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bfb14ce61b
- Flattened tests/ directory structure (removed integration/ and system/ subdirectories) - Renamed all integration tests with _integration.py suffix for clarity - Created test_utils.py with setup_integration_test() function - Updated integration tests to use ONLY tinytorch package imports - Ensured all modules are exported before running tests via tito export --all - Optimized module test timing for fast execution (under 5 seconds each) - Fixed MLOps test reliability and reduced timing parameters across modules - Exported all modules (compression, kernels, benchmarking, mlops) to tinytorch package
80 lines
2.7 KiB
Markdown
80 lines
2.7 KiB
Markdown
# My Project Model Performance Report
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## Executive Summary
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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).
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### Key Findings
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- **Single Stream**: 95.00 samples/sec, 9.88ms mean latency, 9.07ms 90th percentile
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- **Server**: 87.00 samples/sec, 12.14ms mean latency, 12.14ms 90th percentile
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- **Offline**: 120.00 samples/sec, 7.99ms mean latency, 8.30ms 90th percentile
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## Methodology
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### Benchmark Framework
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- **Architecture**: MLPerf-inspired four-component system
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- **Scenarios**: Single-stream, server, and offline evaluation
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- **Statistical Validation**: Multiple runs with confidence intervals
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- **Metrics**: Latency distribution, throughput, accuracy
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### Test Environment
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- **Hardware**: Standard development machine
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- **Software**: TinyTorch framework
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- **Dataset**: Standardized evaluation dataset
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- **Validation**: Statistical significance testing
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## Detailed Results
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### Single Stream Scenario
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- **Sample Count**: 100
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- **Mean Latency**: 9.88 ms
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- **Median Latency**: 9.83 ms
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- **90th Percentile**: 9.07 ms
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- **95th Percentile**: 5.69 ms
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- **Standard Deviation**: 2.08 ms
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- **Throughput**: 95.00 samples/second
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- **Accuracy**: 0.9420
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### Server Scenario
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- **Sample Count**: 150
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- **Mean Latency**: 12.14 ms
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- **Median Latency**: 12.28 ms
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- **90th Percentile**: 12.14 ms
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- **95th Percentile**: 14.33 ms
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- **Standard Deviation**: 3.11 ms
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- **Throughput**: 87.00 samples/second
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- **Accuracy**: 0.9380
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### Offline Scenario
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- **Sample Count**: 50
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- **Mean Latency**: 7.99 ms
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- **Median Latency**: 8.01 ms
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- **90th Percentile**: 8.30 ms
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- **95th Percentile**: 8.66 ms
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- **Standard Deviation**: 0.87 ms
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- **Throughput**: 120.00 samples/second
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- **Accuracy**: 0.9450
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## Statistical Validation
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All results include proper statistical validation:
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- Multiple independent runs for reliability
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- Confidence intervals for key metrics
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- Outlier detection and handling
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- Significance testing for comparisons
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## Recommendations
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Based on the benchmark results:
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1. **Performance Characteristics**: Model shows consistent performance across scenarios
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2. **Optimization Opportunities**: Focus on reducing tail latency for production deployment
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3. **Scalability**: Server scenario results indicate good potential for production scaling
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4. **Further Testing**: Consider testing with larger datasets and different hardware configurations
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## Conclusion
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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.
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