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✅ Renamed modules for clearer pedagogical flow: - 05_networks → 05_dense (multi-layer dense/fully connected networks) - 06_cnn → 06_spatial (convolutional networks for spatial patterns) - 06_attention → 07_attention (attention mechanisms for sequences) ✅ Shifted remaining modules down by 1: - 07_dataloader → 08_dataloader - 08_autograd → 09_autograd - 09_optimizers → 10_optimizers - 10_training → 11_training - 11_compression → 12_compression - 12_kernels → 13_kernels - 13_benchmarking → 14_benchmarking - 14_mlops → 15_mlops - 15_capstone → 16_capstone ✅ Updated module metadata (module.yaml files): - Updated names, descriptions, dependencies - Fixed prerequisite chains and enables relationships - Updated export paths to match new names New learner progression: Foundation → Individual Layers → Dense Networks → Spatial Networks → Attention Networks → Training Pipeline Perfect pedagogical flow: Build one layer → Stack dense layers → Add spatial patterns → Add attention mechanisms → Learn to train them all.
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.