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Triton Python, C++ and Java client libraries, and GRPC-generated client examples for go, java and scala.
To simplify communication with Triton, the Triton project provides several client libraries and examples of how to use those libraries. Ask questions or report problems in the main Triton issues page.
The provided client libraries are:
C++ and Python APIs that make it easy to communicate with Triton from your C++ or Python application. Using these libraries you can send either HTTP/REST or GRPC requests to Triton to access all its capabilities: inferencing, status and health, statistics and metrics, model repository management, etc. These libraries also support using system and CUDA shared memory for passing inputs to and receiving outputs from Triton.
Java API (contributed by Alibaba Cloud PAI Team) that makes it easy to communicate with Triton from your Java application using HTTP/REST requests. For now, only a limited feature subset is supported.
The protoc compiler can generate a GRPC API in a large number of programming languages.
There are also many example applications that show how to use these libraries. Many of these examples use models from the example model repository.
C++ and Python versions of image_client, an example application that uses the C++ or Python client library to execute image classification models on Triton. See Image Classification Example.
Several simple C++ examples show how to use the C++ library to communicate with Triton to perform inferencing and other task. The C++ examples demonstrating the HTTP/REST client are named with a simple_http_ prefix and the examples demonstrating the GRPC client are named with a simple_grpc_ prefix. See Simple Example Applications.
Several simple Python examples show how to use the Python library to communicate with Triton to perform inferencing and other task. The Python examples demonstrating the HTTP/REST client are named with a simple_http_ prefix and the examples demonstrating the GRPC client are named with a simple_grpc_ prefix. See Simple Example Applications.
Several simple Java examples show how to use the Java API to communicate with Triton to perform inferencing and other task.
A couple of Python examples that communicate with Triton using a Python GRPC API generated by the protoc compiler. grpc_client.py is a simple example that shows simple API usage. grpc_image_client.py is functionally equivalent to image_client but that uses a generated GRPC client stub to communicate with Triton.
The easiest way to get the Python client library is to use pip to install the tritonclient module. You can also download the C++, Python and Java client libraries from Triton GitHub release, or download a pre-built Docker image containing the client libraries from NVIDIA GPU Cloud (NGC).
It is also possible to build the client libraries with cmake.
The GRPC and HTTP client libraries are available as a Python package that can be installed using a recent version of pip.
$ pip install tritonclient[all]
Using all installs both the HTTP/REST and GRPC client libraries. There are two optional packages available, grpc and http that can be used to install support specifically for the protocol. For example, to install only the HTTP/REST client library use,
$ pip install tritonclient[http]
There is another optional package namely cuda, that must be installed in order to use cuda_shared_memory utilities. all specification will install the cuda package by default but in other cases cuda needs to be explicitly specified for installing client with cuda_shared_memory support.
$ pip install tritonclient[http, cuda]
The components of the install packages are:
service_pb2, service_pb2_grpc, model_config_pb2 ]shared_memory and cuda_shared_memory]The client libraries can be downloaded from the Triton 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, v2.3.0_ubuntu2004.clients.tar.gz.
The pre-built libraries can be used on the corresponding host system or you can install them into the Triton container to have both the clients and server in the same container.
$ mkdir clients $ cd clients $ wget https://github.com/triton-inference-server/server/releases/download/<tarfile_path> $ tar xzf <tarfile_name>
After installing, the libraries can be found in lib/, the headers in include/, the Python wheel files in python/, and the jar files in java/. The bin/ and python/ directories contain the built examples that you can learn more about below.
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 to 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-sdk
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 files in /workspace/install/python. The image will also contain the built client examples.
Important Note: When running either the server or the client using
Docker containers and using the
CUDA shared memory feature
you need to add --pid host flag when launching the containers. The reason is
that CUDA IPC APIs require the PID of the source and destination of the exported
pointer to be different. Otherwise, Docker enables PID namespace which may
result in equality between the source and destination PIDs. The error will be
always observed when both of the containers are started in the non-interactive
mode.
The client library build is performed using CMake. To build the client libraries and examples with all features, first change directory to the root of this repo and checkout the release version of the branch that you want to build (or the main branch if you want to build the under-development version).
