--- license: mit tags: - distributed-training - old-hardware - int8 - webgpu - webrtc --- # 🌼 DaisyChain-Train β€” Old Hardware Training Pipeline **Part of DaisyChain on πŸ€— Hugging Face β†’ 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** β€” > verified INT8 units (GUDA-style) that stand in for a GPU's math β€” 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 β†’ [Limitations](docs/LIMITS.md). **Use the hardware you already have to train.** Each machine runs the emulated GPU logic (verified INT8 units β€” 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 β€” **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](docs/LIMITS.md)** β€” please read it before relying on it. --- ## Quick start ### Docker (most reliable β€” one command) ```bash 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 .`): ```bash 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 ```bat scripts\setup.bat ``` An interactive menu: Docker, Python node, or just install deps. Full walkthrough: **[docs/QUICKSTART.md](docs/QUICKSTART.md)**. ### πŸ‹ SpikeWhale control panel (sliders β†’ real training) ```bash 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) ```bash 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 β€” there is no plain-float path). Private cross-network rooms via `?room=CODE` with **host approval** β€” 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 β†’ broadcast** so one device can restore the whole group after a failure. **Live demo:** https://huggingface.co/spaces/Quazim0t0/DaisyChain-Web ### Recent updates (July 2026) β€” DaisyChain-Web **Verification stack** β€” the browser trainer's correctness is now *checked by things that run*, not argued ([full results](web/TEST_RESULTS.md)): - **IEEE-754 oracle** (`web/test_ieee.js`): a binary32 oracle built from the standard in exact BigInt arithmetic proves the JS epilogue mirror is spec-correct β€” and rejects the old round-once mirror on 34% of inputs. - **Metamorphic properties + oracle mutation scoring** (`test_metamorphic.js`, `test_corpus.js`): properties needing no reference implementation, scored against an externally-authored bug taxonomy β€” **4/4**, matching the exact differential gate's 4/4. Relations own the loop bugs; two definitional absolutes (ReLU output range, a unit-scale integer anchor) own the value bugs no relation can see. - **Exact kernel gates on every live kernel**, a continuous random-cell audit at live shapes, and a cross-device kernel probe (same seeded int8 GEMM, same hash on every honest device, any backend). - **RDNA2 ISA audit**: reading a real GPU's shader ISA against our determinism assumptions confirmed three of them on silicon (exact packed int8 dot; correctly-rounded f32 add/mul; 1-ULP reciprocal β€” division stays off the GPU) and produced two hardenings. (1) Real ISAs have non-IEEE variants that **flush βˆ’0 to +0**; JS `!==` can't see that (`-0 !== 0` is false), so all gates and audits now compare **bit patterns** β€” exactly what the replica hash sees. (2) FMA contraction of the quantize's `xΒ·inv + 0.5` (one rounding instead of two) turned out to be **floor-invisible by construction** β€” proven in `test_b2b.js` with 175k+ last-ulp anomalies at binade edges, zero surviving `floor()`. Rounding mode and denorm flushing are runtime driver state on real hardware, which is why every device re-runs the exact gates at every init. **Training data** β€” FineWeb-Edu (10BT sample) is the hardcoded dataset. The Space reads random slices of the parquet shards straight off the HF CDN with range requests (pure-JS `hyparquet`, SNAPPY) and serves plain text at `/data` β€” no dependency on the datasets-server rows API and its 503s. **Resilience** β€” the sync guard now *repairs* instead of halting: a roster gradient that reached the leader but not some follower (asymmetric WebRTC mesh) is re-requested from the leader, bit-exact, and the run continues. The guard still stops anything that would fork the weights. **CUTLASS-style kernel work**, each step proven bit-identical or exact-gated: - **Dispatch-optimized backward** (ex. 05/24): independent GEMMs overlapped, sibling trios fused into batch-3 dispatches β€” bit-identical gradients, dormant int8-backward path down from 1.63Γ— to 1.21Γ— vs float. - **QKV dual-GEMM fusion** (ex. 45): q/k/v share one left operand β€” quantized once, one batched dispatch, zero changed bits. - **B2B MLP chain** (ex. 13 + 23): both MLP GEMMs back-to-back on the GPU with a fused per-row absmax reduction; the intermediate is quantized on-device via a WGSL-exact respec (`floor(f32(xΒ·invScale)+0.5)` β€” no GPU division) whose fround-stepped JS mirror keeps mixed GPU/CPU fleets bit-identical. All ten test suites (`cd web && npm test`) pass; results with methodology in [`web/TEST_RESULTS.md`](web/TEST_RESULTS.md). --- ## How it works Each machine runs the **same** command; they form a cluster and train one shared model. Two things happen: 1. **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/`) β€” the emulated GPU math. Rank 0 prints **cluster-wide unit-invocation counts** so you can see the emulated logic doing the work. 2. **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 β”€β”˜ (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](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 β€” 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` β€” 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 β€” **[docs/TAILSCALE.md](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 ```bash pip install torch numpy psutil pip install -e . # exposes: daisychain-train, daisychain-agent, daisychain-dashboard ``` Requires Python β‰₯ 3.9, PyTorch β‰₯ 2.0. Multi-node is reliable on **Linux/macOS**; on **Windows use Docker/WSL** (see [Limitations](docs/LIMITS.md)). --- ## Links - **DaisyChain on Hugging Face:** https://huggingface.co/DaisyChainAI - **This model:** https://huggingface.co/DaisyChainAI/DaisyChain-Train **License:** MIT Β· **Author:** Dean Byrne (Quazim0t0) Β· **Org:** DaisyChainAI ## Citation ```bibtex @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)** Β· 2026