Triton Inference Server Release 22.02

The Triton Inference Server container image, release 22.02, 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 contatiner includes, refer to Deep Learning Frameworks Support Matrix.

Driver Requirements

Release 22.02 is based on NVIDIA CUDA 11.6.0, which requires NVIDIA Driver release 510 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 22.02 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.

Known Issues

  • Starting in 22.02, the Triton container (which uses the 22.02 PyTorch container) will report an error in the PyTorch backend when using scripted models that were exported in the legacy format (using our 19.09 or previous PyTorch NGC containers corresponding to PyTorch 1.2.0 or previous releases). To avoid this error, you will need to re-export the model using a recent version of PyTorch to be able to load the model successfully in Triton.
  • Addition of cache metrics may affect 3rd party tools/calculations for inference/compute latencies in models with caching enabled not accounting for cache hit requests that don't require inference.
  • Triton pip wheels for ARM SBSA are not available from PyPI and pip will install an incorrect Jetson version of Triton for ARM SBSA. The correct 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. See
  • Triton cannot retrieve GPU metrics with MIG-enabled GPU devices (A100 and A30).
  • Triton metrics may not work if the host machine is running a separate DCGM agent, either on bare-metal or in a container.
  • Running a PyTorch TorchScript model using the PyTorch backend, where multiple instances of a model are configured can lead to a slowdown in model execution due to the following PyTorch issue: