26 Aug 17:32

mc-nv

98ee6a1

Release 2.25.0 corresponding to NGC container 22.08

Triton Inference Server

The Triton Inference Server provides a cloud inferencing solution optimized for both CPUs and GPUs. The server provides an inference service via an HTTP or GRPC endpoint, allowing remote clients to request inferencing for any model being managed by the server. For edge deployments, Triton Server is also available as a shared library with an API that allows the full functionality of the server to be included directly in an application.

What's New in 2.25.0

New support for multiple cloud credentials has been enabled. This feature is in beta and is subject to change.
Models using custom backends which implement auto-complete configuration, can be loaded without explicit config.pbtxt file if they are named in form <model_name>.<backend_name>.
Users can specify a maximum memory limit when loading models onto the GPU with the new --model-load-gpu-limit tritonserver option and the TRITONSERVER_ServerOptionsSetModelLoadDeviceLimit C API function
Added new documentation, Performance Tuning, with a step by step guide to optimize models for production
From this release onwards Triton will default to TensorFlow version 2.X. TensorFlow version 1.X can still be manually specified via backend config.
PyTorch backend has improved performance by using a separate CUDA Stream for each model instance when the instance kind is GPU.
Refer to the 22.08 column of the Frameworks Support Matrix for container image versions on which the 22.08 inference server container is based.
Model Analyzer's profile subcommand now analyzes the results after Profile is completed. Usage of the Analyze subcommand is deprecated. See Model Analyzer's documentation for further details.

Known Issues

There is no Jetpack release for 22.08, the latest release is 22.07.
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.
When auto-completing some model configs, backends may generate a model config even though there is not enough metadata (ex. Graphdef models for TensorFlow Backend). The user will see the model successfully load but fail to inference. In this case the user should provide the full model configuration for these models or use the --disable-auto-complete-config CLI option to show which models fail to load.
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: pytorch/pytorch#38273
Auto-complete is not supported in the OpenVINO backend
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 pytorch/pytorch#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.
Model Analyzer reported values for GPU utilization and GPU power are known to be inaccurate and generally lower than reality.

Client Libraries and Examples

Ubuntu 20.04 builds of the client libraries and examples are included in this release in the attached v2.25.0_ubuntu2004.clients.tar.gz file. The SDK is also available for as an Ubuntu 20.04 based NGC Container. The SDK container includes the client libraries and examples, Performance Analyzer and Model Analyzer. Some components are also available in the tritonclient pip package. See Getting the Client Libraries for more information on each of these options.

For windows, the client libraries and some examples are available in the attached tritonserver2.25.0-sdk-win.zip file.

Windows Support

A beta release of Triton for Windows is provided in the attached file:tritonserver2.25.0-win.zip. This is a beta release so functionality is limited and performance is not optimized. Additional features and improved performance will be provided in future releases. Specifically in this release:

HTTP/REST and GRPC endpoints are supported.
ONNX models are supported by the ONNX Runtime backend. The ONNX Runtime version is 1.12.0. The CPU, CUDA, and TensorRT execution providers are supported. The OpenVINO execution provider is not supported.
OpenVINO models are supported. The OpenVINO version is 2021.4.
Prometheus metrics endpoint is not supported.
System and CUDA shared memory are not supported.

To use the Windows version of Triton, you must install all the necessary dependencies on your Windows system. These dependencies are available in the Dockerfile.win10.min. The Dockerfile includes the following CUDA-related components:

CUDA 11.7.1
cuDNN 8.4.1.5
TensorRT 8.4.1.5

Jetson Jetpack Support

NOTE: There is no Jetpack release for 22.08, the latest release is 22.07.

Assets 5

30 Jul 00:26

mc-nv

v2.24.0

1a34a63

Release 2.24.0 corresponding to NGC container 22.07

Triton Inference Server

What's New in 2.24.0

Auto-Complete is enabled by default. The --strict-model-config option has been soft deprecated, use the new --disable-auto-complete-config CLI option instead.
New example backend demonstrating Business Logic Scripting in C++.
Users can provide values for "init_ops" in Tensorflow TF1.x GraphDef models through json file.
New asyncio compatible API to the Python GRPC/HTTP APIs.
Added thread pool to reduce service downtime for concurrently loading models. The thread pool size is configurable with the new --model-load-thread-count tritonserver option. You can find more information here.
Model Analyzer now doesn't require config.pbtxt file for models that can be auto-completed in Triton.
Refer to the 22.07 column of the Frameworks Support Matrix for container image versions on which the 22.07 inference server container is based.

