first commit
This commit is contained in:
Carlos Tello
@@ -0,0 +1,534 @@
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#!/bin/bash
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################################################################################
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# LLM SERVER DEPLOYMENT SCRIPT
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# Ubuntu 22.04 LTS + ASUS H510M + RTX 3090
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#
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# Uso: bash scripts/install.sh [--quick] [--gpu-only]
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#
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# Opciones:
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# --quick Salta verificaciones lentas
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# --gpu-only Solo instala GPU drivers (para re-install)
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################################################################################
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set -e # Exit si hay error
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# Colores
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RED='\033[0;31m'
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GREEN='\033[0;32m'
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YELLOW='\033[1;33m'
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BLUE='\033[0;34m'
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NC='\033[0m' # No Color
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# Variables
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SCRIPT_DIR="$( cd "$( dirname "${BASH_SOURCE[0]}" )" && pwd )"
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PROJECT_DIR="$(dirname "$SCRIPT_DIR")"
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WORKSPACE_DIR="$PROJECT_DIR/workspace"
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VENV_DIR="$PROJECT_DIR/venv"
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LOG_FILE="$PROJECT_DIR/logs/install.log"
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# Crear directorio de logs
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mkdir -p "$PROJECT_DIR/logs"
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# Function: Log con timestamp
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log() {
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local level=$1
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shift
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local message="$@"
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local timestamp=$(date '+%Y-%m-%d %H:%M:%S')
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echo -e "${BLUE}[$timestamp]${NC} ${level}: ${message}" | tee -a "$LOG_FILE"
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}
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# Function: Log success
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log_success() {
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echo -e "${GREEN}✅ $@${NC}" | tee -a "$LOG_FILE"
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}
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# Function: Log error
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log_error() {
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echo -e "${RED}❌ $@${NC}" | tee -a "$LOG_FILE"
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}
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# Function: Log warning
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log_warning() {
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echo -e "${YELLOW}⚠️ $@${NC}" | tee -a "$LOG_FILE"
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}
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# Function: Check command exists
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command_exists() {
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command -v "$1" >/dev/null 2>&1
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}
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# Function: Check if running as root
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check_root() {
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if [[ $EUID -ne 0 ]]; then
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log_error "Este script debe ejecutarse con sudo"
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exit 1
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fi
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}
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################################################################################
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# MAIN INSTALLATION
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################################################################################
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main() {
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local quick_mode=false
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local gpu_only=false
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# Parse arguments
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while [[ $# -gt 0 ]]; do
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case $1 in
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--quick)
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quick_mode=true
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shift
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;;
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--gpu-only)
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gpu_only=true
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shift
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;;
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*)
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log_error "Opción desconocida: $1"
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usage
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exit 1
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;;
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esac
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done
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log "LOG" "=========================================="
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log "LOG" "LLM Server Installation"
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log "LOG" "=========================================="
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log "LOG" "Project Dir: $PROJECT_DIR"
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log "LOG" "Workspace: $WORKSPACE_DIR"
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log "LOG" "Quick Mode: $quick_mode"
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log "LOG" "GPU Only: $gpu_only"
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log "LOG" "=========================================="
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# Checks iniciales
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check_root
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check_os
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check_hardware
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if [ "$gpu_only" = false ]; then
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install_dependencies
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install_docker
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fi
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install_nvidia_drivers
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install_cuda_toolkit
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install_ollama
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setup_python_venv
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create_services
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if [ "$quick_mode" = false ]; then
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download_models
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fi
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setup_directories
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generate_config
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log_success "=========================================="
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log_success "✨ INSTALACIÓN COMPLETADA"
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log_success "=========================================="
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print_next_steps
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}
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################################################################################
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# FUNCIONES AUXILIARES
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################################################################################
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check_os() {
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log "LOG" "Verificando Sistema Operativo..."
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if [ ! -f /etc/os-release ]; then
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log_error "No se pudo detectar el SO"
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exit 1
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fi
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. /etc/os-release
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if [[ "$ID" != "ubuntu" ]]; then
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log_error "Este script solo soporta Ubuntu"
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exit 1
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fi
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if [[ "$VERSION_ID" != "22.04" ]]; then
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log_warning "Se recomienda Ubuntu 22.04, detectado: $VERSION_ID"
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fi
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log_success "Ubuntu $VERSION_ID detectado"
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}
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check_hardware() {
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log "LOG" "Verificando Hardware..."
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# Check GPU
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if ! command_exists nvidia-smi; then
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log_warning "nvidia-smi no disponible aún (se instalará)"
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else
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gpu_info=$(nvidia-smi --query-gpu=name,memory.total --format=csv,noheader)
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log_success "GPU detectada: $gpu_info"
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fi
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# Check CPU
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cpu_count=$(nproc)
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log_success "CPU: $cpu_count cores"
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# Check RAM
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ram_gb=$(free -h | awk '/^Mem:/ {print $2}')
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log_success "RAM: $ram_gb"
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# Check Disk
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disk_info=$(df -h / | awk 'NR==2 {print $2}')
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log_success "Disco: $disk_info disponible"
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}
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install_dependencies() {
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log "LOG" "Instalando dependencias del sistema..."
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apt update
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apt install -y \
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build-essential \
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git \
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wget \
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curl \
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htop \
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nano \
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openssh-server \
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python3.11 \
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python3.11-venv \
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python3.11-dev \
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pkg-config \
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libssl-dev \
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libffi-dev
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log_success "Dependencias instaladas"
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}
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install_docker() {
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log "LOG" "Instalando Docker..."
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if command_exists docker; then
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log_success "Docker ya está instalado"
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return
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fi
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curl -fsSL https://get.docker.com -o /tmp/get-docker.sh
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sh /tmp/get-docker.sh
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# Agregar usuario al grupo docker
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if id "charle" &>/dev/null; then
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usermod -aG docker charle
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log_success "Usuario 'charle' agregado al grupo docker"
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fi
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systemctl start docker
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systemctl enable docker
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log_success "Docker instalado"
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}
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install_nvidia_drivers() {
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log "LOG" "Instalando NVIDIA Drivers..."
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if command_exists nvidia-smi; then
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current_driver=$(nvidia-smi --query-gpu=driver_version --format=csv,noheader | head -1)
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log_success "Driver NVIDIA $current_driver ya instalado"
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return
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fi
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# Agregar repositorio NVIDIA
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apt-key adv --keyserver keyserver.ubuntu.com --recv-keys A4B469963BF863CC 2>&1 | grep -v "Warning" || true
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add-apt-repository "deb http://developer.download.nvidia.com/compute/cuda/repos/ubuntu2204/x86_64/ /" 2>&1 | grep -v "already" || true
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apt update
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apt install -y cuda-drivers
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log_success "NVIDIA Drivers instalados"
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log_warning "Se recomienda reiniciar: sudo reboot"
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}
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install_cuda_toolkit() {
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log "LOG" "Instalando CUDA Toolkit 12.3..."
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if [ -d "/usr/local/cuda-12.3" ]; then
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log_success "CUDA 12.3 ya está instalado"
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return
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fi
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log "LOG" "Descargando CUDA 12.3.0..."
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cd /tmp
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wget -q https://developer.download.nvidia.com/compute/cuda/12.3.0/local_installers/cuda_12.3.0_545.23.06_linux.run \
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-O cuda_12.3.0_545.23.06_linux.run
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log "LOG" "Instalando CUDA (esto puede tardar 10-15 minutos)..."
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chmod +x cuda_12.3.0_545.23.06_linux.run
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./cuda_12.3.0_545.23.06_linux.run --silent --driver=false --toolkit
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# Configurar PATH
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if ! grep -q "cuda-12.3" /root/.bashrc; then
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cat >> /root/.bashrc << 'EOF'
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# CUDA 12.3
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export PATH=/usr/local/cuda-12.3/bin:$PATH
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export LD_LIBRARY_PATH=/usr/local/cuda-12.3/lib64:$LD_LIBRARY_PATH
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EOF
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fi
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source /root/.bashrc
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# Verificar
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if command_exists nvcc; then
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cuda_version=$(nvcc --version | grep "release" | awk '{print $5}')
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log_success "CUDA $cuda_version instalado"
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fi
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# Install cuDNN
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log "LOG" "Instalando cuDNN..."
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apt install -y libcudnn8
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log_success "CUDA Toolkit instalado"
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}
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install_ollama() {
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log "LOG" "Instalando Ollama..."
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if command_exists ollama; then
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log_success "Ollama ya está instalado"
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else
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curl https://ollama.ai/install.sh | sh
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log_success "Ollama instalado"
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fi
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# Configurar servicio systemd
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log "LOG" "Configurando servicio Ollama..."
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mkdir -p /etc/systemd/system
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cat > /etc/systemd/system/ollama.service << 'EOF'
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[Unit]
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Description=Ollama
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After=network-online.target
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[Service]
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ExecStart=/usr/local/bin/ollama serve
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User=charle
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Group=charle
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Restart=always
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RestartSec=3
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Environment="OLLAMA_HOST=0.0.0.0:11434"
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Environment="OLLAMA_MODELS=/home/charle/.ollama/models"
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Environment="OLLAMA_NUM_GPU=1"
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[Install]
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WantedBy=default.target
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EOF
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systemctl daemon-reload
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systemctl enable ollama
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systemctl restart ollama
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sleep 2
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if systemctl is-active --quiet ollama; then
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log_success "Servicio Ollama activo"
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else
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log_error "Error iniciando Ollama"
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fi
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}
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setup_python_venv() {
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log "LOG" "Creando Python Virtual Environment..."
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if [ -d "$VENV_DIR" ]; then
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log_success "VEnv ya existe"
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return
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fi
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python3.11 -m venv "$VENV_DIR"
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source "$VENV_DIR/bin/activate"
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pip install --upgrade pip setuptools wheel
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pip install \
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fastapi \
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uvicorn \
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python-multipart \
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aiofiles \
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pillow \
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python-dotenv \
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requests \
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langchain \
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pydantic
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log_success "Python VEnv configurado"
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}
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create_services() {
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log "LOG" "Creando systemd services..."