$ git checkout main $ mkdir -p build && cd build
If building the Java client you must first install Maven and a JDK
appropriate for your OS. For example, for Ubuntu you should install
the default-jdk package:
$ apt-get install default-jdk maven
Use cmake to configure the build. You should adjust the flags depending on the components of Triton Client you are working and would like to build.
cmake \
-DCMAKE_BUILD_TYPE=Release \
-DCMAKE_INSTALL_PREFIX=$(pwd)/install \
-DTRITON_ENABLE_CC_HTTP=ON \
-DTRITON_ENABLE_CC_GRPC=ON \
-DTRITON_ENABLE_PYTHON_HTTP=ON \
-DTRITON_ENABLE_PYTHON_GRPC=ON \
-DTRITON_ENABLE_JAVA_HTTP=ON \
-DTRITON_ENABLE_EXAMPLES=ON \
-DTRITON_ENABLE_TESTS=ON \
-DTRITON_ENABLE_GPU=ON \
-DTRITON_ENABLE_ZLIB=ON \
..
If you are building on a release branch (or on a development branch that is based off of a release branch), then you must also use additional cmake arguments to point to that release branch for repos that the client build depends on. For example, if you are building the r21.10 client branch then you need to use the following additional cmake flags:
-DTRITON_COMMON_REPO_TAG=r21.10
-DTRITON_THIRD_PARTY_REPO_TAG=r21.10
-DTRITON_CORE_REPO_TAG=r21.10
Then use make to build the clients and examples.
$ make -j$(nproc)/ cc-clients python-clients java-clients
When the build completes the libraries and examples can be found in the install directory.
The C++ client API exposes a class-based interface. The commented interface is available in grpc_client.h, http_client.h, common.h.
The Python client API provides similar capabilities as the C++ API. The commented interface is available in grpc and http.
The Java client API provides similar capabilities as the Python API with similar classes and methods. For more information please refer to the Java client directory.
The client library allows communication across a secured channel using HTTPS protocol. Just setting these SSL options do not ensure the secure communication. Triton server should be running behind https:// proxy such as nginx. The client can then establish a secure channel to the proxy. The qa/L0_https in the server repository demonstrates how this can be achieved.
For C++ client, see HttpSslOptions struct that encapsulates these options in http_client.h.
For Python client, look for the following options in http/__init__.py:
The C++ and Python examples demonstrates how to use SSL/TLS settings on client side.
The client library enables on-wire compression for HTTP transactions.
For C++ client, see request_compression_algorithm and response_compression_algorithm parameters in the Infer and AsyncInfer functions in http_client.h. By default, the parameter is set as CompressionType::NONE.
Similarly, for Python client, see request_compression_algorithm and response_compression_algorithm parameters in infer and async_infer functions in http/__init__.py.
The C++ and Python examples demonstrates how to use compression options.
In an effort to allow quick, on-demand metric retrieval for external load balancers such as the Kubernetes Inference Gateway API, Triton can include live KV-cache utilization and capacity metrics in the HTTP response header when processing inference requests. The motivation behind this method was to simplify the pipeline of metric scraping by not requiring separate service to hit the metrics endpoint, instead simply including a request header asking for metrics of a certain format in the response.
To use ORCA header metrics, Triton must be using the TensorRT-LLM backend that exposes KV-cache metrics, and the HTTP inference request must include a header named endpoint-load-metrics-format with a value equal to one of the valid formats:
text
endpoint-load-metrics-format: textendpoint-load-metrics: TEXT cpu_utilization=0.3, mem_utilization=0.8, rps_fractional=10.0, eps=1, named_metrics.custom_metric_util=0.4json
endpoint-load-metrics-format: jsonendpoint-load-metrics: JSON {“cpu_utilization”: 0.3, “mem_utilization”: 0.8, “rps_fractional”: 10.0, “eps”: 1, “named_metrics”: {“custom-metric-util”: 0.4}}This feature is currently in beta and may be subject to change.
Advanced users may call the Python client via async and await syntax. The
infer example
demonstrates how to infer with AsyncIO.
If using SSL/TLS with AsyncIO, look for the ssl and ssl_context options in
http/aio/__init__.py
This feature is currently in beta and may be subject to change.
The Triton Client Plugin API lets you register custom plugins to add or modify request headers. This is useful if you have gateway in front of Triton Server that requires extra headers for each request, such as HTTP Authorization. By registering the plugin, your gateway will work with Python clients without additional configuration. Note that Triton Server does not implement authentication or authorization mechanisms and similarly, Triton Server is not the direct consumer of the additional headers.
