[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"project-9663":3},{"id":4,"name":5,"fullName":6,"owner":7,"repo":5,"description":8,"homepage":9,"htmlUrl":10,"language":11,"languages":10,"totalLinesOfCode":10,"stars":12,"forks":13,"watchers":14,"openIssues":15,"contributorsCount":16,"subscribersCount":16,"size":16,"stars1d":17,"stars7d":18,"stars30d":19,"stars90d":16,"forks30d":16,"starsTrendScore":18,"compositeScore":20,"rankGlobal":10,"rankLanguage":10,"license":21,"archived":22,"fork":22,"defaultBranch":23,"hasWiki":22,"hasPages":22,"topics":24,"createdAt":10,"pushedAt":10,"updatedAt":32,"readmeContent":33,"aiSummary":34,"trendingCount":16,"starSnapshotCount":16,"syncStatus":35,"lastSyncTime":36,"discoverSource":37},9663,"server","triton-inference-server\u002Fserver","triton-inference-server","The Triton Inference Server provides an optimized cloud and edge inferencing solution. ","https:\u002F\u002Fdocs.nvidia.com\u002Fdeeplearning\u002Ftriton-inference-server\u002Fuser-guide\u002Fdocs\u002Findex.html",null,"Python",10747,1792,143,780,0,3,16,97,93.46,"BSD 3-Clause \"New\" or \"Revised\" License",false,"main",[25,26,27,28,29,30,31],"cloud","datacenter","deep-learning","edge","gpu","inference","machine-learning","2026-06-12 04:00:46","\u003C!--\n# Copyright 2018-2026, NVIDIA CORPORATION & AFFILIATES. All rights reserved.\n#\n# Redistribution and use in source and binary forms, with or without\n# modification, are permitted provided that the following conditions\n# are met:\n#  * Redistributions of source code must retain the above copyright\n#    notice, this list of conditions and the following disclaimer.\n#  * Redistributions in binary form must reproduce the above copyright\n#    notice, this list of conditions and the following disclaimer in the\n#    documentation and\u002For other materials provided with the distribution.\n#  * Neither the name of NVIDIA CORPORATION nor the names of its\n#    contributors may be used to endorse or promote products derived\n#    from this software without specific prior written permission.\n#\n# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS ``AS IS'' AND ANY\n# EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE\n# IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR\n# PURPOSE ARE DISCLAIMED.  IN NO EVENT SHALL THE COPYRIGHT OWNER OR\n# CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL,\n# EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO,\n# PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR\n# PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY\n# OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT\n# (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE\n# OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.\n-->\n[![License](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FLicense-BSD3-lightgrey.svg)](https:\u002F\u002Fopensource.org\u002Flicenses\u002FBSD-3-Clause)\n\n>[!WARNING]\n>You are currently on the `main` branch which tracks under-development progress\n>towards the next release. The current release is version [2.68.0](https:\u002F\u002Fgithub.com\u002Ftriton-inference-server\u002Fserver\u002Freleases\u002Flatest)\n>and corresponds to the 26.04 container release on NVIDIA GPU Cloud (NGC).\n\n# Triton Inference Server\n\nTriton Inference Server is an open source inference serving software that\nstreamlines AI inferencing. Triton enables teams to deploy any AI model from\nmultiple deep learning and machine learning frameworks, including TensorRT,\nPyTorch, ONNX, OpenVINO, Python, RAPIDS FIL, and more. Triton\nInference Server supports inference across cloud, data center, edge and embedded\ndevices on NVIDIA GPUs, x86 and ARM CPU, or AWS Inferentia. Triton Inference\nServer delivers optimized performance for many query types, including real time,\nbatched, ensembles and audio\u002Fvideo streaming. Triton inference Server is part of\n[NVIDIA AI Enterprise](https:\u002F\u002Fwww.nvidia.com\u002Fen-us\u002Fdata-center\u002Fproducts\u002Fai-enterprise\u002F),\na software platform that accelerates the data science pipeline and streamlines\nthe development and deployment of production AI.\n\nMajor features include:\n\n- [Supports multiple deep learning\n  frameworks](https:\u002F\u002Fgithub.com\u002Ftriton-inference-server\u002Fbackend#where-can-i-find-all-the-backends-that-are-available-for-triton)\n- [Supports multiple machine learning\n  frameworks](https:\u002F\u002Fgithub.com\u002Ftriton-inference-server\u002Ffil_backend)\n- [Concurrent model\n  execution](docs\u002Fuser_guide\u002Farchitecture.md#concurrent-model-execution)\n- [Dynamic batching](docs\u002Fuser_guide\u002Fbatcher.md#dynamic-batcher)\n- [Sequence batching](docs\u002Fuser_guide\u002Fbatcher.md#sequence-batcher) and\n  [implicit state management](docs\u002Fuser_guide\u002Farchitecture.md#implicit-state-management)\n  for stateful models\n- Provides [Backend API](https:\u002F\u002Fgithub.com\u002Ftriton-inference-server\u002Fbackend) that\n  allows adding custom backends and pre\u002Fpost processing operations\n- Supports writing custom backends in python, a.k.a.\n  [Python-based backends.](https:\u002F\u002Fgithub.com\u002Ftriton-inference-server\u002Fbackend\u002Fblob\u002Fmain\u002Fdocs\u002Fpython_based_backends.md#python-based-backends)\n- Model pipelines using\n  [Ensembling](docs\u002Fuser_guide\u002Farchitecture.md#ensemble-models) or [Business\n  Logic Scripting\n  (BLS)](https:\u002F\u002Fgithub.com\u002Ftriton-inference-server\u002Fpython_backend#business-logic-scripting)\n- [HTTP\u002FREST and GRPC inference\n  protocols](docs\u002Fcustomization_guide\u002Finference_protocols.md) based on the community\n  developed [KServe\n  protocol](https:\u002F\u002Fgithub.com\u002Fkserve\u002Fkserve\u002Ftree\u002Fmaster\u002Fdocs\u002Fpredict-api\u002Fv2)\n- A [C API](docs\u002Fcustomization_guide\u002Finprocess_c_api.md) and\n  [Java API](docs\u002Fcustomization_guide\u002Finprocess_java_api.md)\n  allow Triton to link directly into your application for edge and other in-process use cases\n- [Metrics](docs\u002Fuser_guide\u002Fmetrics.md) indicating GPU utilization, server\n  throughput, server latency, and more\n\n**New to Triton Inference Server?** Make use of\n[these tutorials](https:\u002F\u002Fgithub.com\u002Ftriton-inference-server\u002Ftutorials)\nto begin your Triton journey!\n\nJoin the [Triton and TensorRT community](https:\u002F\u002Fwww.nvidia.com\u002Fen-us\u002Fdeep-learning-ai\u002Ftriton-tensorrt-newsletter\u002F) and\nstay current on the latest product updates, bug fixes, content, best practices,\nand more.  Need enterprise support?  NVIDIA global support is available for Triton\nInference Server with the\n[NVIDIA AI Enterprise software suite](https:\u002F\u002Fwww.nvidia.com\u002Fen-us\u002Fdata-center\u002Fproducts\u002Fai-enterprise\u002F).\n\n## Serve a Model in 3 Easy Steps\n\n```bash\n# Step 1: Create the example model repository\ngit clone -b r26.04 https:\u002F\u002Fgithub.com\u002Ftriton-inference-server\u002Fserver.git\ncd server\u002Fdocs\u002Fexamples\n.