Claude
c6576178b4
Vendor the functional source of github.com/outis1one/local-ai into ./ai-stack (22 files) and add services/ai-stack.sh, which copies the source to ~/docker/ai-stack and hands off to the app's VRAM-aware installer (local-ai-setup.sh). The stack bundles Ollama, Open WebUI, RAG + MCP servers, ChromaDB, SearXNG, Kiwix, Gitea, InvokeAI, ComfyUI and Portainer. Cloud LLM providers (Groq/DeepInfra/OpenAI/OpenRouter) are optionally wired into Open WebUI via the plural OPENAI_API_BASE_URLS list, with the local RAG connection kept as the first entry so RAG keeps working. Open WebUI ships built-in auth, so Caddy is configured without Authelia. Excludes the upstream's two bundled copies of this very project (ubuntu-post-install.sh, ubuntu-post-install-main.zip) — stale and circular. Coexists with the existing ai-gpu service. Also fix the install-function names for ai-gpu and ai-stack: the dispatcher calls install_<raw-name>, so the function must be install_ai-gpu / install_ai-stack (hyphen), matching the working mail-archiver / wg-easy services. ai-gpu was previously uninstallable. Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com> Claude-Session: https://claude.ai/code/session_01Nb2vJ8W7bHKx1JXVvpCraH
685 lines
31 KiB
Bash
685 lines
31 KiB
Bash
#!/bin/bash
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# services/ai-gpu.sh — GPU AI stack: InvokeAI image gen + Ollama/OpenWebUI LLM + swap portal.
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# Part of the modular post-install system (sourced by setup.sh).
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#
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# Can also be run standalone on any machine:
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# sudo bash ai-gpu.sh
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# (Docker + nvidia-container-toolkit must already be installed)
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#
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# Clones https://github.com/outis1one/ai-6gb-gpu and installs three stacks:
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# image-gen/ — InvokeAI (port 9090), nvidia GPU, optimised for 6 GB VRAM
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# llm/ — Ollama (11434) + Open WebUI (3000) + SearXNG (internal)
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# + optional cloud LLM providers (Groq/DeepInfra/OpenAI/OpenRouter) as OpenAI connections
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# portal/ — Flask app (port 8080), mounts Docker socket, hot-swaps GPU between stacks
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#
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# Because a 6 GB GPU can only run ONE stack at a time, the portal handles the swap:
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# stop the active stack, start the requested one. Stop image-gen before starting llm, and vice versa.
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# ── Standalone bootstrap ──────────────────────────────────────────────────────
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if [[ "${BASH_SOURCE[0]}" == "${0}" ]]; then
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[[ "$(id -u)" == "0" ]] || { echo "Run with sudo: sudo bash $0"; exit 1; }
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_SELF_DIR="$(cd "$(dirname "${BASH_SOURCE[0]}")" && pwd)"
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_COMMON="$_SELF_DIR/../lib/common.sh"
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if [[ -f "$_COMMON" ]]; then
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source "$_COMMON"
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else
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log_info() { echo -e "\033[0;34m[INFO]\033[0m $*"; }
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log_success() { echo -e "\033[0;32m[OK]\033[0m $*"; }
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log_warning() { echo -e "\033[1;33m[WARN]\033[0m $*"; }
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log_error() { echo -e "\033[0;31m[ERROR]\033[0m $*" >&2; }
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require_docker() {
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command -v docker &>/dev/null || {
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log_error "Docker not found. Install it first:"
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log_error " curl -fsSL https://get.docker.com | sudo sh"
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return 1
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}
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docker compose version &>/dev/null || {
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log_error "Docker Compose plugin missing:"
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log_error " sudo apt-get install -y docker-compose-plugin"
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return 1
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}
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}
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ensure_docker_dir_ownership() {
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chown -R "$ACTUAL_USER:$ACTUAL_USER" "$@" 2>/dev/null || true
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}
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prompt_text() {
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local _q="$1" _def="$2" _var="$3" _r
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[[ "${UNATTENDED:-false}" == "true" ]] && { eval "$_var='$_def'"; return; }
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read -r -p " $_q " _r
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eval "$_var='${_r:-$_def}'"
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}
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prompt_yn() {
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local _q="$1" _def="$2" _var="$3" _r
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[[ "${UNATTENDED:-false}" == "true" ]] && { eval "$_var='$_def'"; return; }
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read -r -p " $_q " _r
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eval "$_var='${_r:-$_def}'"
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}
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configure_caddy_for_service() {
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local _name="$1" _upstream="$2" _subdomain="$3" _extra="${4:-}"
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local _caddy_dir="$DOCKER_DIR/caddy"
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local _caddyfile="$_caddy_dir/Caddyfile"
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local _display_port="${_upstream##*:}"
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local _mode="none"
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[[ -d "$_caddy_dir" ]] && _mode="local"
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[[ -n "${CADDY_REMOTE_HOST:-}" ]] && [[ "$_mode" != "local" ]] && _mode="remote"
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[[ "$_mode" == "none" ]] && {
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log_info "Access $_name directly on port $_display_port."
