feat: 完成 tvai 库便捷接口和优化 (阶段五)
全局配置管理系统 - 实现 GlobalSettings 全局设置结构 - 实现 SettingsManager 设置管理器 - 支持配置文件持久化 (TOML 格式) - 自动检测用户配置目录 - 全局设置单例模式访问 预设管理系统 - 实现 VideoPreset 和 ImagePreset 预设结构 - 实现 PresetManager 预设管理器 - 内置 8 种视频处理预设 - 内置 6 种图片处理预设 - 支持自定义预设添加和管理 高级 GPU 检测和优化 - 实现 GpuManager 详细 GPU 检测 - 支持 CUDA、OpenCL、Vulkan 检测 - 详细的 GPU 设备信息获取 - 智能推荐设置生成 - GPU 性能基准测试功能 性能监控和优化 - 实现 PerformanceMonitor 性能监控器 - 支持并发控制和资源管理 - 详细的性能指标收集 - 智能性能建议生成 - 系统优化参数自动调整 增强错误处理系统 - 扩展 TvaiError 错误类型 (12 种错误) - 用户友好的错误信息和建议 - 错误分类和可恢复性判断 - 详细的故障排除指导 - 错误上下文和解决方案 便捷工具函数 - optimize_for_system() 系统优化 - 全局设置和预设访问函数 - GPU 适用性检查函数 - 性能基准测试工具 - 智能参数推荐系统 配置文件支持 - TOML 格式配置文件 - 自动配置目录检测 - 设置持久化和加载 - 配置验证和错误处理 - 跨平台配置管理 完整示例和演示 - 创建 convenience_and_optimization.rs 综合示例 - 全局设置管理演示 - 预设管理系统演示 - GPU 优化和检测演示 - 性能监控和基准测试演示 - 增强错误处理演示 技术特性 - 全局状态管理和配置持久化 - 智能系统检测和优化建议 - 详细的性能监控和分析 - 用户友好的错误处理和恢复 - 跨平台兼容性和配置管理 代码质量 - 所有测试通过 (6/6 单元测试 + 1 文档测试) - 完整的错误处理和用户指导 - 内存安全的资源管理 - 清晰的 API 设计和文档 功能覆盖 - 全局配置管理 (设置持久化) - 预设管理系统 (14 种内置预设) - 高级 GPU 检测 (CUDA/OpenCL/Vulkan) - 性能监控优化 (并发控制/资源管理) - 增强错误处理 (12 种错误类型) - 便捷工具函数 (系统优化/智能推荐) 新增依赖 - toml v0.8 (配置文件支持) - dirs v5.0 (用户目录检测) 项目完成度: 100% - 所有核心功能已实现并测试通过
This commit is contained in:
imeepos
231
cargos/tvai/examples/convenience_and_optimization.rs
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231
cargos/tvai/examples/convenience_and_optimization.rs
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//! Convenience interfaces and optimization examples
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use tvai::*;
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use tvai::config::{global_settings, global_presets};
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use tvai::utils::{GpuManager, PerformanceMonitor, optimize_for_system};
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#[tokio::main]
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async fn main() -> std::result::Result<(), Box<dyn std::error::Error>> {
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println!("Topaz Video AI Library - Convenience and Optimization Examples");
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// Demonstrate global settings management
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demonstrate_global_settings().await?;
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// Demonstrate preset management
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demonstrate_preset_management().await?;
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// Demonstrate GPU detection and optimization
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demonstrate_gpu_optimization().await?;
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// Demonstrate performance monitoring
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demonstrate_performance_monitoring().await?;
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// Demonstrate error handling
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demonstrate_error_handling().await?;
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println!("All convenience and optimization examples completed successfully!");
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Ok(())
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}
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async fn demonstrate_global_settings() -> std::result::Result<(), Box<dyn std::error::Error>> {
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println!("\n=== Global Settings Management Demo ===");
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// Get global settings manager
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let settings_manager = global_settings();
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// Display current settings
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let current_settings = settings_manager.get_settings();
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println!("Current Settings:");
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println!(" Default GPU usage: {}", current_settings.default_use_gpu);
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println!(" Max concurrent jobs: {}", current_settings.max_concurrent_jobs);
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println!(" Auto-detect Topaz: {}", current_settings.auto_detect_topaz);
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println!(" Verbose logging: {}", current_settings.verbose_logging);
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// Update settings
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settings_manager.set_default_use_gpu(true)?;
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settings_manager.set_max_concurrent_jobs(2)?;
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println!("Updated settings successfully");
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// Create config from global settings
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match settings_manager.create_config() {
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Ok(_config) => println!("Successfully created config from global settings"),
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Err(e) => println!("Config creation failed (expected): {}", e.user_friendly_message()),
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}
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Ok(())
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}
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async fn demonstrate_preset_management() -> std::result::Result<(), Box<dyn std::error::Error>> {
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println!("\n=== Preset Management Demo ===");
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// Get global preset manager
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let presets = global_presets();
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let preset_manager = presets.lock().unwrap();
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// List available video presets
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println!("Available Video Presets:");
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for preset_name in preset_manager.list_video_presets() {
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if let Some(preset) = preset_manager.get_video_preset(&preset_name) {
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println!(" {}: {}", preset.name, preset.description);
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}
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}
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// List available image presets
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println!("\nAvailable Image Presets:");
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for preset_name in preset_manager.list_image_presets() {
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if let Some(preset) = preset_manager.get_image_preset(&preset_name) {
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println!(" {}: {}", preset.name, preset.description);
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}
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}
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// Demonstrate using a preset
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if let Some(preset) = preset_manager.get_video_preset("general_2x") {
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println!("\nUsing preset: {}", preset.name);
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if let Some(ref upscale) = preset.upscale {
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println!(" Upscale model: {}", upscale.model.as_str());
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println!(" Scale factor: {}x", upscale.scale_factor);
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}
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}
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Ok(())
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}
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async fn demonstrate_gpu_optimization() -> std::result::Result<(), Box<dyn std::error::Error>> {
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println!("\n=== GPU Optimization Demo ===");
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// Detect detailed GPU information
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let gpu_info = GpuManager::detect_detailed_gpu_info();
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println!("GPU Detection Results:");
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println!(" Available: {}", gpu_info.available);
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println!(" CUDA: {}", gpu_info.cuda_available);
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println!(" OpenCL: {}", gpu_info.opencl_available);
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println!(" Vulkan: {}", gpu_info.vulkan_available);
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// Display detected devices
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println!("\nDetected GPU Devices:");
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for (idx, device) in gpu_info.devices.iter().enumerate() {
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println!(" Device {}: {}", idx, device.name);
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println!(" Vendor: {}", device.vendor);
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if let Some(memory) = device.memory_mb {
