28 Oct 00:17

dzier

5e5db26

Release 2.15.0 corresponding to NGC container 21.10

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 2.15.0

Rate limiter is now available and manages the rate at which requests are scheduled on model instances by Triton.
A beta version of Triton is available for ARM SBSA.
Windows Triton build now supports HTTP protocol.
Triton added support for caching responses to inference requests.
Sequence IDs can now accept strings.
Container composer tool can generate CPU-only Triton containers.

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 v2.15.0_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 tritonserver2.15.0-sdk-win.zip file.

Windows Support

An alpha release of Triton for Windows is provided in the attached file: tritonserver2.15.0-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: tritonserver2.15.0-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.
For this release the TF1 corresponds to the version from the 21.10 NGC TF container but TF2 corresponds to the version from the 21.09 NGC container.

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-2.15.0-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

29 Sep 21:27

dzier

v2.14.0

39b9337

Release 2.14.0 corresponding to NGC container 21.09

Triton Inference Server

What's New In 2.14.0

Full-featured, Beta version of Business Logic Scripting (BLS) released.
Beta version for basic JAVA Client released. See https://github.com/triton-inference-server/client/tree/r21.09/src/java for a list of supported features.
A stack trace is now printed when Triton crashes to aid in debugging.
The Triton Client SDK wheel file is now available directly from PyPI for both Ubuntu and Windows.
The TensorRT backend is now an optional part of Triton just like all the other backends. The compose utility can be used to create a Triton container that does not contain the TensorRT backend.
Model Analyzer can profile with perf_analyzer's C-API.
Model Analyzer can use the CUDA Device Index in addition to the GPU UUID in the --gpus flag.

Known Issues

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 v2.14.0_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 tritonserver2.14.0-sdk-win.zip file.

Windows Support

An alpha release of Triton for Windows is provided in the attached file: tritonserver2.14.0-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:

TensorRT models are supported. The TensorRT version is 8.0.1.6.
ONNX models are supported by the ONNX Runtime backend. The ONNX Runtime version is 1.8.1. The CPU, CUDA, and TensorRT execution providers are supported. The OpenVINO execution provider is not supported.
OpenVINO models are supported. The OpenVINO version is 2021.2.
Only the GRPC endpoint is supported, HTTP/REST is not supported.
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 11.4.1
cuDNN 8.2.2.26
TensorRT 8.0.1.6

Jetson Jetpack Support

A release of Triton for JetPack 4.6 (https://developer.nvidia.com/embedded/jetpack) is provided in the attached tar file: tritonserver2.14.0-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 run the clients the following dependencies must be installed.

apt-get install -y --no-install-recommends \
        curl \
        libopencv-dev=3.2.0+dfsg-4ubuntu0.1 \
        libopencv-core-dev=3.2.0+dfsg-4ubuntu0.1 \
        pkg-config \
        python3 \
        python3-pip \
        python3-dev

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

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-2.14.0-py3-none-linux_aarch64.whl[all]

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

Assets 6

28 Aug 03:42

dzier

v2.13.0

48cc33c

Release 2.13.0 corresponding to NGC container 21.08

Triton Inference Server

What's New In 2.13.0

Initial Beta release for Business Logic Scripting, a new set of utility functions that allow the execution of inference requests on other models being served by Triton as part of executing a Python model.
Release new Container Composition Utility which can be used to create custom Triton containers with specific backends and repository agents.
Starting in 21.08, Triton will release two new containers on NGC.
- nvcr.io/nvidia/tritonserver:21.08-tf-python-py3 - GPU enabled Triton server with only the TensorFlow 2.x and Python backends.
- nvcr.io/nvidia/tritonserver:21.08-pyt-python-py3 - GPU enabled Triton server with only the PyTorch and Python backends.
Added Model Analyzer support for models with custom operations.

Known Issues

Loading models in ONNX Runtime on the Windows build of Triton may be slow due to the JIT compiler being invoked for newer CUDA architectures. For more information, refer to triton-inference-server/onnxruntime_backend#58
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 21.08 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
There are backwards incompatible changes in the example Python client shared-memory support library when that library is used for tensors of type BYTES. The utils.serialize_byte_tensor() and utils.deserialize_byte_tensor() functions now return np.object_ numpy arrays where previously they returned np.bytes_ numpy arrays. Code depending on np.bytes_ must be updated. This change was necessary because the np.bytes_ type removes all trailing zeros from each array element and so binary sequences ending in zero(s) could not be represented with the old behavior. Correct usage of the Python client shared-memory support library is shown in https://github.com/triton-inference-server/server/blob/r21.03/src/clients/python/examples/simple_http_shm_string_client.py.