Known Issues

JetPack release will be published later in the month in order to align with JetPack SDK public availability.
Auto-complete could cause an increase in server start time. To avoid a start time increase, users should provide the full model configuration.
When auto-completing some model configs, backends may generate a model config even though there is not enough metadata (ex. Graphdef models for Tensorflow Backend). The user will see the model successfully load but fail to inference. In this case the user should provide the full model configuration for these models or use the --disable-auto-complete-config CLI option to show which models fail to load.
Can't do autocomplete for PyTorch models, not enough metadata. Can only verify that the number of inputs is correct and the input names match what is specified in the model configuration. No info about number of outputs and datatypes. Related pytorch bug: pytorch/pytorch#38273.
Running inference on multiple TensorRT model instances in Triton may fail with signal(6). The issue is expected to be fixed in a future release. Details can be found at #4566.
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.
Unlike previously noted, 22.07 is the last release that defaults to TensorFlow version 1. From 22.08 onwards Triton will change the default TensorFlow version to 2.X.
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.

Refer to issue pytorch/pytorch#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.
Starting from 22.02, the Triton container, which uses the 22.02 or above PyTorch container, will report an error during model loading 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 load the model successfully in Triton, you need to export the model again by using a recent version of PyTorch.
Model Analyzer reported values for GPU utilization and GPU power are known to be inaccurate and generally lower than reality

Client Libraries and Examples

Ubuntu 20.04 builds of the client libraries and examples are included in this release in the attached v2.24.0_ubuntu2004.clients.tar.gz file. The SDK is also available for as an Ubuntu 20.04 based NGC Container. The SDK container includes the client libraries and examples, Performance Analyzer and Model Analyzer. Some components are also available in the tritonclient pip package. See Getting the Client Libraries for more information on each of these options.

For windows, the client libraries and some examples are available in the attached tritonserver2.24.0-sdk-win.zip file.

Windows Support

A beta release of Triton for Windows is provided in the attached file:tritonserver2.24.0-win.zip. This is a beta release so functionality is limited and performance is not optimized. Additional features and improved performance will be provided in future releases. Specifically in this release:

HTTP/REST and GRPC endpoints are supported.
ONNX models are supported by the ONNX Runtime backend. The ONNX Runtime version is 1.12.0. The CPU, CUDA, and TensorRT execution providers are supported. The OpenVINO execution provider is not supported.
OpenVINO models are supported. The OpenVINO version is 2021.4.
Prometheus metrics endpoint is not supported.
System and CUDA shared memory are not supported.

CUDA 11.7.1
cuDNN 8.4.1.5
TensorRT 8.4.1.5

Jetson Jetpack Support

A release of Triton for JetPack is provided in the attached tar file: A release of Triton for JetPack is provided in the attached tar file: tritonserver2.24.0-jetpack5.0.2.tgz..

This release supports TensorFlow 2.9.1, TensorFlow 1.15.5, TensorRT 8.4.1.5, Onnx Runtime 1.12.0, PyTorch 1.13.0, Python 3.8 and as well as ensembles.
Onnx Runtime backend does not support the OpenVino and TensorRT execution providers. The CUDA execution provider is in Beta.
System shared memory is supported on Jetson. CUDA shared memory is not supported.
GPU metrics, GCS storage, S3 storage and Azure storage are not supported.

The tar file contains the Triton server executable and shared libraries and also the C++ and Python client libraries and examples. For more information on how to install and use Triton on JetPack refer to jetson.md.

The wheel for the Python client library is present in the tar file and can be installed by running the following command:

python3 -m pip install --upgrade clients/python/tritonclient-2.24.0-py3-none-manylinux2014_aarch64.whl[all]

Assets 6

30 Jun 01:24

mc-nv

v2.23.0

e0bec87

Release 2.23.0 corresponding to NGC container 22.06

Triton Inference Server

What's New in 2.23.0

Auto-generated model configuration enables
dynamic batching
in supported models by default.
Python backend models now support
auto-generated model configuration.
Decoupled API
support in Python Backend model is out of beta.
Updated I/O tensors
naming convention
for serving TorchScript models via PyTorch backend.
Improvements to Perf Analyzer stability and profiling logic.
Refer to the 22.06 column of the
Frameworks Support Matrix
for container image versions on which the 22.06 inference server container is based.

Known Issues

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.
22.06 is the last release that defaults to
TensorFlow version 1.
From 22.07 onwards Triton will change the default TensorFlow version to 2.X.
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.

Refer to issue pytorch#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.
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:
pytorch#27902.
Starting from 22.02, the Triton container, which uses the 22.02 or above
PyTorch container, will report an error during model loading 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 load the model successfully in Triton, you need to export the model again
by using a recent version of PyTorch.

Client Libraries and Examples

Ubuntu 20.04 builds of the client libraries and examples are included in this release in the attached v2.23.0_ubuntu2004.clients.tar.gz file. The SDK is also available for as an Ubuntu 20.04 based NGC Container. The SDK container includes the client libraries and examples, Performance Analyzer and Model Analyzer. Some components are also available in the tritonclient pip package. See Getting the Client Libraries for more information on each of these options.

For windows, the client libraries and some examples are available in the attached tritonserver2.23.0-sdk-win.zip file.

