Triton Inference Server Release 22.11

The Triton Inference Server container image, release 22.11, 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.

For the list of what the container includes, refer to Deep Learning Frameworks Support Matrix.

Driver Requirements

Release 22.11 is based on CUDA 11.8.0, which requires NVIDIA Driver release 520 or later. However, if you are running on a data center GPU (for example, T4 or any other data center GPU), you can use NVIDIA driver release 450.51 (or later R450), 470.57 (or later R470), 510.47 (or later R510), or 515.65 (or later R515).

The CUDA driver's compatibility package only supports particular drivers. Thus, users should upgrade from all R418, R440, and R460 drivers, which are not forward-compatible with CUDA 11.8. For a complete list of supported drivers, see the CUDA Application Compatibility topic. For more information, see CUDA Compatibility and Upgrades.

GPU Requirements

Release 22.11 supports CUDA compute capability 6.0 and later. This corresponds to GPUs in the NVIDIA Pascal, NVIDIA Volta™, NVIDIA Turing™, NVIDIA Ampere architecture, and NVIDIA Hopper™ architecture families. For a list of GPUs to which this compute capability corresponds, 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.

  • Support for new TensorRT 8.5 features, including:
    • UINT8 I/O
    • “Data dependent dynamic shapes" operators (i.e. ONNX NMS and NonZero operations)
  • Support for execution environment paths outside model directory. This can be done via the EXECUTION_ENV_PATH parameter in config.pbtxt. Refer to the python backend README for known limitations.
  • Refer to the 22.11 column of the Frameworks Support Matrix for container image versions on which the 22.11 inference server container is based.

NVIDIA Triton Inference Server Container Versions

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

Container Version Triton Inference Server Ubuntu CUDA Toolkit TensorRT
22.11 2.28.0 20.04 NVIDIA CUDA 11.8.0 TensorRT 8.5.1
22.10 2.27.0 TensorRT 8.5 EA
22.09 2.26.0
22.08 2.25.0 NVIDIA CUDA 11.7.1 TensorRT 8.4.2.4
22.07 2.24.0 NVIDIA CUDA 11.7 Update 1 Preview TensorRT 8.4.1
22.06 2.23.0 TensorRT 8.2.5
22.05 2.22.0 NVIDIA CUDA 11.7.0
22.04 2.21.0 NVIDIA CUDA 11.6.2 TensorRT 8.2.4.2 and

for x86 Linux and SBSA

TensorRT 8.4.0 for JetPack/Jetson

22.03 2.20.0 NVIDIA CUDA 11.6.1 TensorRT 8.2.3 and

for x86 Linux and SBSA

TensorRT 8.4.0 for JetPack/Jetson

22.02 2.19.0 NVIDIA CUDA 11.6.0 TensorRT 8.2.3
22.01 2.18.0 TensorRT 8.2.2
21.12 2.17.0 NVIDIA CUDA 11.5.0 TensorRT 8.2.1.8
21.11 2.16.0

TensorRT 8.2.1.8 for x64 Linux

TensorRT 8.0.2.2 for ARM SBSA Linux
21.10 2.15.0 NVIDIA CUDA 11.4.2 with cuBLAS 11.6.5.2
21.09 2.14.0 NVIDIA CUDA 11.4.2 TensorRT 8.0.3
21.08 2.13.0 NVIDIA CUDA 11.4.1 TensorRT 8.0.1.6
21.07 2.12.0 NVIDIA CUDA 11.4.0
21.06.1 2.11.0 NVIDIA CUDA 11.3.1 TensorRT 7.2.3.4
21.06
21.05 2.10.0 NVIDIA CUDA 11.3.0
21.04 2.9.0
21.03 2.8.0 NVIDIA CUDA 11.2.1 TensorRT 7.2.2.3
21.02 2.7.0 NVIDIA CUDA 11.2.0 TensorRT 7.2.2.3+cuda11.1.0.024
20.12 2.6.0 NVIDIA CUDA 11.1.1 TensorRT 7.2.2
20.11 2.5.0

18.04

 NVIDIA CUDA 11.1.0 TensorRT 7.2.1
20.10 2.4.0
20.09 2.3.0 NVIDIA CUDA 11.0.3 TensorRT 7.1.3
20.08

2.2.0
20.07 1.15.0

2.1.0

 NVIDIA CUDA 11.0.194
20.06 1.14.0

2.0.0

 NVIDIA CUDA 11.0.167 TensorRT 7.1.2
20.03.1 1.13.0 NVIDIA CUDA 10.2.89 TensorRT 7.0.0
20.03 1.12.0

20.02

20.01

 1.11.0
1.10.0

19.12

19.11

 1.9.0 TensorRT 6.0.1
1.8.0
19.10 1.7.0 NVIDIA CUDA 10.1.243
19.09 1.6.0
19.08 1.5.0 TensorRT 5.1.5

Known Issues

Here are the known issues in this release:
  • Triton will not release for Jetson in 22.11. Please use the latest version, 22.10 (https://github.com/triton-inference-server/server/releases/tag/v2.27.0), instead.
  • In some rare cases Triton might overwrite input tensors while they are still in use which leads to corrupt input data being used for inference with TensorRT models. If you encounter accuracy issues with your TensorRT model, you can work-around the issue byenabling the output_copy_stream option in your model's configuration.
  • When using a custom operator for the PyTorch backend, the operator may not be loaded due to undefined Python library symbols. This can be work-around by specifying Python library in LD_PRELOAD.
  • Auto-complete may cause an increase in server start time. To avoid a start time increase, users can provide the full model configuration and launch the server with --disable-auto-complete-config.
  • Auto-complete does not support PyTorch models due to lack of metadata in the model. It can only verify that the number of inputs and the input names matches what is specified in the model configuration. There is no model metadata about the number of outputs and datatypes. Related PyTorch bug:https://github.com/pytorch/pytorch/issues/38273
  • Perf Analyzer stability criteria has been changed which may result in reporting instability for scenarios that were previously considered stable. This change has been made to improve the accuracy of Perf Analyzer results. If you observe this message, it can be resolved by increasing the --measurement-interval in the time windows mode or --measurement-request-count in the count windows mode.
  • Triton Client PIP wheels for Arm SBSA are not available from PyPI and pip will install an incorrect Jetson version of Triton Client library for Arm SBSA.

    The correct client wheel file can be pulled directly from the Arm SBSA SDK image and manually installed.

  • Traced models in PyTorch seem to create overflows when int8 tensor values are transformed to int32 on the GPU.

    Refer to https://github.com/pytorch/pytorch/issues/66930 for more information.

  • Triton cannot retrieve GPU metrics with MIG-enabled GPU devices (A100 and A30).
  • Triton metrics might not work if the host machine is running a separate DCGM agent on bare-metal or in a container.