Client Libraries and Examples

Ubuntu 20.04 builds of the client libraries and examples are included in this release in the attached v2.13.0_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 tritonserver2.13.0-sdk-win.zip file.

Windows Support

An alpha release of Triton for Windows is provided in the attached file: tritonserver2.13.0-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:

TensorRT models are supported. The TensorRT version is 8.0.1.6.
ONNX models are supported by the ONNX Runtime backend. The ONNX Runtime version is 1.8.1. The CPU, CUDA, and TensorRT execution providers are supported. The OpenVINO execution provider is not supported.
OpenVINO models are supported. The OpenVINO version is 2021.2.
Only the GRPC endpoint is supported, HTTP/REST is not supported.
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 11.4.0
cuDNN 8.2.2.26
TensorRT 8.0.1.6

Jetson Jetpack Support

A release of Triton for JetPack 4.6 (https://developer.nvidia.com/embedded/jetpack) is provided in the attached file: tritonserver2.13.0-jetpack4.6.tgz. This release supports the TensorFlow 2.5.0, TensorFlow 1.15.5, TensorRT 8.0.1, 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 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 3.18.4. Below is a script to upgrade your cmake version to 3.18.4.

apt remove cmake
wget https://cmake.org/files/v3.18/cmake-3.18.4.tar.gz
tar -xf cmake-3.18.4.tar.gz
(cd cmake-3.18.4 && ./configure && sudo 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 run the clients the following dependencies must be installed.

apt-get install -y --no-install-recommends \
        curl \
        libopencv-dev=3.2.0+dfsg-4ubuntu0.1 \
        libopencv-core-dev=3.2.0+dfsg-4ubuntu0.1 \
        pkg-config \
        python3 \
        python3-pip \
        python3-dev

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

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-2.13.0-py3-none-linux_aarch64.whl[all]

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

Assets 6

27 Jul 19:00

dzier

v2.12.0

6733c3f

Release 2.12.0 corresponding to NGC container 21.07

Triton Inference Server

What's New In 2.12.0

Added support for CPU in RAPIDS FIL Backend.
Inference requests using the C API are now allowed to provide multiple copies of an input tensor in different memories. Triton will choose the most performant copy to use depending on where the inference request is executed.
For ONNX models using TensorRT acceleration, the tensorrt_accelerator option in the model configuration can now specify precision and workspace size. https://github.com/triton-inference-server/server/blob/main/docs/optimization.md#onnx-with-tensorrt-optimization
Model Analyzer added an offline mode, which prioritizes throughput over latency for offline inferencing scenarios. A new set of reports and graphs are created to better analyze the offline use case.

Known Issues

The 21.07 release includes libsystemd and libudev versions that have a known vulnerability that was discovered late in our QA process. See CVE-2021-33910 for details. This will be fixed in the next release.
ONNX Runtime TRT support was removed due to incompatibility with TensorRT 8.0.
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 21.07 includes a feature that works around this issue, but TF1 21.07 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
There are backwards incompatible changes in the example Python client shared-memory support library when that library is used for tensors of type BYTES. The utils.serialize_byte_tensor() and utils.deserialize_byte_tensor() functions now return np.object_ numpy arrays where previously they returned np.bytes_ numpy arrays. Code depending on np.bytes_ must be updated. This change was necessary because the np.bytes_ type removes all trailing zeros from each array element and so binary sequences ending in zero(s) could not be represented with the old behavior. Correct usage of the Python client shared-memory support library is shown in https://github.com/triton-inference-server/server/blob/r21.03/src/clients/python/examples/simple_http_shm_string_client.py.

Client Libraries and Examples

Ubuntu 20.04 builds of the client libraries and examples are included in this release in the attached v2.12.0_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 tritonserver2.12.0-sdk-win.zip file.

