PageSourceSearch

https://www.pipeless.ai/docs/_next/static/chunks/pages/index-be77801be52e01d7.js

js pipeless.ai collected 2026-10-03 21:37:50 UTC 12,500 bytes, 1 lines download raw bytes

1(self.webpackChunk_N_E=self.webpackChunk_N_E||[]).push([[405],{1464:function(e,t,o){(window.__NEXT_P=window.__NEXT_P||[]).push(["/",function(){return o(4726)}])},4726:function(e,t,o){"use strict";o.r(t),o.d(t,{__toc:function(){return c}});var n=o(5893),i=o(2673),s=o(2681),r=o(9369);o(9966);var a=o(2643),d=o(9013),l=o(3147);let c=[{depth:2,value:"What is Pipeless?",id:"what-is-pipeless"},{depth:2,value:"Why Pipeless?",id:"why-pipeless"},{depth:2,value:"How is Pipeless different?",id:"how-is-pipeless-different"},{depth:2,value:"Where to start?",id:"where-to-start"},{depth:2,value:"Documentation Versions",id:"documentation-versions"}];function p(e){let t=Object.assign({h1:"h1",p:"p",h2:"h2",strong:"strong",a:"a",code:"code"},(0,a.a)(),e.components);return(0,n.jsxs)(n.Fragment,{children:[(0,n.jsx)(t.h1,{children:"Introduction"}),"\n","\n",(0,n.jsx)(t.p,{children:"Welcome to the Pipeless documentation!"}),"\n",(0,n.jsx)(t.h2,{id:"what-is-pipeless",children:"What is Pipeless?"}),"\n",(0,n.jsxs)(t.p,{children:["Pipeless is an ",(0,n.jsx)(t.strong,{children:"open-source computer vision framework"})," that ships all the features you\nneed to create and deploy efficient computer vision applications that work in real-time.\nPipeless is the easiest way to create a computer vision application."]}),"\n",(0,n.jsx)(t.h2,{id:"why-pipeless",children:"Why Pipeless?"}),"\n",(0,n.jsx)(t.p,{children:"Next.js (among others) allowed web developers to create applications quickly and easily. However,\nfor computer vision there are no equivalent solutions. It is hard to get started and be\nproductive in a short span of time."}),"\n",(0,n.jsx)(t.p,{children:"Pipeless gives you all the required pieces in one. Simply write short functions that take frames and Pipeless will run them on events.\nYou can easily load industry-standard models or bring your own and load it in one of the provided inference runtimes, they are run using CPUs or GPUs out-of-the-box."}),"\n",(0,n.jsx)(t.h2,{id:"how-is-pipeless-different",children:"How is Pipeless different?"}),"\n",(0,n.jsx)(t.p,{children:"We have taken technology and concepts from other fields, like serverless computing, to create what\nwe think is the ideal solution for computer vision. We replaced the typical HTTP events that trigger\nserverless functions by frame events that trigger your processing code. This makes Pipeless\nto process streams in a highly parallelized environment and allows to efficently process multiple streams\nfrom several sources at the same time."}),"\n",(0,n.jsx)(t.p,{children:"So in short, Pipeless makes your code to run extreamly fast without headaches, you just write simple functions."}),"\n",(0,n.jsx)(t.h2,{id:"where-to-start",children:"Where to start?"}),"\n",(0,n.jsxs)(t.p,{children:["Nobody likes to read docs, so you can start by running some of the examples and playing with their code. If you need more context on what is happening, you can come back to the ",(0,n.jsx)(t.a,{href:"/v1/getting-started",children:"getting started"})," guide and learn from the basics."]}),"\n",(0,n.jsx)(t.p,{children:"In the rare case in which you are one of those persons who read the docs, we hope you enjoy the guides! Any feedback on the docs is really appreciated! You can also contribute to the docs by clicking on the links at the right menu."