Add GPU support and improve Docker deployment

- Add GPU deployment support with NVIDIA runtime
  - Update Dockerfile.allinone with GPU environment variables
  - Add comprehensive GPU_DEPLOYMENT.md guide

- Make port 11434 (Ollama) optional for security
  - Update DEPLOYMENT.md with CPU and GPU deployment options
  - Simplify default docker run commands
  - Update healthcheck to only check web application

- Add memory requirements documentation
  - Create MEMORY_REQUIREMENTS.md with model comparison
  - Add build-8b.sh script for lower memory usage
  - Document OOM troubleshooting steps

- Improve Docker build process
  - Add BUILD_TROUBLESHOOTING.md for common issues
  - Add DISTRIBUTION.md for image distribution methods
  - Update .gitignore to exclude large binary files
  - Improve docker-entrypoint.sh with better diagnostics

- Update .dockerignore to include ollama-linux-amd64.tgz
- Add backup file exclusions to .gitignore

.dockerignore

@@ -14,6 +14,8 @@ build
 .idea
 *.md
 !README.md
+# Include pre-downloaded Ollama binary for offline build
+!ollama-linux-amd64.tgz
 local_docs
 examples
 outputs

.gitignore

@@ -149,6 +149,16 @@ outputs/
 *.log
 local_docs/
 
+# Docker build artifacts (DO NOT commit these - they are huge!)
+ollama-models/
+*.tar
+ollama-linux-amd64.tgz
+system-prompt-optimizer-*.tar
+*.tar.gz
+
+# Backup files from scripts
+*.bak
+
 # Node modules (if any frontend dependencies)
 node_modules/
 package-lock.json

DEPLOYMENT.md

@@ -117,19 +117,22 @@ rsync -avP --progress system-prompt-optimizer-allinone.tar user@server:/path/
 # 加载镜像（需要几分钟）
 docker load -i system-prompt-optimizer-allinone.tar
 
+# 如果遇到权限错误，使用 sudo
+# sudo docker load -i system-prompt-optimizer-allinone.tar
+
 # 验证镜像已加载
 docker images | grep system-prompt-optimizer
 ```
 
 #### 步骤 7: 启动服务
 
+**CPU 模式（默认）:**
+
 ```bash
-# 启动容器
+# 启动容器（推荐：仅暴露 Web 端口）
 docker run -d \
   --name system-prompt-optimizer \
   -p 8010:8010 \
-  -p 11434:11434 \
-  -v $(pwd)/outputs:/app/outputs \
   --restart unless-stopped \
   system-prompt-optimizer:allinone
 