Windows Support

An alpha release of Triton for Windows is provided in the attached file: tritonserver2.12.0-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:

TensorRT models are supported. The TensorRT version is 7.2.2.
ONNX models are supported by the ONNX Runtime backend. The ONNX Runtime version is 1.8.0. 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.
Only the GRPC endpoint is supported, HTTP/REST is not supported.
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 455 or later.
CUDA 11.1.1
cuDNN 8.0.5
TensorRT 7.2.2

Jetson Jetpack Support

A release of Triton for JetPack 4.6 (https://developer.nvidia.com/embedded/jetpack) is provided in the attached file: tritonserver2.12.0-jetpack4.6.tgz. This release supports the TensorFlow 2.5.0, TensorFlow 1.15.5, TensorRT 8.0.1, OnnxRuntime 1.8.0 and as well as ensembles. For the OnnxRuntime backend the TensorRT and OpenVino execution providers are not 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 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 3.18.4. Below is a script to upgrade your cmake version to 3.18.4.

apt remove cmake
wget https://cmake.org/files/v3.18/cmake-3.18.4.tar.gz
tar -xf cmake-3.18.4.tar.gz
(cd cmake-3.18.4 && ./configure && sudo 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 run the clients the following dependencies must be installed.

apt-get install -y --no-install-recommends \
        curl \
        libopencv-dev=3.2.0+dfsg-4ubuntu0.1 \
        libopencv-core-dev=3.2.0+dfsg-4ubuntu0.1 \
        pkg-config \
        python3 \
        python3-pip \
        python3-dev

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

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-2.12.0-py3-none-linux_aarch64.whl[all]

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

Assets 6

25 Jun 20:38

dzier

v2.11.0

30b08ba

Release 2.11.0 corresponding to NGC container 21.06

Triton Inference Server

What's New In 2.11.0

The Forest Inference Library (FIL) backend is added to Triton. The FIL backend allows forest models trained by several popular machine learning frameworks (including XGBoost, LightGBM, Scikit-Learn, and cuML) to be deployed in a Triton.
Windows version of Triton now includes the OpenVino backend.
The Performance Analyzer (perf_analyzer) now supports testing against the Triton C API.
The Python backend now allows the use of conda to create a unique execution environment for your Python model. See https://github.com/triton-inference-server/python_backend#using-custom-python-execution-environments.
Python models that crash or exit unexpectedly are now automatically restarted by Triton.
Model repositories in S3 storage can now be accessed using HTTPS protocol. See https://github.com/triton-inference-server/server/blob/main/docs/model_repository.md#s3 for more information.
Triton now collects GPU metrics for MIG partitions.
Passive model instances can now be specified in the model configuration. A passive model instance will be loaded and initialized by Triton, but no inference requests will be sent to the instance. Passive instances are typically used by a custom backend that uses its own mechanisms to distribute work to the passive instances. See the ModelInstanceGroup section of model_config.proto for the setting.
NVDLA support is added to the TensorRT backend.
ONNX Runtime version updated to 1.8.0.
Windows build documentation simplified and improved.
Improved detailed and summary reports in Model Analyzer.
Added an offline mode to Model Analyzer.
The DALI backend now accepts GPU inputs.
The DALI backend added support for dynamic batching and ragged inputs.

Known Issues

There are backwards incompatible changes in the example Python client shared-memory support library when that library is used for tensors of type BYTES. The utils.serialize_byte_tensor() and utils.deserialize_byte_tensor() functions now return np.object_ numpy arrays where previously they returned np.bytes_ numpy arrays. Code depending on np.bytes_ must be updated. This change was necessary because the np.bytes_ type removes all trailing zeros from each array element and so binary sequences ending in zero(s) could not be represented with the old behavior. Correct usage of the Python client shared-memory support library is shown in https://github.com/triton-inference-server/server/blob/r21.03/src/clients/python/examples/simple_http_shm_string_client.py.
The 21.06 release of Triton was built against the wrong commit of the FIL backend code, causing an incompatible version of RAPIDS to be used instead of the intended RAPIDS 21.06 stable release. This issue is fixed in the new 21.06.1 container released on NGC. Although the Triton server itself and other integrated backends will work, the FIL backend will not work in the 21.06 Triton container.

Client Libraries and Examples

Ubuntu 20.04 builds of the client libraries and examples are included in this release in the attached v2.11.0_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 tritonserver2.11.0-sdk-win.zip file.

