LATEST RELEASE: You are currently on the master branch which tracks under-development progress towards the next release. The latest release of the Triton Inference Server is 2.3.0 and is available on branch r20.09.
Triton Inference Server provides a cloud and edge inferencing solution optimized for both CPUs and GPUs. Triton supports an HTTP/REST and GRPC protocol that allows remote clients to request inferencing for any model being managed by the server. For edge deployments, Triton is available as a shared library with a C API that allows the full functionality of Triton to be included directly in an application.
The current release of the Triton Inference Server is 2.3.0 and corresponds to the 20.09 release of the tensorrtserver container on NVIDIA GPU Cloud (NGC). The branch for this release is r20.09.
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Multiple deep-learning frameworks. Triton can manage any number and mix of models (limited by system disk and memory resources). Triton supports TensorRT, TensorFlow GraphDef, TensorFlow SavedModel, ONNX, PyTorch, and Caffe2 NetDef model formats. Both TensorFlow 1.x and TensorFlow 2.x are supported. Triton also supports TensorFlow-TensorRT and ONNX-TensorRT integrated models.
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Concurrent model execution. Multiple models (or multiple instances of the same model) can run simultaneously on the same GPU or on multiple GPUs.
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Dynamic batching. For models that support batching, Triton implements multiple scheduling and batching algorithms that combine individual inference requests together to improve inference throughput. These scheduling and batching decisions are transparent to the client requesting inference.
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Extensible backends. In addition to deep-learning frameworks, Triton provides a backend API that allows Triton to be extended with any model execution logic implemented in Python or C++, while still benefiting from the CPU and GPU support, concurrent execution, dynamic batching and other features provided by Triton.
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Model pipelines. Triton ensembles represents a pipeline of one or more models and the connection of input and output tensors between those models. A single inference request to an ensemble will trigger the execution of the entire pipeline.
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HTTP/REST and GRPC inference protocols based on the community developed KFServing protocol.
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Metrics indicating GPU utilization, server throughput, and server latency. The metrics are provided in Prometheus data format.
Triton Architecture gives a high-level overview of the structure and capabilities of the inference server. There is also an FAQ. Additional documentation is divided into user and developer sections. The user documentation describes how to use Triton as an inference solution, including information on how to configure Triton, how to organize and configure your models, how to use the C++ and Python clients, etc. The developer documentation describes how to build and test Triton and also how Triton can be extended with new functionality.
The Triton Release Notes and Support Matrix indicate the required versions of the NVIDIA Driver and CUDA, and also describe supported GPUs.
The quickstart walks you through all the steps required to install and run Triton with an example image classification model and then use an example client application to perform inferencing using that model. The quickstart also demonstrates how Triton supports both GPU systems and CPU-only systems.
The first step in using Triton to serve your models is to place one or more models into a model repository. Optionally, depending on the type of the model and on what Triton capabilities you want to enable for the model, you may need to create a model configuration for the model. If your model has custom operations you will need to make sure they are loaded correctly by Triton.
After you have your model(s) available in Triton, you will want to send inference and other requests to Triton from your client application. The Python and C++ client libraries provide APIs to simplify this communication. You can also send HTTP/REST requests directly to Triton using the HTTP/REST JSON-based protocol or generate a GRPC client for many other languages.
Understanding and optimizing performance is an important part of deploying your models. The Triton project provides the Performance Analyzer and the Model Analyzer to help your optimization efforts. Specifically, you will want to optimize scheduling and batching and model instances appropriately for each model. You may also want to consider ensembling multiple models and pre/post-processing into a pipeline. In some cases you may find individual inference request trace data useful when optimizing. A Prometheus metrics endpoint allows you to visualize and monitor aggregate inference metrics.
NVIDIA publishes a number of deep learning examples that use Triton.
As part of you deployment strategy you may want to explicitly manage what models are available by loading and unloading models from a running Triton server. If you are using Kubernetes for deployment a simple example of how to deploy Triton using Kubernetes and Helm may be helpful.
The version 1 to version 2 migration information is helpful if you are moving to version 2 of Triton from previously using version 1.
Triton can be built using Docker or built without Docker. After building you should test Triton.
Starting with the r20.10 release, it is also possible to create a Docker image containing a customized Triton that contains only a subset of the backends.
The Triton project also provides client libraries for Python and C++ that make it easy to communicate with the server. There are also a large number of example clients that demonstrate how to use the libraries. You can also develop your own clients that directly communicate with Triton using HTTP/REST or GRPC protocols. There is also a C API that allows Triton to be linked directly into your application.
A Triton backend is the implementation that executes a model. A backend can interface with a deep learning framework, like PyTorch, TensorFlow, TensorRT or ONNX Runtime; or it can interface with a data processing framework like DALI; or it can be custom C/C++ or Python code for performing any operation. You can even extend Triton by writing your own backend.
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Maximizing Deep Learning Inference Performance with NVIDIA Model Analyzer.
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High-Performance Inferencing at Scale Using the TensorRT Inference Server.
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Accelerate and Autoscale Deep Learning Inference on GPUs with KFServing.
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Deep into Triton Inference Server: BERT Practical Deployment on NVIDIA GPU.
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Maximizing Utilization for Data Center Inference with TensorRT Inference Server.
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NVIDIA TensorRT Inference Server Boosts Deep Learning Inference.
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GPU-Accelerated Inference for Kubernetes with the NVIDIA TensorRT Inference Server and Kubeflow.
Contributions to Triton Inference Server are more than welcome. To contribute make a pull request and follow the guidelines outlined in CONTRIBUTING.md.
We appreciate any feedback, questions or bug reporting regarding this project. When help with code is needed, follow the process outlined in the Stack Overflow (https://stackoverflow.com/help/mcve) document. Ensure posted examples are:
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minimal – use as little code as possible that still produces the same problem
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complete – provide all parts needed to reproduce the problem. Check if you can strip external dependency and still show the problem. The less time we spend on reproducing problems the more time we have to fix it
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verifiable – test the code you're about to provide to make sure it reproduces the problem. Remove all other problems that are not related to your request/question.