Discover effective strategies for model serving with the model-serving repository, delivering a comprehensive guide to implementing both lightweight and efficient solutions. This project provides a detailed analysis of various model serving techniques, ranging from basic PyTorch setups to advanced ONNX Runtime implementations in both Python and Rust. Each method is benchmarked under standardized conditions to evaluate how different serving strategies influence model service load capacity, alongside secondary metrics like image size and container startup time.

Key Features:

• Versatile Model Serving Approaches: Explore three distinct model serving strategies:

  • Naive Model Serving with PyTorch and FastAPI (Python): This basic setup serves models directly from their state_dict using PyTorch with model.eval() and torch.inference_mode() enabled. A valuable baseline for benchmarking, this method is widely used in practice.
  • Optimized Model Serving with ONNX Runtime (Python): This method enhances efficiency by embedding input transformation directly into the model graph, utilizing ONNX Runtime for superior performance over the naive approach.
  • Optimized Model Serving with Rust and ONNX Runtime: Leveraging Rust's capabilities, this strategy combines ONNX Runtime with Actix-Web for a high-performance solution that showcases the advantages of Rust in model serving.

• Comprehensive Benchmarking: The project involves rigorous benchmarking of each serving strategy, assessing performance metrics such as requests per second, latency, and resource consumption. For example, the Rust implementation boasts approximately 9.23 times the throughput compared to the naive PyTorch method, reflecting its high efficiency.

Conclusion Insights:

Utilizing ONNX Runtime consistently improves model serving performance, providing substantial enhancements in throughput and reduced latency. The Rust implementation, despite its higher memory usage, outperforms its Python counterparts in terms of speed and deployment efficiency, demonstrating how optimized models can significantly impact business needs.

For an in-depth exploration of technologies used, click here.

For quick access to essential aspects, explore sections on Benchmark Setup, Benchmark Results, and Conclusions.

Delve into the world of efficient model serving and discover how to optimize your machine learning deployments.