Windows Support

An alpha release of Triton for Windows is provided in the attached file: tritonserver2.11.0-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:

TensorRT models are supported. The TensorRT version is 7.2.2.
ONNX models are supported by the ONNX Runtime backend. The ONNX Runtime version is 1.8.0. 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.
Only the GRPC endpoint is supported, HTTP/REST is not supported.
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 455 or later.
CUDA 11.1.1
cuDNN 8.0.5
TensorRT 7.2.2

Jetson Jetpack Support

A release of Triton for JetPack 4.5 (https://developer.nvidia.com/embedded/jetpack) is provided in the attached file: tritonserver2.11.0-jetpack4.5.tgz. This release supports the TensorFlow 2.4.0, TensorFlow 1.15.5, TensorRT 7.1, OnnxRuntime 1.8.0 and as well as ensembles. For the OnnxRuntime backend the TensorRT execution provider is supported but the OpenVINO execution provider is not 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 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

To run the clients the following dependencies must be installed.

apt-get install -y --no-install-recommends \
        curl \
        libopencv-dev=3.2.0+dfsg-4ubuntu0.1 \
        libopencv-core-dev=3.2.0+dfsg-4ubuntu0.1 \
        pkg-config \
        python3 \
        python3-pip \
        python3-dev

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

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-2.11.0-py3-none-linux_aarch64.whl[all]

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

Assets 6

21 May 14:38

dzier

v2.10.0

021593f

Release 2.10.0 corresponding to NGC container 21.05

Triton Inference Server

What's New In 2.10.0

Triton on Jetson now supports ONNX via the ONNX Runtime backend.
The Triton server and HTTP clients (Python and C++) now support compression.
Ragged batching is now supported for ONNX models.
The Triton clients have moved to a separate repo: https://github.com/triton-inference-server/client
Trace now correctly reports all timestamps for all backends.
NVTX annotations are fixed.
The legacy custom backend support is removed. All custom backends must be implemented using the TRITONBACKEND API described here: https://github.com/triton-inference-server/backend
Added CLI subcommands in Model Analyzer for profile, analyze, and report. See CLI documentation for usage instructions.
- This is a breaking change and requires updating Model Analyzer config files and CLI flags. See Configuring Model Analyzer and Quick Start for more information.
Model Analyzer can create a detailed report of any specific model configuration with the report subcommand.
CPU only mode is now supported in Model Analyzer.

Known Issues

There are backwards incompatible changes in the example Python client shared-memory support library when that library is used for tensors of type BYTES. The utils.serialize_byte_tensor() and utils.deserialize_byte_tensor() functions now return np.object_ numpy arrays where previously they returned np.bytes_ numpy arrays. Code depending on np.bytes_ must be updated. This change was necessary because the np.bytes_ type removes all trailing zeros from each array element and so binary sequences ending in zero(s) could not be represented with the old behavior. Correct usage of the Python client shared-memory support library is shown in https://github.com/triton-inference-server/server/blob/r21.03/src/clients/python/examples/simple_http_shm_string_client.py.
Some versions of Google Kubernetes Engine (GKE) contain a regression in the handling of LD_LIBRARY_PATH that prevents the inference server container from running correctly (see issue 141255952). Use a GKE 1.13 or earlier version or a GKE 1.14.6 or later version to avoid this issue.

Client Libraries and Examples

Ubuntu 20.04 builds of the client libraries and examples are included in this release in the attached v2.10.0_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 tritonserver2.10.0-sdk-win.zip file.

Windows Support

An alpha release of Triton for Windows is provided in the attached file: tritonserver2.10.0-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:

TensorRT models are supported. The TensorRT version is 7.2.2.
ONNX models are supported by the ONNX Runtime backend. The ONNX Runtime version is 1.7.1. The CPU, CUDA, and TensorRT execution providers are supported. The OpenVINO execution provider is not supported.
Only the GRPC endpoint is supported, HTTP/REST is not supported.
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 455 or later.
CUDA 11.1.1
cuDNN 8.0.5
TensorRT 7.2.2

Jetson Jetpack Support

A release of Triton for JetPack 4.5 (https://developer.nvidia.com/embedded/jetpack) is provided in the attached file: tritonserver2.10.0-jetpack4.5.tgz. This release supports the TensorFlow 2.4.0, TensorFlow 1.15.5, TensorRT 7.1, OnnxRuntime 1.7.1 and as well as ensembles. For the OnnxRuntime backend the TensorRT execution provider is supported but the OpenVINO execution provider is not 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 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