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return 0
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}
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echo ""
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local _do_caddy=""
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if [[ "$_mode" == "remote" ]]; then
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log_info "Remote Caddy configured (${CADDY_REMOTE_HOST})."
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log_info "A snippet file will be saved to ~/docker/caddy-snippets/."
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fi
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read -r -p " Configure Caddy reverse proxy for $_name? [y/N]: " _do_caddy
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[[ "${_do_caddy,,}" == "y" ]] || {
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log_info "Skipping — access at: http://localhost:$_display_port"
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return 0
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}
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local _default_domain=""
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if [[ -n "${SITE_DOMAIN:-}" ]] && [[ "$SITE_DOMAIN" != "example.com" ]]; then
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_default_domain="${_subdomain}.${SITE_DOMAIN}"
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log_info "Default: $_default_domain"
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fi
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local _domain=""
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read -r -p " Domain [${_default_domain:-required}]: " _domain
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_domain="${_domain:-$_default_domain}"
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[[ -n "$_domain" ]] || { log_warning "No domain entered — skipping Caddy."; return 0; }
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local _block_upstream="$_upstream"
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if [[ "$_mode" == "remote" ]]; then
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_block_upstream="${CADDY_REMOTE_HOST}:${_display_port}"
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fi
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local _site_block
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_site_block="$(cat << CBLOCK
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# $_name
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${_domain} {
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reverse_proxy ${_block_upstream}
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header {
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Strict-Transport-Security "max-age=31536000; includeSubDomains; preload"
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X-Content-Type-Options "nosniff"
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X-Frame-Options "SAMEORIGIN"
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Referrer-Policy "strict-origin-when-cross-origin"
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}
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log {
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output file /var/log/caddy/${_domain}.log
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format json
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}
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${_extra}
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}
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CBLOCK
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)"
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if [[ "$_mode" == "local" ]]; then
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if [[ -f "$_caddyfile" ]]; then
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local _bk="$_caddy_dir/Caddyfile.backup.$(date +%Y%m%d-%H%M%S)"
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cp "$_caddyfile" "$_bk"
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log_info "Backed up Caddyfile to $(basename "$_bk")"
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else
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touch "$_caddyfile"
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fi
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if grep -q "^${_domain}" "$_caddyfile" 2>/dev/null; then
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log_warning "$_domain already in Caddyfile"
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local _ow=""
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read -r -p " Overwrite? [y/N]: " _ow
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[[ "${_ow,,}" == "y" ]] || { log_info "Keeping existing entry."; return 0; }
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sed -i "/^${_domain}/,/^}/d" "$_caddyfile"
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fi
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printf '%s\n' "$_site_block" >> "$_caddyfile"
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log_success "Added $_domain to Caddyfile"
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docker exec caddy caddy fmt --overwrite /etc/caddy/Caddyfile 2>/dev/null || true
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if docker exec caddy caddy reload --config /etc/caddy/Caddyfile 2>/dev/null; then
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log_success "$_name accessible at: https://$_domain"
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else
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log_warning "Reload failed — check: docker logs caddy"
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log_info "Manual reload: docker exec caddy caddy reload --config /etc/caddy/Caddyfile"
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fi
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else
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local _snippet_dir="$DOCKER_DIR/caddy-snippets"
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local _snippet_file="$_snippet_dir/${_subdomain}.caddy"
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mkdir -p "$_snippet_dir"
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printf '%s\n' "$_site_block" > "$_snippet_file"
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chown "$ACTUAL_USER:$ACTUAL_USER" "$_snippet_file" 2>/dev/null || true
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log_success "Snippet saved: $_snippet_file"
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log_info "Copy to Caddy machine:"
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log_info " scp $_snippet_file caddy-host:~/caddy-snippets/"
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log_info " rsync -av $_snippet_dir/ caddy-host:~/caddy-snippets/ (all at once)"
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fi
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}
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write_readme() {
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local _dir="$1"
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mkdir -p "$_dir"
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[[ "${DRY_RUN:-false}" == "true" ]] && return 0
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cat > "$_dir/README.md"
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}
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generate_password() {
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local _len="${1:-32}"
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tr -dc 'A-Za-z0-9' < /dev/urandom | head -c "$_len"
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echo
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}
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fi
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ACTUAL_USER="${ACTUAL_USER:-${SUDO_USER:-$USER}}"
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ACTUAL_HOME="$(getent passwd "$ACTUAL_USER" 2>/dev/null | cut -d: -f6 || echo "${HOME:-/root}")"
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DOCKER_DIR="${DOCKER_DIR:-$ACTUAL_HOME/docker}"
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DRY_RUN="${DRY_RUN:-false}"
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UNATTENDED="${UNATTENDED:-false}"
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SITE_TZ="${SITE_TZ:-$(cat /etc/timezone 2>/dev/null || echo UTC)}"
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SITE_DOMAIN="${SITE_DOMAIN:-example.com}"
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SITE_CADDY_NET="${SITE_CADDY_NET:-caddy_net}"
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CADDY_REMOTE_HOST="${CADDY_REMOTE_HOST:-}"
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register_service() { :; }
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_RUN_STANDALONE=1
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fi
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# ─────────────────────────────────────────────────────────────────────────────
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register_service ai-gpu utilities "GPU AI stack — InvokeAI image gen + Ollama/OpenWebUI LLM (6 GB VRAM)" 9090
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install_ai-gpu() {
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require_docker || return 1
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log_info "Installing AI GPU stack (InvokeAI + Ollama/OpenWebUI + portal)..."