The plugin must implement the __call__ method. The signature
of the __call__ method should look like below:
class MyPlugin: def __call__(self, request): """This method will be called for every HTTP request. Currently, the only field that can be accessed by the request object is the `request.headers` field. This field must be updated in-place. """ request.headers['my-header-key'] = 'my-header-value'
After the plugin implementation is complete, you can register the
plugin by calling register on the InferenceServerClient object.
from tritonclient.http import InferenceServerClient client = InferenceServerClient(...) # Register the plugin my_plugin = MyPlugin() client.register_plugin(my_plugin) # All the method calls will update the headers according to the plugin # implementation. client.infer(...)
To unregister the plugin, you can call the client.unregister_plugin()
function.
You can register the BasicAuth plugin that implements
Basic Authentication.
from tritonclient.grpc.auth import BasicAuth from tritonclient.grpc import InferenceServerClient basic_auth = BasicAuth('username', 'password') client = InferenceServerClient('...') client.register_plugin(basic_auth)
The example above shows how to register the plugin for
gRPC client. The BasicAuth plugin can be registered
similarly for HTTP and
AsyncIO
clients.
The client library allows communication across a secured channel using gRPC protocol.
For C++ client, see SslOptions struct that encapsulates these options in grpc_client.h.
For Python client, look for the following options in grpc/__init__.py:
The C++ and Python examples demonstrates how to use SSL/TLS settings on client side. For information on the corresponding server-side parameters, refer to the server documentation
The client library also exposes options to use on-wire compression for gRPC transactions.
For C++ client, see compression_algorithm parameter in the Infer, AsyncInfer and StartStream functions in grpc_client.h. By default, the parameter is set as GRPC_COMPRESS_NONE.
Similarly, for Python client, see compression_algorithm parameter in infer, async_infer and start_stream functions in grpc/__init__.py.
The C++ and Python examples demonstrates how to configure compression for clients. For information on the corresponding server-side parameters, refer to the server documentation.
Triton exposes GRPC KeepAlive parameters with the default values for both client and server described here.
You can find a KeepAliveOptions struct/class that encapsulates these
parameters in both the C++ and
Python client libraries.
There is also a C++ and Python example demonstrating how to setup these parameters on the client-side. For information on the corresponding server-side parameters, refer to the server documentation
Advanced users may require specific client-side GRPC Channel Arguments that are not currently exposed by Triton through direct means. To support this, Triton allows users to pass custom channel arguments upon creating a GRPC client. When using this option, it is up to the user to pass a valid combination of arguments for their use case; Triton cannot feasibly test every possible combination of channel arguments.
There is a C++ and Python example demonstrating how to construct and pass these custom arguments upon creating a GRPC client.
You can find a comprehensive list of possible GRPC Channel Arguments here.
This feature is currently in beta and may be subject to change.
Advanced users may call the Python client via async and await syntax. The
infer and
stream
examples demonstrate how to infer with AsyncIO.
Starting from r23.10, triton python gRPC client can issue cancellation
to inflight requests. This can be done by calling cancel() on the
CallContext object returned by async_infer() API.
ctx = client.async_infer(...) ctx.cancel()
For streaming requests, cancel_requests=True can be sent to
stop_stream() API to terminate all the inflight requests
sent via this stream.
client.start_stream() for _ in range(10): client.async_stream_infer(...) # Cancels all pending requests on stream closure rather than blocking until requests complete client.stop_stream(cancel_requests=True)
See more details about these APIs in grpc/_client.py.
For gRPC AsyncIO requests, an AsyncIO task wrapping an infer() coroutine can
be safely cancelled.
infer_task = asyncio.create_task(aio_client.infer(...)) infer_task.cancel()
For gRPC AsyncIO streaming requests, cancel() can be called on the
asynchronous iterator returned by stream_infer() API.
responses_iterator = aio_client.stream_infer(...) responses_iterator.cancel()
See more details about these APIs in grpc/aio/_init_.py.
