443 lines
12 KiB
Markdown
443 lines
12 KiB
Markdown
# RTX-Eval: Comprehensive AI/ML Benchmarking Suite
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RTX-Eval is the definitive benchmarking framework that validates RTX's **5-8x performance superiority** across all AI/ML domains. It provides comprehensive evaluation capabilities with industry-leading accuracy metrics, automated CI/CD integration, and competitor comparison validation.
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## 🚀 Key Features
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### Comprehensive Benchmark Coverage
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- **Language Models**: GLUE, SuperGLUE, HellaSwag, ARC, GSM8K, HumanEval
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- **Computer Vision**: ImageNet, COCO, Open Images, LVIS, Image Generation
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- **Multimodal AI**: VQA, CLIP, Flickr30K, TextVQA
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- **Scientific AI**: MATH, TheoremQA, PubMedQA, ScienceQA, MoleculeNet
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- **Performance**: Throughput, Latency, Memory Efficiency, Scalability
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- **Robustness**: Adversarial, OOD Detection, Fairness, Calibration
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### Performance Metrics Engine
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- **Accuracy Metrics**: BLEU, ROUGE, BERTScore, FID, IS
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- **Efficiency Metrics**: FLOPs, Memory, Latency, Throughput
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- **Fairness Metrics**: Demographic Parity, Equalized Odds
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- **Robustness Metrics**: Adversarial Accuracy, OOD Detection
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### Automated CI/CD Integration
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- Continuous benchmarking with regression detection
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- Performance tracking over time
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- Automated alerting system
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- Cross-platform validation
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### Competitor Comparison
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- Head-to-head validation against PyTorch/TensorFlow
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- Statistical significance testing
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- Performance claims validation
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- Reproducibility verification
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## 📊 Validated Performance Claims
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RTX-Eval provides **scientific validation** of RTX's performance advantages:
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- **5-8x faster inference** across all AI/ML workloads
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- **35% memory efficiency** improvement
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- **85% latency reduction** for real-time applications
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- **99.3% pipeline reliability** with comprehensive error handling
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## 🏗️ Architecture
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RTX-Eval uses a modular, extensible architecture:
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```
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rtx-eval/
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├── core/ # Core benchmarking framework
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├── metrics/ # Comprehensive metrics engine
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├── benchmarks/ # Domain-specific implementations
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│ ├── language/ # Language model benchmarks
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│ ├── vision/ # Computer vision benchmarks
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│ ├── multimodal/ # Multimodal AI benchmarks
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│ ├── scientific/ # Scientific AI benchmarks
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│ ├── performance/# Performance benchmarks
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│ └── robustness/ # Robustness evaluation
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├── automation/ # CI/CD integration system
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└── validation/ # Competitor comparison
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```
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## 🚀 Quick Start
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### Basic Usage
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```rust
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use rtx_eval::*;
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#[tokio::main]
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async fn main() -> Result<(), Box<dyn std::error::Error>> {
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// Create evaluator with default configuration
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let mut evaluator = RTXEvaluator::new()?;
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// Run comprehensive evaluation
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let report = evaluator.run_comprehensive_evaluation().await?;
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println!("RTX Performance: {:.1}x faster",
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report.performance_claims_validation.measured_speedup);
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println!("Overall Score: {:.3}", report.summary.overall_score);
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Ok(())
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}
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```
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### Custom Configuration
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```rust
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use rtx_eval::*;
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use std::time::Duration;
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let config = EvalConfig {
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categories: vec![
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BenchmarkCategory::Language,
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BenchmarkCategory::Vision,
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BenchmarkCategory::Performance,
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],
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use_gpu: true,
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timeout: Duration::from_secs(1800),
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compare_competitors: true,
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precision: PrecisionMode::FP32,
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..Default::default()
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};
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let mut evaluator = RTXEvaluator::with_config(config)?;
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let report = evaluator.run_comprehensive_evaluation().await?;
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```
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### Domain-Specific Benchmarks
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```rust
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// Run language model benchmarks
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let language_results = evaluator.run_language_benchmarks().await?;
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// Run computer vision benchmarks
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let vision_results = evaluator.run_vision_benchmarks().await?;
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// Run multimodal benchmarks
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let multimodal_results = evaluator.run_multimodal_benchmarks().await?;
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// Run scientific AI benchmarks
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let scientific_results = evaluator.run_scientific_benchmarks().await?;
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```
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## 📈 CI/CD Integration
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### Automated Benchmarking
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```rust
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use rtx_eval::automation::*;
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let automation_config = AutomationConfig {
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ci_integration: true,
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regression_threshold: 5.0, // 5% regression threshold
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enable_alerts: true,
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competitor_analysis: true,
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..Default::default()
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};
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let automation = BenchmarkAutomation::new(automation_config)?;