To run the clients the following dependencies must be installed.

apt-get install -y --no-install-recommends \
        curl \
        libopencv-dev=3.2.0+dfsg-4ubuntu0.1 \
        libopencv-core-dev=3.2.0+dfsg-4ubuntu0.1 \
        pkg-config \
        python3 \
        python3-pip \
        python3-dev

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

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-2.10.0-py3-none-linux_aarch64.whl[all]

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

Assets 6

27 Apr 17:19

dzier

v2.9.0

b4d6cc2

Release 2.9.0 corresponding to NGC container 21.04

Triton Inference Server

What's New In 2.9.0

Python backend performance has been increased significantly.
Onnx Runtime update to version 1.7.1.
Triton Server is now available as a GKE Marketplace Application, see https://github.com/triton-inference-server/server/tree/master/deploy/gke-marketplace-app.
The GRPC client libraries now allow compression to be enabled.
Ragged batching is now supported for TensorFlow models.
For TensorFlow models represented with SavedModel format, it is now possible to choose which graph and signature_def to load. See https://github.com/triton-inference-server/tensorflow_backend/tree/r21.04#parameters.
A Helm Chart example is added for AWS. See https://github.com/triton-inference-server/server/tree/master/deploy/aws.
The Model Control API is enhanced to provide an option when unloading an ensemble model. The option allows all contained models to be unloaded as part of unloading the ensemble. See https://github.com/triton-inference-server/server/blob/master/docs/protocol/extension_model_repository.md#model-repository-extension.
Model reloading using the Model Control API previously resulted in the model being unavailable for a short period of time. This is now fixed so that the model remains available during reloading.
Latency statistics and metrics for TensorRT models are fixed. Previously the sum of the "compute input", "compute infer" and "compute output" times accurately indicated the entire compute time but the total time could be incorrectly attributed across the three components. This incorrect attribution is now fixed and all values are now accurate.
Error reporting is improved for the Azure, S3 and GCS cloud file system support.
Fix trace support for ensembles. The models contained within an ensemble are now traced correctly.
Model Analyzer improvements
- Summary report now includes GPU Power usage
- Model Analyzer will find the Top N model configuration across multiple models.

Known Issues

There are backwards incompatible changes in the example Python client shared-memory support library when that library is used for tensors of type BYTES. The utils.serialize_byte_tensor() and utils.deserialize_byte_tensor() functions now return np.object_ numpy arrays where previously they returned np.bytes_ numpy arrays. Code depending on np.bytes_ must be updated. This change was necessary because the np.bytes_ type removes all trailing zeros from each array element and so binary sequences ending in zero(s) could not be represented with the old behavior. Correct usage of the Python client shared-memory support library is shown in https://github.com/triton-inference-server/server/blob/r21.03/src/clients/python/examples/simple_http_shm_string_client.py.
Some versions of Google Kubernetes Engine (GKE) contain a regression in the handling of LD_LIBRARY_PATH that prevents the inference server container from running correctly (see issue 141255952). Use a GKE 1.13 or earlier version or a GKE 1.14.6 or later version to avoid this issue.

Client Libraries and Examples

Ubuntu 20.04 builds of the client libraries and examples are included in this release in the attached v2.9.0_ubuntu2004.clients.tar.gz file. See Getting the Client Libraries for more information on the client libraries and examples. The client SDK is also available as a NGC Container.

Windows Support

An alpha release of Triton for Windows is provided in the attached file: tritonserver2.8.0-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:

TensorRT models are supported. The TensorRT version is 7.2.2.
ONNX models are supported by the ONNX Runtime backend. The ONNX Runtime version is 1.6.0. The CPU, CUDA, and TensorRT execution providers are supported. The OpenVINO execution provider is not supported.
Only the GRPC endpoint is supported, HTTP/REST is not supported.
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 455 or later.
CUDA 11.1.1
cuDNN 8.0.5
TensorRT 7.2.2

Jetson Jetpack Support

A release of Triton for JetPack 4.5 (https://developer.nvidia.com/embedded/jetpack) is provided in the attached file: tritonserver2.9.0-jetpack4.5.tgz. This release supports the TensorFlow 2.4.0, TensorFlow 1.15.5, TensorRT 7.1, and Custom backends as well as ensembles. 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 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 \
        rapidjson-dev \
        patchelf \
        zlib1g-dev

To run the clients the following dependencies must be installed.

apt-get install -y --no-install-recommends \
        curl \
        libopencv-dev=3.2.0+dfsg-4ubuntu0.1 \
        libopencv-core-dev=3.2.0+dfsg-4ubuntu0.1 \
        pkg-config \
        python3 \
        python3-pip \
        python3-dev

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

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-2.9.0-py3-none-linux_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 5

27 Mar 00:16

deadeyegoodwin

v2.8.0

f5b001c

Release 2.8.0 corresponding to NGC container 21.03

Triton Inference Server

What's New In 2.8.0

The Windows build now supports ONNX models.
Repository agent is a new extensibility C API added to Triton that allows implementation of custom authentication, decryption, conversion, or similar operations when a model is loaded. See https://github.com/triton-inference-server/server/blob/master/docs/repository_agents.md
An OpenVINO backend is added to Triton to enable the execution of OpenVINO models on CPUs. https://github.com/triton-inference-server/openvino_backend
The PyTorch backend is now maintained in its own repository: https://github.com/triton-inference-server/pytorch_backend
The ONNX Runtime backend is now maintained in its own repository: https://github.com/triton-inference-server/onnxruntime_backend
The Jetson release of Triton now supports the system shared-memory protocol between clients and the Triton server.
SSL/TLS Mutual Authentication support is added to the GRPC client library.
A new Model Configuration option, "gather_kernel_buffer_threshold", can be specified to instruct Triton to use a CUDA kernel to gather inputs buffers onto the GPU. Using this option can improve inference performance for some models.
The Python client libraries have been improved to more efficiently create numpy arrays for input and output tensors.
The client libraries examples have been improved to more clearly describe how string and byte-blob tensors are supported by the Python Client API. https://github.com/triton-inference-server/server/blob/master/docs/client_examples.md
Ubuntu 20.04 with February 2021 updates.

Known Issues

There are backwards incompatible changes in the example Python client shared-memory support library when that library is used for tensors of type BYTES. The utils.serialize_byte_tensor() and utils.deserialize_byte_tensor() functions now return np.object_ numpy arrays where previously they returned np.bytes_ numpy arrays. Code depending on np.bytes_ must be updated. This change was necessary because the np.bytes_ type removes all trailing zeros from each array element and so binary sequences ending in zero(s) could not be represented with the old behavior. Correct usage of the Python client shared-memory support library is shown in https://github.com/triton-inference-server/server/blob/r21.03/src/clients/python/examples/simple_http_shm_string_client.py.
Some versions of Google Kubernetes Engine (GKE) contain a regression in the handling of LD_LIBRARY_PATH that prevents the inference server container from running correctly (see issue 141255952). Use a GKE 1.13 or earlier version or a GKE 1.14.6 or later version to avoid this issue.

Client Libraries and Examples

Ubuntu 20.04 builds of the client libraries and examples are included in this release in the attached v2.8.0_ubuntu2004.clients.tar.gz file. See Getting the Client Libraries for more information on the client libraries and examples. The client SDK is also available as a NGC Container.

Windows Support

TensorRT models are supported. The TensorRT version is 7.2.2.
ONNX models are supported by the ONNX Runtime backend. The ONNX Runtime version is 1.6.0. The CPU, CUDA, and TensorRT execution providers are supported. The OpenVINO execution provider is not supported.
Only the GRPC endpoint is supported, HTTP/REST is not supported.
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 455 or later.
CUDA 11.1.1
cuDNN 8.0.5
TensorRT 7.2.2

Jetson Jetpack Support

A release of Triton for JetPack 4.5 (https://developer.nvidia.com/embedded/jetpack) is provided in the attached file: tritonserver2.8.0-jetpack4.5.tgz. This release supports the TensorFlow 2.4.0, TensorFlow 1.15.5, TensorRT 7.1, and Custom backends as well as ensembles. 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 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 \
        rapidjson-dev \
        patchelf \
        zlib1g-dev

To run the clients the following dependencies must be installed.

apt-get install -y --no-install-recommends \
        curl \
        libopencv-dev=3.2.0+dfsg-4ubuntu0.1 \
        libopencv-core-dev=3.2.0+dfsg-4ubuntu0.1 \
        pkg-config \
        python3 \
        python3-pip \
        python3-dev

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

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-2.8.0-py3-none-linux_aarch64.whl[all]

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

Assets 5

27 Feb 01:20

dzier

v2.7.0

c7d7b00

Release 2.7.0 corresponding to NGC container 21.02

Triton Inference Server

What's New In 2.7.0

Fix bug in TensorRT backend that could, in rare cases, lead to corruption of output tensors.
Fix performance issue in the HTTP/REST client that occurred when the client does not explicitly request specific outputs.
In this case all outputs are now returned as binary data where previously they were returned as JSON.
Add an example Java and Scala client based on GRPC-generated API.
Extend perf_analyzer to be able to work with TFServing and TorchServe.
The legacy custom backend API is deprecated and will be removed in a future release. The Triton Backend API should be used as the API for custom backends. The Triton Backend API remains fully supported and that support will continue indefinitely.
Model Analyzer parameters and test model configurations can be specified with YAML configuration file.
Model Analyzer will report performance metrics for end-to-end latency and CPU memory usage.
Refer to the 21.02 column of the Frameworks Support Matrix
for container image versions that the 21.02 inference server container is based on.
Ubuntu 20.04 with January 2021 updates.

Known Issues

TensorRT reformat-free I/O is not supported.
Some versions of Google Kubernetes Engine (GKE) contain a regression in the handling of LD_LIBRARY_PATH that prevents the inference server container from running correctly (see issue 141255952). Use a GKE 1.13 or earlier version or a GKE 1.14.6 or later version to avoid this issue.
Observed memory leak in gRPC client library. Suggested workaround: restart client processes periodically or minimize creation of new InferenceServerGrpcClient objects. For more details on the issue in gRPC, please reference: #2517. The memory leak is fixed on master branch by #2533 and the fix will be included in the 21.03 release. If required, the change can be applied to the 21.02 branch and the client library can be rebuilt: https://github.com/triton-inference-server/server/blob/master/docs/client_libraries.md.

Client Libraries and Examples

Ubuntu 20.04 builds of the client libraries and examples are included in this release in the attached v2.7.0_ubuntu2004.clients.tar.gz file. See Getting the Client Libraries for more information on the client libraries and examples. The client SDK is also available as a NGC Container.

Windows Support

An alpha release of Triton for Windows is provided in the attached file: tritonserver2.7.0-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:

Only TensorRT models are supported. The TensorRT version is 7.2.2.
Only the GRPC endpoint is supported, HTTP/REST is not supported.
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 455 or later.
CUDA 11.1.1
cuDNN 8.0.5
TensorRT 7.2.2

Jetson Jetpack Support

A release of Triton for JetPack 4.5 (https://developer.nvidia.com/embedded/jetpack) is provided in the attached file: tritonserver2.7.0-jetpack4.5.tgz. This release supports the TensorFlow 2.4.0, TensorFlow 1.15.5, TensorRT 7.1, and Custom backends as well as ensembles. 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 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 \
        rapidjson-dev \
        patchelf \
        zlib1g-dev

To run the clients the following dependencies must be installed.

apt-get install -y --no-install-recommends \
        curl \
        libopencv-dev=3.2.0+dfsg-4ubuntu0.1 \
        libopencv-core-dev=3.2.0+dfsg-4ubuntu0.1 \
        pkg-config \
        python3 \
        python3-pip \
        python3-dev

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

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-2.7.0-py3-none-linux_aarch64.whl[all]

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

Assets 5

18 Dec 23:54

dzier

v2.6.0

4af943f

Release 2.6.0 corresponding to NGC container 20.12

Triton Inference Server

What's New In 2.6.0

An alpha release Triton for Windows is included in this release. See below for more details.
Due to interactions with Ubuntu 20.04, the ONNX Runtime's OpenVINO execution provider is disabled in this release. OpenVINO support will be re-enabled in a subsequent release.
The Triton *-py3-clientsdk container has been renamed to *-py3-sdk and now contains the Model Analyzer as well as the client libraries and examples.
The PyTorch backend has been moved to a separate repository: https://github.com/triton-inference-server/pytorch_backend. As a result, it is now easy to add or remove it from Triton without requiring a rebuild: https://github.com/triton-inference-server/server/blob/master/docs/compose.md.
Initial release of the Model Analyzer tool in the Triton SDK container and the PIP package, nvidia-triton-model-analyzer, in the NVIDIA Py Index.
Refer to the 20.12 column of the Frameworks Support Matrix
for container image versions that the 20.12 inference server container is based on.
Ubuntu 20.04 with September 2020 updates.

