TensorRT Inference Optimization: The Complete Guide for Developers and Engineers

"TensorRT Inference Optimization"

"TensorRT Inference Optimization" is the definitive guide to harnessing the full power of NVIDIA's TensorRT for high-performance deep learning inference. Beginning with a foundational overview of deep learning inference workflows, model format interoperability, and precision modes, the book systematically explores the architectural principles behind TensorRT and practical strategies for optimizing and deploying neural networks across a spectrum of hardware platforms—from data center GPUs to edge devices. Readers gain an in-depth understanding of model conversion processes, handling custom operators, and ensuring reliable and efficient model deployment in real-world production environments.

The book delves into advanced engine optimization techniques including graph-level transformations, memory management, calibration for lower-precision inference, and the integration of custom plugin layers through high-performance CUDA kernels. Comprehensive coverage is also given to deployment architectures, scalable serving solutions such as Triton, multi-GPU/multi-instance scaling, and robust practices for model versioning, security, and continuous integration/deployment. Profiling and benchmarking chapters provide hands-on methodologies for identifying bottlenecks and balancing throughput, latency, and accuracy, ensuring peak system performance.

Catering to both practitioners and researchers, the guide navigates state-of-the-art topics such as asynchronous multi-stream inference, distributed and federated deployment scenarios, post-training quantization, sequence model optimization, and automation within MLOps pipelines. Concluding with future trends, open challenges, and the expanding TensorRT community ecosystem, this volume is an essential resource for professionals seeking to maximize deep learning inference efficiency and drive innovation in AI-powered applications.

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전자책: 2025년 8월 20일