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Model Benchmarking
OpenVINO Benchmark

OpenVINO Benchmark

Benchmark models using PyTorch and OpenVINO

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What is OpenVINO Benchmark ?

OpenVINO Benchmark is a tool designed to benchmark models using PyTorch and OpenVINO. It allows users to compare the performance of models run through different frameworks, providing insights into speed, accuracy, and resource usage. This tool is particularly useful for optimizing model inference in production environments.

Features

• Gapless PyTorch and OpenVINO Integration: Directly compare model performance between PyTorch and OpenVINO.
• Automated Model Conversion: Seamlessly convert PyTorch models to OpenVINO format for benchmarking.
• Comprehensive Performance Metrics: Measures inference speed, latency, throughput, and memory usage.
• Customizable Workloads: Allows users to define specific input shapes and batch sizes for accurate benchmarking.
• Cross-Architecture Support: Supports benchmarking on CPUs, GPUs, and other specialized hardware.
• Detailed Reporting: Generates clear and actionable reports for performance analysis.

How to use OpenVINO Benchmark ?

  1. Install OpenVINO Benchmark: Clone the repository and install the required dependencies.
  2. Prepare Your Model: Export or convert your PyTorch model to the OpenVINO format using the built-in conversion tools.
  3. Define Benchmark Parameters: Specify input shapes, batch sizes, and hardware targets in a configuration file.
  4. Run the Benchmark: Execute the benchmarking script using the command line or Python API.
  5. Analyze Results: Review the generated reports to compare performance metrics between PyTorch and OpenVINO.

Frequently Asked Questions

What models are supported by OpenVINO Benchmark?
OpenVINO Benchmark supports models developed in PyTorch and compatible with OpenVINO. Models must be exported in a compatible format for benchmarking.

Can I use OpenVINO Benchmark on non-Intel hardware?
Yes, OpenVINO Benchmark supports benchmarking on various architectures, including non-Intel devices.

How do I interpret the benchmarking results?
Results are presented in a detailed report that compares metrics like inference speed, memory usage, and latency. This helps in identifying the most optimized framework for your use case.

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