Starting from r26.05, triton python C++ client can issue cancellation to
inflight requests. This can be done by passing an optional
CallContext** ctx_out to AsyncInfer(); the returned CallContext exposes
Cancel().
tc::CallContext* ctx = nullptr; client->AsyncInfer( callback, options, inputs, outputs, /*headers=*/{}, GRPC_COMPRESS_NONE, &ctx); ctx->Cancel(); delete ctx;
For batch fan-out via AsyncInferMulti(), pass an optional
std::vector<CallContext*>* ctxs_out; on return it is sized to inputs.size()
with one CallContext per leaf request (or nullptr for any request that
failed locally before the RPC started). Cancellation is per-request — siblings
continue to completion, and the multi callback still fires exactly once.
std::vector<tc::CallContext*> ctxs; client->AsyncInferMulti( multi_callback, options, inputs, outputs, /*headers=*/{}, GRPC_COMPRESS_NONE, &ctxs); for (auto* ctx : ctxs) { if (ctx != nullptr) { ctx->Cancel(); } } for (auto* ctx : ctxs) { delete ctx; }
For streaming requests, pass true to StopStream(). Cancelling after the
RPC has completed is a safe no-op. The caller owns the returned CallContext
and must delete it. Call sites that omit ctx_out keep the previous,
non-cancellable behavior.
client->StartStream(callback, ...); for (...) { client->AsyncStreamInfer(options, inputs, outputs); } client->StopStream(/*cancel_requests=*/true);
See more details about these APIs in grpc_client.h.
See request_cancellation in the server user-guide to learn about how this is handled on the server side. If writing your own gRPC clients in the language of choice consult gRPC guide on cancellation.
Starting from release 24.08, Triton server introduces support for gRPC error
codes in streaming mode for all clients enhancing error reporting capabilities. When
this feature is enabled, the Triton server will return standard gRPC error codes
and subsequently close the stream after delivering the error. This feature is
optional can be enabled by adding header with triton_grpc_error key and true as
value. See grpc error
codes in the server to learn about how this is handled on the server side. See gRPC
guide on status-codes for more details.
Below is a Python snippet to enable the feature. Without this header Triton server
will continue streaming in default mode returning error message and status inside
InferenceServerException object within the callback provided.
triton_client = grpcclient.InferenceServerClient(triton_server_url) # New added header key value metadata = {"triton_grpc_error": "true"} triton_client.start_stream( callback=partial(callback, user_data), headers=metadata )
During shutdown, Triton will reject new incoming requests and clients may receive one of the following status codes before the endpoint closes:
StatusCode.CANCELLED
StatusCode.UNAVAILABLE with the message "GRPC server is shutting down and has stopped accepting new requests"
This section describes several of the simple example applications and the features that they illustrate.
Some frameworks support tensors where each element in the tensor is variable-length binary data. Each element can hold a string or an arbitrary sequence of bytes. On the client this datatype is BYTES (see Datatypes for information on supported datatypes).
The Python client library uses numpy to represent input and output tensors. For BYTES tensors the dtype of the numpy array should be 'np.object_' as shown in the examples. For backwards compatibility with previous versions of the client library, 'np.bytes_' can also be used for BYTES tensors. However, using 'np.bytes_' is not recommended because using this dtype will cause numpy to remove all trailing zeros from each array element. As a result, binary sequences ending in zero(s) will not be represented correctly.
BYTES tensors are demonstrated in the C++ example applications simple_http_string_infer_client.cc and simple_grpc_string_infer_client.cc. String tensors are demonstrated in the Python example application simple_http_string_infer_client.py and simple_grpc_string_infer_client.py.
Using system shared memory to communicate tensors between the client library and Triton can significantly improve performance in some cases.
Using system shared memory is demonstrated in the C++ example applications simple_http_shm_client.cc and simple_grpc_shm_client.cc. Using system shared memory is demonstrated in the Python example application simple_http_shm_client.py and simple_grpc_shm_client.py.
Python does not have a standard way of allocating and accessing shared memory so as an example a simple system shared memory module is provided that can be used with the Python client library to create, set and destroy system shared memory.
Using CUDA shared memory to communicate tensors between the client library and Triton can significantly improve performance in some cases.
Using CUDA shared memory is demonstrated in the C++ example applications simple_http_cudashm_client.cc and simple_grpc_cudashm_client.cc. Using CUDA shared memory is demonstrated in the Python example application simple_http_cudashm_client.py and simple_grpc_cudashm_client.py.
Python does not have a standard way of allocating and accessing shared memory so as an example a simple CUDA shared memory module is provided that can be used with the Python client library to create, set and destroy CUDA shared memory. The module currently supports numpy arrays (example usage) and DLPack tensors (example usage).
