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# Triton Inference Server
[![License](https://img.shields.io/badge/License-BSD3-lightgrey.svg)](https://opensource.org/licenses/BSD-3-Clause)

**NOTE: You are currently on the r22.12 branch which tracks stabilization
towards the next release. This branch is not usable during stabilization.**
----
Triton Inference Server is an open source inference serving software that
streamlines AI inferencing. Triton enables teams to deploy any AI model from
multiple deep learning and machine learning frameworks, including TensorRT,
TensorFlow, PyTorch, ONNX, OpenVINO, Python, RAPIDS FIL, and more. Triton
supports inference across cloud, data center,edge and embedded devices on NVIDIA
GPUs, x86 and ARM CPU, or AWS Inferentia. Triton delivers optimized performance
for many query types, including real time, batched, ensembles and audio/video
streaming.

Major features include:

- [Supports multiple deep learning
frameworks](https://github.com/triton-inference-server/backend#where-can-i-find-all-the-backends-that-are-available-for-triton)
- [Supports multiple machine learning
frameworks](https://github.com/triton-inference-server/fil_backend)
- [Concurrent model
execution](docs/user_guide/architecture.md#concurrent-model-execution)
- [Dynamic batching](docs/user_guide/model_configuration.md#dynamic-batcher)
- [Sequence batching](docs/user_guide/model_configuration.md#sequence-batcher) and
[implicit state management](docs/user_guide/architecture.md#implicit-state-management)
for stateful models
- Provides [Backend API](https://github.com/triton-inference-server/backend) that
allows adding custom backends and pre/post processing operations
- Model pipelines using
[Ensembling](docs/user_guide/architecture.md#ensemble-models) or [Business
Logic Scripting
(BLS)](https://github.com/triton-inference-server/python_backend#business-logic-scripting)
- [HTTP/REST and GRPC inference
protocols](docs/customization_guide/inference_protocols.md) based on the community
developed [KServe
protocol](https://github.com/kserve/kserve/tree/master/docs/predict-api/v2)
- A [C API](docs/customization_guide/inference_protocols.md#in-process-triton-server-api) and
[Java API](docs/customization_guide/inference_protocols.md#java-bindings-for-in-process-triton-server-api)
allow Triton to link directly into your application for edge and other in-process use cases
- [Metrics](docs/user_guide/metrics.md) indicating GPU utilization, server
throughput, server latency, and more

Join the [Triton and TensorRT community](https://www.nvidia.com/en-us/deep-learning-ai/triton-tensorrt-newsletter/) and
stay current on the latest product updates, bug fixes, content, best practices,
and more. Need enterprise support? NVIDIA global support is available for Triton
Inference Server with the
[NVIDIA AI Enterprise software suite](https://www.nvidia.com/en-us/data-center/products/ai-enterprise/).

## Serve a Model in 3 Easy Steps

```bash
# Step 1: Create the example model repository
git clone -b r23.01 https://github.com/triton-inference-server/server.git
cd server/docs/examples
./fetch_models.sh

# Step 2: Launch triton from the NGC Triton container
docker run --gpus=1 --rm --net=host -v ${PWD}/model_repository:/models nvcr.io/nvidia/tritonserver:23.01-py3 tritonserver --model-repository=/models

# Step 3: Sending an Inference Request
# In a separate console, launch the image_client example from the NGC Triton SDK container
docker run -it --rm --net=host nvcr.io/nvidia/tritonserver:23.01-py3-sdk
/workspace/install/bin/image_client -m densenet_onnx -c 3 -s INCEPTION /workspace/images/mug.jpg

# Inference should return the following
Image '/workspace/images/mug.jpg':
15.346230 (504) = COFFEE MUG
13.224326 (968) = CUP
10.422965 (505) = COFFEEPOT
```
Please read the [QuickStart](docs/getting_started/quickstart.md) guide for additional information
regarding this example. The quickstart guide also contains an example of how to launch Triton on [CPU-only systems](docs/getting_started/quickstart.md#run-on-cpu-only-system). New to Triton and wondering where to get started? Watch the [Getting Started video](https://youtu.be/NQDtfSi5QF4).

## Examples and Tutorials

Check out [NVIDIA LaunchPad](https://www.nvidia.com/en-us/data-center/products/ai-enterprise-suite/trial/)
for free access to a set of hands-on labs with Triton Inference Server hosted on
NVIDIA infrastructure.

Specific end-to-end examples for popular models, such as ResNet, BERT, and DLRM
are located in the
[NVIDIA Deep Learning Examples](https://github.com/NVIDIA/DeepLearningExamples)
page on GitHub. The
[NVIDIA Developer Zone](https://developer.nvidia.com/nvidia-triton-inference-server)
contains additional documentation, presentations, and examples.

