Hardware Query

A cross-platform Rust library for querying detailed system hardware information with advanced monitoring and power management capabilities.

🚀 Features

Core Hardware Detection

• ✅ Cross-platform hardware detection (Windows, Linux, macOS)
• ✅ Detailed CPU information (cores, threads, cache, features)
• ✅ GPU detection and capabilities (CUDA, ROCm, DirectML support)
• ✅ Memory configuration and status
• ✅ Storage device enumeration and properties
• ✅ Network interface detection and capabilities
• ✅ Hardware acceleration support detection (NPU, TPU, FPGA)
• ✅ PCI/USB device enumeration
• ✅ ARM-specific hardware detection (Raspberry Pi, Jetson, etc.)

🔄 Real-time Monitoring (NEW!)

• ✅ Continuous hardware metrics monitoring
• ✅ Configurable update intervals and thresholds
• ✅ Event-driven notifications for thermal/power alerts
• ✅ Background monitoring with async support
• ✅ Comprehensive monitoring statistics

⚡ Power Management & Efficiency

• ✅ Real-time power consumption tracking
• ✅ Battery life estimation and health monitoring
• ✅ Power efficiency scoring and optimization
• ✅ Thermal throttling risk assessment
• ✅ Power optimization recommendations

🌡️ Enhanced Thermal Management

• ✅ Advanced temperature monitoring with history
• ✅ Thermal throttling prediction algorithms
• ✅ Cooling optimization recommendations
• ✅ Sustained performance capability analysis
• ✅ Fan curve analysis and optimization

🐳 Virtualization & Container Detection

• ✅ Comprehensive virtualization environment detection
• ✅ Container runtime identification (Docker, Kubernetes, etc.)
• ✅ Resource limits and restrictions analysis
• ✅ GPU passthrough capability detection
• ✅ Performance impact assessment
• ✅ Security feature analysis

🎯 AI/ML Optimization

• ✅ Hardware suitability assessment for AI workloads
• ✅ Performance scoring for different AI tasks
• ✅ Memory and compute requirement analysis
• ✅ Acceleration framework compatibility
• ✅ Optimization recommendations

Quick Start

Add this to your Cargo.toml:

[dependencies]
hardware-query = "0.2.0"

# For real-time monitoring features
hardware-query = { version = "0.2.0", features = ["monitoring"] }

🔧 Feature Flags

When to Use the monitoring Feature

Enable monitoring if you need:

• ✅ Continuous hardware monitoring - Track temperatures, power, and performance over time
• ✅ Real-time alerts - Get notified when hardware exceeds thresholds
• ✅ Background monitoring - Async monitoring that doesn't block your application
• ✅ Event-driven architecture - React to hardware changes as they happen
• ✅ Server/system monitoring - Long-running applications that need to track hardware health

Skip monitoring if you only need:

• ❌ One-time hardware queries (use SystemOverview::quick())
• ❌ Static hardware information for configuration
• ❌ Simple compatibility checks
• ❌ Lightweight applications where async dependencies are unwanted

# Lightweight - no async dependencies
hardware-query = "0.2.0"

# Full featured - includes real-time monitoring
hardware-query = { version = "0.2.0", features = ["monitoring"] }

🚀 Simplified API (Recommended for New Users)

For most use cases, start with the simplified API:

use hardware_query::SystemOverview;

// Get essential system info in one line - no hardware expertise needed!
let overview = SystemOverview::quick()?;
println!("CPU: {} ({} cores)", overview.cpu.name, overview.cpu.cores);
println!("Memory: {:.1} GB", overview.memory_gb);
println!("Performance Score: {}/100", overview.performance_score);
println!("AI Ready: {}", overview.is_ai_ready());

For domain-specific needs:

use hardware_query::HardwarePresets;

// AI/ML assessment with built-in expertise
let ai_assessment = HardwarePresets::ai_assessment()?;
println!("AI Score: {}/100", ai_assessment.ai_score);

// Gaming performance recommendations
let gaming = HardwarePresets::gaming_assessment()?;
println!("Recommended: {} at {:?}", 
    gaming.recommended_settings.resolution,
    gaming.recommended_settings.quality_preset);

Advanced Usage

use hardware_query::HardwareInfo;

fn main() -> Result<(), Box<dyn std::error::Error>> {
    // Get complete system information
    let hw_info = HardwareInfo::query()?;
    
    // Access CPU information
    let cpu = hw_info.cpu();
    println!("CPU: {} {} with {} cores, {} threads",
        cpu.vendor(),
        cpu.model_name(),
        cpu.physical_cores(),
        cpu.logical_cores()
    );
    
    // Check virtualization environment
    let virt = hw_info.virtualization();
    if virt.is_virtualized() {
        println!("Running in: {} (Performance impact: {:.1}%)", 
            virt.environment_type,
            (1.0 - virt.get_performance_factor()) * 100.0
        );
    }
    
    // Power management
    if let Some(power) = hw_info.power_profile() {
        println!("Power State: {}", power.power_state);
        if let Some(power_draw) = power.total_power_draw {
            println!("Current Power Draw: {:.1}W", power_draw);
        }
        
        // Get optimization recommendations
        let optimizations = power.suggest_power_optimizations();
        for opt in optimizations {
            println!("💡 {}", opt.recommendation);
        }
    }
    
    // Thermal analysis
    let thermal = hw_info.thermal();
    if let Some(max_temp) = thermal.max_temperature() {
        println!("Max Temperature: {:.1}°C (Status: {})", 
            max_temp, thermal.thermal_status());
        
        // Predict thermal throttling
        let prediction = thermal.predict_thermal_throttling(1.0);
        if prediction.will_throttle {
            println!("⚠️  Thermal throttling predicted: {}", prediction.severity);
        }
        
        // Get cooling recommendations
        let cooling_recs = thermal.suggest_cooling_optimizations();
        for rec in cooling_recs.iter().take(3) {
            println!("🌡️ {}", rec.description);
        }
    }
    
    Ok(())
}

Advanced Real-time Monitoring

use hardware_query::{HardwareMonitor, MonitoringConfig};
use std::time::Duration;

#[tokio::main]
async fn main() -> Result<(), Box<dyn std::error::Error>> {
    // Configure monitoring
    let mut config = MonitoringConfig::default();
    config.update_interval = Duration::from_secs(2);
    config.thermal_threshold = 75.0;
    
    let monitor = HardwareMonitor::with_config(config);
    
    // Add event callbacks
    monitor.on_event(|event| {
        match event {
            MonitoringEvent::ThermalAlert { sensor_name, temperature, .. } => {
                println!("🚨 Thermal Alert: {} at {:.1}°C", sensor_name, temperature);
            }
            MonitoringEvent::PowerAlert { current_power, .. } => {
                println!("⚡ Power Alert: {:.1}W", current_power);
            }
            _ => {}
        }
    }).await;
    
    // Start monitoring
    monitor.start_monitoring().await?;
    
    // Monitor for 60 seconds
    tokio::time::sleep(Duration::from_secs(60)).await;
    
    // Stop and get statistics
    monitor.stop_monitoring().await;
    let stats = monitor.get_stats().await;
    println!("Generated {} events ({} thermal alerts)", 
        stats.total_events, stats.thermal_alerts);
    
    Ok(())
}

// Get GPU information
for (i, gpu) in hw_info.gpus().iter().enumerate() {
    println!("GPU {}: {} {} with {} GB VRAM",
        i + 1,
        gpu.vendor(),
        gpu.model_name(),
        gpu.memory_gb()
    );
    
    // Check GPU capabilities
    println!("  CUDA support: {}", gpu.supports_cuda());
    println!("  ROCm support: {}", gpu.supports_rocm());
    println!("  DirectML support: {}", gpu.supports_directml());
}

// Memory information
let mem = hw_info.memory();
println!("System memory: {:.1} GB total, {:.1} GB available",
    mem.total_gb(),
    mem.available_gb()
);

// Storage information
for (i, disk) in hw_info.storage_devices().iter().enumerate() {
    println!("Disk {}: {} {:.1} GB ({:?} type)",
        i + 1,
        disk.model,
        disk.capacity_gb,
        disk.storage_type
    );
}

// Network interfaces
for interface in hw_info.network_interfaces() {
    println!("Network: {} - {}", 
        interface.name,
        interface.mac_address
    );
}

// Check specialized hardware
for npu in hw_info.npus() {
    println!("NPU detected: {} {}", npu.vendor(), npu.model_name());
}

for tpu in hw_info.tpus() {
    println!("TPU detected: {} {}", tpu.vendor(), tpu.model_name());
}

// Check ARM-specific hardware (Raspberry Pi, Jetson, etc.)
if let Some(arm) = hw_info.arm_hardware() {
    println!("ARM System: {}", arm.system_type);
}

