From 435d1cee7226c8a261cb5fb7acfb91824e28eaed Mon Sep 17 00:00:00 2001 From: Daniel Ecer Date: Mon, 17 Aug 2026 14:24:54 +0100 Subject: [PATCH 1/6] Upgrade to sciencebeam-trainer-delft 1.0 Every model the trainer now produces is a torch state dict, and the 0.0.x pin cannot load one, so serving a freshly trained model needs this first. The 0.0.x line is also closed to security and maintenance updates. TF-era `model_weights.hdf5` stays supported input: delft converts it to a torch state dict in memory on load and never touches the directory, so the model URLs in the shipped config are unchanged. Upstream measured the conversion against a reference capture for the eight CustomBidLSTM_CRF models the biorxiv_elife profile serves, which is why parity is not re-measured here. The graph loses tensorflow, tf-keras, tfa-nightly, keras, tensorboard, libclang, ml-dtypes and pydot, and gains pytorch-crf and sentencepiece; torch moves to 2.11.0 and torchvision to 0.26.0. torch stays declared in the cpu extra rather than left to arrive through delft. That declaration is what binds [tool.uv.sources] to the package name across the whole lock: uv applies a source to a direct dependency, so removing these lines as redundant - delft requires torch==2.11.0 on its own - would let torch resolve from PyPI instead, where the Linux wheel carries four nvidia-* packages and triton. The floors are raised to what actually resolves, since >=2.5.1 said nothing next to delft's exact pin. trainer-delft's own cpu extra is deliberately not requested. It re-pins torch==2.11.0, which delft requires anyway, and its index selection does not propagate to a consumer. The tf extra is removed. Nothing here installed it and it existed only to add TensorFlow for the delft engine, but it is published metadata, so it belongs in the release notes. --- pyproject.toml | 11 +- uv.lock | 1326 +++++++++++++++++------------------------------- 2 files changed, 478 insertions(+), 859 deletions(-) diff --git a/pyproject.toml b/pyproject.toml index 0dcbd43c..4e9d3d3a 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -15,12 +15,14 @@ dependencies = [ ] [project.optional-dependencies] +# torch is declared here, rather than left to arrive through delft, so that +# [tool.uv.sources] binds it to the CPU index across the whole lock cpu = [ - "torch>=2.5.1", - "torchvision>=0.20.1" + "torch>=2.11.0", + "torchvision>=0.26.0" ] delft = [ - "sciencebeam-trainer-delft[delft]>=0.0.38", + "sciencebeam-trainer-delft[delft]>=1.0.0", ] cv = [ "layoutparser==0.3.2", @@ -46,9 +48,6 @@ dev = [ ocr = [ "tesserocr==2.5.2", ] -tf = [ - "tensorflow>=2.17.1", -] [tool.uv.sources] diff --git a/uv.lock b/uv.lock 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An inference image should not change device because of the host it lands on. SCIENCEBEAM_DELFT_DEVICE is the whole mechanism, since Sequence is constructed here without a device argument. Set as an image default rather than baked in, so a GPU variant stays possible and an operator can override it per deployment. --- Dockerfile | 4 ++++ 1 file changed, 4 insertions(+) diff --git a/Dockerfile b/Dockerfile index c3087919..f7317890 100644 --- a/Dockerfile +++ b/Dockerfile @@ -174,6 +174,8 @@ COPY docker/entrypoint.sh ./docker/entrypoint.sh ENV SCIENCEBEAM_DELFT_MAX_SEQUENCE_LENGTH=2000 ENV SCIENCEBEAM_DELFT_INPUT_WINDOW_STRIDE=1800 +# torch would otherwise pick CUDA whenever the host happens to expose a GPU +ENV SCIENCEBEAM_DELFT_DEVICE=cpu CMD [ "--port=8070", "--host=0.0.0.0" ] ENTRYPOINT ["/usr/bin/dumb-init", "--", "/opt/sciencebeam_parser/docker/entrypoint.sh"] @@ -197,6 +199,8 @@ COPY docker/entrypoint.sh ./docker/entrypoint.sh ENV SCIENCEBEAM_DELFT_MAX_SEQUENCE_LENGTH=2000 ENV SCIENCEBEAM_DELFT_INPUT_WINDOW_STRIDE=1800 +# torch would otherwise pick CUDA whenever the host happens to expose a GPU +ENV SCIENCEBEAM_DELFT_DEVICE=cpu # temporary workaround for tesserocr https://github.com/sirfz/tesserocr/issues/165 ENV LC_ALL=C From 529218484e6c391e0dacf52b8bc0f439067b9486 Mon Sep 17 00:00:00 2001 From: Daniel Ecer Date: Mon, 17 Aug 2026 14:25:08 +0100 Subject: [PATCH 3/6] Run the full test suite on Linux arm64 The job installed sciencebeam-trainer-delft outside the lock and ran a hand-picked subset, because TensorFlow had no arm64 wheels and the delft extra would not install. Every distribution in that extra now has an aarch64 wheel, torch included, so arm64 installs like every other platform and stops being the one job testing an unlocked version. The selection widens to match macOS: the whole suite minus cv_models. Both native jobs now also deselect slow, which covers the cases that download model artifacts - the Docker pytest target runs those. --- .github/workflows/github-actions-ci.yml | 24 ++++++------------------ 1 file changed, 6 insertions(+), 18 deletions(-) diff --git a/.github/workflows/github-actions-ci.yml b/.github/workflows/github-actions-ci.yml index a04cc10e..85667224 100644 --- a/.github/workflows/github-actions-ci.yml +++ b/.github/workflows/github-actions-ci.yml @@ -117,18 +117,8 @@ jobs: include: - runs-on: macos-latest install-poppler: brew install poppler - pytest-args: >- - --ignore=tests/cv_models - tests/ - runs-on: ubuntu-24.04-arm install-poppler: sudo apt-get update && sudo apt-get install -y poppler-utils - pytest-args: >- - tests/external/pdfalto/url_test.py - tests/external/pdfalto/parser_test.py - tests/document - tests/utils - tests/config - tests/lookup runs-on: ${{ matrix.runs-on }} steps: - name: Check out repository code @@ -144,16 +134,14 @@ jobs: - name: Install dependencies run: uv sync --frozen --group dev --extra delft - if: runner.os != 'Linux' || runner.arch != 'ARM64' - - - name: Install dependencies (Linux arm64 - skip delft extras with no arm64 wheels) - run: | - uv sync --frozen --group dev - uv pip install "sciencebeam-trainer-delft>=0.0.36" - if: runner.os == 'Linux' && runner.arch == 'ARM64' + # the slow cases download model artifacts; the Docker pytest target runs them - name: Run pytest - run: uv run python -m pytest -p no:cacheprovider ${{ matrix.pytest-args }} + run: >- + uv run python -m pytest -p no:cacheprovider + -m "not slow" + --ignore=tests/cv_models + tests/ testpypi-publish: From bb1b37d7a5979e08b0cc40c0f7960575f82c6cf3 Mon Sep 17 00:00:00 2001 From: Daniel Ecer Date: Mon, 17 Aug 2026 14:25:22 +0100 Subject: [PATCH 4/6] Cover loading a TensorFlow-era delft model Upstream measured the conversion's scores, but nothing showed the load path works in this package's environment, and CI stopped exercising it when the default profile became wapiti. The biorxiv_elife header release is the model that exercises conversion and nothing else: TF-era model_weights.hdf5, and no word embeddings, so it needs nothing from the embedding registry. Loading the whole profile would be the wrong shape for CI, since its other models resolve glove-840B. The case goes through DelftModelImpl rather than the models API. The router is already covered by mocked tests and the default profile is wapiti, so a router-level case would need a config override to reach a delft model at all; the load-and-convert path is what was unproven. Input comes from HeaderDataGenerator rather than a hand-written feature