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log_info "Requires: nvidia GPU with 6 GB+ VRAM, nvidia-container-toolkit installed."
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local AI_DIR="$DOCKER_DIR/ai-gpu"
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local REPO_URL="https://github.com/outis1one/ai-6gb-gpu.git"
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local REPO_DIR="$AI_DIR/src"
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if [ "$DRY_RUN" = true ]; then
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echo "[DRY-RUN] Would clone $REPO_URL to $REPO_DIR"
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echo "[DRY-RUN] Would create stacks: image-gen (InvokeAI:9090), llm (Ollama:11434 + OpenWebUI:3000), portal (8080)"
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echo "[DRY-RUN] Would prompt for Ollama and InvokeAI model selection"
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echo "[DRY-RUN] Would optionally wire cloud LLM providers (Groq/DeepInfra/OpenAI/OpenRouter) into Open WebUI"
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echo "[DRY-RUN] Would auto-pull selected Ollama models after LLM stack starts"
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echo "[DRY-RUN] Would queue InvokeAI starter model via REST API"
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return 0
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fi
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# ── Timezone ──────────────────────────────────────────────────────────────
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local TZ_VAL="${SITE_TZ:-UTC}"
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prompt_text "Timezone (e.g. America/New_York) [$TZ_VAL]:" "$TZ_VAL" TZ_VAL
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TZ_VAL="${TZ_VAL:-UTC}"
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# ── Ollama model selection ────────────────────────────────────────────────
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echo ""
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log_info "Ollama LLM models — select which to download (enter numbers separated by spaces):"
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log_info "All models are quantized (Q4_K_M) and run comfortably on 6 GB VRAM."
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echo ""
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log_info " 1) llama3.2:3b ~2.0 GB Fast general chat. Great all-rounder. ← Recommended"
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log_info " 2) llama3.2:1b ~1.3 GB Ultra-fast. Light tasks, low latency."
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log_info " 3) qwen2.5:7b ~4.7 GB Top code + math model. Strong reasoning."
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log_info " 4) mistral:7b ~4.1 GB Solid all-rounder. Good at instruction follow."
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log_info " 5) phi4-mini ~2.5 GB Microsoft Phi-4 mini. Excellent for coding."
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log_info " 6) gemma3:4b ~2.5 GB Google Gemma 3. Well-rounded, multilingual."
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log_info " 7) deepseek-r1:7b ~4.7 GB Strong reasoning and math. Think-step model."
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log_info " 8) nomic-embed-text ~274 MB Embedding model — enables RAG/doc search."
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log_info " Recommended to add alongside a chat model."
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echo ""
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log_info " Example: '1 8' pulls llama3.2:3b + nomic-embed-text"
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log_info " Enter '0' or leave blank to skip and pull models manually later."
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echo ""
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local OLLAMA_CHOICES=""
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prompt_text "Models to download [1 8]:" "1 8" OLLAMA_CHOICES
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declare -a OLLAMA_MODELS=()
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for _n in $OLLAMA_CHOICES; do
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case "$_n" in
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1) OLLAMA_MODELS+=("llama3.2:3b") ;;
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2) OLLAMA_MODELS+=("llama3.2:1b") ;;
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3) OLLAMA_MODELS+=("qwen2.5:7b") ;;
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4) OLLAMA_MODELS+=("mistral:7b") ;;
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5) OLLAMA_MODELS+=("phi4-mini") ;;
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6) OLLAMA_MODELS+=("gemma3:4b") ;;
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7) OLLAMA_MODELS+=("deepseek-r1:7b") ;;
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8) OLLAMA_MODELS+=("nomic-embed-text") ;;
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esac
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done
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# ── Cloud LLM providers (optional) ─────────────────────────────────────────────
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echo ""
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log_info "Cloud LLM providers — optional, wired into Open WebUI alongside local Ollama."
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log_info "All are OpenAI-compatible. Pick any combination (you enter a key for each):"
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echo ""
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log_info " 1) Groq Fast LPU inference, generous free tier. Open models (Llama, Qwen, gpt-oss, Kimi)."
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log_info " Key: https://console.groq.com/keys"
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log_info " 2) DeepInfra Cheapest host for open models. Zero-retention, no training (US)."
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log_info " Key: https://deepinfra.com/dash/api_keys"
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log_info " 3) OpenAI GPT-5.x, o-series, gpt-image. Pay-as-you-go."
