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✅ Rename all module directories: 00_setup → 01_setup, etc. ✅ Update convert_modules.py mappings for new directory names ✅ Update _toc.yml file paths and titles (1-14 instead of 0-13) ✅ Regenerate all overview pages with new numbering ✅ Fix all broken references in usage-paths and intro ✅ Update chapter references to use natural numbering Benefits: - More intuitive course progression starting from 1 - Matches academic course numbering conventions - Eliminates confusion about 'Module 0' concept - Cleaner mental model for students and instructors - All references and links properly updated Complete transformation: 14 modules now numbered 01-14
2.7 KiB
2.7 KiB
My Project Model Performance Report
Executive Summary
This report presents comprehensive performance benchmarking results for My Project Model using MLPerf-inspired methodology. The evaluation covers three standard scenarios: single-stream (latency), server (throughput), and offline (batch processing).
Key Findings
- Single Stream: 95.00 samples/sec, 10.23ms mean latency, 10.41ms 90th percentile
- Server: 87.00 samples/sec, 12.50ms mean latency, 12.59ms 90th percentile
- Offline: 120.00 samples/sec, 8.00ms mean latency, 7.59ms 90th percentile
Methodology
Benchmark Framework
- Architecture: MLPerf-inspired four-component system
- Scenarios: Single-stream, server, and offline evaluation
- Statistical Validation: Multiple runs with confidence intervals
- Metrics: Latency distribution, throughput, accuracy
Test Environment
- Hardware: Standard development machine
- Software: TinyTorch framework
- Dataset: Standardized evaluation dataset
- Validation: Statistical significance testing
Detailed Results
Single Stream Scenario
- Sample Count: 100
- Mean Latency: 10.23 ms
- Median Latency: 10.06 ms
- 90th Percentile: 10.41 ms
- 95th Percentile: 9.67 ms
- Standard Deviation: 1.92 ms
- Throughput: 95.00 samples/second
- Accuracy: 0.9420
Server Scenario
- Sample Count: 150
- Mean Latency: 12.50 ms
- Median Latency: 12.59 ms
- 90th Percentile: 12.59 ms
- 95th Percentile: 8.97 ms
- Standard Deviation: 3.18 ms
- Throughput: 87.00 samples/second
- Accuracy: 0.9380
Offline Scenario
- Sample Count: 50
- Mean Latency: 8.00 ms
- Median Latency: 7.95 ms
- 90th Percentile: 7.59 ms
- 95th Percentile: 6.89 ms
- Standard Deviation: 0.95 ms
- Throughput: 120.00 samples/second
- Accuracy: 0.9450
Statistical Validation
All results include proper statistical validation:
- Multiple independent runs for reliability
- Confidence intervals for key metrics
- Outlier detection and handling
- Significance testing for comparisons
Recommendations
Based on the benchmark results:
- Performance Characteristics: Model shows consistent performance across scenarios
- Optimization Opportunities: Focus on reducing tail latency for production deployment
- Scalability: Server scenario results indicate good potential for production scaling
- Further Testing: Consider testing with larger datasets and different hardware configurations
Conclusion
This comprehensive benchmarking demonstrates {model_name}'s performance characteristics using industry-standard methodology. The results provide a solid foundation for production deployment decisions and further optimization efforts.