matrix, which would risk a width mismatch against the model's config.json and fail for a reason unrelated to the upgrade. Assertions are shape only - a pair per token, labels drawn from the model's own set - because asserting particular labels would re-measure parity here and tie the test to output upstream has already checked. Marked slow: it downloads 6.6 MB, so the native jobs deselect it. --- tests/models/delft_model_impl_test.py | 84 +++++++++++++++++++++++++++ 1 file changed, 84 insertions(+) create mode 100644 tests/models/delft_model_impl_test.py diff --git a/tests/models/delft_model_impl_test.py b/tests/models/delft_model_impl_test.py new file mode 100644 index 00000000..838857c5 --- /dev/null +++ b/tests/models/delft_model_impl_test.py @@ -0,0 +1,84 @@ +import logging + +import pytest +from sciencebeam_trainer_delft.sequence_labelling.reader import load_data_crf_lines +from sciencebeam_trainer_delft.utils.download_manager import DownloadManager + +from sciencebeam_parser.app.context import AppContext +from sciencebeam_parser.config.config import AppConfig, get_download_dir +from sciencebeam_parser.document.layout_document import ( + LayoutBlock, + LayoutDocument, + LayoutLine, + LayoutToken +) +from sciencebeam_parser.external.wapiti.wrapper import LazyWapitiBinaryWrapper +from sciencebeam_parser.models.data import DEFAULT_DOCUMENT_FEATURES_CONTEXT +from sciencebeam_parser.models.delft_model_impl import DelftModelImpl +from sciencebeam_parser.models.header.data import HeaderDataGenerator +from sciencebeam_parser.models.model import iter_data_lines_for_model_data_iterables +from sciencebeam_parser.resources.default_config import DEFAULT_CONFIG_FILE + + +LOGGER = logging.getLogger(__name__) + + +# The biorxiv_elife header model: TF-era `model_weights.hdf5`, and no word embeddings, +# so loading it needs nothing from the embedding registry. +HEADER_MODEL_URL = ( + 'https://github.com/eLifePathways/sciencebeam-models/releases/download' + '/v0.0.1/2020-10-04-delft-grobid-header-biorxiv-no-word-embedding.tar.gz' +) + +HEADER_LINE_TOKEN_TEXTS = [ + ['A', 'Study', 'of', 'Something'], + ['Jane', 'Doe', 'and', 'John', 'Smith'] +] + + +@pytest.fixture(name='app_context', scope='module') +def _app_context() -> AppContext: + app_config = AppConfig.load_yaml(DEFAULT_CONFIG_FILE) + download_manager = DownloadManager(download_dir=get_download_dir(app_config)) + return AppContext( + app_config=app_config, + download_manager=download_manager, + lazy_wapiti_binary_wrapper=LazyWapitiBinaryWrapper( + download_manager=download_manager + ) + ) + + +def _get_layout_document() -> LayoutDocument: + return LayoutDocument.for_blocks([ + LayoutBlock(lines=[ + LayoutLine([LayoutToken(text) for text in line_token_texts]) + for line_token_texts in HEADER_LINE_TOKEN_TEXTS + ]) + ]) + + +@pytest.mark.slow +class TestDelftModelImpl: + def test_should_load_tensorflow_era_model_and_tag_every_token( + self, app_context: AppContext + ): + model_impl = DelftModelImpl(HEADER_MODEL_URL, app_context) + data_generator = HeaderDataGenerator(DEFAULT_DOCUMENT_FEATURES_CONTEXT) + model_data_list = list( + data_generator.iter_model_data_for_layout_document(_get_layout_document()) + ) + data_lines = list(iter_data_lines_for_model_data_iterables([model_data_list])) + texts, features = load_data_crf_lines(data_lines) + tag_result = model_impl.predict_labels( + texts=texts.tolist(), + features=features.tolist(), + output_format=None + ) + LOGGER.debug('tag_result: %r', tag_result) + assert len(tag_result) == 1 + assert [token for token, _ in tag_result[0]] == list(texts[0]) + preprocessor = model_impl.model.p + assert preprocessor is not None + assert preprocessor.indice_tag is not None + assert {label for _, label in tag_result[0]} <= set(preprocessor.indice_tag.values()) From 24b2edb6324b55d24653d48535b26dd410da02da Mon Sep 17 00:00:00 2001 From: Daniel Ecer Date: Mon, 17 Aug 2026 14:25:22 +0100 Subject: [PATCH 5/6] Document installing CPU-only PyTorch The delft extra pulls in torch, and on Linux the default PyPI wheel is the CUDA build. This project's index configuration is not part of its published metadata, so it cannot reach a downstream consumer; documentation is the only mechanism available. The non-obvious part is not the index block but that torch has to be declared as a direct dependency for a uv source to apply. A project that receives torch only through sciencebeam-parser has no binding at all, which is the same reason the cpu extra keeps its torch line. Also states that there is no TensorFlow extra and that model URLs need no change, since TF-era artifacts are converted on load. --- Makefile | 4 ++-- doc/python_library.md | 38 ++++++++++++++++++++++++++++++++++++++ 2 files changed, 40 insertions(+), 2 deletions(-) diff --git a/Makefile b/Makefile index b1ad4d6c..a91e968d 100644 --- a/Makefile +++ b/Makefile @@ -117,8 +117,8 @@ dev-venv: venv-create dev-install # Lightweight host venv for running the benchmark scripts (incl. benchmarks.run) # directly on the host. Installs only the benchmark group + light core deps, NOT the -# cpu/delft/cv extras (torch/TensorFlow), so it works where dev-install can't (e.g. Mac -# Intel). benchmarks.run orchestrates the GROBID/parser containers via the host docker CLI. +# cpu/delft/cv extras (torch), so it works where dev-install can't (e.g. Mac Intel). +# benchmarks.run orchestrates the GROBID/parser containers via the host docker CLI. benchmark-install: $(UV) sync --active --frozen --no-dev --group benchmark diff --git a/doc/python_library.md b/doc/python_library.md index b29e48f7..13f07982 100644 --- a/doc/python_library.md +++ b/doc/python_library.md @@ -8,6 +8,44 @@ ScienceBeam Parser allows you to parse scientific documents. It provides a REST pip install sciencebeam-parser[delft,cpu] ``` +The `delft` extra provides the PyTorch-based sequence labelling engine. There is no TensorFlow +extra: the delft engine runs on PyTorch, and TF-era model artifacts are converted to a torch +state dict in memory when they are loaded, so the model URLs in the +[default config.yml](../sciencebeam_parser/resources/default_config/config.yml) need no change +and the artifacts themselves are never modified. + +### Installing CPU-only PyTorch + +On Linux the default PyTorch wheel on PyPI is the CUDA build, which adds several `nvidia-*` +packages and `triton` that a CPU-only deployment never uses. Index configuration is not part of +published package metadata, so this project's own cannot reach you — install torch from the CPU +index yourself, before the rest: + +```bash +pip install torch --index-url https://download.pytorch.org/whl/cpu +pip install sciencebeam-parser[delft,cpu] +``` + +With `uv`, declare `torch` as a direct dependency of your own project and point it at the CPU +index. Declaring it directly is what makes the source apply — receiving torch only through +`sciencebeam-parser` leaves it resolving from PyPI: + +```toml +[project] +dependencies = [ + "sciencebeam-parser[delft,cpu]", + "torch", +] + +[tool.uv.sources] +torch = [{ index = "torch-cpu" }] + +[[tool.uv.index]] +name = "torch-cpu" +url = "https://download.pytorch.org/whl/cpu" +explicit = true +``` + ## CLI ### CLI: Start Server From 10e0b9cedd0f9daa65bcd5ab0ebe0cc8bc6affea