mirror of https://github.com/MLSysBook/TinyTorch.git synced 2026-04-28 15:13:23 -05:00

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Vijay Janapa Reddi 05391eb550 feat: Restructure integration tests and optimize module timing

- 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

2025-07-14 23:37:50 -04:00

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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, 9.88ms mean latency, 9.07ms 90th percentile
Server: 87.00 samples/sec, 12.14ms mean latency, 12.14ms 90th percentile
Offline: 120.00 samples/sec, 7.99ms mean latency, 8.30ms 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: 9.88 ms
Median Latency: 9.83 ms
90th Percentile: 9.07 ms
95th Percentile: 5.69 ms
Standard Deviation: 2.08 ms
Throughput: 95.00 samples/second
Accuracy: 0.9420

Server Scenario

Sample Count: 150
Mean Latency: 12.14 ms
Median Latency: 12.28 ms
90th Percentile: 12.14 ms
95th Percentile: 14.33 ms
Standard Deviation: 3.11 ms
Throughput: 87.00 samples/second
Accuracy: 0.9380

Offline Scenario

Sample Count: 50
Mean Latency: 7.99 ms
Median Latency: 8.01 ms
90th Percentile: 8.30 ms
95th Percentile: 8.66 ms
Standard Deviation: 0.87 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.

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