\u002Ffetch_models.sh\n\n# Step 2: Launch triton from the NGC Triton container\ndocker run --gpus=1 --rm --net=host -v ${PWD}\u002Fmodel_repository:\u002Fmodels nvcr.io\u002Fnvidia\u002Ftritonserver:26.04-py3 tritonserver --model-repository=\u002Fmodels --model-control-mode explicit --load-model densenet_onnx\n\n# Step 3: Sending an Inference Request\n# In a separate console, launch the image_client example from the NGC Triton SDK container\ndocker run -it --rm --net=host nvcr.io\u002Fnvidia\u002Ftritonserver:26.04-py3-sdk \u002Fworkspace\u002Finstall\u002Fbin\u002Fimage_client -m densenet_onnx -c 3 -s INCEPTION \u002Fworkspace\u002Fimages\u002Fmug.jpg\n\n# Inference should return the following\nImage '\u002Fworkspace\u002Fimages\u002Fmug.jpg':\n    15.346230 (504) = COFFEE MUG\n    13.224326 (968) = CUP\n    10.422965 (505) = COFFEEPOT\n```\nPlease read the [QuickStart](docs\u002Fgetting_started\u002Fquickstart.md) guide for additional information\nregarding this example. The quickstart guide also contains an example of how to launch Triton on [CPU-only systems](docs\u002Fgetting_started\u002Fquickstart.md#run-on-cpu-only-system). New to Triton and wondering where to get started? Watch the [Getting Started video](https:\u002F\u002Fyoutu.be\u002FNQDtfSi5QF4).\n\n## Examples and Tutorials\n\nCheck out [NVIDIA LaunchPad](https:\u002F\u002Fwww.nvidia.com\u002Fen-us\u002Fdata-center\u002Fproducts\u002Fai-enterprise-suite\u002Ftrial\u002F)\nfor free access to a set of hands-on labs with Triton Inference Server hosted on\nNVIDIA infrastructure.\n\nSpecific end-to-end examples for popular models, such as ResNet, BERT, and DLRM\nare located in the\n[NVIDIA Deep Learning Examples](https:\u002F\u002Fgithub.com\u002FNVIDIA\u002FDeepLearningExamples)\npage on GitHub. The\n[NVIDIA Developer Zone](https:\u002F\u002Fdeveloper.nvidia.com\u002Fnvidia-triton-inference-server)\ncontains additional documentation, presentations, and examples.\n\n## Documentation\n\n### Build and Deploy\n\nThe recommended way to build and use Triton Inference Server is with Docker\nimages.\n\n- [Install Triton Inference Server with Docker containers](docs\u002Fcustomization_guide\u002Fbuild.md#building-with-docker) (*Recommended*)\n- [Install Triton Inference Server without Docker containers](docs\u002Fcustomization_guide\u002Fbuild.md#building-without-docker)\n- [Build a custom Triton Inference Server Docker container](docs\u002Fcustomization_guide\u002Fcompose.md)\n- [Build Triton Inference Server from source](docs\u002Fcustomization_guide\u002Fbuild.md#building-on-unsupported-platforms)\n- [Build Triton Inference Server for Windows 10](docs\u002Fcustomization_guide\u002Fbuild.md#building-for-windows-10)\n- Examples for deploying Triton Inference Server with Kubernetes and Helm on [GCP](deploy\u002Fgcp\u002FREADME.md),\n  [AWS](deploy\u002Faws\u002FREADME.md), and [NVIDIA FleetCommand](deploy\u002Ffleetcommand\u002FREADME.md)\n- [Secure Deployment Considerations](docs\u002Fcustomization_guide\u002Fdeploy.md)\n\n### Using Triton\n\n#### Preparing Models for Triton Inference Server\n\nThe first step in using Triton to serve your models is to place one or\nmore models into a [model repository](docs\u002Fuser_guide\u002Fmodel_repository.md). Depending on\nthe type of the model and on what Triton capabilities you want to enable for\nthe model, you may need to create a [model\nconfiguration](docs\u002Fuser_guide\u002Fmodel_configuration.md) for the model.\n\n- [Add custom operations to Triton if needed by your model](docs\u002Fuser_guide\u002Fcustom_operations.md)\n- Enable model pipelining with [Model Ensemble](docs\u002Fuser_guide\u002Farchitecture.md#ensemble-models)\n  and [Business Logic Scripting (BLS)](https:\u002F\u002Fgithub.com\u002Ftriton-inference-server\u002Fpython_backend#business-logic-scripting)\n- Optimize your models setting [scheduling and batching](docs\u002Fuser_guide\u002Farchitecture.md#models-and-schedulers)\n  parameters and [model instances](docs\u002Fuser_guide\u002Fmodel_configuration.md#instance-groups).\n- Use the [Model Analyzer tool](https:\u002F\u002Fgithub.com\u002Ftriton-inference-server\u002Fmodel_analyzer)\n  to help optimize your model configuration with profiling\n- Learn how to [explicitly manage what models are available by loading and\n  unloading models](docs\u002Fuser_guide\u002Fmodel_management.md)\n\n#### Configure and Use Triton Inference Server\n\n- Read the [Quick Start Guide](docs\u002Fgetting_started\u002Fquickstart.md) to run Triton Inference\n  Server on both GPU and CPU\n- Triton supports multiple execution engines, called\n  [backends](https:\u002F\u002Fgithub.com\u002Ftriton-inference-server\u002Fbackend#where-can-i-find-all-the-backends-that-are-available-for-triton), including\n  [TensorRT](https:\u002F\u002Fgithub.com\u002Ftriton-inference-server\u002Ftensorrt_backend),\n  [PyTorch](https:\u002F\u002Fgithub.com\u002Ftriton-inference-server\u002Fpytorch_backend),\n  [ONNX](https:\u002F\u002Fgithub.com\u002Ftriton-inference-server\u002Fonnxruntime_backend),\n  [OpenVINO](https:\u002F\u002Fgithub.com\u002Ftriton-inference-server\u002Fopenvino_backend),\n  [Python](https:\u002F\u002Fgithub.com\u002Ftriton-inference-server\u002Fpython_backend), and more\n- Not all the above backends are supported on every platform supported by Triton.\n  Look at the\n  [Backend-Platform Support Matrix](https:\u002F\u002Fgithub.com\u002Ftriton-inference-server\u002Fbackend\u002Fblob\u002Fmain\u002Fdocs\u002Fbackend_platform_support_matrix.md)\n  to learn which backends are supported on your target platform.\n- Learn how to [optimize performance](docs\u002Fuser_guide\u002Foptimization.md) using the\n  [Performance Analyzer](https:\u002F\u002Fgithub.com\u002Ftriton-inference-server\u002Fperf_analyzer\u002Fblob\u002Fmain\u002FREADME.md)\n  and\n  [Model Analyzer](https:\u002F\u002Fgithub.com\u002Ftriton-inference-server\u002Fmodel_analyzer)\n- Learn how to [manage loading and unloading models](docs\u002Fuser_guide\u002Fmodel_management.md) in\n  Triton\n- Send requests directly to Triton with the [HTTP\u002FREST JSON-based\n  or gRPC protocols](docs\u002Fcustomization_guide\u002Finference_protocols.md#httprest-and-grpc-protocols)\n\n#### Client Support and Examples\n\nA Triton *client* application sends inference and other requests to Triton. The\n[Python and C++ client libraries](https:\u002F\u002Fgithub.com\u002Ftriton-inference-server\u002Fclient)\nprovide APIs to simplify this communication.\n\n- Review client examples for [C++](https:\u002F\u002Fgithub.com\u002Ftriton-inference-server\u002Fclient\u002Fblob\u002Fmain\u002Fsrc\u002Fc%2B%2B\u002Fexamples),\n  [Python](https:\u002F\u002Fgithub.com\u002Ftriton-inference-server\u002Fclient\u002Fblob\u002Fmain\u002Fsrc\u002Fpython\u002Fexamples),\n  and [Java](https:\u002F\u002Fgithub.com\u002Ftriton-inference-server\u002Fclient\u002Fblob\u002Fmain\u002Fsrc\u002Fjava\u002Fsrc\u002Fmain\u002Fjava\u002Ftriton\u002Fclient\u002Fexamples)\n- Configure [HTTP](https:\u002F\u002Fgithub.com\u002Ftriton-inference-server\u002Fclient#http-options)\n  and [gRPC](https:\u002F\u002Fgithub.com\u002Ftriton-inference-server\u002Fclient#grpc-options)\n  client options\n- Send input data (e.g. a jpeg image) directly to Triton in the [body of an HTTP\n  request without any additional metadata](https:\u002F\u002Fgithub.com\u002Ftriton-inference-server\u002Fserver\u002Fblob\u002Fmain\u002Fdocs\u002Fprotocol\u002Fextension_binary_data.md#raw-binary-request)\n\n### Extend Triton\n\n[Triton