate") + ax.spines[["top", "right", "left"]].set_visible(False) + ax.set_axisbelow(True) + ax.grid(axis="x", alpha=0.15) + macro = summary["official_macro_score"] * 100 + qualifier = "Full benchmark" if args.mode == "full" else "21-page smoke — not a benchmark result" + fig.suptitle(f"Kraken BLLA + PP-OCRv6 medium\n{qualifier}", fontsize=15) + low, high = [x * 100 for x in summary["official_ci_95"]] + ax.set_title( + f"Overall {macro:.1f}% · 95% CI {low:.1f}–{high:.1f}% · " + f"{summary['pages']:,} pages\n300 DPI RGB · raw line text · one candidate", + fontsize=10, pad=15, + ) + fig.savefig(root / "category-scores.png", dpi=180) + fig.savefig(root / "category-scores.svg") + plt.close(fig) + + records = [json.loads(line) for line in (root / "test-results.jsonl").read_text().splitlines()] + pages = read_json(args.bucket / "manifest.json") + page_map = {(p["pdf"], p["page"]): p for p in pages} + chosen = {} + # A fixed rule selects the first failure of each test type, plus one passed test. + # This is diagnostic selection after scoring, never a change to the scored set. + for record in records: + group = f"failed-{record['type']}" if not record["passed"] else "passed-example" + if group not in chosen: + chosen[group] = record + for label, record in sorted(chosen.items()): + page = page_map[(record["pdf"], record["page"])] + variant = "staged" if args.mode == "smoke" else f"shard-{page['shard']}" + source = args.bucket / "runs" / args.run_id / variant + geometry = read_json(Path(str(source / "pages" / key(page)) + ".geometry.json")) + result = read_json(Path(str(source / "pages" / key(page)) + ".result.json")) + image, _ = render(page) + folder = root / "examples" / label + folder.mkdir(parents=True, exist_ok=True) + overlay(image, geometry["geometry"], folder / "lines.png") + image.thumbnail((1400, 1800)) + image.save(folder / "page.png") + (folder / "prediction.txt").write_text(result["text"]) + write_json(folder / "test.json", record) + print(f"Saved category chart and {len(chosen)} diagnostic examples to {root}", flush=True) + + +if __name__ == "__main__": + main() diff --git a/experiments/kraken-olmocr-bench/runner.py b/experiments/kraken-olmocr-bench/runner.py new file mode 100644 index 0000000..1d7300f --- /dev/null +++ b/experiments/kraken-olmocr-bench/runner.py @@ -0,0 +1,404 @@ +"""Pinned Kraken batch inference; smoke and full runs share this Jobs entrypoint.""" + +import argparse +import cProfile +import hashlib +import importlib.metadata +import json +import os +import platform +import pstats +import sys +import time +import traceback +from dataclasses import asdict +from pathlib import Path + +DATASET = "allenai/olmOCR-bench" +DATA_REV = "54a96a6fb6a2bd3b297e59869491db4d3625b711" +MODEL = "small-models-for-glam/kraken-ppocrv6-medium" +MODEL_REV = "b26109b54a73e27b91c3fa83558ccba1ed485e03" +WEIGHTS_SHA = "15313b51ace64cbfa81f8f6ef25ad64f04e5a6fb7f7823e67b107527bc081ac9" +KRAKEN_REV = "3e6893a0d5eb06273494bfcdb9d3cac6159e0611" +IMAGE = "ghcr.io/astral-sh/uv@sha256:85d4cb1afa769a7338e095b927bee941cf5ec92266c7424b3f6c0f2748567248" +CATEGORIES = ( + "arxiv_math", "headers_footers", "long_tiny_text", "multi_column", + "old_scans", "old_scans_math", "table_tests", +) +START = time.perf_counter() + + +def log(message): + print(f"[{time.perf_counter() - START:.1f}s] {message}", flush=True) + + +def write_json(path, value): + path.parent.mkdir(parents=True, exist_ok=True) + path.write_text(json.dumps(value, ensure_ascii=False, indent=2) + "\n") + + +def read_json(path): + # Mounted Bucket writes can become visible after close rather than immediately. + # Bound retries so absent/corrupt checkpoints remain hard failures. + for attempt in range(31): + try: + return json.loads(path.read_text()) + except (FileNotFoundError, json.JSONDecodeError): + if attempt == 30: + raise + time.sleep(1) + + +def digest(path): + return hashlib.sha256(path.read_bytes()).hexdigest() + + +def key(page): + return f"{Path(page['pdf']).with_suffix('')}_pg{page['page']}" + + +def manifest(bucket): + from huggingface_hub import hf_hub_download + + if (bucket / "manifest.json").exists(): + return read_json(bucket / "manifest.json") + pages = {} + tests = [] + for category in CATEGORIES: + source = Path(hf_hub_download( + DATASET, f"bench_data/{category}.jsonl", repo_type="dataset", revision=DATA_REV, + )) + for line in source.read_text().splitlines(): + test = json.loads(line) + tests.append(test) + ident = (test["pdf"], test["page"]) + if ident in pages: + assert pages[ident]["category"] == category + pages[ident] = {"pdf": test["pdf"], "page": test["page"], "category": category} + ordered = [] + for category in CATEGORIES: + group = sorted( + [p for p in pages.values() if p["category"] == category], + key=lambda p: hashlib.sha256(f"kraken-smoke-v1:{key(p)}".encode()).hexdigest(), + ) + for index, page in enumerate(group): + ordered.append({**page, "smoke": index < 3, "shard": index % 4}) + assert len(ordered) == 1403 and len(tests) == 7019 + write_json(bucket / "manifest.json", ordered) + return ordered + + +def render(page): + import pypdfium2 as pdfium + from huggingface_hub import hf_hub_download + from PIL import Image + + path = Path("/tmp/rendered") / f"{key(page)}.png" + meta_path = path.with_suffix(".json") + if not path.exists(): + pdf = Path(hf_hub_download( + DATASET, f"bench_data/pdfs/{page['pdf']}", repo_type="dataset", revision=DATA_REV, + )) + path.parent.mkdir(parents=True, exist_ok=True) + with pdfium.PdfDocument(pdf) as doc: + pdf_page = doc[page["page"] - 1] + try: + bitmap = pdf_page.render(scale=300 / 72) + im = bitmap.to_pil().convert("RGB") + im.save(path) + write_json(meta_path, { + "pdf_sha256": digest(pdf), "image_sha256": digest(path), + "width": im.width, "height": im.height, "dpi": 300, + }) + bitmap.close() + finally: + pdf_page.close() + with Image.open(path) as im: + return im.convert("RGB"), read_json(meta_path) + + +def load_stack(weights, prepared): + import torch + from kraken.configs import RecognitionInferenceConfig, SegmentationInferenceConfig + from kraken.tasks import RecognitionTaskModel, SegmentationTaskModel + + segmenter = SegmentationTaskModel.load_model() + recognizer = RecognitionTaskModel.load_model(str(weights)) + seg_config = SegmentationInferenceConfig( + text_direction="horizontal-lr", accelerator="cuda", precision="32-true", + ) + rec_config = RecognitionInferenceConfig( + batch_size=32, num_line_workers=0, padding=16, bidi_reordering=True, + accelerator="cuda", precision="32-true", + ) + + def prepare_once(net, config): + # Preserve the high-level task API, including merge and reading-order processing. + with torch.inference_mode(): + net.prepare_for_inference(config) + assert next(net.parameters()).device.type == "cuda" + + def already_prepared(requested): + if requested is not config: + raise RuntimeError("Prepared model cannot be reused with a different config") + + net.prepare_for_inference = already_prepared + + if prepared: + for net in segmenter.seg_models: + prepare_once(net, seg_config) + prepare_once(recognizer.net, rec_config) + return segmenter, recognizer, seg_config, rec_config + + +def canonical_geometry(value): + # UUIDs identify lines/regions but are randomly allocated by each segmentation call. + # Canonicalise identities, preserving every coordinate, sequence and membership edge. + identifiers = {} + + def collect(node): + if isinstance(node, dict): + if "id" in node and node["id"] not in identifiers: + identifiers[node["id"]] = f"id-{len(identifiers)}" + for child in node.values(): + collect(child) + elif isinstance(node, list): + for child in node: + collect(child) + + collect(value) + + def replace(node): + if isinstance(node, dict): + return {k: replace(v) for k, v in node.items() if k != "imagename"} + if isinstance(node, (list, tuple)): + return [replace(v) for v in node] + if isinstance(node, str): + return identifiers.get(node, node) + return node + + return replace(value) + + +def synchronize(): + import torch + + torch.cuda.synchronize() + + +def recognise(recognizer, config, image, segmentation): + if not segmentation.lines: + return [] + records = list(recognizer.predict(image, segmentation, config)) + return [record.prediction for record in records] + + +def overlay(image, geometry, path): + from PIL import ImageDraw + + image = image.copy() + draw = ImageDraw.Draw(image) + for index, line in enumerate(geometry["lines"] or []): + boundary = line.get("boundary") + baseline = line.get("baseline") + if boundary: + points = [tuple(p) for p in boundary] + draw.line(points + [points[0]], fill="red", width=3) + if baseline: + points = [tuple(p) for p in baseline] + draw.line(points, fill="blue", width=3) + draw.text(points[0], str(index + 1), fill="blue", stroke_width=1) + image.thumbnail((1400, 1800)) + path.parent.mkdir(parents=True, exist_ok=True) + image.save(path) + + +def run_pages(pages, root, weights, staged, resume_check=False): + from kraken.containers import Segmentation + + start = time.perf_counter() + segmenter, recognizer, seg_config, rec_config = load_stack(weights, staged) + if resume_check: + def unexpected_prediction(*args, **kwargs): + raise AssertionError("Resume must skip completed predictions") + + segmenter.predict = unexpected_prediction + recognizer.predict = unexpected_prediction + synchronize() + setup_seconds = time.perf_counter() - start + root.mkdir(parents=True, exist_ok=True) + write_json(root / "selected_pages.json", pages) + results = [] + + def paths(page): + stem = root / "pages" / key(page) + return Path(str(stem) + ".geometry.json"), Path(str(stem) + ".result.json") + + def segment(page): + geometry_path, result_path = paths(page) + image, render_meta = render(page) + if geometry_path.exists(): + checkpoint = read_json(geometry_path) + assert checkpoint["render"] == render_meta + return image, checkpoint, Segmentation(**checkpoint["geometry"]) + synchronize() + t0 = time.perf_counter() + profile = cProfile.Profile() if not results and page == pages[0] else None + if profile: + profile.enable() + segmentation = segmenter.predict(im=image, config=seg_config) + if profile: + profile.disable() + with (root / "segmentation-profile.txt").open("w") as stream: + pstats.Stats(profile, stream=stream).sort_stats("cumulative").print_stats(60) + synchronize() + checkpoint = { + "page": page, "render": render_meta, "geometry": asdict(segmentation), + "seg_seconds": time.perf_counter() - t0, + } + write_json(geometry_path, checkpoint) + log(f"SEG {key(page)} {checkpoint['seg_seconds']:.2f}s {len(segmentation.lines or [])} lines") + return image, checkpoint, segmentation + + def recognition(page, image, checkpoint, original_segmentation=None): + _, result_path = paths(page) + if result_path.exists(): + result = read_json(result_path) + if result.get("status") == "ok": + results.append(result) + return + geometry = checkpoint["geometry"] + segmentation = Segmentation(**geometry) + assert canonical_geometry(asdict(segmentation)) == canonical_geometry(geometry) + if original_segmentation is not None: + segmentation = original_segmentation + synchronize() + t0 = time.perf_counter() + lines = recognise(recognizer, rec_config, image, segmentation) + synchronize() + result = { + "page": page, "status": "ok", "render": checkpoint["render"], + "seg_seconds": checkpoint["seg_seconds"], "rec_seconds": time.perf_counter() - t0, + "n_lines": len(segmentation.lines or []), "n_records": len(lines), + "lines": lines, "text": "\n".join(lines), + } + assert result["n_lines"] == result["n_records"] + output = root / "candidate" / f"{key(page)}_repeat1.md" + output.parent.mkdir(parents=True, exist_ok=True) + output.write_text(result["text"]) + if page["smoke"]: + overlay(image, geometry, root / "overlays" / f"{key(page)}.png") + write_json(result_path, result) + results.append(result) + log(f"OCR {key(page)} {result['rec_seconds']:.2f}s {len(result['text'])} chars") + + failures = [] + + def failed(page, phase): + error = {"page": page, "phase": phase, "error": traceback.format_exc()} + failures.append(error) + write_json(root / "errors.json", failures) + log(f"ERROR {key(page)} {phase}: {error['error']}") + + if staged: + for page in pages: + try: + segment(page) + except Exception: + failed(page, "segmentation") + for page in pages: + try: + geometry_path, _ = paths(page) + if not geometry_path.exists(): + continue + image, _ = render(page) + recognition(page, image, read_json(geometry_path)) + except Exception: + failed(page, "recognition") + else: + for page in pages: + try: + image, checkpoint, segmentation = segment(page) + recognition(page, image, checkpoint, segmentation) + except Exception: + failed(page, "stock") + summary = { + "pages": len(pages), "completed": len(results), "errors": len(failures), + "staged": staged, "setup_seconds": setup_seconds, + "elapsed_seconds": time.perf_counter() - start, + "seg_seconds": sum(p["seg_seconds"] for p in results), + "rec_seconds": sum(p["rec_seconds"] for p in results), + } + write_json(root / ("resume-summary.json" if resume_check else "summary.json"), summary) + log(json.dumps(summary)) + if failures or len(results) != len(pages): + raise RuntimeError("Incomplete inference; inspect errors and resume") + + +def main(): + import torch + from huggingface_hub import hf_hub_download + + parser = argparse.ArgumentParser() + parser.add_argument("--mode", choices=("smoke", "full"), required=True) + parser.add_argument("--shard", type=int, choices=range(4), default=0) + parser.add_argument("--run-id", default="v1") + parser.add_argument("--verify-only", action="store_true") + parser.add_argument("--bucket", type=Path, default=Path("/bucket")) + args = parser.parse_args() + assert torch.cuda.is_available(), "GPU acceleration is required" + torch.set_num_threads(4) + torch.set_num_interop_threads(1) + provenance = { + "python": sys.version, "platform": platform.platform(), "gpu": torch.cuda.get_device_name(), + "torch": torch.__version__, "cuda": torch.version.cuda, "kraken_commit": KRAKEN_REV, + "model": MODEL, "model_revision": MODEL_REV, "model_sha256": WEIGHTS_SHA, + "dataset": DATASET, "dataset_revision": DATA_REV, "image": IMAGE, + "args": {k: str(v) if isinstance(v, Path) else v for k, v in vars(args).items()}, + "packages": {d.metadata["Name"]: d.version for d in importlib.metadata.distributions()}, + "source_sha256": digest(Path(__file__)), "lock_sha256": digest(Path("uv.lock")), + "cpu_count": os.cpu_count(), "torch_threads": torch.get_num_threads(), + } + run_root = args.bucket / "runs" / args.run_id + suffix = "verification" if args.verify_only else "environment" + write_json(run_root / f"{args.mode}-{args.shard}-{suffix}.json", provenance) + log(f"GPU verified: {provenance['gpu']}; torch {torch.__version__}") + pages = manifest(args.bucket) + weights = Path(hf_hub_download(MODEL, "medium.safetensors", revision=MODEL_REV)) + assert digest(weights) == WEIGHTS_SHA, "Recognition weights hash mismatch" + if args.mode == "smoke": + pages = [p for p in pages if p["smoke"]] + if not args.verify_only: + run_pages(pages, run_root / "stock", weights, staged=False) + import gc + + gc.collect() + torch.cuda.empty_cache() + run_pages(pages, run_root / "staged", weights, staged=True) + probe = run_root / f"io-probe-{time.time_ns()}.json" + write_json(probe, {"round_trip": True}) + assert read_json(probe) == {"round_trip": True} + comparisons = [] + for page in pages: + def artifact(variant, suffix): + return read_json(Path(str(run_root / variant / "pages" / key(page)) + suffix)) + comparisons.append({ + "page": page, + "geometry_equal": canonical_geometry(artifact("stock", ".geometry.json")["geometry"]) + == canonical_geometry(artifact("staged", ".geometry.json")["geometry"]), + "text_equal": artifact("stock", ".result.json")["text"] + == artifact("staged", ".result.json")["text"], + }) + write_json(run_root / "equivalence.json", comparisons) + assert all(p["geometry_equal"] and p["text_equal"] for p in comparisons) + log("PASS: all 21 pages have identical ordered geometry and raw text") + run_pages(pages, run_root / "staged", weights, staged=True, resume_check=True) + log("PASS: all 21 completed pages resume without calling either prediction model") + else: + assert not args.verify_only, "Verification-only mode requires a smoke run" + pages = [p for p in pages if p["shard"] == args.shard] + run_pages(pages, run_root / f"shard-{args.shard}", weights, staged=True) + + +if __name__ == "__main__": + main() diff --git a/experiments/kraken-olmocr-bench/score-bootstrap.sh b/experiments/kraken-olmocr-bench/score-bootstrap.sh new file mode 100644 index 0000000..c9788a5 --- /dev/null +++ b/experiments/kraken-olmocr-bench/score-bootstrap.sh @@ -0,0 +1,21 @@ +#!