Windows Support

A beta release of Triton for Windows is provided in the attached file:tritonserver2.23.0-win.zip. This is a beta release so functionality is limited and performance is not optimized. Additional features and improved performance will be provided in future releases. Specifically in this release:

HTTP/REST and GRPC endpoints are supported.
ONNX models are supported by the ONNX Runtime backend. The ONNX Runtime version is 1.10.0. The CPU, CUDA, and TensorRT execution providers are supported. The OpenVINO execution provider is not supported.
OpenVINO models are supported. The OpenVINO version is 2021.4.
Prometheus metrics endpoint is not supported.
System and CUDA shared memory are not supported.

CUDA 11.5.0
cuDNN 8.3.2.44
TensorRT 8.2.2.1

Jetson Jetpack Support

A release of Triton for JetPack 5.0 Developer Preview is provided in the attached tar file: tritonserver2.23.0-jetpack5.0.tgz.

This release supports TensorFlow 2.8.0, TensorFlow 1.15.5, TensorRT 8.4.0.9, Onnx Runtime 1.10.0, PyTorch 1.12.0, Python 3.8 and as well as ensembles.
Onnx Runtime backend does not support the OpenVino and TensorRT execution providers. The CUDA execution provider is in Beta.
System shared memory is supported on Jetson. CUDA shared memory is not supported.
GPU metrics, GCS storage, S3 storage and Azure storage are not supported.

The wheel for the Python client library is present in the tar file and can be installed by running the following command:

python3 -m pip install --upgrade clients/python/tritonclient-2.23.0-py3-none-manylinux2014_aarch64.whl[all]

Assets 6

26 May 19:35

mc-nv

v2.22.0

be57909

Release 2.22.0 corresponding to NGC container 22.05

Triton Inference Server

What's New in 2.22.0

Triton In-Process API is available in Java.
Python backend supports the decoupled API as BETA release.
You may load models with file content provided during the Triton Server API invocation.
Triton supports BF16 data type.
PyTorch backend supports 1-dimensional String I/O.
Explicit model control mode supports loading all models at startup.
You may specify customized GRPC channel settings in the GRPC client library.
Triton In-Process API supports dynamic model repository registration.
Improve build pipeline in build.py and generate build scripts used for pipeline examination.
ONNX Runtime backend updated to ONNX Runtime version 1.11.1 in both Ubuntu and Windows versions of Triton.
Refer to the 22.05 column of the Frameworks Support Matrix for container image versions on which the 22.05 inference server container is based.

Known Issues

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.

Refer to pytorch/pytorch#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.
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: pytorch/pytorch#27902.
Starting in 22.02, the Triton container, which uses the 22.05 PyTorch container, will report an error during model loading 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 load the model successfully in Triton, you need to export the model again by using a recent version of PyTorch.
A protobuf python package version that satisfies protobuf>=3.5.0,<3.20 must be installed before installing the Triton ARM SBSA wheels or any tritonclient version of 2.22.0 or earlier. Tritonclient versions of 2.22.3 or newer for jetson, x86, and windows will work normally.

Client Libraries and Examples

Ubuntu 20.04 builds of the client libraries and examples are included in this release in the attached v2.22.0_ubuntu2004.clients.tar.gz file. The SDK is also available for as an Ubuntu 20.04 based NGC Container. The SDK container includes the client libraries and examples, Performance Analyzer and Model Analyzer. Some components are also available in the tritonclient pip package. See Getting the Client Libraries for more information on each of these options.

For windows, the client libraries and some examples are available in the attached tritonserver2.22.0-sdk-win.zip file.

Windows Support

A beta release of Triton for Windows is provided in the attached file:tritonserver2.22.0-win.zip. This is a beta release so functionality is limited and performance is not optimized. Additional features and improved performance will be provided in future releases. Specifically in this release:

HTTP/REST and GRPC endpoints are supported.
ONNX models are supported by the ONNX Runtime backend. The ONNX Runtime version is 1.10.0. The CPU, CUDA, and TensorRT execution providers are supported. The OpenVINO execution provider is not supported.
OpenVINO models are supported. The OpenVINO version is 2021.4.
Prometheus metrics endpoint is not supported.
System and CUDA shared memory are not supported.

CUDA 11.5.0
cuDNN 8.3.2.44
TensorRT 8.2.2.1

Jetson Jetpack Support

A release of Triton for JetPack 5.0 Developer Preview is provided in the attached tar file: tritonserver2.22.0-jetpack5.0.tgz.

This release supports TensorFlow 2.8.0, TensorFlow 1.15.5, TensorRT 8.4.0.9, Onnx Runtime 1.10.0, PyTorch 1.12.0, Python 3.8 and as well as ensembles.
Onnx Runtime backend does not support the OpenVino and TensorRT execution providers. The CUDA execution provider is in Beta.
System shared memory is supported on Jetson. CUDA shared memory is not supported.
GPU metrics, GCS storage, S3 storage and Azure storage are not supported.

