The inference server client libraries make it easy to communicate with the Triton Inference Server from your C++ or Python application. Using these libraries you can send either HTTP or GRPC requests to the server to check status or health and to make inference requests. These libraries also support using system and CUDA shared memory for passing inputs to and receiving outputs from the inference server. Client Examples describes examples that show the use of both the C++ and Python libraries.
You can also communicate with the inference server by using the protoc compiler to generate the GRPC client stub in a large number of programming languages. The grpc_image_client example in Client Examples illustrates how to use the GRPC client stub.
This section shows how to get the client libraries by either building or downloading, and also describes how to build your own client using these libraries.
Getting the Client Libraries¶
The provided Dockerfile.client and CMake support can be used to build the client libraries. As an alternative to building, it is also possible to download the pre-build client libraries from GitHub or a pre-built Docker image containing the client libraries from NVIDIA GPU Cloud (NGC).
Build Using Dockerfile¶
To build the libraries using Docker, first change directory to the root of the repo and checkout the release version of the branch that you want to build (or the master branch if you want to build the under-development version). The branch you use for the client build should match the version of the inference server you are using:
$ git checkout r20.03
Then, issue the following command to build the C++ client library and a Python wheel file for the Python client library:
$ docker build -t tritonserver_client -f Dockerfile.client .
You can optionally add --build-arg “BASE_IMAGE=<base_image>” to set the base image that you want the client library built for. Must be a Ubuntu CUDA devel image to be able to build CUDA shared memory support. If CUDA shared memory support is not required, you can use an Ubuntu 16.04 or 18.04 as the base image.
After the build completes the tritonserver_client docker image will contain the built client libraries in /workspace/install/lib, the corresponding headers in /workspace/install/include, and the Python wheel file in /workspace/install/python. The image will also contain the built client examples that you can learn more about in Client Examples.
Build Using CMake¶
The client library build is performed using CMake. The build dependencies and requirements are shown in Dockerfile.client. To build without Docker you must first install those dependencies. This section describes the client build for Ubuntu 16.04, Ubuntu 18.04, and Windows 10 systems. The CMake build can also be targeted for other OSes and platforms. We welcome any updates that expand the build functionality and allow the clients to be built on additional platforms.
To build the libraries using CMake, first change directory to the root of the repo and checkout the release version of the branch that you want to build (or the master branch if you want to build the under-development version):
$ git checkout r20.03
Ubuntu 16.04 / Ubuntu 18.04¶
For Ubuntu, the dependencies and how to install them can be found in Dockerfile.client. Also note that the dependency name may be different depending on the version of the system.
To build on Ubuntu, change to the build/ directory and run the following to configure and build:
$ cd build
$ cmake -DCMAKE_BUILD_TYPE=Release
$ make -j8 trtis-clients
When the build completes the libraries can be found in trtis-clients/install/lib, the corresponding headers in trtis-clients/install/include, and the Python wheel file in trtis-clients/install/python. The trtis-clients/install directory will also contain the built client examples that you can learn more about in Client Examples.
For Windows, the dependencies can be installed using pip and vcpkg which is a C++ library management tool on Windows. The following shows how to install the dependencies using them, and you can also install the dependencies in other ways that you prefer:
> .\vcpkg.exe install openssl:x64-windows zlib:x64-windows
> .\pip.exe install grpcio-tools wheel
The vcpkg step above installs openssl and zlib, “:x64-windows” specifies the target and it is optional. The path to the libraries should be added to environment variable “PATH”, by default it is \path\to\vcpkg\installed\<target>\bin. Update the pip to get the proper wheel from PyPi. Users may need to invoke pip.exe from a command line ran as an administrator.
To build the client for Windows, as there is no default build system available, you will need to specify the generator for CMake to match the build system you are using. For instance, if you are using Microsoft Visual Studio, you should do the following:
> cd build
> cmake -G"Visual Studio 16 2019" -DCMAKE_BUILD_TYPE=Release
> MSBuild.exe trtis-clients.vcxproj -p:Configuration=Release
When the build completes the libraries can be found in trtis-clients\install\lib, the corresponding headers in trtis-clients\install\include, and the Python wheel file in trtis-clients\install\python. The trtis-clients\install directory will also contain the built client Python examples that you can learn more about in Client Examples. At this time the Windows build does not include the C++ examples.
The MSBuild.exe may need to be invoked twice for a successfull build.
Download From GitHub¶
An alternative to building the client library is to download the pre-built client libraries from the GitHub release page corresponding to the release you are interested in. The client libraries are found in the “Assets” section of the release page in a tar file named after the version of the release and the OS, for example, v1.2.0_ubuntu1604.clients.tar.gz.
