🌼 DaisyChain-Train — Old Hardware Training Pipeline

Part of DaisyChain on 🤗 Hugging Face → https://huggingface.co/DaisyChainAI Model page (weights + card): https://huggingface.co/DaisyChainAI/DaisyChain-Train

In plain terms: DaisyChain-Train lets you use old / spare machines to train neural networks. The training runs through emulated GPU logic — verified INT8 units (GUDA-style) that stand in for a GPU's math — so machines without a modern GPU can still do the work. Chain several together and they train one shared model as a cluster. Before you rely on it, see what it can't do → Limitations.

Use the hardware you already have to train. Each machine runs the emulated GPU logic (verified INT8 units — multiply / requantize / ReLU) to compute the model, and DaisyChain pools the machines data-parallel: device selection, capacity-weighted sharding, gradient sync, a P2P setup, and a live dashboard. Two ways to run — Docker or Python.

⚠️ Read this first

DaisyChain-Train is for small models on spare hardware. It pools compute, not memory (the model must fit on one machine), scaling is sublinear, and it is not a substitute for a real GPU on large models. Full envelope in docs/LIMITS.md — please read it before relying on it.

Quick start

Docker (most reliable — one command)

docker compose -f docker/docker-compose.yml up --build
# open http://localhost:8080

Brings up a 3-node demo cluster + dashboard on one machine.

Python (real machines)

On every machine (pip install -e .):

export MASTER_ADDR=100.101.102.10   # coordinator IP (Tailscale 100.x recommended)
export MASTER_PORT=29560
export WORLD_SIZE=3
export RANK=0                        # 1, 2, ... on the others
export GLOO_SOCKET_IFNAME=tailscale0 # your mesh / LAN NIC
daisychain-train

Windows helper

scripts\setup.bat

An interactive menu: Docker, Python node, or just install deps.

Full walkthrough: docs/QUICKSTART.md.

🐋 SpikeWhale control panel (sliders → real training)

python -m daisychain.spikewhale_panel
# open http://localhost:8899

A web control panel: pick model size / training settings with sliders, choose any HuggingFace dataset you have access to (default: streamed FineWeb-Edu), hit Start, and watch the live loss. Stop and re-adjust any time with ← Back to settings. Launches the real DaisyChain training underneath.

🌐 DaisyChain-Web (train by opening a browser tab)

cd web && npm install && node server.js
# open http://localhost:8787 on every device

Zero-install browser training: devices on the same network auto-group (Snapdrop-style) and train a shared model peer-to-peer over WebRTC, computing through the same verified INT8 units (WebGPU, with the identical units on CPU for machines without it — there is no plain-float path). Private cross-network rooms via ?room=CODE with host approval — the room creator accepts each device before it can join. Includes gradient averaging with a deterministic Adam optimizer (identical state on every peer, nothing extra over the wire), checkpoint download (.pt) and upload → broadcast so one device can restore the whole group after a failure.

Live demo: https://huggingface.co/spaces/Quazim0t0/DaisyChain-Web

How it works

Each machine runs the same command; they form a cluster and train one shared model. Two things happen:

The compute runs through the emulated GPU logic. By default the model is built from VerifiedLinear layers, so every forward multiply / requantize / ReLU is done by the bundled verified INT8 units (daisychain/verified/) — the emulated GPU math. Rank 0 prints cluster-wide unit-invocation counts so you can see the emulated logic doing the work.
The machines are pooled data-parallel. Each node trains on its own shard; gradients are capacity-weighted and combined into the exact full-batch gradient, so replicas stay bit-identical. Faster machines automatically take a bigger share.

  old machine A ─┐
  old machine B ─┼─►  each runs the emulated GPU logic on its shard  ─►  one model
  old machine C ─┘        (gradients combined across the cluster)

Bring your own model

DaisyChain-Train trains any Task (build_model / sample / loss). Copy examples/my_task_template.py, set DAISY_TASK=your_module:YourTask. Use VerifiedLinear (see daisychain/verified_task.py) to run your model's compute through the emulated units. See docs/CUSTOM_TASK.md.

Plain-float alternative

To skip the emulated units and train with normal float math on each machine, set DAISY_TASK=daisychain.example_task:ExampleTask. Same cluster, same pooling — the model math just runs as ordinary float instead of through the verified units.

The dashboard

daisychain-dashboard (or the Docker service) serves a Tailwind page at :8080 — readiness banner, P2P connectivity scan, pooled cores/RAM + capacity plan (per-node device, weight, batch), and live training loss.

Networking

Use Tailscale for a P2P mesh so machines on different networks get stable IPs on one interface — docs/TAILSCALE.md.

Layout

daisychain/cluster.py        capacity-weighted CPU/GPU data-parallel trainer
daisychain/train.py          entry point (daisychain-train)
daisychain/verified/         bundled trained N/N units + VerifiedLinear (train through them)
daisychain/verified_task.py  default task: forward runs on the verified units
daisychain/example_task.py   plain-float alternative task
daisychain/task.py           the Task interface + loader
daisychain/dashboard/        agent + P2P scanner + Tailwind server
docker/                      Dockerfile, dashboard image, compose (demo cluster)
scripts/setup.bat / setup.sh interactive setup helpers
config/                      nodes + cluster env examples
examples/my_task_template.py starting point for your own model
docs/                        QUICKSTART, LIMITS, CUSTOM_TASK, TAILSCALE
daisychain/spikewhale_task.py   trains the real SpikeWhale on streamed HF datasets
daisychain/spikewhale_panel.py  slider control panel (localhost:8899)
web/                         DaisyChain-Web: P2P browser training (WebRTC + WebGPU)
export_luts_web.py           regenerates web/public LUTs from the trained units

Install

pip install torch numpy psutil
pip install -e .          # exposes: daisychain-train, daisychain-agent, daisychain-dashboard

Requires Python ≥ 3.9, PyTorch ≥ 2.0. Multi-node is reliable on Linux/macOS; on Windows use Docker/WSL (see Limitations).

Citation

@misc{byrne2026daisychain,
  title        = {DaisyChain-Train: An Old Hardware Training Pipeline},
  author       = {Byrne, Dean (Quazim0t0)},
  year         = {2026},
  howpublished = {\url{https://huggingface.co/DaisyChainAI/DaisyChain-Train}},
  note         = {Chain spare/old machines into a data-parallel training cluster}
}

Dean Byrne (Quazim0t0) · 2026

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