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log_info " Key: https://platform.openai.com/api-keys"
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log_info " 4) OpenRouter One key, 300+ models across many providers (incl. free variants)."
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log_info " Key: https://openrouter.ai/keys"
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echo ""
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log_info " Example: '1 2' wires Groq + DeepInfra. Leave blank to skip cloud providers."
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echo ""
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local CLOUD_CHOICES=""
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prompt_text "Cloud providers to add []:" "" CLOUD_CHOICES
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# Parallel arrays: display name, OpenAI-compatible base URL, and entered key
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declare -a CLOUD_NAMES=() CLOUD_URLS=() CLOUD_KEYS=()
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local _c _cname _curl _ckey
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for _c in $CLOUD_CHOICES; do
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_cname="" ; _curl=""
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case "$_c" in
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1) _cname="Groq"; _curl="https://api.groq.com/openai/v1" ;;
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2) _cname="DeepInfra"; _curl="https://api.deepinfra.com/v1/openai" ;;
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3) _cname="OpenAI"; _curl="https://api.openai.com/v1" ;;
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4) _cname="OpenRouter"; _curl="https://openrouter.ai/api/v1" ;;
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*) log_warning "Ignoring unknown choice '$_c'"; continue ;;
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esac
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_ckey=""
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prompt_text "$_cname API key (enter to skip):" "" _ckey
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if [ -n "$_ckey" ]; then
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CLOUD_NAMES+=("$_cname")
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CLOUD_URLS+=("$_curl")
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CLOUD_KEYS+=("$_ckey")
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else
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log_warning "No key for $_cname — skipping."
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fi
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done
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# Semicolon-joined lists for Open WebUI (OPENAI_API_BASE_URLS / OPENAI_API_KEYS)
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local CLOUD_URLS_JOINED="" CLOUD_KEYS_JOINED="" _i
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for _i in "${!CLOUD_NAMES[@]}"; do
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CLOUD_URLS_JOINED+="${CLOUD_URLS[$_i]};"
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CLOUD_KEYS_JOINED+="${CLOUD_KEYS[$_i]};"
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done
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CLOUD_URLS_JOINED="${CLOUD_URLS_JOINED%;}"
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CLOUD_KEYS_JOINED="${CLOUD_KEYS_JOINED%;}"
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# ── InvokeAI model selection ──────────────────────────────────────────────
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echo ""
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log_info "InvokeAI image generation models (for 6 GB VRAM with partial GPU offload):"
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log_info "InvokeAI uses VRAM=3 GB + 8 GB RAM cache, so all models below work on 6 GB."
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echo ""
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log_info " 1) stabilityai/stable-diffusion-v1-5 ~4 GB SD 1.5 — fast, huge style/LoRA library."
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log_info " Best starting model for most uses."
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log_info " 2) stabilityai/sdxl-turbo ~7 GB SDXL Turbo — 4-step generation."
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log_info " Fast, high quality, slightly slower on 6 GB."
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log_info " 3) stabilityai/stable-diffusion-xl-base-1.0"
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log_info " ~7 GB SDXL base — best quality at 1024px."
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log_info " Slowest due to RAM offload on 6 GB."
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log_info " 4) Skip — install models via the Model Manager at http://localhost:9090"
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echo ""
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log_info " Tip: SD 1.5 (choice 1) is fastest and most compatible. Start here."
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log_info " HuggingFace token: required for some gated models (free at huggingface.co/settings/tokens)"
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echo ""
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local INVOKE_CHOICE=""
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prompt_text "InvokeAI starter model [1]:" "1" INVOKE_CHOICE
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local INVOKE_MODEL_SOURCE=""
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local INVOKE_MODEL_NAME=""
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case "$INVOKE_CHOICE" in
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2) INVOKE_MODEL_SOURCE="stabilityai/sdxl-turbo"
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INVOKE_MODEL_NAME="SDXL Turbo" ;;
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3) INVOKE_MODEL_SOURCE="stabilityai/stable-diffusion-xl-base-1.0"
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INVOKE_MODEL_NAME="SDXL Base" ;;
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4) INVOKE_MODEL_SOURCE=""
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INVOKE_MODEL_NAME="" ;;
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*) INVOKE_MODEL_SOURCE="stabilityai/stable-diffusion-v1-5"
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INVOKE_MODEL_NAME="SD 1.5" ;;
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esac
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local HF_TOKEN=""
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if [ -n "$INVOKE_MODEL_SOURCE" ]; then
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prompt_text "HuggingFace token (optional — needed for gated models, enter to skip):" "" HF_TOKEN
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fi
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# ── Clone / update repo ───────────────────────────────────────────────────
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mkdir -p "$AI_DIR"
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if [ -d "$REPO_DIR/.git" ]; then
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log_info "Updating ai-6gb-gpu repo..."
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git -C "$REPO_DIR" pull --ff-only 2>/dev/null \
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&& log_success "Repo updated" \
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|| log_warning "Could not pull latest — using existing version"
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else
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log_info "Cloning ai-6gb-gpu repo..."
