spikeforge

CI Status: pre-1.0 License: BSD-3-Clause Python 3.10–3.13 Ruff PRs Welcome Docs

A spiking-neural-network (SNN) toolkit built on snnTorch and PyTorch. Loads MNIST-style and neuromorphic event datasets, encodes them into rate, latency, delta, and random spikes, and trains, validates, exports, and deploys LIF networks — with a live browser dashboard served over WebSockets.

Pre-1.0 and unpublished. install.sh is the supported path today; PyPI ships once the distributions are published. Before trusting any number this produces, read Implications and boundaries.

Quickstart

git clone https://github.com/capsize-games/spikeforge.git
cd spikeforge
docker compose up --build

Open http://localhost:8877 — the dashboard connects to the WebSocket on the same host and port. No separate backend or proxy to run.

Prefer a local install, a headless example, or the CLI tools instead? See Usage and Quickstart for every path (./install.sh, local dev with Vite, examples/, and the eight spikeforge-* console scripts).

Features

  • Encoding — rate, latency, delta, and random spike coders.
  • Training — fully-connected and convolutional LIF networks with surrogate-gradient cross-entropy, checkpointing, and opt-in AMP / gradient checkpointing / truncated BPTT / multi-GPU.
  • Topologies — fc_legacy, fc_small, conv_net, recurrent_net, plus the sequence presets sequence_mlp and sequence_attn.
  • Datasets — MNIST, Fashion-MNIST, KMNIST, QMNIST, USPS, EMNIST, CIFAR-10, and (via the events extra) N-MNIST, DVS128 Gesture, CIFAR10-DVS, and Spiking Speech Commands.
  • Interpreter spine — NIR export, an independent NIR interpreter, and numerical drift validation.
  • Introspection — educational-mode U[t]/I[t]/S[t] traces, trajectory metrics, and surrogate-derivative curves.
  • Deployment — a capability matrix, weight quantization, energy accounting, and executable reference, norse, and lava_loihi2 backends.
  • Model hub — a curated, offline-first catalog plus optional live Hugging Face search.
  • Dashboard — a React + TypeScript UI with training, introspection, analysis, targets, energy, and hub panels, and seven guided walkthroughs.

Packages

This repository is a single workspace that publishes four distributions:

Distribution Import root Purpose
spikeforge spikeforge Core package: encoders, topologies, training, simulator, NIR bridge, tracking
spikeforge-targets spikeforge_targets Deployment targets, quantization, energy accounting, sparse event runtime
spikeforge-hub spikeforge_hub Curated model hub and optional Hugging Face access
spikeforge-server server FastAPI + WebSocket server and dashboard hosting

Documentation

This README stays short on purpose. documentation/ is the full reference — install paths, the CLI tools, architecture, module layout, and the dev workflow — written for contributors and coding agents alike. Also see COOKBOOK.md for copy-pasteable recipes, examples/ for runnable end-to-end scripts, and plans/ for design documents and the roadmap.

See CONTRIBUTING.md and rules.md before opening a pull request.

Citing

If spikeforge is useful in your research, please cite it — see CITATION.cff (GitHub renders a "Cite this repository" button from it automatically).

License

Released under the BSD 3-Clause License — see LICENSE and AUTHORS.

Project links

Data verified by PyPI on Sep 12, 2026
Data provided by the project maintainers, verified at the time the release was uploaded to PyPI.

Key dates

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  • Released:
Newer release available (0.3.2)

1 maintainer

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Credits

Author/Maintainer: Capsize LLC

GitHub Statistics

Data verified by PyPI on Sep 12, 2026
The GitHub source repository was provided by the project maintainers and verified by PyPI at the time of upload. Stars, forks, and open issues/PRs are derived from that repository and have not been independently verified.

License expression

BSD-3-Clause
View SPDX License List

Requires

Python >=3.10

Provides Extra

dev

Tags

spiking neural networks fastapi websockets dashboard

Classifiers

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