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println!(" Memory: {} MB", memory);
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}
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println!(" AI Support: {}", device.supports_ai);
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}
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// Display recommendations
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println!("\nRecommended Settings:");
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println!(" Use GPU: {}", gpu_info.recommended_settings.use_gpu);
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if let Some(device_idx) = gpu_info.recommended_settings.preferred_device {
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println!(" Preferred device: {}", device_idx);
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}
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if let Some(memory_limit) = gpu_info.recommended_settings.memory_limit_mb {
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println!(" Memory limit: {} MB", memory_limit);
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}
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println!(" Concurrent streams: {}", gpu_info.recommended_settings.concurrent_streams);
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// Check if GPU is suitable for AI
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let suitable = GpuManager::is_gpu_suitable_for_ai();
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println!("\nGPU suitable for AI workloads: {}", suitable);
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// Benchmark GPU performance
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println!("\nRunning GPU benchmark...");
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match GpuManager::benchmark_gpu_performance().await {
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Ok(benchmark) => {
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println!("Benchmark Results:");
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println!(" Processing time: {:?}", benchmark.processing_time);
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println!(" Memory bandwidth: {:.1} GB/s", benchmark.memory_bandwidth_gbps);
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println!(" Compute score: {}", benchmark.compute_score);
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println!(" Recommended for AI: {}", benchmark.recommended_for_ai);
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}
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Err(e) => println!("Benchmark failed: {}", e),
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}
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Ok(())
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}
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async fn demonstrate_performance_monitoring() -> std::result::Result<(), Box<dyn std::error::Error>> {
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println!("\n=== Performance Monitoring Demo ===");
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// Create optimized performance settings
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let settings = optimize_for_system();
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println!("Optimized Performance Settings:");
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println!(" Max concurrent ops: {}", settings.max_concurrent_ops);
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println!(" Processing mode: {:?}", settings.processing_mode);
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println!(" Chunk size: {} MB", settings.chunk_size_mb);
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println!(" Monitoring enabled: {}", settings.enable_monitoring);
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// Create performance monitor
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let mut monitor = PerformanceMonitor::new(settings);
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// Simulate a processing operation
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println!("\nSimulating processing operation...");
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let metrics = {
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let _permit = monitor.acquire_slot().await?;
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let operation_monitor = monitor.start_operation("video_upscale", 100.0);
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// Simulate processing time
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tokio::time::sleep(std::time::Duration::from_millis(500)).await;
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// Finish monitoring
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operation_monitor.finish(200.0)
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};
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// Record metrics after permit is dropped
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monitor.record_metrics(metrics.clone());
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println!("Operation completed:");
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println!(" Input size: {:.1} MB", metrics.input_size_mb);
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println!(" Output size: {:.1} MB", metrics.output_size_mb);
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println!(" Processing time: {:?}", metrics.processing_time);
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println!(" Throughput: {:.2} MB/s", metrics.throughput_mbps);
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// Get performance summary
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let summary = monitor.get_summary();
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println!("\nPerformance Summary:");
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println!(" Total operations: {}", summary.total_operations);
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println!(" Average throughput: {:.2} MB/s", summary.average_throughput_mbps);
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if !summary.recommendations.is_empty() {
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println!(" Recommendations:");
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for rec in summary.recommendations {
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println!(" • {}", rec);
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}
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}
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Ok(())
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}
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async fn demonstrate_error_handling() -> std::result::Result<(), Box<dyn std::error::Error>> {
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println!("\n=== Enhanced Error Handling Demo ===");
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// Demonstrate different error types and their user-friendly messages
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let errors = vec![
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TvaiError::TopazNotFound("/invalid/path".to_string()),
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TvaiError::FfmpegError("Invalid codec".to_string()),
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TvaiError::InvalidParameter("Scale factor out of range".to_string()),
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TvaiError::GpuError("CUDA out of memory".to_string()),
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TvaiError::UnsupportedFormat("WEBM".to_string()),
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TvaiError::InsufficientResources("Not enough memory".to_string()),
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TvaiError::PermissionDenied("Cannot write to output directory".to_string()),
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];
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for error in errors {
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println!("\nError Type: {}", error.category());
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println!("Recoverable: {}", error.is_recoverable());
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println!("User-friendly message:");
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println!("{}", error.user_friendly_message());
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println!("{}", "-".repeat(50));
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}
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Ok(())
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}
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// Helper function to create a progress callback
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fn create_progress_callback(operation_name: &str) -> ProgressCallback {
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let name = operation_name.to_string();
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Box::new(move |progress| {
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let percentage = (progress * 100.0) as u32;
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println!("{}: {}%", name, percentage);
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})
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}
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