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git clone --depth 1 "$REPO_URL" "$REPO_DIR" \
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|| { log_error "Clone failed — check network and git access"; return 1; }
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fi
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# ── Image-gen stack (InvokeAI) ────────────────────────────────────────────
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local IMAGE_GEN_DIR="$AI_DIR/image-gen"
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mkdir -p "$IMAGE_GEN_DIR"
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if [ -d "$REPO_DIR/ai-image-gen" ]; then
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cp -rn "$REPO_DIR/ai-image-gen/." "$IMAGE_GEN_DIR/" 2>/dev/null || true
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find "$IMAGE_GEN_DIR" -name "docker-compose.yml" -exec \
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sed -i "s|America/New_York|$TZ_VAL|g" {} \;
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fi
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cat > "$IMAGE_GEN_DIR/.env" << IMGENV
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# InvokeAI — image generation
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TZ=${TZ_VAL}
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# VRAM cap: 3 GB leaves headroom on a 6 GB card; remaining model layers go to RAM
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INVOKEAI_vram=3
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# RAM cache size for model layer offload
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INVOKEAI_ram=8
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${HF_TOKEN:+HUGGING_FACE_HUB_TOKEN=${HF_TOKEN}}
|
|
CADDY_NET=${SITE_CADDY_NET}
|
|
IMGENV
|
|
chmod 600 "$IMAGE_GEN_DIR/.env"
|
|
|
|
# ── LLM stack (Ollama + Open WebUI + SearXNG) ─────────────────────────────
|
|
local LLM_DIR="$AI_DIR/llm"
|
|
mkdir -p "$LLM_DIR"
|
|
if [ -d "$REPO_DIR/ai-llm" ]; then
|
|
cp -rn "$REPO_DIR/ai-llm/." "$LLM_DIR/" 2>/dev/null || true
|
|
find "$LLM_DIR" -name "docker-compose.yml" -exec \
|
|
sed -i "s|America/New_York|$TZ_VAL|g" {} \;
|
|
fi
|
|
|
|
# Wire selected cloud providers into Open WebUI as OpenAI-compatible
|
|
# connections (idempotent). Keys live in .env; only ${VAR} refs go in compose.
|
|
if [ -n "$CLOUD_URLS_JOINED" ]; then
|
|
local LLM_COMPOSE="$LLM_DIR/docker-compose.yml"
|
|
if [ -f "$LLM_COMPOSE" ] && ! grep -q "OPENAI_API_BASE_URLS" "$LLM_COMPOSE"; then
|
|
sed -i '/- OLLAMA_BASE_URL=http:\/\/ollama:11434/a\
|
|
- ENABLE_OPENAI_API=true\
|
|
- OPENAI_API_BASE_URLS=${OPENAI_API_BASE_URLS}\
|
|
- OPENAI_API_KEYS=${OPENAI_API_KEYS}' "$LLM_COMPOSE"
|
|
grep -q "OPENAI_API_BASE_URLS" "$LLM_COMPOSE" \
|
|
&& log_success "Cloud providers wired into Open WebUI: ${CLOUD_NAMES[*]}" \
|
|
|| log_warning "Could not patch docker-compose.yml — add ENABLE_OPENAI_API/OPENAI_API_BASE_URLS/OPENAI_API_KEYS to the open-webui service's environment manually"
|
|
fi
|
|
fi
|
|
|
|
local WEBUI_SECRET
|
|
WEBUI_SECRET="$(generate_password 32)"