The wheel for the Python client library is present in the tar file and can be installed by running the following command:

python3 -m pip install --upgrade clients/python/tritonclient-2.22.0-py3-none-manylinux2014_aarch64.whl[all]

Assets 6

29 Apr 00:31

mc-nv

v2.21.0

a31b118

Release 2.21.0 corresponding to NGC container 22.04

Triton Inference Server

What's New In 2.21.0

Users can now specify a customized temp directory with the --tmp-dir argument to build.py during the container build.
Users can now send a raw binary request to eliminate the need for the specification of inference header.
Ensembles now recognize optional inputs.
Users can now add custom metrics to the existing Triton metrics endpoint in their custom backends and applications using the Triton C API. Documentation can be found here.
Official support for multiple cloud repositories. This includes the same as well as different cloud storage providers i.e. a single instance of Triton can load models from two S3 buckets, two GCS buckets and two Azure Storage containers.
ONNX Runtime backend now uses execution providers when available when autocomplete is enabled. This fixes the old behavior where it would always use the CPU execution provider.
The build.py and compose.py now support PyTorch and TensorFlow 1 backends for the CPU-only builds.
Refer to the 22.04 column of the Frameworks Support Matrix for container image versions on which the 22.04 inference server container is based.

Known Issues

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 pytorch/pytorch#66930.
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: pytorch/pytorch#27902
Starting in 22.02, the Triton container (which uses the 22.04 PyTorch container) will report an error during model loading 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). You will need to re-export the model using a recent version of PyTorch to be able to load the model successfully in Triton.
To best ensure the security and reliability of our RPM and Debian package repositories, NVIDIA is updating and rotating the signing keys used by the apt, dnf/yum, and zypper package managers beginning April 27, 2022.
Triton r22.04 and prior release branches have not updated these repository signing keys. Due to this users should expect package management errors when attempting to access or install packages from CUDA repositories.
Please follow these recommendations to mitigate the issue.
Please update your branches prior to building to include the updated signing key(s). These changes are captured in this commit.

Client Libraries and Examples

Ubuntu 20.04 builds of the client libraries and examples are included in this release in the attached v2.21.0_ubuntu2004.clients.tar.gz file. The SDK is also available for as an Ubuntu 20.04 based NGC Container. The SDK container includes the client libraries and examples, Performance Analyzer and Model Analyzer. Some components are also available in the tritonclient pip package. See Getting the Client Libraries for more information on each of these options.

For windows, the client libraries and some examples are available in the attached tritonserver2.21.0-sdk-win.zip file.

Windows Support

A beta release of Triton for Windows is provided in the attached file: tritonserver2.21.0-win.zip. This is a beta release so functionality is limited and performance is not optimized. Additional features and improved performance will be provided in future releases. Specifically in this release:

HTTP/REST and GRPC endpoints are supported.
ONNX models are supported by the ONNX Runtime backend. The ONNX Runtime version is 1.10.0. The CPU, CUDA, and TensorRT execution providers are supported. The OpenVINO execution provider is not supported.
OpenVINO models are supported. The OpenVINO version is 2021.4.
Prometheus metrics endpoint is not supported.
System and CUDA shared memory are not supported.

CUDA 11.5.0
cuDNN 8.3.2.44
TensorRT 8.2.2.1

Jetson Jetpack Support

A release of Triton for JetPack 5.0 Developer Preview is provided in the attached tar file: tritonserver2.21.0-jetpack5.0.tgz.

This release supports TensorFlow 2.8.0, TensorFlow 1.15.5, TensorRT 8.4.0.9, Onnx Runtime 1.10.0, PyTorch 1.12.0, Python 3.8 and as well as ensembles.
Onnx Runtime backend does not support the OpenVino and TensorRT execution providers. The CUDA execution provider is in Beta.
System shared memory is supported on Jetson. CUDA shared memory is not supported.
GPU metrics, GCS storage, S3 storage and Azure storage are not supported.

The wheel for the Python client library is present in the tar file and can be installed by running the following command:

python3 -m pip install --upgrade clients/python/tritonclient-2.21.0-py3-none-manylinux2014_aarch64.whl[all]

Assets 6

29 Mar 21:27

mc-nv

v2.20.0

e8d7325

Release 2.20.0 corresponding to NGC container 22.03

Triton Inference Server

What's New In 2.20.0

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.

Known Issues

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 pytorch/pytorch#66930.
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: pytorch/pytorch#27902
Starting in 22.03, the Triton container (which uses the 22.03 PyTorch container) will report an error during model loading 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). You will need to re-export the model using a recent version of PyTorch to be able to load the model successfully in Triton.

Client Libraries and Examples

Ubuntu 20.04 builds of the client libraries and examples are included in this release in the attached v2.20.0_ubuntu2004.clients.tar.gz file. The SDK is also available for as an Ubuntu 20.04 based NGC Container. The SDK container includes the client libraries and examples, Performance Analyzer and Model Analyzer. Some components are also available in the tritonclient pip package. See Getting the Client Libraries for more information on each of these options.

For windows, the client libraries and some examples are available in the attached tritonserver2.20.0-sdk-win.zip file.

Windows Support

A beta release of Triton for Windows is provided in the attached file: tritonserver2.20.0-win.zip. This is a beta release so functionality is limited and performance is not optimized. Additional features and improved performance will be provided in future releases. Specifically in this release:

HTTP/REST and GRPC endpoints are supported.
ONNX models are supported by the ONNX Runtime backend. The ONNX Runtime version is 1.10.0. The CPU, CUDA, and TensorRT execution providers are supported. The OpenVINO execution provider is not supported.
OpenVINO models are supported. The OpenVINO version is 2021.4.
Prometheus metrics endpoint is not supported.
System and CUDA shared memory are not supported.

CUDA 11.5.0
cuDNN 8.3.2.44
TensorRT 8.2.2.1

Jetson Jetpack Support

NOTE: The Jetson tritonserver2.20.0-jetpack5.0.tgz file will be added at a later date.

A release of Triton for JetPack 5.0 EA will be provided in the attached tar file: tritonserver2.20.0-jetpack5.0.tgz. It will be uploaded once JetPack 5.0 is released publicly.

This release supports TensorFlow 2.8.0, TensorFlow 1.15.5, TensorRT 8.4.0.6, Onnx Runtime 1.10.0, PyTorch 1.12.0, Python 3.8 and as well as ensembles.
Onnx Runtime backend does not support the OpenVino and TensorRT execution providers. The CUDA execution provider is in Beta.
System shared memory is supported on Jetson. CUDA shared memory is not supported.
GPU metrics, GCS storage, S3 storage and Azure storage are not supported.

The wheel for the Python client library is present in the tar file and can be installed by running the following command:

python3 -m pip install --upgrade clients/python/tritonclient-2.20.0-py3-none-manylinux2014_aarch64.whl[all]

Assets 6

25 Feb 17:19

dzier

v2.19.0

f6ad9ca

Release 2.19.0 corresponding to NGC container 22.02

Triton Inference Server

What's New In 2.19.0

Enabled full-fledged SSL support in HTTP C++ client library.
New cache metrics added to Prometheus metrics.
PyTorch Backend now supports passing inputs in the form of a dictionary of tensors.

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 pytorch/pytorch#66930.
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: pytorch/pytorch#27902
Starting in 22.02, the Triton container (which uses the 22.02 PyTorch container) will report an error during model loading 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). You will need to re-export the model using a recent version of PyTorch to be able to load the model successfully in Triton.

Client Libraries and Examples

Ubuntu 20.04 builds of the client libraries and examples are included in this release in the attached v2.19.0_ubuntu2004.clients.tar.gz file. The SDK is also available for as an Ubuntu 20.04 based NGC Container. The SDK container includes the client libraries and examples, Performance Analyzer and Model Analyzer. Some components are also available in the tritonclient pip package. See Getting the Client Libraries for more information on each of these options.

For windows, the client libraries and some examples are available in the attached tritonserver2.19.0-sdk-win.zip file.

Windows Support

A beta release of Triton for Windows is provided in the attached file: tritonserver2.19.0-win.zip. This is a beta release so functionality is limited and performance is not optimized. Additional features and improved performance will be provided in future releases. Specifically in this release:

HTTP/REST and GRPC endpoints are supported.
ONNX models are supported by the ONNX Runtime backend. The ONNX Runtime version is 1.10.0. The CPU, CUDA, and TensorRT execution providers are supported. The OpenVINO execution provider is not supported.
OpenVINO models are supported. The OpenVINO version is 2021.2.
Prometheus metrics endpoint is not supported.
System and CUDA shared memory are not supported.

CUDA 11.5.0
cuDNN 8.3.2.44
TensorRT 8.2.2.1

Jetson Jetpack Support

A release of Triton for JetPack 4.6.1 is provided in the attached tar file: tritonserver2.19.0-jetpack4.6.1.tgz.

This release supports TensorFlow 2.7.0, TensorFlow 1.15.5, TensorRT 8.2.1.8, Onnx Runtime 1.10.0, PyTorch 1.11.0, Python 3.6 and as well as ensembles.
Onnx Runtime backend does not support the OpenVino execution provider. The TensorRT execution provider however is supported.
System shared memory is supported on Jetson. CUDA shared memory is not supported.
GPU metrics, GCS storage, S3 storage and Azure storage are not supported.

The wheel for the Python client library is present in the tar file and can be installed by running the following command:

python3 -m pip install --upgrade clients/python/tritonclient-2.19.0-py3-none-manylinux2014_aarch64.whl[all]

Assets 6

31 Jan 21:03

dzier

v2.18.0

b6721bf

Release 2.18.0 corresponding to NGC container 22.01

Triton Inference Server

What's New In 2.18.0

Triton CPU-only build now supports TensorFlow2 backend for Linux x86.
Implicit state management can be used for ONNX Runtime and TensorRT backends.
State initialization from a constant is now supported in Implicit State management.
PyTorch and TensorFlow models now support batching on Inferentia.
PyTorch and Python backends are now supported on Jetson.
ARM Support has been added for the Performance Analyzer and Model Analyzer.

Known Issues

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 pytorch/pytorch#66930.
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: pytorch/pytorch#27902

Client Libraries and Examples

Ubuntu 20.04 builds of the client libraries and examples are included in this release in the attached v2.18.0_ubuntu2004.clients.tar.gz file. The SDK is also available for as an Ubuntu 20.04 based NGC Container. The SDK container includes the client libraries and examples, Performance Analyzer and Model Analyzer. Some components are also available in the tritonclient pip package. See Getting the Client Libraries for more information on each of these options.

For windows, the client libraries and some examples are available in the attached tritonserver2.18.0-sdk-win.zip file.

Windows Support

A beta release of Triton for Windows is provided in the attached file: tritonserver2.18.0-win.zip. This is a beta release so functionality is limited and performance is not optimized. Additional features and improved performance will be provided in future releases. Specifically in this release:

HTTP/REST and GRPC endpoints are supported.
ONNX models are supported by the ONNX Runtime backend. The ONNX Runtime version is 1.10.0. The CPU, CUDA, and TensorRT execution providers are supported. The OpenVINO execution provider is not supported.
OpenVINO models are supported. The OpenVINO version is 2021.2.
Prometheus metrics endpoint is not supported.
System and CUDA shared memory are not supported.

CUDA 11.5.0
cuDNN 8.3.2.44
TensorRT 8.2.2.1

Jetson Jetpack Support

NOTE: Jetson release of Triton is skipped for 2.18.0 (22.01) and the next release will be 2.19.0 (22.02).

Assets 5

21 Dec 06:34

dzier

v2.17.0

dd5d189

Release 2.17.0 corresponding to NGC container 21.12

Triton Inference Server

What's New In 2.17.0

Improved Inferentia support to use Neuron Runtime 2.x and multiple instances.
Models from MLflow can now be deployed to Triton with the MLflow plugin.
The preview release of TorchTRT models is now supported. PyTorch models optimized using TensorRT can now be loaded into Triton in the same way as regular PyTorch (TorchScript) models.
At the end of each Model Analyzer phase, an example command line will be
printed to run the next phase.
ONNX Runtime backend updated to ONNX Runtime version 1.10.0 in both Ubuntu and Window versions of Triton.

Known Issues

There was a bug in the GRPC protobuf implementation that was resolved by triton-inference-server/common#34. If the client code uses the 'byte_contents' field, the code must be updated to instead use 'bytes_contents'.
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 pytorch/pytorch#66930.
Triton’s TensorRT support depends on the input-consumed feature of TensorRT. In some rare cases using TensorRT 8.0 and earlier versions, the input-consumed event fires earlier than expected, causing Triton to overwrite input tensors while they are still in use and leading to corrupt input data being used for inference. This situation occurs when the inputs feed directly into a TensorRT layer that is optimized into a ForeignNode in the builder log. If you encounter accuracy issues with your TensorRT model, you can work-around the issue by enabling the output_copy_stream option in your model’s configuration (https://github.com/triton-inference-server/common/blob/main/protobuf/model_config.proto#L816)
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: pytorch/pytorch#27902

Client Libraries and Examples

Ubuntu 20.04 builds of the client libraries and examples are included in this release in the attached v2.17.0_ubuntu2004.clients.tar.gz file. The SDK is also available for as an Ubuntu 20.04 based NGC Container. The SDK container includes the client libraries and examples, Performance Analyzer and Model Analyzer. Some components are also available in the tritonclient pip package. See Getting the Client Libraries for more information on each of these options.

For windows, the client libraries and some examples are available in the attached tritonserver2.17.0-sdk-win.zip file.

Windows Support

An alpha release of Triton for Windows is provided in the attached file: tritonserver2.17.0-win.zip. This is an alpha release so functionality is limited and performance is not optimized. Additional features and improved performance will be provided in future releases. Specifically in this release:

HTTP/REST and GRPC endpoints are now supported.
ONNX models are supported by the ONNX Runtime backend. The ONNX Runtime version is 1.10.0. The CPU, CUDA, and TensorRT execution providers are supported. The OpenVINO execution provider is not supported.
OpenVINO models are supported. The OpenVINO version is 2021.2.
Prometheus metrics endpoint is not supported.
System and CUDA shared memory are not supported.

NVIDIA Driver release 470 or later.
CUDA 11.4.2
cuDNN 8.2.4.15
TensorRT 8.0.3.4

Jetson Jetpack Support

A release of Triton for JetPack 4.6 (https://developer.nvidia.com/embedded/jetpack) is provided in the attached tar file: tritonserver2.17.0-jetpack4.6.tgz.

This release supports TensorFlow 2.6.0, TensorFlow 1.15.5, TensorRT 8.0.1.6, Onnx Runtime 1.10.0 and as well as ensembles.
For the Onnx Runtime backend the OpenVino execution provider is not supported but the TensorRT execution provider is supported.
System shared memory is supported on Jetson.
GPU metrics, GCS storage, S3 storage and Azure storage are not supported.

The tar file contains the Triton server executable and shared libraries and also the C++ and Python client libraries and examples.

Installation and Usage

The following dependencies must be installed before building / running Triton.

apt-get update && \
    apt-get install -y --no-install-recommends \
        software-properties-common \
        autoconf \
        automake \
        build-essential \
        cmake \
        git \
        libb64-dev \
        libre2-dev \
        libssl-dev \
        libtool \
        libboost-dev \
        libcurl4-openssl-dev \
        libopenblas-dev \
        rapidjson-dev \
        patchelf \
        zlib1g-dev

Note: When building Triton on Jetson, you will require a newer version of cmake. We recommend using cmake 3.21.0. Below is a script to upgrade your cmake version to 3.21.0. You can use cmake 3.18.4 if you are not enabling OnnxRuntime support.

apt remove cmake
wget https://cmake.org/files/v3.21/cmake-3.21.0.tar.gz
tar -xf cmake-3.21.0.tar.gz
(cd cmake-3.21.0 && ./configure && make install)

Note: Seeing a core dump when using numpy 1.19.5 on Jetson is a known issue. We recommend using numpy version 1.19.4 or earlier to work around this issue.

To build / run the Triton client libraries and examples on Jetson, the following dependencies must be installed.

apt-get install -y --no-install-recommends \
        curl \
        pkg-config \
        python3 \
        python3-pip \
        python3-dev

pip3 install --upgrade wheel setuptools cython && \
pip3 install --upgrade grpcio-tools numpy==1.19.4 future attrdict

Note: OpenCV 4.1.1 is installed as a part of JetPack 4.6. It is one of the dependencies for the client build.

The Python wheel for the python client library is present in the tar file and can be installed by running the following command:

python3 -m pip install --upgrade clients/python/tritonclient-2.17.0-py3-none-manylinux2014_aarch64.whl[all]

On Jetson, the backend directory needs to be explicitly set with the --backend-directory flag. Triton also defaults to using TensorFlow 1.x and a version string is required to specify TensorFlow 2.x.

  tritonserver --model-repository=/path/to/model_repo --backend-directory=/path/to/tritonserver/backends \
         --backend-config=tensorflow,version=2

Assets 6

19 Nov 23:09

dzier

v2.16.0

a9367c6

Release 2.16.0 corresponding to NGC container 21.11

Triton Inference Server

What's New In 2.16.0

Added support for LightGBM models with categorical features in FIL backend.
Added Jetson examples in documentation.
Completed proof of concept of Inferentia support.
Added ARM Support for Model Analyzer.

Known Issues

Traced models in PyTorch seem to create overflows when int8 tensor values are transformed to int32 on the GPU. See pytorch/pytorch#66930.
Triton’s TensorRT support depends on the input-consumed feature of TensorRT. In some rare cases using TensorRT 8.0 and earlier versions, the input-consumed event fires earlier than expected, causing Triton to overwrite input tensors while they are still in use and leading to corrupt input data being used for inference. This situation occurs when the inputs feed directly into a TensorRT layer that is optimized into a ForeignNode in the builder log. If you encounter accuracy issues with your TensorRT model, you can work-around the issue by enabling the output_copy_stream option in your model’s configuration (https://github.com/triton-inference-server/common/blob/main/protobuf/model_config.proto#L816)
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
There is a known issue in TensorRT 8.0 regarding accuracy for a certain case of int8 inferencing on A40 and similar GPUs. The version of TF-TRT in TF2 21.09 includes a feature that works around this issue, but TF1 21.08 does not include that feature and therefore Triton users may experience the accuracy drop for a small subset of model/data type/batch size combinations on A40 when TF-TRT is used through the TF1 backend. This will be fixed in the next version of TensorRT.
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: pytorch/pytorch#27902

Client Libraries and Examples

Ubuntu 20.04 builds of the client libraries and examples are included in this release in the attached v2.16.0_ubuntu2004.clients.tar.gz file. The SDK is also available for as an Ubuntu 20.04 based NGC Container. The SDK container includes the client libraries and examples, Performance Analyzer and Model Analyzer. Some components are also available in the tritonclient pip package. See Getting the Client Libraries for more information on each of these options.

For windows, the client libraries and some examples are available in the attached tritonserver2.16.0-sdk-win.zip file.