Inference Server's architecture](docs\u002Fuser_guide\u002Farchitecture.md) is specifically\ndesigned for modularity and flexibility\n\n- [Customize Triton Inference Server container](docs\u002Fcustomization_guide\u002Fcompose.md) for your use case\n- [Create custom backends](https:\u002F\u002Fgithub.com\u002Ftriton-inference-server\u002Fbackend)\n  in either [C\u002FC++](https:\u002F\u002Fgithub.com\u002Ftriton-inference-server\u002Fbackend\u002Fblob\u002Fmain\u002FREADME.md#triton-backend-api)\n  or [Python](https:\u002F\u002Fgithub.com\u002Ftriton-inference-server\u002Fpython_backend)\n- Create [decoupled backends and models](docs\u002Fuser_guide\u002Fdecoupled_models.md) that can send\n  multiple responses for a request or not send any responses for a request\n- Use a [Triton repository agent](docs\u002Fcustomization_guide\u002Frepository_agents.md) to add functionality\n  that operates when a model is loaded and unloaded, such as authentication,\n  decryption, or conversion\n- Deploy Triton on [Jetson and JetPack](docs\u002Fuser_guide\u002Fjetson.md)\n- [Use Triton on AWS\n   Inferentia](https:\u002F\u002Fgithub.com\u002Ftriton-inference-server\u002Fpython_backend\u002Ftree\u002Fmain\u002Finferentia)\n\n### Additional Documentation\n\n- [FAQ](docs\u002Fuser_guide\u002Ffaq.md)\n- [User Guide](docs\u002FREADME.md#user-guide)\n- [Customization Guide](docs\u002FREADME.md#customization-guide)\n- [Release Notes](https:\u002F\u002Fdocs.nvidia.com\u002Fdeeplearning\u002Ftriton-inference-server\u002Frelease-notes\u002Findex.html)\n- [GPU, Driver, and CUDA Support\nMatrix](https:\u002F\u002Fdocs.nvidia.com\u002Fdeeplearning\u002Fdgx\u002Fsupport-matrix\u002Findex.html)\n\n## Contributing\n\nContributions to Triton Inference Server are more than welcome. To\ncontribute please review the [contribution\nguidelines](CONTRIBUTING.md). If you have a backend, client,\nexample or similar contribution that is not modifying the core of\nTriton, then you should file a PR in the [contrib\nrepo](https:\u002F\u002Fgithub.com\u002Ftriton-inference-server\u002Fcontrib).\n\n## Reporting problems, asking questions\n\nWe appreciate any feedback, questions or bug reporting regarding this project.\nWhen posting [issues in GitHub](https:\u002F\u002Fgithub.com\u002Ftriton-inference-server\u002Fserver\u002Fissues),\nfollow the process outlined in the [Stack Overflow document](https:\u002F\u002Fstackoverflow.com\u002Fhelp\u002Fmcve).\nEnsure posted examples are:\n- minimal – use as little code as possible that still produces the\n  same problem\n- complete – provide all parts needed to reproduce the problem. Check\n  if you can strip external dependencies and still show the problem. The\n  less time we spend on reproducing problems the more time we have to\n  fix it\n- verifiable – test the code you're about to provide to make sure it\n  reproduces the problem. Remove all other problems that are not\n  related to your request\u002Fquestion.\n\nFor issues, please use the provided bug report and feature request templates.\n\nFor questions, we recommend posting in our community\n[GitHub Discussions.](https:\u002F\u002Fgithub.com\u002Ftriton-inference-server\u002Fserver\u002Fdiscussions)\n\n## For more information\n\nPlease refer to the [NVIDIA Developer Triton page](https:\u002F\u002Fdeveloper.nvidia.com\u002Fnvidia-triton-inference-server)\nfor more information.\n","Triton Inference Server 是一个优化的云和边缘推理解决方案。它支持多种深度学习和机器学习框架，包括TensorRT、PyTorch、ONNX等，能够在NVIDIA GPU、x86及ARM CPU或AWS Inferentia上运行。Triton提供了实时、批量、集成模型以及音视频流等多种查询类型的优化性能。适用于需要高性能推理服务的数据中心、云端以及边缘设备场景，帮助团队简化AI模型部署流程并提升效率。",2,"2026-06-11 03:24:03","top_topic"]