25 Jun 20:38

dzier

30b08ba

Release 2.11.0 corresponding to NGC container 21.06

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 2.11.0

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]

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

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

20 Nov 23:24

dzier

v2.5.0

b431e28

Release 2.5.0 corresponding to NGC container 20.11

NVIDIA Triton Inference Server

The NVIDIA Triton Inference Server provides a cloud inferencing solution optimized for NVIDIA 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 2.5.0

ONNX Runtime backend updated to use ONNX Runtime 1.5.3.
The PyTorch backend is moved to a dedicated repo triton-inference-server/pytorch_backend.
The Caffe2 backend is removed. Caffe2 models are no longer supported.
Fix handling of failed model reloads. If a model reload fails, the currently loaded version of the model will remain loaded and its availability will be uninterrupted.
Releasing Triton ModelAnalyzer in the Triton SDK container and as a PIP package available in NVIDIA PyIndex.
Refer to the 20.11 column of the Frameworks Support Matrix
for container image versions that the 20.11 inference server container is based on.
Ubuntu 18.04 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 18.04 builds of the client libraries and examples are included in this release in the attached v2.5.0_ubuntu1804.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.

Jetson Jetpack Support

A release of Triton for JetPack 4.4 (https://developer.nvidia.com/embedded/jetpack) is provided in the attached file: 2.5.0-jetpack4.4-1795341.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 and S3 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-2.5.0-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 4

27 Oct 01:21

dzier

v2.4.0

bca98c2

Release 2.4.0 corresponding to NGC container 20.10

Triton Inference Server

What's New In 2.4.0

A new Python backend allows Python code to run as a model within Triton. See https://github.com/triton-inference-server/python_backend.
A new DALI backend allows running pre-processing and augmentation pipelines within Triton. See https://github.com/triton-inference-server/dali_backend.
The perf_client application is renamed to perf_analyzer, functionality remains the same.
A new Model Analyzer project is started with the goal of providing analysis and guidance on how to best optimize single or multiple models within Triton. The initial release analyzes GPU memory usage. See https://github.com/triton-inference-server/model_analyzer.
Triton documentation now resides on GitHub and is reachable from https://github.com/triton-inference-server/server/blob/master/README.md.
Build process for Triton has changed, see https://github.com/triton-inference-server/server/blob/master/docs/build.md.
Triton backends are moving to separate repositories. In this release the TensorFlow, ONNX Runtime, Python and DALI backends are moved, see https://github.com/triton-inference-server/backend#where-can-i-find-all-the-backends-that-are-available-for-triton.
Refer to the 20.10 column of the Frameworks Support Matrix
for container image versions that the 20.09 inference server container is based on.
Ubuntu 18.04 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 18.04 builds of the client libraries and examples are included in this release in the attached v2.4.0_ubuntu1804.clients.tar.gz file. See the documentation section 'Building the Client Libraries and Examples' for more information on using these files. The client SDK is also available as a NGC Container.

Jetson Jetpack Support

A release of Triton for the Developer Preview of JetPack 4.4 (https://developer.nvidia.com/embedded/jetpack) is provided in the attached file: v2.4.0-jetpack4.4-1718105.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 and S3 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-2.4.0-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 4

02 Oct 02:10

dzier

v2.3.0

3159e73

Release 2.3.0 corresponding to NGC container 20.09

NVIDIA Triton Inference Server

What's New In 2.3.0

Python Client library is now a pip package available from the NVIDIA pypi index. See Python client documentation for more information.
The custom backend API, custom.h and associated custom backend SDK are no longer provided as part of the Triton release. Existing custom backends will continue to work with Triton and older releases of the SDK can still be used to create "legacy" custom backends. However, all users are strongly encouraged to move to the new Triton backend API.
Fix a performance issue with the HTTP/REST protocol and the Python client library that caused reduced performance when outputs were not requested explicitly in an inference request.
Fix some bugs in reporting of statistics for ensemble models.
GRPC updated to version 1.25.0.

Known Issues

The KFServing HTTP/REST and GRPC protocols and corresponding V2 experimental Python and C++ clients are beta quality and are likely to change. Specifically:
- The data returned by the statistics API will be changing to include additional information.
- The data returned by the repository index API will be changing to include additional information.
The new C API specified in tritonserver.h is beta quality and is likely to change.
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 18.04 builds of the client libraries and examples are included in this release in the attached v2.3.0_ubuntu1804.clients.tar.gz file. See the documentation section 'Building the Client Libraries and Examples' for more information on using these files. The client SDK is also available as a NGC Container.

Jetson Jetpack Support

An experimental release of Triton for the Developer Preview of JetPack 4.4 is available as part of the 20.06 release. See 20.06 release for more information.

Assets 3

27 Aug 19:57

dzier

v2.2.0

4479257

Release 2.2.0 corresponding to NGC container 20.08

NVIDIA Triton Inference Server

What's New In 2.2.0

TensorFlow 2.x is now supported in addition to TensorFlow 1.x. See the Frameworks Support Matrix for the supported TensorFlow versions. The version of TensorFlow used can be selected when launching Triton with the --backend-config=tensorflow,version=<version> flag. Set <version> to 1 or 2 to select TensorFlow1 or TensorFlow2 respectively. By default TensorFlow 1 is used.
Add inference request timeout option to Python and C++ client libraries.
GRPC inference protocol updated to fix performance regression.
Explicit major/minor versioning added to TRITONSERVER and TRITONBACKED APIs.
New CMake option TRITON_CLIENT_SKIP_EXAMPLES to disable building the client examples.

Known Issues

The KFServing HTTP/REST and GRPC protocols and corresponding V2 experimental Python and C++ clients are beta quality and are likely to change. Specifically:
- The data returned by the statistics API will be changing to include additional information.
- The data returned by the repository index API will be changing to include additional information.
The new C API specified in tritonserver.h is beta quality and is likely to change.
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 18.04 builds of the client libraries and examples are included in this release in the attached v2.2.0_ubuntu1804.clients.tar.gz file. See the documentation section 'Building the Client Libraries and Examples' for more information on using these files. The client SDK is also available as a NGC Container.

Custom Backend SDK

Ubuntu 18.04 builds of the custom backend SDK are included in this release in the attached v2.2.0_ubuntu1804.custombackend.tar.gz file. See the documentation section 'Building a Custom Backend' for more information on using these files.

Jetson Jetpack Support

An experimental release of Triton for the Developer Preview of JetPack 4.4 is available as part of the 20.06 release. See 20.06 release for more information.

Assets 4

Releases: triton-inference-server/server

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

Installation and Usage

Release 2.5.0 corresponding to NGC container 20.11

NVIDIA Triton Inference Server

What's New In 2.5.0

Known Issues

Client Libraries and Examples

Jetson Jetpack Support

Installation and Usage

Release 2.4.0 corresponding to NGC container 20.10

Triton Inference Server

What's New In 2.4.0

Known Issues

Client Libraries and Examples

Jetson Jetpack Support

Installation and Usage

Release 2.3.0 corresponding to NGC container 20.09

NVIDIA Triton Inference Server

What's New In 2.3.0

Known Issues

Client Libraries and Examples

Jetson Jetpack Support

Release 2.2.0 corresponding to NGC container 20.08

NVIDIA Triton Inference Server

What's New In 2.2.0

Known Issues

Client Libraries and Examples

Custom Backend SDK

Jetson Jetpack Support