Windows Support

An alpha release of Triton for Windows is provided in the attached file: tritonserver2.16.0-win.zip. This is an alpha release so functionality is limited and performance is not optimized. Additional features and improved performance will be provided in future releases. Specifically in this release:

HTTP/REST and GRPC endpoints are now supported.
ONNX models are supported by the ONNX Runtime backend. The ONNX Runtime version is 1.9. The CPU, CUDA, and TensorRT execution providers are supported. The OpenVINO execution provider is not supported.
OpenVINO models are supported. The OpenVINO version is 2021.2.
Prometheus metrics endpoint is not supported.
System and CUDA shared memory are not supported.

The following components are required for this release and must be installed on the Windows system:

NVIDIA Driver release 470 or later.
CUDA 11.4.2
cuDNN 8.2.4.15
TensorRT 8.0.3.4

Jetson Jetpack Support

A release of Triton for JetPack 4.6 (https://developer.nvidia.com/embedded/jetpack) is provided in the attached tar file: tritonserver2.16.0-jetpack4.6.tgz.

This release supports the TensorFlow 2.6.0, TensorFlow 1.15.5, TensorRT 8.0.1.6, OnnxRuntime 1.8.1 and as well as ensembles.
For the OnnxRuntime backend the OpenVino execution provider is not supported but the TensorRT execution provider is supported.
System shared memory is supported on Jetson.
GPU metrics, GCS storage, S3 storage and Azure storage are not supported.

The tar file contains the Triton server executable and shared libraries and also the C++ and Python client libraries and examples.

Installation and Usage

The following dependencies must be installed before building / running Triton.

apt-get update && \
    apt-get install -y --no-install-recommends \
        software-properties-common \
        autoconf \
        automake \
        build-essential \
        cmake \
        git \
        libb64-dev \
        libre2-dev \
        libssl-dev \
        libtool \
        libboost-dev \
        libcurl4-openssl-dev \
        libopenblas-dev \
        rapidjson-dev \
        patchelf \
        zlib1g-dev

apt remove cmake
wget https://cmake.org/files/v3.21/cmake-3.21.0.tar.gz
tar -xf cmake-3.21.0.tar.gz
(cd cmake-3.21.0 && ./configure && make install)

Note: Seeing a core dump when using numpy 1.19.5 on Jetson is a known issue. We recommend using numpy version 1.19.4 or earlier to work around this issue.

To build / run the Triton client libraries and examples on Jetson, the following dependencies must be installed.

apt-get install -y --no-install-recommends \
        curl \
        pkg-config \
        python3 \
        python3-pip \
        python3-dev

pip3 install --upgrade wheel setuptools cython && \
pip3 install --upgrade grpcio-tools numpy==1.19.4 future attrdict

Note: OpenCV 4.1.1 is installed as a part of JetPack 4.6. It is one of the dependencies for the client build.

The Python wheel for the python client library is present in the tar file and can be installed by running the following command:

python3 -m pip install --upgrade clients/python/tritonclient-2.16.0-py3-none-manylinux2014_aarch64.whl[all]

  tritonserver --model-repository=/path/to/model_repo --backend-directory=/path/to/tritonserver/backends \
         --backend-config=tensorflow,version=2

Assets 6

Releases: triton-inference-server/server

Release 2.25.0 corresponding to NGC container 22.08

Triton Inference Server

What's New in 2.25.0

Known Issues

Client Libraries and Examples

Windows Support

Jetson Jetpack Support

Release 2.24.0 corresponding to NGC container 22.07

Triton Inference Server

What's New in 2.24.0

Known Issues

Client Libraries and Examples

Windows Support

Jetson Jetpack Support

Release 2.23.0 corresponding to NGC container 22.06

Triton Inference Server

What's New in 2.23.0

Known Issues

Client Libraries and Examples

Windows Support

Jetson Jetpack Support

Release 2.22.0 corresponding to NGC container 22.05

Triton Inference Server

What's New in 2.22.0

Known Issues

Client Libraries and Examples

Windows Support

Jetson Jetpack Support

Release 2.21.0 corresponding to NGC container 22.04

Triton Inference Server

What's New In 2.21.0

Known Issues

Client Libraries and Examples

Windows Support

Jetson Jetpack Support

Release 2.20.0 corresponding to NGC container 22.03

Triton Inference Server

What's New In 2.20.0

Known Issues

Client Libraries and Examples

Windows Support

Jetson Jetpack Support

Release 2.19.0 corresponding to NGC container 22.02

Triton Inference Server

What's New In 2.19.0

Known Issues

Client Libraries and Examples

Windows Support

Jetson Jetpack Support

Release 2.18.0 corresponding to NGC container 22.01

Triton Inference Server

What's New In 2.18.0

Known Issues

Client Libraries and Examples

Windows Support

Jetson Jetpack Support

Release 2.17.0 corresponding to NGC container 21.12

Triton Inference Server

What's New In 2.17.0

Known Issues

Client Libraries and Examples

Windows Support

Jetson Jetpack Support

Installation and Usage

Release 2.16.0 corresponding to NGC container 21.11

Triton Inference Server

What's New In 2.16.0

Known Issues

Client Libraries and Examples

Windows Support

Jetson Jetpack Support

Installation and Usage