TensorRT Release 18.09
The NVIDIA container image of TensorRT, release 18.09, is available.
Contents of TensorRT
- 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/html directory.
- The TensorRT Python samples and Python API documentation. The Python samples can be found in the /workspace/tensorrt/samples/python directory. Many Python samples can be run using python <script.py> -d /workspace/tensorrt/python/data. The Python API documentation can be found in the /workspace/tensorrt/doc/python directory.
- Ubuntu 16.04
- NVIDIA CUDA 10.0.130 including CUDA® Basic Linear Algebra Subroutines library™ (cuBLAS) 10.0.130
- NVIDIA CUDA® Deep Neural Network library™ (cuDNN) 7.3.0
- NCCL 2.3.4 (optimized for NVLink™ )
Driver Requirements
Release 18.09 is based on CUDA 10, which requires NVIDIA Driver release 410.xx. However, if you are running on Tesla (Tesla V100, Tesla P4, Tesla P40, or Tesla P100), you may use NVIDIA driver release 384. For more information, see CUDA Compatibility and Upgrades.
Key Features and Enhancements
- TensorRT container image version 18.09 is based on TensorRT 5.0.0 RC.
- Latest version of cuDNN 7.3.0.
- Latest version of CUDA 10.0.130 which includes support for DGX-2, Turing, and Jetson Xavier.
- Latest version of cuBLAS 10.0.130.
- Latest version of NCCL 2.3.4.
- Ubuntu 16.04 with August 2018 updates
Installing Required Python Modules
Some samples require data files that are not included within the TensorRT container either due to licensing restrictions or because they are too large. The following script has been added to the container to install these missing Python modules and their dependencies if desired: /opt/tensorrt/python/python_setup.sh
Samples which do not include all the required data files include a README.txt file in the corresponding source directory informing you how to obtain the necessary data files. You may need to first run the Python setup script in order to complete some of the samples.