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Training framework being used, this may not be possible without patching the Installation of cuDNN on the single system. Some reason strongly undesirable, be careful to properly manage the side-by-side Training frameworks as expected by TensorRT. Installed, the simplest strategy is to use the same version of cuDNN for the If the target system has both TensorRT and one or more training frameworks.

The ONNX-TensorRT parser has been tested with ONNX 1.12.0 and supports opset 16.

The PyTorch examples have been tested with PyTorch 1.13.1, but may work with older.

Some Python samples require TensorFlow 2.5.1, such asĪddition, the deprecated UFF model export from TensorFlow requires TensorFlow 1.15.5.

TensorRT 8.6.1 supports NVIDIA cuDNN 8.9.0. Review the NVIDIA cuDNN Installation Guide for If you require cuDNN, then verify that you have cuDNN

cuDNN is now an optional dependency for TensorRT and is only used to speed-up a.

Instructions on how to install the CUDA Toolkit. Is not already installed, review the NVIDIA CUDA Installation Guide for

Verify that you have the NVIDIA CUDA™ Toolkit installed.

Ensure you are familiar with the NVIDIA TensorRT Release Notes.

On your system, refer to the NVIDIA CUDA-Python Installation Guide.

If you are using the TensorRT Python API and CUDA-Python isn’t already installed.

Pascal, NVIDIA Volta™, NVIDIA Turing™, NVIDIA AmpereĪrchitecture, NVIDIA Ada Lovelace architecture, and NVIDIA Hopper™ TensorRT also includes optional high speed mixed precision capabilities with the NVIDIA TensorRT also supplies a runtime that you can use to execute this network onĪll of NVIDIA’s GPU’s from the NVIDIA Pascal™ generation onwards. Implementation of that model leveraging a diverse collection of highly optimized

Optimizations, layer fusions, among other optimizations, while also finding the fastest TensorRT to optimize and run them on an NVIDIA GPU. The Network Definition API or load a pre-defined model via the parsers that allow TensorRT provides APIs via C++ and Python that help to express deep learning models via Trained parameters, and produces a highly optimized runtime engine that performs TensorRT takes a trained network, which consists of a network definition and a set of That facilitates high-performance inference on NVIDIA graphics processing units (GPUs). The core of NVIDIA ® TensorRT™ is a C++ library

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