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 2.2.0 and is available on branch r20.08.
Triton V2: Starting with the 20.06 release, Triton moves to version 2. The master branch currently tracks V2 development and is likely to be more unstable than usual due to the significant changes during the transition from V1 to V2. A legacy V1 version of Triton will be released from the master-v1 branch. The V1 version of Triton is deprecated and no releases beyond 20.07 are planned.
Triton Inference Server provides a cloud inferencing solution optimized for both CPUs and GPUs. Triton provides an inference service via an HTTP/REST or GRPC endpoint, allowing remote clients to request inferencing for any model being managed by the server. For edge deployments, Triton is also available as a shared library with a C API that allows the full functionality of Triton to be included directly in an application. Triton provides the following features:
- Multiple framework support. Triton can manage any number and mix of models (limited by system disk and memory resources). Supports TensorRT, TensorFlow GraphDef, TensorFlow SavedModel, ONNX, PyTorch, and Caffe2 NetDef model formats. Both TensorFlow 1.x and TensorFlow 2.x are supported. Also supports TensorFlow-TensorRT and ONNX-TensorRT integrated models. Variable-size input and output tensors are allowed if supported by the framework. See Capabilities for information for each framework.
- Concurrent model execution support. Multiple models (or multiple instances of the same model) can run simultaneously on the same GPU.
- Batching support. For models that support batching, Triton can accept requests for a batch of inputs and respond with the corresponding batch of outputs. Triton also supports 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.
- Custom backend support. Triton allows individual models to be implemented with custom backends instead of by a deep-learning framework. With a custom backend a model can implement any logic desired, while still benefiting from the CPU and GPU support, concurrent execution, dynamic batching and other features provided by Triton.
- Ensemble support. An ensemble 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.
- Multi-GPU support. Triton can distribute inferencing across all system GPUs.
- Triton provides multiple modes for model management. These model management modes allow for both implicit and explicit loading and unloading of models without requiring a server restart.
- Model repositories may reside on a locally accessible file system (e.g. NFS), in Google Cloud Storage or in Amazon S3.
- HTTP/REST and GRPC inference protocols based on the community developed KFServing protocol.
- Readiness and liveness health endpoints suitable for any orchestration or deployment framework, such as Kubernetes.
- Metrics indicating GPU utilization, server throughput, and server latency. The metrics are provided in Prometheus data format.
- C library inferface allows the full functionality of Triton to be included directly in an application.
The current release of the Triton Inference Server is 2.2.0 and corresponds to the 20.08 release of the tensorrtserver container on NVIDIA GPU Cloud (NGC). The branch for this release is r20.08.
Version 2 of Triton is beta quality, so you should expect some changes to the server and client protocols and APIs. Version 2 of Triton does not generally maintain backwards compatibility with version 1. Specifically, you should take the following items into account when transitioning from version 1 to version 2:
- The Triton executables and libraries are in /opt/tritonserver. The Triton executable is /opt/tritonserver/bin/tritonserver.
- Some tritonserver command-line arguments are removed, changed or
have different default behavior in version 2.
- --api-version, --http-health-port, --grpc-infer-thread-count, --grpc-stream-infer-thread-count,--allow-poll-model-repository, --allow-model-control and --tf-add-vgpu are removed.
- The default for --model-control-mode is changed to none.
- --tf-allow-soft-placement and --tf-gpu-memory-fraction are renamed
- to --backend-config="tensorflow,allow-soft-placement=<true,false>" and --backend-config="tensorflow,gpu-memory-fraction=<float>".
- The HTTP/REST and GRPC protocols, while conceptually similar to version 1, are completely changed in version 2. See the inference protocols section of the documentation for more information.
- Python and C++ client libraries are re-implemented to match the new HTTP/REST and GRPC protocols. The Python client no longer depends on a C++ shared library and so should be usable on any platform that supports Python. See the client libraries section of the documentaion for more information.
- The version 2 cmake build requires these changes:
- The cmake flag names have changed from having a TRTIS prefix to having a TRITON prefix. For example, TRITON_ENABLE_TENSORRT.
- The build targets are server, client and custom-backend to build the server, client libraries and examples, and custom backend SDK, respectively.
- In the Docker containers the environment variables indicating the Triton version have changed to have a TRITON prefix, for example, TRITON_SERVER_VERSION.
The User Guide, Developer Guide, and API Reference documentation for the current release provide guidance on installing, building, and running Triton Inference Server.
You can also view the documentation for the master branch and for earlier releases.
NVIDIA publishes a number of deep learning examples that use Triton.
An FAQ provides answers for frequently asked questions.
READMEs for deployment examples can be found in subdirectories of deploy/, for example, deploy/single_server/README.rst.
The Release Notes and Support Matrix indicate the required versions of the NVIDIA Driver and CUDA, and also describe which GPUs are supported by Triton.
- Maximizing Deep Learning Inference Performance with NVIDIA Model Analyzer.
- High-Performance Inferencing at Scale Using the TensorRT Inference Server.
- Accelerate and Autoscale Deep Learning Inference on GPUs with KFServing.
- Deep into Triton Inference Server: BERT Practical Deployment on NVIDIA GPU.
- Maximizing Utilization for Data Center Inference with TensorRT Inference Server.
- NVIDIA TensorRT Inference Server Boosts Deep Learning Inference.
- 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 the Contributing document.
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:
- minimal – use as little code as possible that still produces the same problem
- 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
- 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.