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Method 4: Tar File Installation#

Recommended for: Multiple TensorRT versions, custom installation paths, C++ and Python development on Linux

Advantages:

✓ High flexibility in installation location ✓ No root privileges needed for installation ✓ Multiple versions can coexist ✓ Includes C++ headers ✓ Complete control over environment

Limitations:

✗ Requires manual dependency management ✗ Manual LD_LIBRARY_PATH configuration ✗ No automatic updates ✗ More complex setup than other methods

Platform Support#

Supported Operating Systems:

  • Linux x86-64: Ubuntu 22.04+, RHEL 8+, Debian 12+, SLES 15+

  • Linux ARM SBSA and JetPack: Ubuntu 24.04+, Debian 12+

Prerequisites:

  • CUDA Toolkit installed (tar file or package manager)

Installation Steps#

Step 1: Download the TensorRT tar file

From the TensorRT download page, download the tar file that matches the CPU architecture and CUDA version you are using.

Step 2: Choose installation directory

Choose where you want to install TensorRT. The tar file will install everything into a subdirectory called TensorRT-10.x.x.x, where 10.x.x.x is your TensorRT version.

Step 3: Extract the tar file

version="10.x.x.x"
arch=$(uname -m)
cuda="cuda-x.x"
tar -xzvf TensorRT-${version}.Linux.${arch}-gnu.${cuda}.tar.gz

Where 10.x.x.x is your TensorRT version and cuda-x.x is CUDA version.

Step 4: Set environment variables

Add the absolute path to the TensorRT lib directory to the environment variable LD_LIBRARY_PATH:

export LD_LIBRARY_PATH=<TensorRT-${version}/lib>:$LD_LIBRARY_PATH

For permanent configuration, add this line to ~/.bashrc or ~/.profile:

echo 'export LD_LIBRARY_PATH=<TensorRT-${version}/lib>:$LD_LIBRARY_PATH' >> ~/.bashrc
source ~/.bashrc

Step 5 (Optional): Install Python wheels

Install the Python TensorRT wheel file. Replace cp3x with the desired Python version (such as cp310 for Python 3.10):

cd TensorRT-${version}/python
python3 -m pip install tensorrt-*-cp3x-none-linux_x86_64.whl

Optionally, install the TensorRT lean and dispatch runtime wheel files:

python3 -m pip install tensorrt_lean-*-cp3x-none-linux_x86_64.whl
python3 -m pip install tensorrt_dispatch-*-cp3x-none-linux_x86_64.whl

Verification#

Ensure that the installed files are located in the correct directories. For example, run the tree -d command to check whether all supported installed files are in place in the bin, lib, include, and other directories.

C++ Verification:

Compile and run a sample, such as sampleOnnxMNIST. Samples and sample data are only available from GitHub. The instructions to prepare the sample data can be found within the samples README.md. To build all the samples, use the following commands:

$ cd <cloned_tensorrt_dir>
$ mkdir build && cd build
$ cmake .. \
   -DTRT_LIB_DIR=$TRT_LIBPATH \
   -DTRT_OUT_DIR=`pwd`/out \
   -DBUILD_SAMPLES=ON \
   -DBUILD_PARSERS=OFF \
   -DBUILD_PLUGINS=OFF
$ cmake --build . --parallel 4
$ ./out/sample_onnx_mnist

For information about the samples, refer to TensorRT Sample Support Guide.

Python Verification:

import tensorrt as trt
print(trt.__version__)
assert trt.Builder(trt.Logger())

Troubleshooting#

Issue: error while loading shared libraries: libnvinfer.so.10

  • Solution: Ensure LD_LIBRARY_PATH is set correctly. Check:

    echo $LD_LIBRARY_PATH

    It should include $TENSORRT_INSTALL_DIR/lib.

Issue: Samples fail to compile

  • Solution: Install build essentials and CUDA development headers:

    sudo apt-get install build-essential cmake

Issue: Wrong Python wheel version

  • Solution: Check your Python version:

    python3 --version

    Download the matching wheel (cp38 for Python 3.8, cp39 for Python 3.9, and so on).