@@ -137,7 +140,41 @@ docker run -d \
 docker logs -f system-prompt-optimizer
 ```
 
-**重要**：首次启动需要等待 30-60 秒，Ollama 服务需要初始化。
+**GPU 模式（推荐，如果有 NVIDIA GPU）:**
+
+```bash
+# 使用所有可用 GPU（推荐）
+docker run -d \
+  --name system-prompt-optimizer \
+  --gpus all \
+  -p 8010:8010 \
+  --restart unless-stopped \
+  system-prompt-optimizer:allinone
+
+# 或指定特定 GPU
+docker run -d \
+  --name system-prompt-optimizer \
+  --gpus '"device=0"' \
+  -p 8010:8010 \
+  --restart unless-stopped \
+  system-prompt-optimizer:allinone
+
+# 查看启动日志
+docker logs -f system-prompt-optimizer
+```
+
+**GPU 部署前提条件**:
+- 已安装 NVIDIA 驱动 (`nvidia-smi` 可用)
+- 已安装 NVIDIA Container Toolkit
+- GPU 显存 ≥ 10GB (14b 模型) 或 ≥ 6GB (8b 模型)
+
+**详细 GPU 部署指南**: 参见 [GPU_DEPLOYMENT.md](GPU_DEPLOYMENT.md)
+
+**重要**：
+- 首次启动需要等待 30-60 秒（CPU）或 10-20 秒（GPU），Ollama 服务需要初始化
+- GPU 模式下推理速度提升 5-10 倍
+- 端口 11434 (Ollama) 是可选的，仅在需要外部访问 Ollama 时暴露
+- 不暴露 11434 更安全，因为 Ollama API 没有身份验证
 
 #### 步骤 8: 验证部署
 
@@ -225,7 +262,8 @@ docker run -d \
 
 ### 端口映射
 
-- **8010**: Web 界面和 API 端口
+- **8010**: Web 界面和 API 端口（必需）
+- **11434**: Ollama API 端口（可选，仅用于调试或外部访问 Ollama）
 
 ### 数据持久化
 
@@ -233,6 +271,71 @@ docker run -d \
 
 ## 故障排查
 
+### 0. Docker 守护进程连接错误
+
+**问题**: 运行 `docker` 命令时提示 "Cannot connect to the Docker daemon"
+
+**症状**:
+```
+Cannot connect to the Docker daemon at unix:///var/run/docker.sock. Is the docker daemon running?
+```
+
+**解决方案**:
+
+**方法 1: 检查 Docker 服务状态**
+```bash
+# 检查 Docker 是否运行
+sudo systemctl status docker
+
+# 如果未运行，启动它
+sudo systemctl start docker
+
+# 设置开机自启
+sudo systemctl enable docker
+```
+
+**方法 2: 添加用户到 docker 组（推荐）**
+```bash
+# 将当前用户添加到 docker 组
+sudo usermod -aG docker $USER
+
+# 应用组变更（需要重新登录或使用 newgrp）
+newgrp docker
+
+# 或者直接注销并重新登录
+
+# 验证
+docker info
+```
+
+**方法 3: 修复 Docker socket 权限**
+```bash
+# 检查 socket 权限
+ls -l /var/run/docker.sock
+
+# 修复权限
+sudo chown root:docker /var/run/docker.sock
+sudo chmod 660 /var/run/docker.sock
+```
+
+**方法 4: 临时使用 sudo**
+```bash
+# 如果上述方法不可行，使用 sudo 运行 Docker 命令
+sudo docker load -i system-prompt-optimizer-allinone.tar
+sudo docker run -d --name system-prompt-optimizer ...
+```
+
+**验证修复**:
+```bash
+# 应该能正常显示 Docker 信息
+docker info
+
+# 应该能看到当前用户在 docker 组中
+groups | grep docker
+```
+
+---
+
 ### 1. 无法连接 Ollama 服务
 
 **问题**: 容器内无法访问宿主机的 Ollama 服务

Dockerfile.allinone

@@ -1,16 +1,20 @@
-FROM python:3.10-slim
+FROM --platform=linux/amd64 python:3.10-slim
 
 # Set working directory
 WORKDIR /app
 
-# Install system dependencies including curl for Ollama
+# Install system dependencies
 RUN apt-get update && apt-get install -y \
     curl \
     ca-certificates \
     && rm -rf /var/lib/apt/lists/*
 
-# Install Ollama
+# Install Ollama manually for amd64
-RUN curl -fsSL https://ollama.com/install.sh | sh
+# Copy pre-downloaded Ollama binary to avoid slow downloads during build
+# Using v0.13.1 (latest stable as of Dec 2024)
+COPY ollama-linux-amd64.tgz /tmp/ollama-linux-amd64.tgz
+RUN tar -C /usr -xzf /tmp/ollama-linux-amd64.tgz \
+    && rm /tmp/ollama-linux-amd64.tgz
 
 # Copy requirements file
 COPY requirements.txt .
@@ -36,14 +40,18 @@ EXPOSE 8010 11434
 # Set environment variables
 ENV PYTHONUNBUFFERED=1
 ENV OLLAMA_HOST=http://localhost:11434
+# Enable GPU support for Ollama (will auto-detect NVIDIA GPU if available)
+ENV NVIDIA_VISIBLE_DEVICES=all
+ENV NVIDIA_DRIVER_CAPABILITIES=compute,utility
 
 # Copy startup script
 COPY docker-entrypoint.sh /docker-entrypoint.sh
 RUN chmod +x /docker-entrypoint.sh
 
 # Health check
-HEALTHCHECK --interval=30s --timeout=10s --start-period=40s --retries=3 \
