From 835225c470563ffe181396e6494699595ac1ed8d Mon Sep 17 00:00:00 2001 From: Misha Chornyi <99709299+mc-nv@users.noreply.github.com> Date: Tue, 26 Mar 2024 18:29:49 -0700 Subject: [PATCH] Update README.md 2.44.0 / 24.03 (#7032) --- README.md | 229 +++++++++++++++++++++++++++++++++++++++++++++++++++++- 1 file changed, 227 insertions(+), 2 deletions(-) diff --git a/README.md b/README.md index ea95c8534f..fbea24b260 100644 --- a/README.md +++ b/README.md @@ -30,5 +30,230 @@ [![License](https://img.shields.io/badge/License-BSD3-lightgrey.svg)](https://opensource.org/licenses/BSD-3-Clause) -> [!WARNING] -> THIS BRANCH IS UNDER DEVELOPENT AND IS NOT YET STABLE.44 \ No newline at end of file +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 +Inference Server supports inference across cloud, data center, edge and embedded +devices on NVIDIA GPUs, x86 and ARM CPU, or AWS Inferentia. Triton Inference +Server delivers optimized performance for many query types, including real time, +batched, ensembles and audio/video streaming. Triton inference Server is part of +[NVIDIA AI Enterprise](https://www.nvidia.com/en-us/data-center/products/ai-enterprise/), +a software platform that accelerates the data science pipeline and streamlines +the development and deployment of production AI. + +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 +- Supports writing custom backends in python, a.k.a. + [Python-based backends.](https://github.com/triton-inference-server/backend/blob/r24.03/docs/python_based_backends.md#python-based-backends) +- 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 + +**New to Triton Inference Server?** Make use of +[these tutorials](https://github.com/triton-inference-server/tutorials) +to begin your Triton journey! + +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 r24.02 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:24.02-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:24.02-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) +- [Secure Deployment Considerations](docs/customization_guide/deploy.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/r24.03/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](https://github.com/triton-inference-server/client/blob/r24.03/src/c++/perf_analyzer/README.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/r24.03/src/c%2B%2B/examples), + [Python](https://github.com/triton-inference-server/client/blob/r24.03/src/python/examples), + and [Java](https://github.com/triton-inference-server/client/blob/r24.03/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/r24.03/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/r24.03/README.md#triton-backend-api) + or [Python](https://github.com/triton-inference-server/python_backend) +- Create [decoupled 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 issues, please use the provided bug report and feature request templates. + +For questions, we recommend posting in our community +[GitHub Discussions.](https://github.com/triton-inference-server/server/discussions) + +## For more information + +Please refer to the [NVIDIA Developer Triton page](https://developer.nvidia.com/nvidia-triton-inference-server) +for more information.