Known Issues

TensorRT reformat-free I/O is not supported.
Some versions of Google Kubernetes Engine (GKE) contain a regression in the handling of LD_LIBRARY_PATH that prevents the inference server container from running correctly (see issue 141255952). Use a GKE 1.13 or earlier version or a GKE 1.14.6 or later version to avoid this issue.

Client Libraries and Examples

Ubuntu 20.04 builds of the client libraries and examples are included in this release in the attached v2.6.0_ubuntu2004.clients.tar.gz file. See Getting the Client Libraries for more information on the client libraries and examples. The client SDK is also available as a NGC Container.

Windows Support

An alpha release of Triton for Windows is provided in the attached file: tritonserver2.6.0-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:

Only TensorRT models are supported. The TensorRT version is 7.2.2.
Only the GRPC endpoint is supported, HTTP/REST is not supported.
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 455 or later.
CUDA 11.1.1
cuDNN 8.0.5
TensorRT 7.2.2

Jetson Jetpack Support

A release of Triton for JetPack 4.4 (https://developer.nvidia.com/embedded/jetpack) is provided in the attached file: tritonserver2.6.0-jetpack4.4.tgz. This release supports the TensorFlow 2.3.1, TensorFlow 1.15.4, TensorRT 7.1, and Custom backends as well as ensembles. 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 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 \
        rapidjson-dev \
        patchelf \
        zlib1g-dev

To run the clients the following dependencies must be installed.

apt-get install -y --no-install-recommends \
        curl \
        libopencv-dev=3.2.0+dfsg-4ubuntu0.1 \
        libopencv-core-dev=3.2.0+dfsg-4ubuntu0.1 \
        pkg-config \
        python3 \
        python3-pip \
        python3-dev

python3 -m pip install --upgrade wheel setuptools
python3 -m pip install --upgrade grpcio-tools numpy pillow

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-2.6.0-py3-none-linux_aarch64.whl[all]

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

Assets 5

Releases: triton-inference-server/server

Release 2.15.0 corresponding to NGC container 21.10

Triton Inference Server

What's New In 2.15.0

Known Issues

Client Libraries and Examples

Windows Support

Jetson Jetpack Support

Installation and Usage

Release 2.14.0 corresponding to NGC container 21.09

Triton Inference Server

What's New In 2.14.0

Known Issues

Client Libraries and Examples

Windows Support

Jetson Jetpack Support

Installation and Usage

Release 2.13.0 corresponding to NGC container 21.08

Triton Inference Server

What's New In 2.13.0

Known Issues

Client Libraries and Examples

Windows Support

Jetson Jetpack Support

Installation and Usage

Release 2.12.0 corresponding to NGC container 21.07

Triton Inference Server

What's New In 2.12.0

Known Issues

Client Libraries and Examples

Windows Support

Jetson Jetpack Support

Installation and Usage

Release 2.11.0 corresponding to NGC container 21.06

Triton Inference Server

What's New In 2.11.0

Known Issues

Client Libraries and Examples

Windows Support

Jetson Jetpack Support

Installation and Usage

Release 2.10.0 corresponding to NGC container 21.05

Triton Inference Server

What's New In 2.10.0

Known Issues

Client Libraries and Examples

Windows Support

Jetson Jetpack Support

Installation and Usage

Release 2.9.0 corresponding to NGC container 21.04

Triton Inference Server

What's New In 2.9.0

Known Issues

Client Libraries and Examples

Windows Support

Jetson Jetpack Support

Installation and Usage

Release 2.8.0 corresponding to NGC container 21.03

Triton Inference Server

What's New In 2.8.0

Known Issues

Client Libraries and Examples

Windows Support

Jetson Jetpack Support

Installation and Usage

Release 2.7.0 corresponding to NGC container 21.02

Triton Inference Server

What's New In 2.7.0

Known Issues

Client Libraries and Examples

Windows Support

Jetson Jetpack Support

Installation and Usage

Release 2.6.0 corresponding to NGC container 20.12

Triton Inference Server

What's New In 2.6.0

Known Issues

Client Libraries and Examples

Windows Support

Jetson Jetpack Support