01 Feb 04:47

mc-nv

ba482cf

Release 2.30.0 corresponding to NGC container 23.01

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 2.30.0

The dynamic batcher now accepts user-defined batching constraints, allowing users to specify custom batching strategies.
Relaxed Python client gRPC version requirement.
Refer to the 23.01 column of the Frameworks Support Matrix for container image versions on which the 23.01 inference server container is based.

Known Issues

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 by enabling the output_copy_stream option in your model's configuration.
Some systems which implement malloc() may not release memory back to the operating system right away causing a false memory leak. This can be mitigated by using a different malloc implementation. Tcmalloc is installed in the Triton container and can be used by specifying the library in LD_PRELOAD.
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: pytorch/pytorch#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 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.

Client Libraries and Examples

Ubuntu 20.04 builds of the client libraries and examples are included in this release in the attached v2.30.0_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 tritonserver2.30.0-sdk-win.zip file.

Windows Support

A beta release of Triton for Windows is provided in the attached file:tritonserver2.30.0-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 1.13.1. 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 11.8.0
cuDNN 8.7.0.84
TensorRT 8.5.1.7

Jetson Jetpack Support

A release of Triton for JetPack is provided in the attached tar file: tritonserver2.30.0-jetpack5.1.tgz.

This release supports TensorFlow 2.11.0, TensorFlow 1.15.5, TensorRT 8.5.2.1, Onnx Runtime 1.13.1, PyTorch 1.14.0, 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-2.30.0-py3-none-manylinux2014_aarch64.whl[all]

Assets 6

20 Dec 19:59

mc-nv

v2.29.0

2d77fd0

Release 2.29.0 corresponding to NGC container 22.12

Triton Inference Server

What's New in 2.29.0

Improvements to container and non-container builds on Windows.
Refer to the 22.12 column of the Frameworks Support Matrix for container image versions on which the 22.12 inference server container is based.

Known Issues

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 by enabling the output_copy_stream option in your model's configuration.
Some systems which implement malloc() may not release memory back to the operating system right away causing a false memory leak. This can be mitigated by using a different malloc implementation. Tcmalloc is installed in the Triton container and can be used by specifying the library in LD_PRELOAD.
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: pytorch/pytorch#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 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.

Client Libraries and Examples

Ubuntu 20.04 builds of the client libraries and examples are included in this release in the attached v2.29.0_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 tritonserver2.29.0-sdk-win.zip file.

Windows Support

A beta release of Triton for Windows is provided in the attached file:tritonserver2.29.0-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 1.13.1. 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 11.8.0
cuDNN 8.7.0.84
TensorRT 8.5.1.7

Jetson Jetpack Support

NOTE: There is no Jetpack release for 22.12, the latest release is 22.10.

Assets 5

22 Nov 21:26

mc-nv

v2.28.0

75a8480

Release 2.28.0 corresponding to NGC container 22.11

Triton Inference Server

What's New in 2.28.0

Support new TensorRT 8.5 features. Including:
- UINT8 I/O
- “Data dependent dynamic shapes" operators (i.e. ONNX NMS and NonZero
  operations)
Support execution environment paths outside model directory. This can be done via 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.

Known Issues

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 by enabling the output_copy_stream option in your model's configuration.
Triton's TensorRT support depends on the CUDA event synchronization. In some rare cases the events may be triggered 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. 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.
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: pytorch/pytorch#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 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.

Client Libraries and Examples

Ubuntu 20.04 builds of the client libraries and examples are included in this release in the attached v2.28.0_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 tritonserver2.28.0-sdk-win.zip file.

Windows Support

A beta release of Triton for Windows is provided in the attached file:tritonserver2.28.0-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 1.13.1. 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 11.8.0
cuDNN 8.7.0.80
TensorRT 8.5.1.7

Jetson Jetpack Support

NOTE: There is no Jetpack release for 22.11, the latest release is 22.10.

Assets 5

02 Nov 22:20

mc-nv

v2.27.0

386d05d

Release 2.27.0 corresponding to NGC container 22.10

Triton Inference Server

What's New in 2.27.0

Added an example to demonstrate the use of JAX in python models.
Improved and enhanced Server Wrapper API to include missing features such as decoupled model and tracing support.
Multiple concurrent models can be profiled and analyzed by Model Analyzer. See Multi-Model Search Mode for additional details.

Known Issues

Triton's TensorRT support depends on the CUDA event synchronization. In some rare cases the events may be triggered 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. 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.
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: pytorch/pytorch#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 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.

Client Libraries and Examples

Ubuntu 20.04 builds of the client libraries and examples are included in this release in the attached v2.27.0_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 tritonserver2.27.0-sdk-win.zip file.

Windows Support

A beta release of Triton for Windows is provided in the attached file:tritonserver2.27.0-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 1.13.1. 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 11.8.0
cuDNN 8.6.0.163
TensorRT 8.5.0.12

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: tritonserver2.27.0-jetpack5.0.2.tgz.

This release supports TensorFlow 2.10.0, TensorFlow 1.15.5, TensorRT 8.4.1.5, Onnx Runtime 1.13.1, 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 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-2.27.0-py3-none-manylinux2014_aarch64.whl[all]

Assets 6

04 Oct 00:55

mc-nv

v2.26.0

ed30e90

Release 2.26.0 corresponding to NGC container 22.09

Triton Inference Server

What's New in 2.26.0

Added developer tools Github repository that provides a simplified interface for users to interact with the Triton Core shared library.
These developer tools are in beta and are subject to change.
Added CPU metrics reporting in Triton’s Prometheus metrics endpoint.
Added logging protocol extension for users to change logging configuration dynamically.
Users can specify the custom plugins to be loaded for TensorRT backend through command line option in addition to LD_PRELOAD.
Enabled auto-completion for OpenVINO backend.
Enabled Python backend to log messages through Triton’s logger.
Refer to the 22.09 column of the Frameworks Support Matrix for container image versions on which the 22.09 inference server container is based.
Added quick search algorithm to Model Analyzer to drastically reduce search time.
Added GPU metrics gathering to Perf Analyzer, which is also used by Model Analyzer to improve accuracy of those metrics.
NGC container release 22.09 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.

Known Issues

In certain rare cases with specific backends, triton server may crash with segmentation fault when exiting. Preliminary analysis shows that there might be a race condition in clean up of backend/model/instance state objects. Exact root cause is still unknown.
Triton's TensorRT support depends on the CUDA event synchronization. In some rare cases the events may be triggered 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. 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.
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: pytorch/pytorch#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 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.

Client Libraries and Examples

Ubuntu 20.04 builds of the client libraries and examples are included in this release in the attached v2.26.0_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 tritonserver2.26.0-sdk-win.zip file.

Windows Support

A beta release of Triton for Windows is provided in the attached file:tritonserver2.26.0-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 1.12.1. 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 11.8.0
cuDNN 8.6.0.163
TensorRT 8.5.0.12

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: tritonserver2.26.0-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 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-2.26.0-py3-none-manylinux2014_aarch64.whl[all]

Assets 6

26 Aug 17:32

mc-nv

v2.25.0

98ee6a1

Release 2.25.0 corresponding to NGC container 22.08

Triton Inference Server

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.

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 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

Releases: triton-inference-server/server

Release 2.30.0 corresponding to NGC container 23.01

Triton Inference Server

What's New in 2.30.0

Known Issues

Client Libraries and Examples

Windows Support

Jetson Jetpack Support

Release 2.29.0 corresponding to NGC container 22.12

Triton Inference Server

What's New in 2.29.0

Known Issues

Client Libraries and Examples

Windows Support

Jetson Jetpack Support

Release 2.28.0 corresponding to NGC container 22.11

Triton Inference Server

What's New in 2.28.0

Known Issues

Client Libraries and Examples

Windows Support

Jetson Jetpack Support

Release 2.27.0 corresponding to NGC container 22.10

Triton Inference Server

What's New in 2.27.0

Known Issues

Client Libraries and Examples

Windows Support

Jetson Jetpack Support

Release 2.26.0 corresponding to NGC container 22.09

Triton Inference Server

What's New in 2.26.0

Known Issues

Client Libraries and Examples

Windows Support

Jetson Jetpack Support

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