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Old-hardware training through emulated GPU logic
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#!/usr/bin/env bash
# DaisyChain setup helper (Linux/macOS).
set -e
cd "$(dirname "$0")/.."
echo "🌼 DaisyChain — Old Hardware Training Pipeline"
echo " [1] Docker (demo cluster on this box)"
echo " [2] Python node (join a real cluster)"
echo " [3] Install deps only"
read -rp " Choice: " c
case "$c" in
1)
command -v docker >/dev/null || { echo "Install Docker first."; exit 1; }
docker compose -f docker/docker-compose.yml up --build -d
echo "Dashboard: http://localhost:8080 (stop: docker compose -f docker/docker-compose.yml down)"
;;
3|2)
python3 -m pip install --upgrade pip
python3 -m pip install torch numpy psutil
python3 -m pip install -e .
echo "Installed."
if [ "$c" = "2" ]; then
read -rp " MASTER_ADDR (coordinator IP): " MA
read -rp " WORLD_SIZE: " WS
read -rp " RANK (0=coordinator): " RK
read -rp " GLOO_SOCKET_IFNAME (e.g. tailscale0): " IF
export MASTER_ADDR="$MA" MASTER_PORT=29560 WORLD_SIZE="$WS" RANK="$RK"
export GLOO_SOCKET_IFNAME="$IF" USE_LIBUV=0
python3 -m daisychain.train
fi
;;
*) echo "bye" ;;
esac