// Check FPGA hardware
for fpga in hw_info.fpgas() {
    println!("FPGA: {} {} with {} logic elements",
        fpga.vendor,
        fpga.family,
        fpga.logic_elements.unwrap_or(0)
    );
}

// Serialize hardware information to JSON
let hw_json = hw_info.to_json()?;
println!("Hardware JSON: {}", hw_json);

Ok(())

}

## Specialized Hardware Support

The library provides comprehensive detection for AI/ML-oriented hardware:

- **NPUs**: Intel Movidius, GNA, XDNA; Apple Neural Engine; Qualcomm Hexagon
- **TPUs**: Google Cloud TPU and Edge TPU; Intel Habana
- **ARM Systems**: Raspberry Pi, NVIDIA Jetson, Apple Silicon with power management
- **FPGAs**: Intel/Altera and Xilinx families with AI optimization scoring

```rust
use hardware_query::HardwareInfo;

let hw_info = HardwareInfo::query()?;

// Check for specialized AI hardware
for npu in hw_info.npus() {
    println!("NPU: {} {}", 
        npu.vendor(), 
        npu.model_name()
    );
}

for tpu in hw_info.tpus() {
    println!("TPU: {} {}", 
        tpu.vendor(),
        tpu.model_name()
    );
}

Platform Support

Platform	CPU	GPU	Memory	Storage	Network	Battery	Thermal	PCI	USB
Windows	✅	✅	✅	✅	✅	✅	✅	✅	✅
Linux	✅	✅	✅	✅	✅	✅	✅	✅	✅
macOS	✅	✅	✅	✅	✅	✅	✅	✅	✅

Optional Features

Enable additional GPU support with feature flags:

[dependencies]
hardware-query = { version = "0.2.0", features = ["nvidia", "amd", "intel"] }

• nvidia: NVIDIA GPU support via NVML
• amd: AMD GPU support via ROCm
• intel: Intel GPU support

Examples

See the examples directory for complete usage examples:

• 🚀 Simplified API (Start Here!) - Easy-to-use API for common scenarios
• Basic Hardware Detection - Fundamental hardware querying
• Enhanced Hardware - Advanced hardware analysis
• Comprehensive Hardware - Complete system analysis
• Advanced Usage - Expert-level features
• Comprehensive AI Hardware - AI/ML-specific analysis
• Enhanced Monitoring Demo - Real-time monitoring (requires monitoring feature)

Building

# Build with default features
cargo build

# Build with GPU support
cargo build --features="nvidia,amd,intel"

# Run tests
cargo test

# Try the simplified API first (recommended for new users!)
cargo run --example 01_simplified_api

# Or run the basic hardware example
cargo run --example 02_basic_hardware

Dependencies

• sysinfo - Cross-platform system information
• serde - Serialization framework
• thiserror - Error handling
• num_cpus - CPU core detection

Optional Dependencies

• nvml-wrapper - NVIDIA GPU support (feature: nvidia)
• wmi - Windows Management Instrumentation (Windows only)
• libc - Linux system calls (Linux only)
• core-foundation - macOS system APIs (macOS only)

Performance

The library is designed for performance with:

• Lazy evaluation of hardware information
• Minimal system calls
• Efficient data structures
• Optional caching for repeated queries

Typical query times:

• Complete hardware scan: 10-50ms
• CPU information: 1-5ms
• GPU information: 5-20ms
• Memory information: 1-3ms

Error Handling

The library uses comprehensive error handling:

use hardware_query::{HardwareInfo, HardwareQueryError};

match HardwareInfo::query() {
    Ok(hw_info) => {
        // Use hardware information
    }
    Err(HardwareQueryError::SystemInfoUnavailable(msg)) => {
        eprintln!("System info unavailable: {}", msg);
    }
    Err(HardwareQueryError::PermissionDenied(msg)) => {
        eprintln!("Permission denied: {}", msg);
    }
    Err(e) => {
        eprintln!("Hardware query error: {}", e);
    }
}

Contributing

1. Fork the repository
2. Create your feature branch (git checkout -b feature/amazing-feature)
3. Commit your changes (git commit -m 'Add amazing feature')
4. Push to the branch (git push origin feature/amazing-feature)
5. Open a Pull Request

License

This project is licensed under the MIT OR Apache-2.0 License - see the LICENSE-MIT and LICENSE-APACHE files for details.

Acknowledgments

• Built on top of the excellent sysinfo crate
• Inspired by the need for better hardware detection in AI workload placement
• Thanks to all contributors who helped improve cross-platform compatibility