|
|
|
|
cat > "$LLM_DIR/.env" << LLMENV
|
|
# Ollama + Open WebUI + SearXNG
|
|
TZ=${TZ_VAL}
|
|
WEBUI_SECRET_KEY=${WEBUI_SECRET}
|
|
# Open WebUI: enable SearXNG for web search in chats
|
|
ENABLE_RAG_WEB_SEARCH=true
|
|
RAG_WEB_SEARCH_ENGINE=searxng
|
|
SEARXNG_QUERY_URL=http://searxng:8080/search?q=<query>&format=json
|
|
# Cloud LLM providers for Open WebUI — OpenAI-compatible, semicolon-separated,
|
|
# matched by position. Blank = local Ollama only. Add/rotate later: append a base
|
|
# URL + its key to these two lines (same order), then run docker compose up -d.
|
|
# Groq https://api.groq.com/openai/v1 key: https://console.groq.com/keys
|
|
# DeepInfra https://api.deepinfra.com/v1/openai key: https://deepinfra.com/dash/api_keys
|
|
# OpenAI https://api.openai.com/v1 key: https://platform.openai.com/api-keys
|
|
# OpenRouter https://openrouter.ai/api/v1 key: https://openrouter.ai/keys
|
|
OPENAI_API_BASE_URLS=${CLOUD_URLS_JOINED}
|
|
OPENAI_API_KEYS=${CLOUD_KEYS_JOINED}
|
|
CADDY_NET=${SITE_CADDY_NET}
|
|
LLMENV
|
|
chmod 600 "$LLM_DIR/.env"
|
|
|
|
# ── Portal stack (Flask GPU swap controller) ──────────────────────────────
|
|
local PORTAL_DIR="$AI_DIR/portal"
|
|
mkdir -p "$PORTAL_DIR"
|
|
if [ -d "$REPO_DIR/ai-portal" ]; then
|
|
cp -rn "$REPO_DIR/ai-portal/." "$PORTAL_DIR/" 2>/dev/null || true
|
|
if [ -f "$PORTAL_DIR/docker-compose.yml" ]; then
|
|
sed -i \
|
|
"s|/home/[^/]*/docker:|${ACTUAL_HOME}/docker:|g" \
|
|
"$PORTAL_DIR/docker-compose.yml"
|
|
sed -i "s|America/New_York|$TZ_VAL|g" "$PORTAL_DIR/docker-compose.yml"
|
|
fi
|
|
fi
|
|
|
|
cat > "$PORTAL_DIR/.env" << PORTALENV
|
|
# AI Portal — GPU stack swap controller
|
|
TZ=${TZ_VAL}
|
|
# Paths inside the container (/docker maps to ${ACTUAL_HOME}/docker via volume mount)
|
|
IMAGE_STACK=/docker/ai-gpu/image-gen
|
|
LLM_STACK=/docker/ai-gpu/llm
|
|
CADDY_NET=${SITE_CADDY_NET}
|
|
PORTALENV
|
|
chmod 600 "$PORTAL_DIR/.env"
|
|
|
|
ensure_docker_dir_ownership "$AI_DIR"
|
|
|
|
echo ""
|
|
log_success "AI GPU stacks configured under $AI_DIR"
|
|
log_warning "Only ONE GPU stack can run at a time on a 6 GB card."
|
|
log_info "Use the portal (port 8080) to hot-swap between image-gen and llm."
|
|
echo ""
|
|
|
|
# ── Caddy ─────────────────────────────────────────────────────────────────
|
|
configure_caddy_for_service "AI Portal" "ai-portal:8080" "localai"
|
|
configure_caddy_for_service "InvokeAI" "invokeai:9090" "images"
|
|
|
|
# ── README ────────────────────────────────────────────────────────────────
|
|
write_readme "$AI_DIR" << MD
|
|
# AI GPU Stack
|
|
|
|
Three Docker stacks optimised for a 6 GB VRAM nvidia GPU.
|
|
Source: https://github.com/outis1one/ai-6gb-gpu
|
|
|
|
## Stacks
|
|
|
|
| Stack | Service | Port | Notes |
|
|
|-------|---------|------|-------|
|
|
| \`image-gen/\` | InvokeAI | 9090 | Image generation (SD 1.5, SDXL, Flux…) |
|
|
| \`llm/\` | Ollama | 11434 | LLM inference engine |
|
|
| \`llm/\` | Open WebUI | 3000 | Chat UI — models, RAG, web search |
|
|
| \`llm/\` | SearXNG | internal | Web search backend for RAG in OpenWebUI |
|
|
| \`portal/\` | AI Portal | 8080 | GPU swap controller — start/stop stacks |
|
|
|
|
## GPU time-sharing (important)
|
|
|
|
A 6 GB GPU can only run one AI stack at a time.
|
|
Use the portal at http://localhost:8080 to swap.
|
|
|
|
Manual swap:
|
|
\`\`\`bash
|
|
docker compose -f $AI_DIR/llm/docker-compose.yml down
|
|
docker compose -f $AI_DIR/image-gen/docker-compose.yml up -d
|
|
\`\`\`
|
|
|
|
## InvokeAI — add more models
|
|
|
|
Models installed at setup are in the Model Manager. To add more:
|
|
1. Open http://localhost:9090 → Model Manager → Add Model
|
|
2. Paste a HuggingFace repo ID (e.g. \`stabilityai/stable-diffusion-2-1\`)
|
|
3. Or import a local .safetensors file
|
|
|
|
For gated models, add \`HUGGING_FACE_HUB_TOKEN=xxx\` to \`image-gen/.env\`.
|
|
|
|
Recommended models for 6 GB (with partial GPU offload):
|
|
|
|
| Model | Source | Notes |
|
|
|-------|--------|-------|
|
|
| SD 1.5 | \`stabilityai/stable-diffusion-v1-5\` | Fast, huge LoRA library |
|
|
| SDXL Turbo | \`stabilityai/sdxl-turbo\` | 4-step, good quality |
|
|
| SDXL Base | \`stabilityai/stable-diffusion-xl-base-1.0\` | Best quality, slower |
|
|
| SD 2.1 | \`stabilityai/stable-diffusion-2-1\` | Good mid-size choice |
|
|
|
|
## Ollama — add more models
|
|
|
|
\`\`\`bash
|
|
# Pull any model while llm stack is running
|
|
docker exec ollama ollama pull llama3.2:3b
|
|
docker exec ollama ollama pull nomic-embed-text # RAG embeddings
|
|
docker exec ollama ollama list # see installed models
|
|
\`\`\`
|
|
|
|
Browse models at: https://ollama.com/library
|
|
For 6 GB cards, stick to 7B or smaller with Q4_K_M quantisation (~4.5 GB).