/bin/sh +set -eu +mkdir -p /tmp/kraken-score +cp /input/score.py /input/report.py /input/runner.py /input/scoring/pyproject.toml /tmp/kraken-score/ +cd /tmp/kraken-score +if [ -f /input/scoring/uv.lock ]; then + cp /input/scoring/uv.lock uv.lock +elif [ -f /bucket/scoring-environment/uv.lock ]; then + cp /bucket/scoring-environment/uv.lock uv.lock +else + uv lock + mkdir -p /bucket/scoring-environment + cp uv.lock /bucket/scoring-environment/uv.lock + cp pyproject.toml /bucket/scoring-environment/pyproject.toml +fi +uv sync --locked +sh -n /input/launch.sh /input/bootstrap.sh /input/score-bootstrap.sh +uv run --locked ruff check score.py runner.py report.py +uv run --locked python -m playwright install --with-deps chromium +uv run --locked python score.py "$@" +uv run --locked python report.py "$@" diff --git a/experiments/kraken-olmocr-bench/score.py b/experiments/kraken-olmocr-bench/score.py new file mode 100644 index 0000000..9ddb0d3 --- /dev/null +++ b/experiments/kraken-olmocr-bench/score.py @@ -0,0 +1,149 @@ +"""Prepare frozen raw predictions and call the unmodified official olmOCR scorer.""" + +import argparse +import contextlib +import importlib.metadata +import json +import random +import shutil +import sys +import time +from collections import defaultdict +from dataclasses import asdict +from pathlib import Path + +import numpy as np +from huggingface_hub import hf_hub_download + +from runner import CATEGORIES, DATA_REV, DATASET, digest, key, read_json, write_json + +SCORER_REV = "f7cfe4c22098b154c76b6ec950d1c0a464eecf8d" +CANDIDATE = "kraken_ppocrv6_medium" + + +def main(): + from olmocr.bench import benchmark + + start = time.perf_counter() + parser = argparse.ArgumentParser() + parser.add_argument("--bucket", type=Path, default=Path("/bucket")) + parser.add_argument("--run-id", required=True) + parser.add_argument("--mode", choices=("smoke", "full"), required=True) + args = parser.parse_args() + pages = read_json(args.bucket / "manifest.json") + if args.mode == "smoke": + pages = [p for p in pages if p["smoke"]] + selected = {(p["pdf"], p["page"]) for p in pages} + workspace = Path("/tmp/scorer-input") + output = args.bucket / "scores" / args.run_id + workspace.mkdir(parents=True, exist_ok=True) + output.mkdir(parents=True, exist_ok=True) + test_categories = {} + for category in CATEGORIES: + source = Path(hf_hub_download( + DATASET, f"bench_data/{category}.jsonl", repo_type="dataset", revision=DATA_REV, + )) + rows = [json.loads(line) for line in source.read_text().splitlines()] + rows = [r for r in rows if (r["pdf"], r["page"]) in selected] + with (workspace / f"{category}.jsonl").open("w") as stream: + for row in rows: + stream.write(json.dumps(row, ensure_ascii=False) + "\n") + test_categories[row["id"]] = f"{category}.jsonl" + coverage = [] + for page in pages: + variant = "staged" if args.mode == "smoke" else f"shard-{page['shard']}" + root = args.bucket / "runs" / args.run_id / variant + result = read_json(Path(str(root / "pages" / key(page)) + ".result.json")) + if result["status"] != "ok" or result["page"] != page: + raise RuntimeError(f"Missing or inconsistent successful output: {key(page)}") + candidate = root / "candidate" / f"{key(page)}_repeat1.md" + # Score saved candidate bytes, checking that they are exactly the recorded raw output. + text = candidate.read_text() + if text != result["text"]: + raise RuntimeError(f"Candidate text mismatch: {key(page)}") + dest = workspace / CANDIDATE / f"{key(page)}_repeat1.md" + dest.parent.mkdir(parents=True, exist_ok=True) + dest.write_text(text) + pdf = Path(hf_hub_download( + DATASET, f"bench_data/pdfs/{page['pdf']}", repo_type="dataset", revision=DATA_REV, + )) + if digest(pdf) != result["render"]["pdf_sha256"]: + raise RuntimeError(f"PDF identity mismatch: {page['pdf']}") + dest_pdf = workspace / "pdfs" / page["pdf"] + dest_pdf.parent.mkdir(parents=True, exist_ok=True) + shutil.copyfile(pdf, dest_pdf) + coverage.append({"page": page, "characters": len(text), "lines": result["n_lines"]}) + write_json(output / "coverage.json", coverage) + print(f"Validated {len(coverage)} page predictions and PDF identities", flush=True) + + captured = {} + original_evaluate = benchmark.evaluate_candidate + original_ci = benchmark.calculate_bootstrap_ci + + def capture_evaluation(*positional, **kwargs): + result = original_evaluate(*positional, **kwargs) + captured["evaluation"] = result + return result + + def capture_ci(*positional, **kwargs): + ci = original_ci(*positional, **kwargs) + captured["ci"] = [float(x) for x in ci] + return ci + + # Observe the official calls without replacing tests, grouping, scoring or bootstrap code. + benchmark.evaluate_candidate = capture_evaluation + benchmark.calculate_bootstrap_ci = capture_ci + random.seed(20260909) + np.random.seed(20260909) + sys.argv = [ + "olmocr.bench.benchmark", "--dir", str(workspace), "--candidate", CANDIDATE, + "--bootstrap_samples", "1000", "--confidence_level", "0.95", + "--output_failed", str(output / "failed-tests.jsonl"), + ] + # Keep the full official output (including individual failures) as a durable artifact. + with (output / "official-output.txt").open("w") as stream: + with contextlib.redirect_stdout(stream): + benchmark.main() + micro, total, errors, failures, by_type, _, details = captured["evaluation"] + write_json(output / "candidate-errors.json", errors) + if errors: + raise RuntimeError(f"Official scorer reported {len(errors)} candidate errors") + categories = defaultdict(lambda: {"passed": 0, "total": 0}) + records = [] + for pdf, pdf_pages in details.items(): + for page_number, tests in pdf_pages.items(): + for test, passed, explanation in tests: + category = test_categories.get(test.id, "baseline") + categories[category]["total"] += 1 + categories[category]["passed"] += int(passed) + records.append({ + "id": test.id, "pdf": pdf, "page": page_number, "category": category, + "type": test.type, "passed": passed, "explanation": explanation, + "test": asdict(test), + }) + if len(records) != total: + raise RuntimeError(f"Incomplete test-result capture: {len(records)} / {total}") + for values in categories.values(): + values["score"] = values["passed"] / values["total"] + score = sum(v["score"] for v in categories.values()) / len(categories) + summary = { + "candidate": CANDIDATE, "mode": args.mode, "pages": len(pages), "tests": total, + "official_macro_score": score, "official_ci_95": captured.get("ci"), + "micro_score": micro, "categories": dict(sorted(categories.items())), + "test_types": {k: {"total": len(v), "score": sum(v) / len(v)} for k, v in by_type.items()}, + "failed_tests": len(failures), "candidate_errors": 0, + "elapsed_seconds": time.perf_counter() - start, + "scorer_revision": SCORER_REV, "dataset_revision": DATA_REV, + "python": sys.version, "seed": 20260909, + "packages": {d.metadata["Name"]: d.version for d in importlib.metadata.distributions()}, + "lock_sha256": digest(Path("uv.lock")), "source_sha256": digest(Path(__file__)), + } + with (output / "test-results.jsonl").open("w") as stream: + for record in sorted(records, key=lambda r: r["id"]): + stream.write(json.dumps(record, ensure_ascii=False, default=str) + "\n") + write_json(output / "summary.json", summary) + print(json.dumps({k: v for k, v in summary.items() if k != "packages"}, indent=2), flush=True) + + +if __name__ == "__main__": + main() diff --git a/experiments/kraken-olmocr-bench/scoring/pyproject.toml b/experiments/kraken-olmocr-bench/scoring/pyproject.toml new file mode 100644 index 0000000..8a96d8f 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upload-time = "2026-07-20T02:05:57.554Z" }, + { url = "https://files.pythonhosted.org/packages/61/02/962c1cbfc401a30c1d034dc67ff395f64b52302c6d62de556c1fca99acc0/yarl-1.24.5-py3-none-any.whl", hash = "sha256:a33700d13d9b7d84fd10947b09ff69fb9a792e519c8cb9764a3ca70baa6c23a7", size = 58612, upload-time = "2026-07-20T02:07:43.461Z" }, +] From 53b2f344d372f75fa015a241862a6abe9e06e736 Mon Sep 17 00:00:00 2001 From: Daniel van Strien Date: Wed, 9 Sep 2026 20:52:38 +0100 Subject: [PATCH 2/5] Handle large benchmark renders and preserve resume history --- experiments/kraken-olmocr-bench/README.md | 8 ++++++ experiments/kraken-olmocr-bench/launch.sh | 7 ++++- experiments/kraken-olmocr-bench/runner.py | 34 +++++++++++++++++++++-- 3 files changed, 45 insertions(+), 4 deletions(-) diff --git a/experiments/kraken-olmocr-bench/README.md b/experiments/kraken-olmocr-bench/README.md index f0be0d1..9fc107b 100644 --- a/experiments/kraken-olmocr-bench/README.md +++ b/experiments/kraken-olmocr-bench/README.md @@ -12,6 +12,10 @@ test (three fixed pages per category) and the full 1,403-page benchmark. - Pin the model and dataset revisions in `runner.py`; verify recognition weight bytes against SHA-256 before loading. - Render PDF pages to RGB at 300 DPI using pypdfium2. No PDF text extraction. +- The pinned `old_scans/48.pdf`, `51.pdf` and `57.pdf` pages render to + 253,864,905, 331,857,945 and 323,914,932 pixels at this DPI. Their cached PNGs + have explicit Pillow size allowances and dimension checks; other pages retain + Pillow's default limit. No page is downscaled to bypass it. - Use fp32, batch size 32, in-process line extraction, default reading order, bidi reordering and 16-pixel recognition padding. Torch CPU threads are limited to four on A10G-small. @@ -72,6 +76,10 @@ when applying a cumulative budget; do not blindly relaunch all shards after one fails. Inspect `errors.json` and resume only the incomplete shard with the same run identifier and inference settings. Read retries are bounded at 30 seconds for mounted-Bucket visibility delays; missing/corrupt artifacts remain hard failures. +For example, `sh "$experiment/launch.sh" resume-0 "$bucket" full-v1` resumes +shard zero with a 45-minute cap. A successful complete resume preserves the earlier +error ledger as `errors-before-successful-resume.json` before clearing its active +`errors.json` marker. Include both Job attempts in the cost total. ## Artifacts and interpretation diff --git a/experiments/kraken-olmocr-bench/launch.sh b/experiments/kraken-olmocr-bench/launch.sh index 09958a4..831321a 