Triton Inference Server Release 22.03
The Triton Inference Server container image, release 22.03, 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 22.03 is based on NVIDIA CUDA® 11.6.1, 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), 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 specific drivers. For a complete list of supported drivers, see CUDA Application Compatibility. For more information, see CUDA Compatibility and Upgrades and NVIDIA CUDA and Drivers Support.
GPU Requirements
Release 22.03 supports CUDA compute capability 6.0 and later. This corresponds to GPUs in the NVIDIA Pascal, NVIDIA Volta™, NVIDIA Turing™, and NVIDIA Ampere Architecture GPU 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.
- Models can now load from a serialized model_config message with the Triton Server API.
- ONNX Runtime, TensorRT, and Tensorflow backends now support server-side, multi-dimensional ragged batching.
- Cache miss statistics have been added to the Prometheus metrics.
- Trace settings can be configured with the Triton Server Trace Protocol.
- Refer to the 22.03 column of the Frameworks Support Matrix for container image versions on which the 22.03 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.
Known Issues
- Starting in 22.02, the Triton container, which uses the
22.02 PyTorch container, reports 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 and successfully load the model in Triton, you need to export the model again by using a later version of PyTorch.
- Triton pip wheels for ARM SBSA are not available from
PyPI, so 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.
Refer to https://github.com/pytorch/pytorch/issues/66930 for more information.
- Triton cannot retrieve GPU metrics with MIG-enabled GPU devices such as 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.
- Running a PyTorch TorchScript model by using the PyTorch backend, where multiple instances of a model are configured, can lead to a slowdown in model execution because of the following PyTorch issue: