Overview
InferBench is a comprehensive, vendor-neutral benchmarking framework for evaluating ML inference performance. It provides standardized benchmarks for comparing inference engines, hardware platforms, and optimization techniques across different ML frameworks and deployment scenarios.
Key Features
- Vendor-Neutral: Compare across different inference engines and platforms
- Comprehensive Metrics: Latency, throughput, memory usage, and more
- Multiple Frameworks: Support for TensorFlow, PyTorch, ONNX, and others
- Standardized Benchmarks: Industry-standard benchmark suites
- Detailed Reporting: Rich visualization and analysis of results
- Reproducible: Ensure consistent and reproducible benchmarking
Technical Implementation
Benchmarking Components
- Benchmark Suite: Collection of standardized benchmarks
- Model Loader: Support for multiple model formats
- Performance Profiler: Detailed performance measurement
- Result Analyzer: Statistical analysis of results
- Report Generator: Comprehensive benchmark reports
- Visualization: Charts and graphs for result comparison
Measurement Capabilities
- Latency measurement (p50, p95, p99)
- Throughput measurement
- Memory profiling
- CPU/GPU utilization
- Power consumption
- Accuracy validation
Key Capabilities
- Multi-framework benchmarking
- Hardware-agnostic evaluation
- Batch and streaming inference
- Model quantization evaluation
- Optimization technique comparison
- Statistical analysis
- Reproducible results
- Detailed performance reports
Code Repository
Explore the implementation on GitHub:
git clone https://github.com/Kernel-ML/inferbench.git
cd inferbench
pip install -e .
inferbench run --model model.onnx --config benchmark.yaml
Use Cases
- Evaluating inference engine performance
- Comparing hardware platforms
- Assessing optimization techniques
- Model selection based on performance
- Performance regression testing
- Capacity planning
Future Enhancements
- Support for more frameworks
- Advanced statistical analysis
- Real-world workload simulation
- Automated performance optimization
- Enhanced visualization tools
Technologies Used
PythonBenchmarkingPerformance Analysis