100644 --- a/experiments/kraken-olmocr-bench/launch.sh +++ b/experiments/kraken-olmocr-bench/launch.sh @@ -2,7 +2,7 @@ # Submit the reproducible experiment. Inference and all runtime checks run on Jobs. set -eu if [ "$#" -ne 3 ]; then - echo "Usage: sh launch.sh smoke|full|score-smoke|score-full NAMESPACE/BUCKET RUN_ID" >&2 + echo "Usage: sh launch.sh smoke|full|resume-0|resume-1|resume-2|resume-3|score-smoke|score-full NAMESPACE/BUCKET RUN_ID" >&2 exit 2 fi phase=$1 @@ -35,6 +35,11 @@ case "$phase" in sh /input/bootstrap.sh --mode full --run-id "$run_id" --shard "$shard" done ;; + resume-0|resume-1|resume-2|resume-3) + shard=${phase#resume-} + submit a10g-small 45m "kraken-$run_id-resume-$shard" \ + sh /input/bootstrap.sh --mode full --run-id "$run_id" --shard "$shard" + ;; score-smoke|score-full) mode=${phase#score-} submit cpu-upgrade 1h "kraken-$run_id-score" \ diff --git a/experiments/kraken-olmocr-bench/runner.py b/experiments/kraken-olmocr-bench/runner.py index 1d7300f..a0675a7 100644 --- a/experiments/kraken-olmocr-bench/runner.py +++ b/experiments/kraken-olmocr-bench/runner.py @@ -25,6 +25,13 @@ "arxiv_math", "headers_footers", "long_tiny_text", "multi_column", "old_scans", "old_scans_math", "table_tests", ) +# These pinned benchmark pages exceed Pillow's default limit at the fixed +# 300 DPI. Allow their known sizes only when reopening our own rendered PNGs. +LARGE_RENDER_PIXELS = { + ("old_scans/48.pdf", 1): 253864905, + ("old_scans/51.pdf", 1): 331857945, + ("old_scans/57.pdf", 1): 323914932, +} START = time.perf_counter() @@ -113,8 +120,17 @@ def render(page): bitmap.close() finally: pdf_page.close() - with Image.open(path) as im: - return im.convert("RGB"), read_json(meta_path) + expected_pixels = LARGE_RENDER_PIXELS.get((page["pdf"], page["page"])) + original_limit = Image.MAX_IMAGE_PIXELS + try: + if expected_pixels is not None: + Image.MAX_IMAGE_PIXELS = expected_pixels + with Image.open(path) as im: + if expected_pixels is not None and im.width * im.height != expected_pixels: + raise RuntimeError(f"Unexpected dimensions for known large page: {key(page)}") + return im.convert("RGB"), read_json(meta_path) + finally: + Image.MAX_IMAGE_PIXELS = original_limit def load_stack(weights, prepared): @@ -229,6 +245,7 @@ def unexpected_prediction(*args, **kwargs): root.mkdir(parents=True, exist_ok=True) write_json(root / "selected_pages.json", pages) results = [] + existing_errors = read_json(root / "errors.json") if (root / "errors.json").exists() else [] def paths(page): stem = root / "pages" / key(page) @@ -297,7 +314,7 @@ def recognition(page, image, checkpoint, original_segmentation=None): def failed(page, phase): error = {"page": page, "phase": phase, "error": traceback.format_exc()} failures.append(error) - write_json(root / "errors.json", failures) + write_json(root / "errors.json", existing_errors + failures) log(f"ERROR {key(page)} {phase}: {error['error']}") if staged: @@ -333,6 +350,17 @@ def failed(page, phase): log(json.dumps(summary)) if failures or len(results) != len(pages): raise RuntimeError("Incomplete inference; inspect errors and resume") + error_path = root / "errors.json" + if error_path.exists(): + # Retain the original attempt's errors, but clear the active error marker + # only after every page has completed successfully on this resumed Job. + previous = read_json(error_path) + archive = root / "errors-before-successful-resume.json" + if archive.exists(): + previous = read_json(archive) + previous + write_json(archive, previous) + assert read_json(archive) == previous + error_path.unlink() def main(): From 8ea25cb7426a657947b94c413cf0e52bfcd066e1 Mon Sep 17 00:00:00 2001 From: Daniel van Strien Date: Wed, 9 Sep 2026 21:21:23 +0100 Subject: [PATCH 3/5] Retry transient Hub download failures in benchmark Jobs --- experiments/kraken-olmocr-bench/README.md | 3 +++ experiments/kraken-olmocr-bench/runner.py | 30 +++++++++++++++++------ experiments/kraken-olmocr-bench/score.py | 7 +++--- 3 files changed, 29 insertions(+), 11 deletions(-) diff --git a/experiments/kraken-olmocr-bench/README.md b/experiments/kraken-olmocr-bench/README.md index 9fc107b..e0e5d50 100644 --- a/experiments/kraken-olmocr-bench/README.md +++ b/experiments/kraken-olmocr-bench/README.md @@ -80,6 +80,9 @@ For example, `sh "$experiment/launch.sh" resume-0 "$bucket" full-v1` resumes shard zero with a 45-minute cap. A successful complete resume preserves the earlier error ledger as `errors-before-successful-resume.json` before clearing its active `errors.json` marker. Include both Job attempts in the cost total. +Hub downloads retry transient transport errors and HTTP 408/429/500/502/503/504 responses +up to five attempts, with bounded backoff. Permanent errors still fail the Job; +successful retries retain the same revision and subsequent byte-identity checks. ## Artifacts and interpretation diff --git a/experiments/kraken-olmocr-bench/runner.py b/experiments/kraken-olmocr-bench/runner.py index a0675a7..4a3bf08 100644 --- a/experiments/kraken-olmocr-bench/runner.py +++ b/experiments/kraken-olmocr-bench/runner.py @@ -39,6 +39,26 @@ def log(message): print(f"[{time.perf_counter() - START:.1f}s] {message}", flush=True) +def hub_download(*args, **kwargs): + from huggingface_hub import hf_hub_download + from huggingface_hub.errors import HfHubHTTPError + from requests.exceptions import ConnectionError, Timeout + + for attempt in range(5): + try: + return hf_hub_download(*args, **kwargs) + except (HfHubHTTPError, ConnectionError, Timeout) as exc: + if isinstance(exc, HfHubHTTPError): + status = getattr(exc.response, "status_code", None) + if status not in (408, 429, 500, 502, 503, 504): + raise + if attempt == 4: + raise + delay = 2 ** (attempt + 1) + log(f"Retrying Hub download after {type(exc).