The pre-built libraries can be used on the corresponding host system (for example Ubuntu-16.04 or Ubuntu-18.04) or you can install them into the Triton Inference Server container to have both the clients and server in the same container:
$ mkdir clients
$ cd clients
$ wget https://github.com/NVIDIA/triton-inference-server/releases/download/<tarfile_path>
$ tar xzf <tarfile_name>
After installing the libraries can be found in lib/, the corresponding headers in include/, and the Python wheel file in python/. The bin/ and python/ directories contain the built examples that you can learn more about in Client Examples.
Download Docker Image From NGC¶
A Docker image containing the client libraries and examples is available from NVIDIA GPU Cloud (NGC). Before attempting to pull the container ensure you have access and are logged into NGC. For step-by-step instructions, see the NGC Getting Started Guide.
Use docker pull to get the client libraries and examples container from NGC:
$ docker pull nvcr.io/nvidia/tritonserver:<xx.yy>-py3-clientsdk
Where <xx.yy> is the version that you want to pull.
Within the container the client libraries are in /workspace/install/lib, the corresponding headers in /workspace/install/include, and the Python wheel file in /workspace/install/python. The image will also contain the built client examples that you can learn more about in Client Examples.
Building Your Own Client¶
No matter how you get the client libraries (Dockerfile, CMake or download), using them to build your own client application is the same. The install directory contains all the libraries and includes needed for your client.
For Python you just need to install the wheel from from the python/ directory. The wheel contains everything you need to communicate with the inference server from you Python application, as shown in Client Examples.
For C++ the lib/ directory contains both shared and static libraries and then include/ directory contains the corresponding headers. The src/ directory contains an example application and CMake file to show how you can build your C++ application to use the libraries and includes. To build the example you must first install dependencies appropriate for your platform. For example, for Ubuntu 18.04:
$ apt-get update
$ apt-get install build-essential cmake git zlib1g-dev libssl-dev
Then you can build the example application:
$ cd build
$ cmake -DTRTIS_CLIENT_CMAKE_DIR:PATH=`pwd`/../lib/cmake/TRTIS .
$ make -j8 trtis-clients
The example CMake file that illustrates how to build is in build/trtis-clients/CMakeLists.txt. The build produces both a statically and dynamically linked version of the example application into build/trtis-clients/install/bin.
The C++ client API exposes a class-based interface for querying server and model status and for performing inference. The commented interface is available at src/clients/c++/library/request.h.in and in the API Reference.
The Python client API provides similar capabilities as the C++ API. The commented interface is available at src/clients/python/api_v1/library/__init__.py and in the API Reference.
A simple C++ example application at src/clients/c++/examples/simple_client.cc.in and a Python version at src/clients/python/api_v1/examples/simple_client.py demonstrate basic client API usage.
To run the C++ version of the simple example, first build or download it as described in Getting the Client Examples and then:
0 + 1 = 1
0 - 1 = -1
1 + 1 = 2
1 - 1 = 0
2 + 1 = 3
2 - 1 = 1
14 - 1 = 13
15 + 1 = 16
15 - 1 = 14
To run the Python version of the simple example, first build or download it as described in Getting the Client Examples and install the tensorrtserver whl, then:
$ python simple_client.py
Some frameworks support tensors where each element in the tensor is a string (see Datatypes for information on supported datatypes). For the most part, the Client API is identical for string and non-string tensors. One exception is that in the C++ API a string input tensor must be initialized with SetFromString() instead of SetRaw().
String tensors are demonstrated in the C++ example application at src/clients/c++/examples/simple_string_client.cc and a Python version at src/clients/python/api_v1/examples/simple_string_client.py.
Client API for Stateful Models¶
When performing inference using a stateful model, a client must identify which inference requests belong to the same sequence and also when a sequence starts and ends.
Each sequence is identified with a correlation ID that is provided when the inference context is created (in either the Python of C++ APIs). It is up to the clients to create a unique correlation ID. For each sequence the first inference request should be marked as the start of the sequence and the last inference requests should be marked as the end of the sequence. Start and end are marked using the flags provided with the RunOptions in the C++ API and the run() and async_run() methods in the Python API.
The use of correlation ID and start and end flags are demonstrated in the C++ example application at src/clients/c++/examples/simple_sequence_client.cc and a Python version at src/clients/python/api_v1/examples/simple_sequence_client.py.
TensorRT models allow shape tensors. The inference server client libraries support these tensors using the existing APIs. A shape tensor should provide values for only a single batch-1, even for a batch request. This single shape tensor is used for the entire batch but the batch size should not be included as one of the shape values in the tensor.
See section–model-configuration for correctly specifying model configuration to use shape tensors.