+# Only check the web application, not Ollama (internal service)
-    CMD curl -f http://localhost:8010/health && curl -f http://localhost:11434/api/tags || exit 1
+HEALTHCHECK --interval=30s --timeout=10s --start-period=60s --retries=3 \
+    CMD curl -f http://localhost:8010/health || exit 1
 
 # Run the startup script
 ENTRYPOINT ["/docker-entrypoint.sh"]

build-8b.sh

@@ -0,0 +1,141 @@
+#!/bin/bash
+
+# Quick build script for qwen3:8b (lower memory usage)
+# Use this if your server has less than 12GB RAM
+
+set -e
+
+echo "=========================================="
+echo "Building with qwen3:8b (Lower Memory)"
+echo "=========================================="
+echo ""
+echo "Memory requirements:"
+echo "  - qwen3:8b: ~5GB RAM"
+echo "  - qwen3:14b: ~10GB RAM"
+echo ""
+
+# Check if 8b model is available
+if ! ollama list | grep -q "qwen3:8b"; then
+    echo "ERROR: qwen3:8b model not found!"
+    echo ""
+    echo "Please download it first:"
+    echo "  ollama pull qwen3:8b"
+    echo ""
+    exit 1
+fi
+
+# Clean up
+echo "Cleaning up previous builds..."
+rm -rf ollama-models/
+docker rmi system-prompt-optimizer:allinone 2>/dev/null || true
+
+# Export 8b model
+echo ""
+echo "Exporting qwen3:8b model..."
+mkdir -p ollama-models/models/{manifests/registry.ollama.ai/library,blobs}
+
+# Function to get blob hashes from manifest
+get_blobs_from_manifest() {
+    local manifest_file=$1
+    grep -o 'sha256:[a-f0-9]\{64\}' "$manifest_file" | sed 's/sha256://' | sort -u
+}
+
+# Function to copy model files
+copy_model() {
+    local model_name=$1
+    local model_tag=$2
+    local manifest_dir="$HOME/.ollama/models/manifests/registry.ollama.ai/library/$model_name"
+    
+    if [ ! -d "$manifest_dir" ]; then
+        echo "ERROR: Model manifest not found: $manifest_dir"
+        return 1
+    fi
+    
+    echo "  Copying $model_name:$model_tag manifest..."
+    mkdir -p "ollama-models/models/manifests/registry.ollama.ai/library/$model_name"
+    
+    if [ -f "$manifest_dir/$model_tag" ]; then
+        cp "$manifest_dir/$model_tag" "ollama-models/models/manifests/registry.ollama.ai/library/$model_name/"
+        
+        echo "  Finding blob files for $model_name:$model_tag..."
+        local blob_hashes=$(get_blobs_from_manifest "$manifest_dir/$model_tag")
+        local blob_count=0
+        
+        for blob_hash in $blob_hashes; do
+            local blob_file="$HOME/.ollama/models/blobs/sha256-$blob_hash"
+            if [ -f "$blob_file" ]; then
+                cp "$blob_file" "ollama-models/models/blobs/" 2>/dev/null
+                blob_count=$((blob_count + 1))
+            fi
+        done
+        
+        echo "  ✓ $model_name:$model_tag copied ($blob_count blobs)"
+    else
+        echo "ERROR: Manifest file not found: $manifest_dir/$model_tag"
+        return 1
+    fi
+}
+
+# Copy models
+copy_model "qwen3" "8b" || exit 1
+copy_model "qwen3-embedding" "4b" || exit 1
+
+echo ""
+echo "✓ Models exported successfully"
+echo ""
+
+# Update config.py to use 8b
+echo "Updating config.py to use qwen3:8b..."
+sed -i.bak 's/DEFAULT_CHAT_MODEL = "qwen3:14b"/DEFAULT_CHAT_MODEL = "qwen3:8b"/' config.py
+
+# Update docker-entrypoint.sh to check for 8b
+echo "Updating docker-entrypoint.sh to check for qwen3:8b..."
+sed -i.bak 's/qwen3:14b/qwen3:8b/g' docker-entrypoint.sh
+
+# Build image
+echo ""
+echo "Building Docker image..."
+docker build --platform linux/amd64 \
+    -f Dockerfile.allinone \
+    -t system-prompt-optimizer:allinone .
+
+if [ $? -ne 0 ]; then
+    echo ""
+    echo "Build failed!"