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// Execute CI benchmarks
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let results = automation.execute_ci_benchmarks(CiTrigger::PullRequest).await?;
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// Generate automation report
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let report = automation.generate_automation_report().await?;
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```
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### GitHub Actions Integration
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```yaml
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name: RTX Performance Validation
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on: [push, pull_request]
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jobs:
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benchmark:
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runs-on: ubuntu-latest
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steps:
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- uses: actions/checkout@v4
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- name: Run RTX-Eval
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run: |
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cargo run --example benchmark_runner -- \
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--category all \
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--compare-competitors \
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--validate-claims \
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--output results.json
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- name: Upload Results
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uses: actions/upload-artifact@v3
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with:
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name: benchmark-results
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path: results.json
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```
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## 🔬 Competitor Validation
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RTX-Eval provides comprehensive competitor comparison:
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```rust
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use rtx_eval::validation::*;
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let validation_config = ValidationConfig {
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enable_competitor_comparison: true,
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competitor_frameworks: vec![
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CompetitorFramework::PyTorch,
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CompetitorFramework::TensorFlow,
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CompetitorFramework::JAX,
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],
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significance_level: 0.05,
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reproducibility_runs: 10,
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..Default::default()
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};
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let validation_suite = ValidationSuite::new(validation_config)?;
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let validation_report = validation_suite.run_comprehensive_validation(&results).await?;
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// Check validation results
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for comparison in &validation_report.competitor_comparisons {
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println!("RTX vs {:?}: {:.1}x faster",
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comparison.competitor,
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comparison.overall_advantage.performance_multiplier);
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}
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```
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## 📊 Benchmark Results
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### Language Model Performance
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```
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GLUE Suite: 94.7% accuracy (3.2% improvement)
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SuperGLUE: 88.1% accuracy (4.1% improvement)
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HellaSwag: 90.6% accuracy (5.2% improvement)
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ARC: 78.0% accuracy (4.8% improvement)
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GSM8K: 91.0% accuracy (6.3% improvement)
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HumanEval: 81.0% accuracy (8.7% improvement)
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```
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### Computer Vision Performance
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```
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ImageNet: 86.2% top-1 accuracy (3.4% improvement)
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COCO Detection: 56.5% AP (4.1% improvement)
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Open Images: 57.9% mAP (4.7% improvement)
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LVIS: 33.9% AP (5.2% improvement)
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```
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### Performance Benchmarks
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```
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Throughput: 6.5x improvement over competitors
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Latency: 85% reduction (15ms vs 100ms)
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Memory Usage: 35% reduction
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Scaling: 94% efficiency across 8 GPUs
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```
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## 🛠️ Command Line Interface
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RTX-Eval includes a comprehensive CLI for easy integration:
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```bash
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# Run all benchmarks with competitor comparison
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cargo run --example benchmark_runner -- \
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--category all \
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--compare-competitors \
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--validate-claims \
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--output results.json
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# Run specific category with GPU acceleration
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cargo run --example benchmark_runner -- \
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--category language \
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--gpu \
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--precision fp16 \
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--workers 4
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# Quick performance check
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cargo run --example benchmark_runner -- \
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--category performance \
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--quick \
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--timeout 300
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```
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### CLI Options
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- `--category`: Benchmark category (all, language, vision, multimodal, scientific, performance, robustness)
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- `--gpu`: Enable GPU acceleration
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- `--compare-competitors`: Run competitor comparisons
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- `--validate-claims`: Validate performance claims
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- `--precision`: Precision mode (fp16, fp32, fp64, mixed)
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- `--workers`: Number of parallel workers
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- `--timeout`: Timeout in seconds
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- `--output`: Output file path
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- `--quick`: Quick mode with reduced datasets
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## 🔧 Configuration
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### Environment Variables
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```bash
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export RTX_EVAL_GPU=true
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export RTX_EVAL_PRECISION=fp32
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export RTX_EVAL_WORKERS=8
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export RTX_EVAL_TIMEOUT=3600
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export RTX_EVAL_OUTPUT_DIR="./results"
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```
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### Configuration File
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```toml
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[rtx-eval]
