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]

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

Release 2.4.0 corresponding to NGC container 20.10

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 2.4.0

• 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]

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

Release 2.3.0 corresponding to NGC container 20.09

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 2.3.0

• 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.

Release 2.2.0 corresponding to NGC container 20.08

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 2.2.0

• 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.

Release 2.1.0 corresponding to NGC container 20.07

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 2.1.0

• Added TensorFlow optimization option that enables automatic FP16 optimization of the model.
• The PyTorch backend now includes support for TorchVision operations.

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 v2.1.0_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 v2.1.0_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.

Release 1.15.0 corresponding to NGC container 20.07

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 1.15.0

• Support for the legacy V1 HTTP/REST, GRPC and corresponding client libraries is released on GitHub branch r20.07-v1 and as NGC container 20.07-v1-py3.

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 v1.15.0_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 v1.15.0_ubuntu1804.custombackend.tar.gz file. See the documentation section 'Building a Custom Backend' for more information on using these files.

Release 2.0.0 corresponding to NGC container 20.06

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 2.0.0

• Updates for KFserving HTTP/REST and GRPC protocols and corresponding Python and C++ client libraries.
• Migration from Triton V1 to Triton V2 requires signficant changes, see the “Backwards Compatibility” and “Roadmap” sections of the GitHub README for more information.

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 v2.0.0_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 v2.0.0_ubuntu1804.custombackend.tar.gz file. See the documentation section 'Building a Custom Backend' for more information on using these files.

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: v2.0.0-jetpack4.4ga.tgz. This experimental release supports the TensorFlow (1.15.2), 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 \
        libgoogle-glog0v5 \
        libre2-dev \
        libssl-dev \
        libtool \
        libboost-dev \
        libcurl4-openssl-dev \
        rapidjson-dev \
        patchelf \
        zlib1g-dev

Additionally, 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/triton*.whl

Release 1.14.0 corresponding to NGC container 20.06

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 1.14.0

• Support for the legacy V1 HTTP/REST, GRPC and corresponding client libraries
  is released on GitHub branch r20.06-v1 and as NGC container
  20.06-v1-py3.

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 v1.14.0_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 v1.14.0_ubuntu1804.custombackend.tar.gz file. See the documentation section 'Building a Custom Backend' for more information on using these files.

Release 1.13.0 corresponding to NGC container 20.03.1

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 1.13.0

• Updates for KFserving HTTP/REST and GRPC protocols and corresponding Python and C++ client libraries. See Roadmap section of README for more information.
• Update GRPC version to 1.24.0.
• Several issues with S3 storage were resolved.
• Fix last_inferrence_timestamp value to correctly show the time when inference last occurred for each model.
• The Caffe2 backend is deprecated. Support for Caffe2 models will be removed in a future release.

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.
• When using the experimental V2 HTTP/REST C++ client, classification results are not supported for output tensors. This issue will be fixed in the next release.
• When using the experimental V2 perf_client_v2, for high concurrency values perf_client_v2 may not be able to achieve throughput as high as V1 perf_client. This will be fixed in the next release.
• 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 v1.13.0_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 v1.13.0_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 20.03 release. See 20.03 release for more information.

Release 1.12.0 corresponding to NGC container 20.03

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 1.12.0

• Add queuing policies for dynamic batching scheduler. These policies are specified in the model configuration and allow each model to set maximum queue size, time outs, and priority levels for inference requests.
• Support for large ONNX models where weights are stored in separate files.
• Allow ONNX Runtime optimization level to be configured via the model configuration optimization setting.
• Experimental Python client and server support for community standard GRPC inferencing API.
• Add --min-supported-compute-capability flag to allow Triton Server to use older, unsupported GPUs.
• Fix perf_client shared memory support. In some cases shared-memory option did not work correctly due to the input and output tensor names. This issue is now resolved.

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 v1.12.0_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 v1.12.0_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 (https://developer.nvidia.com/embedded/jetpack) is provided in the attached file: v1.12.0-jetpack4.4dp.tgz. This experimental release supports the TensorFlow (1.15.2), 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 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 \
        libgoogle-glog0v5 \
        libre2-dev \
        libssl-dev \
        libtool \
        libboost-dev \
        libcurl4-openssl-dev \
        zlib1g-dev

Additionally, 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/tensorrtserver-*.whl