DeepLog

DeepLog is an operational framework for building neurosymbolic (NeSy) systems. Instead of presenting a monolithic stack, DeepLog provides high-performance building blocks that plug directly into PyTorch-first workflows so you can compose differentiable learning and symbolic reasoning with predictable interfaces.

• Symbol-first modeling – declare symbolic tensors and shapes, then let DeepLog check interfaces between modules before you wire them into larger systems.
• Logic-aware modules – compile formulas into differentiable modules, wrap existing PyTorch components, and connect them to multiple reasoning backends.
• Backend flexibility – start with the pure Python engine or enable the Janus/SWI-Prolog backend for lower latency inference without changing your training code.
• Batteries included – tutorial notebooks, example projects (semantic loss, MNIST addition, …), and a Sphinx site walk you from “hello world” to custom NeSy stacks.
• Research to production – typing, validation, and extensive tests keep advanced reasoning pipelines trustworthy when you move from prototypes to production workloads.

DeepLog publishes optional extras so you can extend the base install as needed:

Combine extras as needed, for example pip install ".[examples,tests]".

The Janus backend depends on a local SWI-Prolog installation. On Ubuntu you can install it with:

sudo apt-get install software-properties-common
sudo apt-add-repository ppa:swi-prolog/stable && sudo apt-get update
sudo apt-get install swi-prolog

Once SWI-Prolog is available, install the Python bindings via pip install "pydeeplog[janus_engine]".

git clone https://github.com/ML-KULeuven/deeplog.git
cd deeplog
pip install -e ".[examples,tests]"

Editable installs refresh automatically when you change the source tree, which is handy when contributing.

The root Dockerfile builds a CPU CI base via a DEVICE build arg (default CPU). For local builds:

docker build -t deeplog:cpu --build-arg DEVICE=cpu .

CI builds a CPU image from this Dockerfile. The devcontainer image is built from the same base.

The same image definition is used in CI. For local validation, build it with the command above and then run the contributor checks from CONTRIBUTING.md in your working tree.

import torch
from deeplog import SymTensor, WrappedModule, parse_symbol

# Wrap a plain PyTorch head with symbolic input/output shapes so DeepLog can
# validate every tensor that flows through it.
digits = SymTensor([parse_symbol("digit_a"), parse_symbol("digit_b")])
sigmoid_head = WrappedModule(torch.nn.Sigmoid(), digits, digits, name="sigmoid_head")

print(sigmoid_head(torch.tensor([[1.0, -2.0]])))
# tensor([[0.7311, 0.1192]])

WrappedModule validates tensor shapes against the symbolic specification, so interface mismatches surface as Python errors instead of silent shape bugs when you wire modules into larger reasoning pipelines.

For a taste of the symbolic side, compile a logical formula straight into a differentiable module:

from deeplog import parse_formula_to_module

# Count satisfying assignments of A ∨ B over booleans (= 3: TT, TF, FT).
module = parse_formula_to_module(
    "sum(A): sum(B): =(A,true)_boolean or =(B,true)_boolean"
)
print(int(module()))  # 3

See examples/ for end-to-end notebooks (MNIST addition, semantic loss, DIMACS CNF, LTN, …) and the full API reference on the docs site.

• Landing page & docs – All documentation lives under site/. Build it locally with the steps in Building the documentation site.
• Notebooks – Reproduces the semantic loss workflow, DeepLog module deep dives, MNIST addition with DeepProbLog, and more. Launch them from the examples/ directory after installing deeplog[examples].
• API reference – Generated automatically via sphinx-autoapi, covering symbols, shapes, modules, and engine utilities.

See CONTRIBUTING.md for code quality and testing guidelines.

Public bug reports, questions, and feature requests live on GitHub. Development and code review happen privately, and tagged releases (v*) are mirrored to the public GitHub repository after release.

The landing page and docs live under site/ (Sphinx + PyData theme). To preview them locally:

1. Install the site tooling (from the repo root):

   pip install -r site/requirements.txt

2. Build the static HTML:

   (cd site && make html)

3. Serve the generated pages from site/build/html. Any static file server works, e.g.:

   python -m http.server --directory site/build/html 8000

Visit http://localhost:8000 to browse the site. Tools like sphinx-autobuild can provide live reloads, but the above workflow is the canonical build pipeline.

• Bugs & questions – Open an issue on GitHub and include reproduction steps plus relevant version information.
• Security reports – Please do not file public issues for security vulnerabilities; instead reach the maintainers privately (see CONTRIBUTING.md for details).
• Roadmap discussions – Feature proposals, architectural questions, and broader discussions are welcome via issues.

We welcome contributions of all kinds—bug reports, docs, examples, and new modules. Review the contributing guide for coding standards, triage practices, and tips for a smooth review cycle. If you are looking for a first issue, check the tracker for good first issue and help wanted labels.

DeepLog is released under the LGPL-2.1 license © DTAI Research Group, KU Leuven.