__name__} in {delay}s") + time.sleep(delay) + + def write_json(path, value): path.parent.mkdir(parents=True, exist_ok=True) path.write_text(json.dumps(value, ensure_ascii=False, indent=2) + "\n") @@ -65,14 +85,12 @@ def key(page): def manifest(bucket): - from huggingface_hub import hf_hub_download - if (bucket / "manifest.json").exists(): return read_json(bucket / "manifest.json") pages = {} tests = [] for category in CATEGORIES: - source = Path(hf_hub_download( + source = Path(hub_download( DATASET, f"bench_data/{category}.jsonl", repo_type="dataset", revision=DATA_REV, )) for line in source.read_text().splitlines(): @@ -97,13 +115,12 @@ def manifest(bucket): def render(page): import pypdfium2 as pdfium - from huggingface_hub import hf_hub_download from PIL import Image path = Path("/tmp/rendered") / f"{key(page)}.png" meta_path = path.with_suffix(".json") if not path.exists(): - pdf = Path(hf_hub_download( + pdf = Path(hub_download( DATASET, f"bench_data/pdfs/{page['pdf']}", repo_type="dataset", revision=DATA_REV, )) path.parent.mkdir(parents=True, exist_ok=True) @@ -365,7 +382,6 @@ def failed(page, phase): def main(): import torch - from huggingface_hub import hf_hub_download parser = argparse.ArgumentParser() parser.add_argument("--mode", choices=("smoke", "full"), required=True) @@ -392,7 +408,7 @@ def main(): write_json(run_root / f"{args.mode}-{args.shard}-{suffix}.json", provenance) log(f"GPU verified: {provenance['gpu']}; torch {torch.__version__}") pages = manifest(args.bucket) - weights = Path(hf_hub_download(MODEL, "medium.safetensors", revision=MODEL_REV)) + weights = Path(hub_download(MODEL, "medium.safetensors", revision=MODEL_REV)) assert digest(weights) == WEIGHTS_SHA, "Recognition weights hash mismatch" if args.mode == "smoke": pages = [p for p in pages if p["smoke"]] diff --git a/experiments/kraken-olmocr-bench/score.py b/experiments/kraken-olmocr-bench/score.py index 9ddb0d3..0ded1cd 100644 --- a/experiments/kraken-olmocr-bench/score.py +++ b/experiments/kraken-olmocr-bench/score.py @@ -13,9 +13,8 @@ from pathlib import Path import numpy as np -from huggingface_hub import hf_hub_download -from runner import CATEGORIES, DATA_REV, DATASET, digest, key, read_json, write_json +from runner import CATEGORIES, DATA_REV, DATASET, digest, hub_download, key, read_json, write_json SCORER_REV = "f7cfe4c22098b154c76b6ec950d1c0a464eecf8d" CANDIDATE = "kraken_ppocrv6_medium" @@ -40,7 +39,7 @@ def main(): output.mkdir(parents=True, exist_ok=True) test_categories = {} for category in CATEGORIES: - source = Path(hf_hub_download( + source = Path(hub_download( DATASET, f"bench_data/{category}.jsonl", repo_type="dataset", revision=DATA_REV, )) rows = [json.loads(line) for line in source.read_text().splitlines()] @@ -64,7 +63,7 @@ def main(): dest = workspace / CANDIDATE / f"{key(page)}_repeat1.md" dest.parent.mkdir(parents=True, exist_ok=True) dest.write_text(text) - pdf = Path(hf_hub_download( + pdf = Path(hub_download( DATASET, f"bench_data/pdfs/{page['pdf']}", repo_type="dataset", revision=DATA_REV, )) if digest(pdf) != result["render"]["pdf_sha256"]: From fc51682e0e4eb258a86effb2ffe5ae4a6de7215e Mon Sep 17 00:00:00 2001 From: Daniel van Strien Date: Thu, 10 Sep 2026 09:19:42 +0100 Subject: [PATCH 4/5] Document full Kraken benchmark results and public artifacts --- experiments/kraken-olmocr-bench/README.md | 6 ++ .../kraken-olmocr-bench/RESULTS-2026-09-09.md | 93 +++++++++++++++++++ 2 files changed, 99 insertions(+) create mode 100644 experiments/kraken-olmocr-bench/RESULTS-2026-09-09.md diff --git a/experiments/kraken-olmocr-bench/README.md b/experiments/kraken-olmocr-bench/README.md index e0e5d50..676e653 100644 --- a/experiments/kraken-olmocr-bench/README.md +++ b/experiments/kraken-olmocr-bench/README.md @@ -5,6 +5,12 @@ through Kraken's Python API on Hugging Face Jobs, then scores the raw outputs wi the pinned official olmOCR-bench implementation. It includes a 21-page smoke test (three fixed pages per category) and the full 1,403-page benchmark. +The [completed 9 September 2026 run](RESULTS-2026-09-09.md) scored **36.8% overall** +(95% CI 35.9–37.7), including **74.4% on long/tiny text**. Its +[public artifact bundle](https://huggingface.co/buckets/davanstrien/kraken-olmbench-results-20260909) +contains scores and execution metadata; source document images and text remain +in a separate archive. + ## Method - Pin Kraken to `3e6893a0d5eb06273494bfcdb9d3cac6159e0611`, which includes the GPU diff --git a/experiments/kraken-olmocr-bench/RESULTS-2026-09-09.md b/experiments/kraken-olmocr-bench/RESULTS-2026-09-09.md new file mode 100644 index 0000000..81870fa --- /dev/null +++ b/experiments/kraken-olmocr-bench/RESULTS-2026-09-09.md @@ -0,0 +1,93 @@ +# Full Kraken run — 9 September 2026 + +Kraken's bundled BLLA segmentation and PP-OCRv6 medium recognition scored +**36.8% overall** on all **1,403 olmOCR-bench pages**, with a 95% bootstrap +confidence interval of **35.9–37.7%**. All pages