TensorRT Release 21.12

The NVIDIA container image for TensorRT, release 21.12, is available on NGC.

Contents of the TensorRT container

This container includes the following:
  • The TensorRT C++ samples and C++ API documentation. The samples can be built by running make in the /workspace/tensorrt/samples directory. The resulting executables are in the /workspace/tensorrt/bin directory. The C++ API documentation can be found in the /workspace/tensorrt/doc/cpp directory.
  • The TensorRT Python samples and Python API documentation. The Python samples can be found in the /workspace/tensorrt/samples/python directory. Refer to the respective README documents for more samples. Many Python samples can be run using python <script.py> -d /workspace/tensorrt/data. For example:
    python onnx_resnet50.py -d /workspace/tensorrt/data
    The Python API documentation can be found in the /workspace/tensorrt/doc/python directory.
  • TensorRT 8.2.1.8. Note that the ONNX parser and plugin libraries bundled with this container are built from TensorRT Open Source Software: https://github.com/NVIDIA/TensorRT/releases/tag/21.12.
The container also includes the following:

Driver Requirements

Release 21.12 is based on NVIDIA CUDA 11.5.0, which requires NVIDIA Driver release 495 or later. However, if you are running on a Data Center GPU (for example, T4 or any other Tesla board), you may use NVIDIA driver release 418.40 (or later R418), 440.33 (or later R440), 450.51 (or later R450), 460.27 (or later R460), or 470.57 (or later R470). The CUDA driver's compatibility package only supports particular drivers. For a complete list of supported drivers, see the CUDA Application Compatibility topic. For more information, see CUDA Compatibility and Upgrades and NVIDIA CUDA and Drivers Support.

GPU Requirements

Release 21.12 supports CUDA compute capability 3.5 and higher. This corresponds to GPUs in the Kepler, Maxwell, Pascal, Volta, Turing, and NVIDIA Ampere Architecture GPU families. Specifically, for a list of GPUs that this compute capability corresponds to, see CUDA GPUs. For additional support details, see Deep Learning Frameworks Support Matrix.

Key Features and Enhancements

This TensorRT container release includes the following key features and enhancements.
  • TensorRT container image version 21.12 is based on TensorRT 8.2.1.8. For a list of the new features and enhancements introduced in TensorRT 8.2.1 refer to the TensorRT 8.2.1 release notes.
  • Ubuntu 20.04 with November 2021 updates.

Announcements

  • Starting with the 21.12 release, a beta version of the TensorRT container is available for the ARM SBSA platform. Pulling the Docker image nvcr.io/nvidia/tensorrt:21.12-py3 on an ARM SBSA machine will automatically fetch the ARM-specific image.
  • DLProf v1.8, which is included in the 21.12 container, will be the last release of DLProf. Starting with the 22.01 container, DLProf will no longer be included. It can still be manually installed via a pip wheel on the nvidia-pyindex.

Obtaining Missing Data Files

Some samples require data files that are not included within the TensorRT container either due to licensing restrictions or because they are too large. Samples which do not include all the required data files include a README.md file in the corresponding source directory informing you how to obtain the necessary data files.

Installing Required Python Modules

You may need to first run the Python setup script in order to complete some of the samples. The following script has been added to the container to install the missing Python modules and their dependencies if desired: /opt/tensorrt/python/python_setup.sh

Installing Open Source Components

A script has been added to clone, build and replace the provided plugin, Caffe parser, and ONNX parser libraries with the open source ones based off the 21.10 tag on the official TensorRT open source repository.

To install the open source components inside the container, run the following script:

/opt/tensorrt/install_opensource.sh

For more information see GitHub: TensorRT 21.12.

Limitations

Known Issues

  • None.