## Documentation

### Build and Deploy

The recommended way to build and use Triton Inference Server is with Docker
images.

- [Install Triton Inference Server with Docker containers](docs/customization_guide/build.md#building-with-docker) (*Recommended*)
- [Install Triton Inference Server without Docker containers](docs/customization_guide/build.md#building-without-docker)
- [Build a custom Triton Inference Server Docker container](docs/customization_guide/compose.md)
- [Build Triton Inference Server from source](docs/customization_guide/build.md#building-on-unsupported-platforms)
- [Build Triton Inference Server for Windows 10](docs/customization_guide/build.md#building-for-windows-10)
- Examples for deploying Triton Inference Server with Kubernetes and Helm on [GCP](deploy/gcp/README.md),
[AWS](deploy/aws/README.md), and [NVIDIA FleetCommand](deploy/fleetcommand/README.md)

### Using Triton

#### Preparing Models for Triton Inference Server

The first step in using Triton to serve your models is to place one or
more models into a [model repository](docs/user_guide/model_repository.md). 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](docs/user_guide/model_configuration.md) for the model.

- [Add custom operations to Triton if needed by your model](docs/user_guide/custom_operations.md)
- Enable model pipelining with [Model Ensemble](docs/user_guide/architecture.md#ensemble-models)
and [Business Logic Scripting (BLS)](https://github.com/triton-inference-server/python_backend#business-logic-scripting)
- Optimize your models setting [scheduling and batching](docs/user_guide/architecture.md#models-and-schedulers)
parameters and [model instances](docs/user_guide/model_configuration.md#instance-groups).
- Use the [Model Analyzer tool](https://github.com/triton-inference-server/model_analyzer)
to help optimize your model configuration with profiling
- Learn how to [explicitly manage what models are available by loading and
unloading models](docs/user_guide/model_management.md)

#### Configure and Use Triton Inference Server

- Read the [Quick Start Guide](docs/getting_started/quickstart.md) to run Triton Inference
Server on both GPU and CPU
- Triton supports multiple execution engines, called
[backends](https://github.com/triton-inference-server/backend#where-can-i-find-all-the-backends-that-are-available-for-triton), including
[TensorRT](https://github.com/triton-inference-server/tensorrt_backend),
[TensorFlow](https://github.com/triton-inference-server/tensorflow_backend),
[PyTorch](https://github.com/triton-inference-server/pytorch_backend),
[ONNX](https://github.com/triton-inference-server/onnxruntime_backend),
[OpenVINO](https://github.com/triton-inference-server/openvino_backend),
[Python](https://github.com/triton-inference-server/python_backend), and more
- Not all the above backends are supported on every platform supported by Triton.
Look at the
[Backend-Platform Support Matrix](https://github.com/triton-inference-server/backend/blob/main/docs/backend_platform_support_matrix.md)
to learn which backends are supported on your target platform.
- Learn how to [optimize performance](docs/user_guide/optimization.md) using the
[Performance Analyzer](docs/user_guide/perf_analyzer.md) and
[Model Analyzer](https://github.com/triton-inference-server/model_analyzer)
- Learn how to [manage loading and unloading models](docs/user_guide/model_management.md) in
Triton
- Send requests directly to Triton with the [HTTP/REST JSON-based
or gRPC protocols](docs/customization_guide/inference_protocols.md#httprest-and-grpc-protocols)

#### Client Support and Examples

A Triton *client* application sends inference and other requests to Triton. The
[Python and C++ client libraries](https://github.com/triton-inference-server/client)
provide APIs to simplify this communication.

- Review client examples for [C++](https://github.com/triton-inference-server/client/blob/main/src/c%2B%2B/examples),
[Python](https://github.com/triton-inference-server/client/blob/main/src/python/examples),
and [Java](https://github.com/triton-inference-server/client/blob/main/src/java/src/main/java/triton/client/examples)
- Configure [HTTP](https://github.com/triton-inference-server/client#http-options)
and [gRPC](https://github.com/triton-inference-server/client#grpc-options)
client options
- Send input data (e.g. a jpeg image) directly to Triton in the [body of an HTTP
request without any additional metadata](https://github.com/triton-inference-server/server/blob/main/docs/protocol/extension_binary_data.md#raw-binary-request)

### Extend Triton

[Triton Inference Server's architecture](docs/user_guide/architecture.md) is specifically
designed for modularity and flexibility