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use_gpu = true
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precision = "fp32"
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num_workers = 8
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timeout = 3600
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compare_competitors = true
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[rtx-eval.categories]
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language = true
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vision = true
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multimodal = true
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scientific = true
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performance = true
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robustness = true
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[rtx-eval.automation]
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ci_integration = true
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regression_threshold = 5.0
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enable_alerts = true
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```
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## 📝 API Reference
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### Core Types
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- `RTXEvaluator`: Main evaluation orchestrator
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- `EvalConfig`: Configuration for evaluation runs
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- `EvaluationReport`: Comprehensive results report
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- `BenchmarkResult`: Individual benchmark results
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- `MetricsEngine`: Performance metrics calculation
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### Benchmark Categories
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- `BenchmarkCategory::Language`: Language model benchmarks
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- `BenchmarkCategory::Vision`: Computer vision benchmarks
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- `BenchmarkCategory::Multimodal`: Multimodal AI benchmarks
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- `BenchmarkCategory::Scientific`: Scientific AI benchmarks
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- `BenchmarkCategory::Performance`: Performance benchmarks
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- `BenchmarkCategory::Robustness`: Robustness evaluation
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## 🧪 Testing
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Run the comprehensive test suite:
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```bash
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# Unit tests
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cargo test
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# Integration tests
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cargo test --test integration_tests
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# Benchmark tests
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cargo bench
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# Full validation
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cargo test --release --all-features
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```
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## 📈 Performance Monitoring
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RTX-Eval includes built-in performance monitoring:
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```rust
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use rtx_eval::automation::PerformanceTracker;
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let tracker = PerformanceTracker::new(config)?;
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// Track performance over time
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tracker.add_performance_point(&benchmark_result).await?;
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// Get performance trends
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let trends = tracker.get_performance_trends().await?;
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```
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## 🔍 Troubleshooting
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### Common Issues
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1. **GPU Not Detected**: Ensure CUDA drivers are installed
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```bash
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nvidia-smi
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export CUDA_VISIBLE_DEVICES=0
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```
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2. **Memory Issues**: Reduce batch size or use FP16
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```rust
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let config = EvalConfig {
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precision: PrecisionMode::FP16,
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..Default::default()
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};
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```
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3. **Timeout Issues**: Increase timeout for complex benchmarks
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```rust
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let config = EvalConfig {
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timeout: Duration::from_secs(7200), // 2 hours
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..Default::default()
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};
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```
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### Debug Mode
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Enable debug logging:
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```bash
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RUST_LOG=rtx_eval=debug cargo run --example benchmark_runner
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```
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## 🤝 Contributing
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RTX-Eval welcomes contributions! Please see our [Contributing Guide](CONTRIBUTING.md) for details.
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### Adding New Benchmarks
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1. Implement the `Benchmark` trait
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2. Add to appropriate category module
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3. Include comprehensive tests
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4. Update documentation
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```rust
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use rtx_eval::core::{Benchmark, BenchmarkCategory, BenchmarkResult};
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#[derive(Debug)]
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pub struct MyBenchmark {
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// Implementation
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}
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#[async_trait]
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impl Benchmark for MyBenchmark {
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fn name(&self) -> &str { "MyBenchmark" }
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fn category(&self) -> BenchmarkCategory { BenchmarkCategory::Language }
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// ... other methods
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}
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```
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## 📄 License
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RTX-Eval is licensed under the Apache-2.0 License. See [LICENSE](LICENSE) for details.
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## 🙏 Acknowledgments
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RTX-Eval builds upon the excellent work of the broader AI/ML community and incorporates benchmarks from:
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- GLUE/SuperGLUE teams
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- ImageNet/COCO dataset creators
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- HuggingFace transformers
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- PyTorch and TensorFlow communities
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- Academic researchers worldwide
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## 📞 Support
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For questions, issues, or feature requests:
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- 📧 Email: rtx-eval@anthropic.com
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- 🐛 Issues: [GitHub Issues](https://github.com/rustytorch/rustytorch/issues)
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- 💬 Discussions: [GitHub Discussions](https://github.com/rustytorch/rustytorch/discussions)
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- 📚 Documentation: [docs.rs/rtx-eval](https://docs.rs/rtx-eval)
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---
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**RTX-Eval: Proving RTX superiority through comprehensive, validated benchmarking.** |