completed; the official scorer +evaluated **8,413 tests** with zero unresolved producer or candidate errors. + +The [public results bundle](https://huggingface.co/buckets/davanstrien/kraken-olmbench-results-20260909) +contains the exact aggregate result, per-test outcomes, per-page timings and +hashes, environment/source snapshots, a category chart and the complete compute +cost ledger. [Method and launch commands](README.md). + +| Category | Passed / tests | Score | +|---|---:|---:| +| Long / tiny text | 329 / 442 | 74.4% | +| Multiple columns | 512 / 884 | 57.9% | +| Headers / footers | 285 / 760 | 37.5% | +| Old scans | 127 / 526 | 24.1% | +| Tables | 2 / 1,022 | 0.2% | +| Old scans: math | 0 / 458 | 0.0% | +| ArXiv math | 0 / 2,927 | 0.0% | +| Automatic baseline category | 1,394 / 1,394 | 100.0% | + +The official macro score averages these eight rows. Baseline checks overlap the +page categories. There are nine additional baseline checks in the manifests: +seven in headers/footers and two in tables. All 1,403 baseline checks passed; +**zero of 1,020 actual table-structure tests** and **zero of 3,385 math tests** +passed. Micro-average: 31.49% (2,649 / 8,413). + +## Configuration and validation + +The recognizer has approximately 15.92M parameters; the segmenter is additional. +Pages were rendered to RGB at 300 DPI and processed in CUDA fp32, batch size 32, +with four Torch CPU threads and four A10G-small shards. Output was raw +newline-joined text without dehyphenation, header suppression, table reconstruction +or LaTeX generation. The official scorer ran on CPU-upgrade. + +All inference, runtime checks and scoring ran on Hugging Face Jobs. The 21-page +smoke verified identical ordered geometry and text for stock/staged inference +and completed-page resumption without model calls. Staging did not establish a +meaningful speedup. + +| Component | Pinned revision | +|---|---| +| Reproducer code | `8ea25cb7426a657947b94c413cf0e52bfcd066e1` | +| Kraken | `3e6893a0d5eb06273494bfcdb9d3cac6159e0611` | +| `small-models-for-glam/kraken-ppocrv6-medium` | `b26109b54a73e27b91c3fa83558ccba1ed485e03` | +| `allenai/olmOCR-bench` | `54a96a6fb6a2bd3b297e59869491db4d3625b711` | +| Official `allenai/olmocr` scorer | `f7cfe4c22098b154c76b6ec950d1c0a464eecf8d` | + +The bundle's `run.json` records configuration and the recognition weight hash. +Container digest, package versions, dependency locks and source hashes are in +the environment records. Bootstrap: seed 20260909, 1,000 samples, 95% confidence. + +## Runtime and cost + +- Segmentation: **24,390.1s**, or **85.5%** of aggregate measured inference time. +- Recognition: **4,139.8s**. Total aggregate inference: **7.925 hours**. +- Full inference including recovery: **2h 26m 25s** elapsed; through scoring + completion: **2h 53m 28s**. +- **$9.56** estimated list-rate compute, or **$9.65** with each Job rounded up to + a minute, including all 15 setup/smoke/full/recovery/scoring attempts. All stopped. + Costs are before credits and are not an invoice. + +Three very large old scans needed page-specific Pillow size allowances and +resumed inference after the original attempt completed 1,400 pages. Their +resolution and model settings stayed unchanged. The first full scorer attempt +failed on a transient Hub HTTP 502; bounded download retries allowed scoring to +complete. Both source revisions and every attempt remain accounted for. + +## Interpretation and artifacts + +These are pipeline test pass rates, not CER or isolated reading-order accuracy. +Headers/footers mainly test omission, and order tests require matching text +anchors. In multiple columns, 356 of 372 failures lacked an anchor, preventing +the test from assessing order. The model's training card does not quantify the +historical share of its training data; this run does not isolate a domain-transfer +effect. + +```sh +hf buckets sync hf://buckets/davanstrien/kraken-olmbench-results-20260909 ./kraken-run-results +``` + +The public bundle includes all test outcomes with reference passages, original +source URLs and detailed explanations removed. Page statistics include input and +prediction hashes without the predictions themselves. Source document images, +transcriptions, geometry and detailed scorer logs remain in a separate private +archive. Public metadata supports aggregate checks; independently rescoring +predictions requires regenerating them with the pinned scripts and upstream dataset. + +Contains information from [allenai/olmOCR-bench](https://huggingface.co/datasets/allenai/olmOCR-bench), +made available under the [ODC Attribution License 1.0](https://opendatacommons.org/licenses/by/1-0/). +The bundle includes `NOTICE.md` and a SHA-256 manifest. From 650ce74d87ae690e741f6a47436a321b4ddfd32a Mon Sep 17 00:00:00 2001 From: Daniel van Strien Date: Thu, 10 Sep 2026 09:50:12 +0100 Subject: [PATCH 5/5] Remove trailing blank line from scoring project metadata --- experiments/kraken-olmocr-bench/scoring/pyproject.toml | 1 - 1 file changed, 1 deletion(-) diff --git a/experiments/kraken-olmocr-bench/scoring/pyproject.toml b/experiments/kraken-olmocr-bench/scoring/pyproject.toml index 8a96d8f..2825789 100644 --- a/experiments/kraken-olmocr-bench/scoring/pyproject.toml +++ b/experiments/kraken-olmocr-bench/scoring/pyproject.toml @@ -10,4 +10,3 @@ dependencies = [ "matplotlib", "ruff==0.15.6", ] -