- [Customize Triton Inference Server container](docs/customization_guide/compose.md) for your use case
- [Create custom backends](https://github.com/triton-inference-server/backend)
in either [C/C++](https://github.com/triton-inference-server/backend/blob/main/README.md#triton-backend-api)
or [Python](https://github.com/triton-inference-server/python_backend)
- Create [decouple backends and models](docs/user_guide/decoupled_models.md) that can send
multiple responses for a request or not send any responses for a request
- Use a [Triton repository agent](docs/customization_guide/repository_agents.md) to add functionality
that operates when a model is loaded and unloaded, such as authentication,
decryption, or conversion
- Deploy Triton on [Jetson and JetPack](docs/user_guide/jetson.md)
- [Use Triton on AWS
Inferentia](https://github.com/triton-inference-server/python_backend/tree/main/inferentia)

### Additional Documentation

- [FAQ](docs/user_guide/faq.md)
- [User Guide](docs/README.md#user-guide)
- [Customization Guide](docs/README.md#customization-guide)
- [Release Notes](https://docs.nvidia.com/deeplearning/triton-inference-server/release-notes/index.html)
- [GPU, Driver, and CUDA Support
Matrix](https://docs.nvidia.com/deeplearning/dgx/support-matrix/index.html)

## Contributing

Contributions to Triton Inference Server are more than welcome. To
contribute please review the [contribution
guidelines](CONTRIBUTING.md). If you have a backend, client,
example or similar contribution that is not modifying the core of
Triton, then you should file a PR in the [contrib
repo](https://github.com/triton-inference-server/contrib).

## Reporting problems, asking questions

We appreciate any feedback, questions or bug reporting regarding this project.
When posting [issues in GitHub](https://github.com/triton-inference-server/server/issues),
follow the process outlined in the [Stack Overflow document](https://stackoverflow.com/help/mcve).
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 dependencies 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.

## For more information

Please refer to the [NVIDIA Developer Triton page](https://developer.nvidia.com/nvidia-triton-inference-server)
for more information.
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# Release Notes for 2.30.0

## New Freatures and Improvements

* The dynamic batcher now accepts user-defined batching constraints, allowing
users to specify
[custom batching strategies](https://github.com/triton-inference-server/server/blob/r23.01/docs/user_guide/model_configuration.md#custom-batching).
* Relaxed Python client gRPC version requirement.
* Refer to the 23.01 column of the
[Frameworks Support Matrix](https://docs.nvidia.com/deeplearning/frameworks/support-matrix/index.html)
for container image versions on which the 23.01 inference server container is
based.


## Known Issues

* In some rare cases Triton might overwrite input tensors while they are still
in use which leads to corrupt input data being used for inference with
TensorRT models. If you encounter accuracy issues with your TensorRT model,
you can work-around the issue by
[enabling the output_copy_stream option](https://github.com/triton-inference-server/common/blob/r22.12/protobuf/model_config.proto#L843-L852)
in your model's configuration.

* Some systems which implement `malloc()` may not release memory back to the
operating system right away causing a false memory leak. This can be mitigated
by using a different malloc implementation. Tcmalloc is installed in the
Triton container and can be
[used by specifying the library in LD_PRELOAD](https://github.com/triton-inference-server/server/blob/r22.12/docs/user_guide/model_management.md#model-control-mode-explicit).

* When using a custom operator for the PyTorch backend, the operator may not be
loaded due to undefined Python library symbols. This can be work-around by
[specifying Python library in LD_PRELOAD](https://github.com/triton-inference-server/server/blob/r22.12/qa/L0_custom_ops/test.sh#L114-L117).

* Auto-complete may cause an increase in server start time. To avoid a start
time increase, users can provide the full model configuration and launch the
server with `--disable-auto-complete-config`.

* Auto-complete does not support PyTorch models due to lack of metadata in the
model. It can only verify that the number of inputs and the input names
matches what is specified in the model configuration. There is no model
metadata about the number of outputs and datatypes. Related PyTorch bug:
https://github.com/pytorch/pytorch/issues/38273

* Perf Analyzer stability criteria has been changed which may result in
reporting instability for scenarios that were previously considered stable.
This change has been made to improve the accuracy of Perf Analyzer results.
If you observe this message, it can be resolved by increasing the
`--measurement-interval` in the time windows mode or
`--measurement-request-count` in the count windows mode.

* Triton Client PIP wheels for ARM SBSA are not available from PyPI and pip will
install an incorrect Jetson version of Triton Client library for Arm SBSA.

The correct client wheel file can be pulled directly from the Arm SBSA SDK
image and manually installed.

* Traced models in PyTorch seem to create overflows when int8 tensor values are
transformed to int32 on the GPU.

Refer to https://github.com/pytorch/pytorch/issues/66930 for more information.

* Triton cannot retrieve GPU metrics with MIG-enabled GPU devices (A100 and A30).

* Triton metrics might not work if the host machine is running a separate DCGM
agent on bare-metal or in a container.

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