|
|
|
|
## Open WebUI — first login
|
|
|
|
Open http://localhost:3000 and create your admin account on first visit.
|
|
Models pulled into Ollama appear automatically in the model dropdown.
|
|
Enable web search: Settings → Admin → Web Search (SearXNG is pre-configured).
|
|
|
|
## Cloud LLM providers
|
|
|
|
$([ ${#CLOUD_NAMES[@]} -gt 0 ] && echo "Configured at install time: ${CLOUD_NAMES[*]} — these appear in the Open WebUI model dropdown alongside local Ollama." || echo "None configured. To add one or more later:")
|
|
|
|
Open WebUI is OpenAI-compatible, so these plug in as extra connections. They share
|
|
two semicolon-separated lists, matched by position:
|
|
|
|
| Provider | Base URL | API key |
|
|
|----------|----------|---------|
|
|
| Groq | \`https://api.groq.com/openai/v1\` | https://console.groq.com/keys |
|
|
| DeepInfra | \`https://api.deepinfra.com/v1/openai\` | https://deepinfra.com/dash/api_keys |
|
|
| OpenAI | \`https://api.openai.com/v1\` | https://platform.openai.com/api-keys |
|
|
| OpenRouter | \`https://openrouter.ai/api/v1\` | https://openrouter.ai/keys |
|
|
|
|
Add/rotate providers:
|
|
\`\`\`bash
|
|
# llm/.env — semicolon-separated, SAME order in both lists:
|
|
OPENAI_API_BASE_URLS=https://api.groq.com/openai/v1;https://api.deepinfra.com/v1/openai
|
|
OPENAI_API_KEYS=gsk_xxx;di_xxx
|
|
|
|
# llm/docker-compose.yml — open-webui service needs these under 'environment:'
|
|
# - ENABLE_OPENAI_API=true
|
|
# - OPENAI_API_BASE_URLS=\${OPENAI_API_BASE_URLS}
|
|
# - OPENAI_API_KEYS=\${OPENAI_API_KEYS}
|
|
|
|
cd $AI_DIR/llm && docker compose up -d # recreate with the new keys
|
|
\`\`\`
|
|
Tip: Groq has a generous free tier; DeepInfra is the cheapest host for open models
|
|
with zero-retention privacy. Both are far faster than local Ollama on a 6 GB card.
|
|
|
|
## Manage individual stacks
|
|
\`\`\`bash
|
|
cd $AI_DIR/image-gen && docker compose up -d # start InvokeAI
|
|
cd $AI_DIR/llm && docker compose up -d # start Ollama + OpenWebUI
|
|
cd $AI_DIR/portal && docker compose up -d # start portal
|
|
docker compose -f $AI_DIR/image-gen/docker-compose.yml logs -f invokeai
|
|
docker compose -f $AI_DIR/llm/docker-compose.yml logs -f openwebui
|
|
\`\`\`
|
|
|
|
## Update
|
|
\`\`\`bash
|
|
cd $REPO_DIR && git pull
|
|
cd $AI_DIR/image-gen && docker compose pull && docker compose up -d
|
|
cd $AI_DIR/llm && docker compose pull && docker compose up -d
|
|
cd $AI_DIR/portal && docker compose build --pull && docker compose up -d
|
|
\`\`\`
|
|
MD
|
|
|
|
# ── Start stacks + pull models ────────────────────────────────────────────
|
|
echo ""
|
|
log_info "What would you like to start now?"
|
|
log_info " 1) Portal only — start the swap controller, configure the rest later"
|
|
log_info " 2) Portal + LLM stack — start Ollama/OpenWebUI and pull selected models"
|
|
log_info " 3) Portal + image-gen — start InvokeAI and queue the starter model download"
|
|
log_info " 4) None — start manually later"
|
|
echo ""
|
|
|
|
local START_CHOICE=""
|
|
prompt_text "Choice [2]:" "2" START_CHOICE
|
|
|
|
# Always start portal if any stack is starting
|
|
if [[ "$START_CHOICE" =~ ^[123]$ ]]; then
|
|
docker compose -f "$PORTAL_DIR/docker-compose.yml" up -d \
|
|
&& log_success "Portal started — http://localhost:8080" \
|
|
|| log_warning "Portal start failed — check: docker compose -f $PORTAL_DIR/docker-compose.yml logs"
|
|
fi
|
|
|
|
if [[ "$START_CHOICE" == "2" ]]; then
|
|
# Start LLM stack
|
|
docker compose -f "$LLM_DIR/docker-compose.yml" up -d \
|
|
&& log_success "LLM stack started" \
|
|
|| { log_warning "LLM stack start failed"; START_CHOICE="0"; }
|
|
|
|
# Pull Ollama models if any were selected
|
|
if [ ${#OLLAMA_MODELS[@]} -gt 0 ] && [[ "$START_CHOICE" == "2" ]]; then
|
|
log_info "Waiting for Ollama to be ready..."
