DaisyChain Quickstart

Two ways to run. Docker is the most reliable (especially on Windows).

A. Docker — one command (demo cluster on one machine)

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

This starts 3 node containers + the dashboard on a Docker network — the whole pipeline, so you can see connectivity, the capacity plan, and live training. Stop with docker compose -f docker/docker-compose.yml down.

Windows: just run scripts\setup.bat and pick [1] Docker.

B. Python — real machines

On every machine:

pip install torch numpy psutil
pip install -e .            # from the repo, or `pip install daisychain`

Set the cluster env (copy config/cluster.example.env), changing only RANK per machine, then run:

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: run scripts\setup.bat and pick [2] Python (it prompts for these).

Watch it

Run the dashboard anywhere that can reach the nodes:

# edit config/nodes.example.json with your node hosts, then:
daisychain-dashboard        # http://localhost:8080

Train your own model

The default is a tiny example task. To train your model, see CUSTOM_TASK.md — copy examples/my_task_template.py, fill in build_model / sample / loss, and set DAISY_TASK=your_module:YourTask.

Before you rely on it, read LIMITS.md. DaisyChain pools compute, not memory, and is for small models on spare hardware — not GPU-class training.