+    # Restore backups
+    mv config.py.bak config.py
+    mv docker-entrypoint.sh.bak docker-entrypoint.sh
+    exit 1
+fi
+
+# Export image
+echo ""
+echo "Exporting Docker image..."
+docker save -o system-prompt-optimizer-allinone.tar system-prompt-optimizer:allinone
+
+# Restore original files
+mv config.py.bak config.py
+mv docker-entrypoint.sh.bak docker-entrypoint.sh
+
+echo ""
+echo "=========================================="
+echo "Build Complete!"
+echo "=========================================="
+ls -lh system-prompt-optimizer-allinone.tar
+echo ""
+echo "This image uses qwen3:8b (~5GB RAM required)"
+echo ""
+echo "Transfer to server and run:"
+echo ""
+echo "  CPU mode:"
+echo "  docker load -i system-prompt-optimizer-allinone.tar"
+echo "  docker run -d -p 8010:8010 --restart unless-stopped system-prompt-optimizer:allinone"
+echo ""
+echo "  GPU mode (recommended):"
+echo "  docker load -i system-prompt-optimizer-allinone.tar"
+echo "  docker run -d --gpus all -p 8010:8010 --restart unless-stopped system-prompt-optimizer:allinone"
+echo ""
+echo "Note: GPU mode provides 5-10x faster inference."
+echo "      See GPU_DEPLOYMENT.md for GPU setup instructions."
+echo ""
+

build-allinone.sh

@@ -15,12 +15,18 @@ echo "=========================================="
 echo ""
 echo "This will create a Docker image containing:"
 echo "  - Python application"
-echo "  - Ollama service"
+echo "  - Ollama service (v0.13.1)"
 echo "  - qwen3:14b model"
 echo "  - qwen3-embedding:4b model"
 echo ""
+echo "Target platform: linux/amd64 (x86_64)"
+echo ""
 echo "WARNING: The final image will be 10-20GB in size!"
 echo ""
+echo "NOTE: If you're building on Apple Silicon (M1/M2/M3),"
+echo "      Docker will use emulation which may be slower."
+echo "      The image will still work on x86_64 servers."
+echo ""
 
 # Check if ollama-models directory exists
 if [ ! -d "ollama-models" ]; then
@@ -33,6 +39,19 @@ fi
 echo "✓ Found ollama-models directory"
 echo ""
 
+# Check if Ollama binary exists
+if [ ! -f "ollama-linux-amd64.tgz" ]; then
+    echo "ERROR: ollama-linux-amd64.tgz not found!"
+    echo ""
+    echo "Please download it first:"
+    echo "  curl -L -o ollama-linux-amd64.tgz https://github.com/ollama/ollama/releases/download/v0.13.1/ollama-linux-amd64.tgz"
+    echo ""
+    exit 1
+fi
+
+echo "✓ Found ollama-linux-amd64.tgz"
+echo ""
+
 # Check disk space
 AVAILABLE_SPACE=$(df -h . | awk 'NR==2 {print $4}')
 echo "Available disk space: $AVAILABLE_SPACE"
@@ -50,7 +69,12 @@ echo ""
 echo "=========================================="
 echo "Building Docker image..."
 echo "=========================================="
-docker build -f Dockerfile.allinone -t ${IMAGE_NAME}:${IMAGE_TAG} .
+echo "Platform: linux/amd64 (x86_64)"
+echo "This may take 20-40 minutes depending on your machine..."
+echo ""
+
+# Build for amd64 platform explicitly
+docker build --platform linux/amd64 -f Dockerfile.allinone -t ${IMAGE_NAME}:${IMAGE_TAG} .
 
 echo ""
 echo "=========================================="
@@ -83,14 +107,25 @@ echo "2. On target server, load the image:"
 echo "   docker load -i ${EXPORT_FILE}"
 echo ""
 echo "3. Run the container:"
+echo ""
+echo "   CPU mode:"
 echo "   docker run -d \\"
 echo "     --name system-prompt-optimizer \\"
 echo "     -p 8010:8010 \\"
-echo "     -p 11434:11434 \\"
-echo "     -v \$(pwd)/outputs:/app/outputs \\"
 echo "     --restart unless-stopped \\"
 echo "     ${IMAGE_NAME}:${IMAGE_TAG}"
 echo ""
+echo "   GPU mode (recommended if NVIDIA GPU available):"
+echo "   docker run -d \\"
