TRT installation post

AI & Data Science Deep Learning (Training & Inference) TensorRT
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guide me on how to setup tensorrt, where i have NVIDIA hardware with SM 7.5 or higher capability, with onnx support.
Pls help,

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To set up TensorRT on your NVIDIA hardware with SM 75 or higher capability and ONNX support, follow these steps:

Hardware Requirements

  • NVIDIA GPU with SM 75 or higher capability (e.g., NVIDIA Ampere or later)
  • CUDA Toolkit installed (version 11.0 or later)
  • cuDNN library installed (version 8.0 or later)

Software Requirements

  • TensorRT 8.6.13 or later (download from the NVIDIA website)
  • ONNX parser library (included with TensorRT)

Setup Steps

  1. Install CUDA Toolkit: Download and install the CUDA Toolkit from the NVIDIA website. Make sure to select the correct version for your GPU architecture.
  2. Install cuDNN library: Download and install the cuDNN library from the NVIDIA website. Make sure to select the correct version for your CUDA Toolkit version.
  3. Install TensorRT: Download and install TensorRT from the NVIDIA website. Make sure to select the correct version for your CUDA Toolkit version.
  4. Set Environment Variables: Set the following environment variables:
    • CUDA_HOME: path to the CUDA Toolkit installation directory
    • CUDNN_HOME: path to the cuDNN library installation directory
    • TENSORRT_HOME: path to the TensorRT installation directory
  5. Verify Installation: Verify that TensorRT is installed correctly by running the following command:
trtexec --version

This should display the TensorRT version number.

ONNX Support

  • ONNX Parser Library: The ONNX parser library is included with TensorRT. You don’t need to install it separately.
  • ONNX Model Support: TensorRT supports ONNX models with the following features:
    • ONNX operators: TensorRT supports most ONNX operators, including convolution, pooling, activation functions, and more.
    • ONNX data types: TensorRT supports ONNX data types, including float32, float16, and int8.

Example Usage

  • Running an ONNX Model with TensorRT: Use the following command to run an ONNX model with TensorRT:
trtexec --onnx=<model_file> --useDLACore=<core_id> --fp16 --allowGPUFallback

Replace <model_file> with the path to your ONNX model file, <core_id> with the ID of the DLA core you want to use, and adjust the other options as needed.

Comparison with Other Frameworks

  • TensorFlow: TensorFlow is another popular deep learning framework that supports ONNX models. However, TensorFlow requires additional steps to convert ONNX models to TensorFlow format, whereas TensorRT can run ONNX models directly.
  • PyTorch: PyTorch is another popular deep learning framework that supports ONNX models. However, PyTorch requires additional steps to convert ONNX models to PyTorch format, whereas TensorRT can run ONNX models directly.

By following these steps, you should be able to set up TensorRT on your NVIDIA hardware with SM 75 or higher capability and ONNX support.

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