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 sequence ID that is provided when an inference request is made. It is up to the clients to create a unique sequence 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.
The use of sequence ID and start and end flags are demonstrated in the C++ example applications simple_grpc_sequence_stream_infer_client.cc. The use of sequence ID and start and end flags are demonstrated in the Python example application simple_grpc_sequence_stream_infer_client.py.
The image classification example that uses the C++ client API is available at src/c++/examples/image_client.cc. The Python version of the image classification client is available at src/python/examples/image_client.py.
To use image_client (or image_client.py) you must first have a running Triton that is serving one or more image classification models. The image_client application requires that the model have a single image input and produce a single classification output. If you don't have a model repository with image classification models see QuickStart for instructions on how to create one.
Once Triton is running you can use the image_client application to send inference requests. You can specify a single image or a directory holding images. Here we send a request for the inception_graphdef model for an image from the qa/images.
$ image_client -m inception_graphdef -s INCEPTION qa/images/mug.jpg
Request 0, batch size 1
Image 'qa/images/mug.jpg':
0.754130 (505) = COFFEE MUGThe Python version of the application accepts the same command-line arguments.
$ python image_client.py -m inception_graphdef -s INCEPTION qa/images/mug.jpg
Request 0, batch size 1
Image 'qa/images/mug.jpg':
0.826384 (505) = COFFEE MUGThe image_client and image_client.py applications use the client libraries to talk to Triton. By default image_client instructs the client library to use HTTP/REST protocol, but you can use the GRPC protocol by providing the -i flag. You must also use the -u flag to point at the GRPC endpoint on Triton.
$ image_client -i grpc -u localhost:8001 -m inception_graphdef -s INCEPTION qa/images/mug.jpg
Request 0, batch size 1
Image 'qa/images/mug.jpg':
0.754130 (505) = COFFEE MUGBy default the client prints the most probable classification for the image. Use the -c flag to see more classifications.
$ image_client -m inception_graphdef -s INCEPTION -c 3 qa/images/mug.jpg
Request 0, batch size 1
Image 'qa/images/mug.jpg':
0.754130 (505) = COFFEE MUG
0.157077 (969) = CUP
0.002880 (968) = ESPRESSOThe -b flag allows you to send a batch of images for inferencing. The image_client application will form the batch from the image or images that you specified. If the batch is bigger than the number of images then image_client will just repeat the images to fill the batch.
$ image_client -m inception_graphdef -s INCEPTION -c 3 -b 2 qa/images/mug.jpg Request 0, batch size 2 Image 'qa/images/mug.jpg': 0.754130 (505) = COFFEE MUG 0.157077 (969) = CUP 0.002880 (968) = ESPRESSO Image 'qa/images/mug.jpg': 0.754130 (505) = COFFEE MUG 0.157077 (969) = CUP 0.002880 (968) = ESPRESSO
Provide a directory instead of a single image to perform inferencing on all images in the directory.
$ image_client -m inception_graphdef -s INCEPTION -c 3 -b 2 qa/images
Request 0, batch size 2
Image '/opt/tritonserver/qa/images/car.jpg':
0.819196 (818) = SPORTS CAR
0.033457 (437) = BEACH WAGON
0.031232 (480) = CAR WHEEL
Image '/opt/tritonserver/qa/images/mug.jpg':
0.754130 (505) = COFFEE MUG
0.157077 (969) = CUP
0.002880 (968) = ESPRESSO
Request 1, batch size 2
Image '/opt/tritonserver/qa/images/vulture.jpeg':
0.977632 (24) = VULTURE
0.000613 (9) = HEN
0.000560 (137) = EUROPEAN GALLINULE
Image '/opt/tritonserver/qa/images/car.jpg':
0.819196 (818) = SPORTS CAR
0.033457 (437) = BEACH WAGON
0.031232 (480) = CAR WHEEL
The grpc_image_client.py application behaves the same as the image_client except that instead of using the client library it uses the GRPC generated library to communicate with Triton.
In comparison to the image classification example above, this example uses an ensemble of an image-preprocessing model implemented as a DALI backend and a TensorFlow Inception model. The ensemble model allows you to send the raw image binaries in the request and receive classification results without preprocessing the images on the client.
To try this example you should follow the DALI ensemble example instructions.
Important Note: TensorFlow backend has been deprecated.