+echo "     --name system-prompt-optimizer \\"
+echo "     --gpus all \\"
+echo "     -p 8010:8010 \\"
+echo "     --restart unless-stopped \\"
+echo "     ${IMAGE_NAME}:${IMAGE_TAG}"
+echo ""
+echo "   Note: Port 11434 (Ollama) is optional and only needed for debugging."
+echo "   GPU mode provides 5-10x faster inference. See GPU_DEPLOYMENT.md for details."
+echo ""
 echo "4. Access the application:"
 echo "   http://<server-ip>:8010/ui/opro.html"
 echo ""

docker-entrypoint.sh

@@ -2,34 +2,102 @@
 
 set -e
 
+echo "=========================================="
+echo "System Prompt Optimizer - Starting Up"
+echo "=========================================="
+echo ""
+
+# Check if Ollama binary exists
+if ! command -v ollama &> /dev/null; then
+    echo "ERROR: Ollama binary not found!"
+    echo "Expected location: /usr/bin/ollama or /usr/local/bin/ollama"
+    ls -la /usr/bin/ollama* 2>/dev/null || echo "No ollama in /usr/bin/"
+    ls -la /usr/local/bin/ollama* 2>/dev/null || echo "No ollama in /usr/local/bin/"
+    exit 1
+fi
+
+echo "✓ Ollama binary found: $(which ollama)"
+echo ""
+
+# Check if model files exist
+echo "Checking model files..."
+if [ ! -d "/root/.ollama/models" ]; then
+    echo "ERROR: /root/.ollama/models directory not found!"
+    exit 1
+fi
+
+MANIFEST_COUNT=$(find /root/.ollama/models/manifests -type f 2>/dev/null | wc -l)
+BLOB_COUNT=$(find /root/.ollama/models/blobs -type f 2>/dev/null | wc -l)
+
+echo "✓ Found $MANIFEST_COUNT manifest files"
+echo "✓ Found $BLOB_COUNT blob files"
+
+if [ "$BLOB_COUNT" -lt 10 ]; then
+    echo "WARNING: Very few blob files found. Models may not be complete."
+fi
+echo ""
+
 echo "Starting Ollama service..."
-ollama serve &
+ollama serve > /tmp/ollama.log 2>&1 &
+OLLAMA_PID=$!
 
 # Wait for Ollama to be ready
 echo "Waiting for Ollama to start..."
-for i in {1..30}; do
+OLLAMA_READY=false
+for i in {1..60}; do
     if curl -s http://localhost:11434/api/tags > /dev/null 2>&1; then
         echo "Ollama is ready!"
+        OLLAMA_READY=true
         break
     fi
-    echo "Waiting for Ollama... ($i/30)"
+    echo "Waiting for Ollama... ($i/60)"
-    sleep 2
+    sleep 3
 done
 
+if [ "$OLLAMA_READY" = false ]; then
+    echo ""
+    echo "ERROR: Ollama failed to start within 3 minutes!"
+    echo ""
+    echo "Ollama logs:"
+    cat /tmp/ollama.log
+    echo ""
+    echo "Check full logs with: docker logs system-prompt-optimizer"
+    exit 1
+fi
+
 # Check if models exist, if not, show warning
+echo ""
 echo "Checking for models..."
 ollama list
 
+echo ""
 if ! ollama list | grep -q "qwen3:14b"; then
-    echo "WARNING: qwen3:14b model not found!"
+    echo "ERROR: qwen3:14b model not found!"
     echo "The application requires qwen3:14b to function properly."
+    echo ""
+    echo "Available models:"
+    ollama list
+    echo ""
+    exit 1
 fi
 
 if ! ollama list | grep -q "qwen3-embedding"; then
     echo "WARNING: qwen3-embedding model not found!"
     echo "The application requires qwen3-embedding:4b for embeddings."
+    echo "Continuing anyway, but embeddings may not work."
 fi
 
+echo ""
+echo "✓ All required models are available"
+echo ""
+echo "=========================================="
 echo "Starting FastAPI application..."
+echo "=========================================="
+echo "Application will be available at:"
+echo "  - Web UI: http://localhost:8010/ui/opro.html"
+echo "  - API Docs: http://localhost:8010/docs"
+echo "  - Ollama: http://localhost:11434"
+echo ""
+
 exec uvicorn _qwen_xinference_demo.api:app --host 0.0.0.0 --port 8010