Triton Inference Server Release 20.09

The Triton Inference Server container image, release 20.09, is available on NGC and is open source on GitHub.

Contents of the Triton Inference Server container

The Triton Inference Server Docker image contains the inference server executable and related shared libraries in /opt/tritonserver.

Driver Requirements

Release 20.09 is based on NVIDIA CUDA 11.0.3, which requires NVIDIA Driver release 450 or later. However, if you are running on Tesla (for example, T4 or any other Tesla board), you may use NVIDIA driver release 418.xx or 440.30. 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.

GPU Requirements

Release 20.09 supports CUDA compute capability 6.0 and higher. This corresponds to GPUs in the NVIDIA Pascal, Volta, Turing, and 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 Inference Server release includes the following key features and enhancements.

  • Python Client library is now a pip package available from the NVIDIA pypi index. See for more information.
  • Fixed a performance issue with the HTTP/REST protocol and the Python client library that caused reduced performance when outputs were not requested explicitly in an inference request.
  • Fixed some issues in reporting of statistics for ensemble models.
  • GRPC updated to version 1.25.0.
  • Refer to the 20.09 column of the Frameworks Support Matrix for container image versions that the 20.09 inference server container is based on.
  • Ubuntu 18.04 with August 2020 updates.

NVIDIA Triton Inference Server Container Versions

The following table shows what versions of Ubuntu, CUDA, Triton Inference Server, and TensorRT are supported in each of the NVIDIA containers for Triton Inference Server. For older container versions, refer to the Frameworks Support Matrix.

Known Issues

  • The KFServing HTTP/REST and GRPC protocols and corresponding Python and C++ clients are beta quality and are likely to change.

  • The new C API specified in tritonserver.h is beta quality and is likely to change.
  • TensorRT reformat-free I/O is not supported.
  • Some versions of Google Kubernetes Engine (GKE) contain a regression in the handling of LD_LIBRARY_PATH that prevents the inference server container from running correctly (see issue 141255952). Use a GKE 1.13 or earlier version or a GKE 1.14.6 or later version to avoid this issue.