Mon Sep 17 00:00:00 2001 From: Daniel Ecer Date: Tue, 18 Aug 2026 08:58:21 +0100 Subject: [PATCH 6/6] Require sciencebeam-trainer-delft 1.0.1 1.0.1 is the first release that validates SCIENCEBEAM_DELFT_DEVICE and logs the device it resolved. Both runtime images set that variable, and until now a typo in it surfaced deep inside a tensor move rather than at construction, while a correct value left no record of which device was actually used. The floor states that dependency rather than leaving it to whatever resolves. It also drops the cpu and gpu extras, which were byte-identical once published and so promised a wheel choice they could not make. Nothing here requested them - torch is bound to the CPU index through this project's own direct declaration - so the resolution is unchanged: delft stays 1.0.1 and torch stays 2.11.0+cpu. --- pyproject.toml | 2 +- uv.lock | 8 ++++---- 2 files changed, 5 insertions(+), 5 deletions(-) diff --git a/pyproject.toml b/pyproject.toml index 4e9d3d3a..a26ac83f 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -22,7 +22,7 @@ cpu = [ "torchvision>=0.26.0" ] delft = [ - "sciencebeam-trainer-delft[delft]>=1.0.0", + "sciencebeam-trainer-delft[delft]>=1.0.1", ] cv = [ "layoutparser==0.3.2", diff --git a/uv.lock b/uv.lock index 849e8242..6217253b 100644 --- a/uv.lock +++ b/uv.lock @@ -3399,7 +3399,7 @@ requires-dist = [ { name = "pdf2image", specifier = "==1.16.0" }, { name = "pyyaml", specifier = ">=6.0.3" }, { name = "sciencebeam-judge", specifier = ">=0.0.26" }, - { name = "sciencebeam-trainer-delft", extras = ["delft"], marker = "extra == 'delft'", specifier = ">=1.0.0" }, + { name = "sciencebeam-trainer-delft", extras = ["delft"], marker = "extra == 'delft'", specifier = ">=1.0.1" }, { name = "torch", marker = "extra == 'cpu'", specifier = ">=2.11.0", index = "https://download.pytorch.org/whl/cpu" }, { name = "torchvision", marker = "extra == 'cpu'", specifier = ">=0.26.0", index = "https://download.pytorch.org/whl/cpu" }, ] @@ -3426,7 +3426,7 @@ ocr = [{ name = "tesserocr", specifier = "==2.5.2" }] [[package]] name = "sciencebeam-trainer-delft" -version = "1.0.0" +version = "1.0.1" source = { registry = "https://pypi.org/simple" } dependencies = [ { name = "fsspec" }, @@ -3438,9 +3438,9 @@ dependencies = [ { name = "tqdm" }, { name = "typing-extensions" }, ] -sdist = { url = "https://files.pythonhosted.org/packages/07/93/535b62470736679c49ea1ef1bcd850f5c3d9b444b0e8ad383c57fd56ca66/sciencebeam_trainer_delft-1.0.0.tar.gz", hash = "sha256:4c8a133cc69de9b87712a0302de00fa2e7447868a53cffd451b589a664c2f139", size = 121110, upload-time = "2026-08-17T11:08:51.11Z" } +sdist = { url = "https://files.pythonhosted.org/packages/5a/11/bd5750fe4505759bd742c544431e491fa0c2f0ba7f77c753c211922c9e06/sciencebeam_trainer_delft-1.0.1.tar.gz", hash = "sha256:57e7271fd99af1dd31cabda461af7a1a566c446751d590da98cdc27914ce3cfa", size = 123683, upload-time = "2026-08-18T07:39:27.248Z" } wheels = [ - { url = "https://files.pythonhosted.org/packages/d6/0b/2d3ce13b822aba02be669448bcf5e54e457254db446ee5349da104558834/sciencebeam_trainer_delft-1.0.0-py3-none-any.whl", hash = "sha256:90dc3672e1610e15364def8d75cfcd026ed51f56dde78beb9d1c35bd63a2deb8", size = 126236, upload-time = "2026-08-17T11:08:50.068Z" }, + { url = "https://files.pythonhosted.org/packages/ee/11/3860f33a85e9143d082eb51514b76474310ea8501f7d2e67b2036b4b5518/sciencebeam_trainer_delft-1.0.1-py3-none-any.whl", hash = "sha256:e76b1ec098f71e641e429661d2ed9dfc091f54008fc4374beaaf3f02e83745db", size = 127578, upload-time = "2026-08-18T07:39:26.098Z" }, ] [package.optional-dependencies]