|
|
local _w=0
|
|
while ! curl -sf "http://localhost:11434/api/version" &>/dev/null; do
|
|
sleep 3; _w=$((_w+3))
|
|
[[ $_w -ge 90 ]] && { log_warning "Ollama not responding after 90s — pull models manually later"; break; }
|
|
done
|
|
|
|
if curl -sf "http://localhost:11434/api/version" &>/dev/null; then
|
|
for _m in "${OLLAMA_MODELS[@]}"; do
|
|
log_info "Pulling $_m (this may take a while)..."
|
|
docker exec ollama ollama pull "$_m" \
|
|
&& log_success "$_m ready" \
|
|
|| log_warning "Pull failed for $_m — retry: docker exec ollama ollama pull $_m"
|
|
done
|
|
log_success "Open WebUI ready at: http://localhost:3000"
|
|
log_info "Create your admin account on the first visit."
|
|
fi
|
|
fi
|
|
fi
|
|
|
|
if [[ "$START_CHOICE" == "3" ]]; then
|
|
# Start image-gen stack
|
|
docker compose -f "$IMAGE_GEN_DIR/docker-compose.yml" up -d \
|
|
&& log_success "InvokeAI started" \
|
|
|| { log_warning "InvokeAI start failed — check: docker compose -f $IMAGE_GEN_DIR/docker-compose.yml logs"; START_CHOICE="0"; }
|
|
|
|
# Queue starter model via InvokeAI REST API
|
|
if [ -n "$INVOKE_MODEL_SOURCE" ] && [[ "$START_CHOICE" == "3" ]]; then
|
|
log_info "Waiting for InvokeAI to be ready (model database initialises on first start)..."
|
|
local _w=0
|
|
while ! curl -sf "http://localhost:9090/api/v1/app/version" &>/dev/null; do
|
|
sleep 5; _w=$((_w+5))
|
|
[[ $_w -ge 180 ]] && { log_warning "InvokeAI not responding after 3 min"; break; }
|
|
done
|
|
|
|
if curl -sf "http://localhost:9090/api/v1/app/version" &>/dev/null; then
|
|
log_info "Queuing $INVOKE_MODEL_NAME download..."
|
|
local _resp
|
|
_resp=$(curl -s -X POST "http://localhost:9090/api/v2/models/install" \
|
|
-H "Content-Type: application/json" \
|
|
-d "{\"source\": \"${INVOKE_MODEL_SOURCE}\"}" 2>/dev/null)
|
|
if echo "$_resp" | grep -q '"id"'; then
|
|
log_success "$INVOKE_MODEL_NAME queued — downloading in background"
|
|
log_info "Track progress: http://localhost:9090 → Model Manager → In Progress"
|
|
else
|
|
log_warning "Could not queue via API. Install manually:"
|
|
log_info " Open http://localhost:9090 → Model Manager → Add Model"
|
|
log_info " Source: $INVOKE_MODEL_SOURCE"
|
|
fi
|
|
fi
|
|
fi
|
|
fi
|
|
|
|
if [[ "$START_CHOICE" == "4" ]] || [[ "$START_CHOICE" == "0" ]]; then
|
|
log_info "Start when ready:"
|
|
log_info " docker compose -f $PORTAL_DIR/docker-compose.yml up -d"
|
|
log_info " docker compose -f $LLM_DIR/docker-compose.yml up -d"
|
|
log_info " docker compose -f $IMAGE_GEN_DIR/docker-compose.yml up -d"
|
|
fi
|
|
|
|
echo ""
|
|
echo " Portal: http://localhost:8080 (GPU swap controller)"
|
|
echo " InvokeAI: http://localhost:9090 (image-gen stack)"
|
|
echo " Open WebUI: http://localhost:3000 (llm stack)"
|
|
echo " Ollama API: http://localhost:11434 (llm stack)"
|
|
echo " Source repo: $REPO_DIR"
|
|
echo ""
|
|
if [ ${#OLLAMA_MODELS[@]} -gt 0 ]; then
|
|
echo " Ollama models queued: ${OLLAMA_MODELS[*]}"
|
|
fi
|
|
if [ -n "$INVOKE_MODEL_NAME" ]; then
|
|
echo " InvokeAI starter: $INVOKE_MODEL_NAME ($INVOKE_MODEL_SOURCE)"
|
|
fi
|
|
if [ ${#CLOUD_NAMES[@]} -gt 0 ]; then
|
|
echo " Cloud LLM providers: ${CLOUD_NAMES[*]} (wired into Open WebUI)"
|
|
fi
|
|
echo ""
|
|
}
|
|
|
|
[[ "${_RUN_STANDALONE:-0}" == 1 ]] && install_ai-gpu
|