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# API Service
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cat > /etc/systemd/system/llm-api.service << EOF
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[Unit]
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Description=LLM API Server
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After=network.target ollama.service
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[Service]
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Type=simple
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User=charle
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WorkingDirectory=$PROJECT_DIR
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Environment="PATH=$VENV_DIR/bin"
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Environment="OLLAMA_URL=http://localhost:11434"
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ExecStart=$VENV_DIR/bin/python $PROJECT_DIR/backend.py
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Restart=always
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RestartSec=10
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[Install]
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WantedBy=multi-user.target
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EOF
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systemctl daemon-reload
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systemctl enable llm-api
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log_success "Systemd services creados"
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}
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download_models() {
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log "LOG" "Descargando modelos (esto puede tardar 20-30 minutos)..."
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log_warning "Esto es OPCIONAL. Presiona Ctrl+C para cancelar."
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sleep 5
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source "$VENV_DIR/bin/activate"
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# Esperar a que Ollama esté listo
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for i in {1..30}; do
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if curl -s http://localhost:11434/api/tags > /dev/null; then
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log_success "Ollama está listo"
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break
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fi
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log "LOG" "Esperando Ollama... ($i/30)"
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sleep 2
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done
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log "LOG" "Descargando Phi..."
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ollama pull phi:latest
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log "LOG" "Descargando DeepSeek Coder 33B..."
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ollama pull deepseek-coder:33b
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log_success "Modelos descargados"
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}
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setup_directories() {
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log "LOG" "Creando directorios..."
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mkdir -p "$WORKSPACE_DIR"
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mkdir -p "$PROJECT_DIR/uploads"
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mkdir -p "$PROJECT_DIR/logs"
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# Cambiar permisos
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chown -R charle:charle "$PROJECT_DIR"
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chmod -R 755 "$PROJECT_DIR"
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log_success "Directorios creados"
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}
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generate_config() {
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log "LOG" "Generando archivos de configuración..."
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# .env file
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if [ ! -f "$PROJECT_DIR/.env" ]; then
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cat > "$PROJECT_DIR/.env" << EOF
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# LLM Server Configuration
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LLM_SERVER_URL=http://localhost:8000
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LLM_FAST_MODEL=phi
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LLM_POWER_MODEL=deepseek-coder:33b
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OLLAMA_URL=http://localhost:11434
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WORKSPACE_DIR=$WORKSPACE_DIR
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# Server
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HOST=0.0.0.0
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API_PORT=8000
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WEB_PORT=5000
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# Logging
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LOG_LEVEL=INFO
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EOF
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log_success ".env creado"
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fi
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# Config JSON
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if [ ! -f "$PROJECT_DIR/config/server.json" ]; then
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mkdir -p "$PROJECT_DIR/config"
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cat > "$PROJECT_DIR/config/server.json" << 'EOF'
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{
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"server": {
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"host": "0.0.0.0",
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"api_port": 8000,
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"web_port": 5000,
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"workers": 4
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},
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"models": {
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"fast": "phi",
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"power": "deepseek-coder:33b"
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},
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"gpu": {
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"memory_fraction": 0.9,
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"max_batch_size": 8
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},
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"logging": {
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"level": "INFO",
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"format": "%(asctime)s - %(name)s - %(levelname)s - %(message)s"
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}
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}
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EOF
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log_success "config/server.json creado"
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fi
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}
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print_next_steps() {
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cat << EOF
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${BLUE}=========================================${NC}
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${GREEN}✨ PRÓXIMOS PASOS:${NC}
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${BLUE}=========================================${NC}
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1. ${YELLOW}Verificar instalación:${NC}
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sudo systemctl status ollama
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sudo systemctl status llm-api
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2. ${YELLOW}Ver logs:${NC}
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sudo journalctl -u ollama -f
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sudo journalctl -u llm-api -f
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3. ${YELLOW}Iniciar servicios:${NC}
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sudo systemctl start ollama
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sudo systemctl start llm-api
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4. ${YELLOW}Acceder a la API:${NC}
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curl http://localhost:8000/health
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5. ${YELLOW}Descargar modelos (si no se descargaron):${NC}
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source $VENV_DIR/bin/activate
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ollama pull phi:latest
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ollama pull deepseek-coder:33b
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6. ${YELLOW}Ver estado en tiempo real:${NC}
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watch -n 1 nvidia-smi
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${BLUE}=========================================${NC}
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||||
${GREEN}📁 Archivos importantes:${NC}
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||||
${BLUE}=========================================${NC}
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||||
Config: $PROJECT_DIR/config/server.json
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.env: $PROJECT_DIR/.env
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||||
Logs: $PROJECT_DIR/logs/
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||||
Workspace: $WORKSPACE_DIR/
|
||||
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||||
${BLUE}=========================================${NC}
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||||
EOF
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}
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||||
# Ejecutar main
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||||
main "$@"
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||||
Reference in New Issue
Block a user