}),"\n",(0,n.jsxs)(d.oy,{children:[(0,n.jsx)(d.Zb,{icon:(0,n.jsx)(l.XC,{}),title:"Getting Started",href:"/v1/getting-started"}),(0,n.jsx)(d.Zb,{icon:(0,n.jsx)(l.KW,{}),title:"Examples",href:"/v1/examples"})]}),"\n",(0,n.jsx)(t.h2,{id:"documentation-versions",children:"Documentation Versions"}),"\n",(0,n.jsxs)(t.p,{children:["Pipeless versioning follows the semver standard, except for versions annotated with ",(0,n.jsx)(t.code,{children:"alpha"})," or ",(0,n.jsx)(t.code,{children:"beta"}),", which may include breaking changes without a major version bump."]}),"\n",(0,n.jsx)(d.oy,{children:(0,n.jsx)(d.Zb,{icon:(0,n.jsx)(l.UD,{}),title:"V1.x.x",href:"/v1"})})]})}let u={MDXContent:function(){let e=arguments.length>0&&void 0!==arguments[0]?arguments[0]:{},{wrapper:t}=Object.assign({},(0,a.a)(),e.components);return t?(0,n.jsx)(t,{...e,children:(0,n.jsx)(p,{...e})}):p(e)},pageOpts:{filePath:"pages/index.mdx",route:"/",frontMatter:{title:"Introduction",description:"Introduction to Pipeless, the open-source and production ready computer vision framework. Enjoy serverless development experience for embedded computer vision."},timestamp:1704370957e3,pageMap:[{kind:"Meta",data:{index:"Introduction",contributing:"Contributing","-- Versions":{type:"separator",title:"Versions"},v1:"v1.x.x",v0:"v0.x.x",about:{title:"Home",type:"page",href:"https://pipeless.ai"},contact:{title:"Contact ↗",type:"page",href:"https://lfkz9noht13.typeform.com/to/aeOqKGPt",newWindow:!0}}},{kind:"MdxPage",name:"contributing",route:"/contributing",frontMatter:{title:"Contributing Guidelines",description:"Contribute to the Pipeless open-source computer vision framework in GitHub"}},{kind:"MdxPage",name:"index",route:"/",frontMatter:{title:"Introduction",description:"Introduction to Pipeless, the open-source and production ready computer vision framework. Enjoy serverless development experience for embedded computer vision."}},{kind:"MdxPage",name:"v0",route:"/v0",frontMatter:{title:"Pipeless v0.x",description:"Documentation for Pipeless ver
1sions 0.x. The open-source computer vision framework"}},{kind:"Folder",name:"v1",route:"/v1",children:[{kind:"Meta",data:{"getting-started":"\uD83D\uDE80 Getting Started","rest-api":"Pipeless REST API",formats:"Stream Formats and Protocols",inference:"Inference Runtimes",kvstore:"Key Value Store",container:"\uD83D\uDC0B Container images",deployment:"Deploying your application",models:"Ready to Use CV Models",examples:"\uD83C\uDF1F Examples",benchmark:"Benchmark",changes:"Version Notable Changes",troubleshooting:"Troubleshooting","restart-policy":"Processing Restart Policy"}},{kind:"MdxPage",name:"benchmark",route:"/v1/benchmark",frontMatter:{title:"Benchmark",description:"A benchmark obtained by running the Pipeless computer vision framework on different devices to measure the overhead"}},{kind:"MdxPage",name:"changes",route:"/v1/changes",frontMatter:{title:"Notable version changes",description:"A list of important changes between Pipeless versions"}},{kind:"MdxPage",name:"container",route:"/v1/container",frontMatter:{title:"Container Images",description:"Learn to run and deploy Pipeless using container images. There are images available for CPU, and GPU (CUDA and TensorRT). The container images can also be used to easily deploy Pipeless to the cloud."}},{kind:"MdxPage",name:"deployment",route:"/v1/deployment",frontMatter:{title:"Deploying your pipeless application",description:"Learn how to deploy a Pipeless application to either an edge device or to the cloud. Pipeless ca run in distributed cloud servers as well as edge and IoT devices."}},{kind:"Folder",name:"examples",route:"/v1/examples",children:[{kind:"Meta",data:{w
1attermark:"Adding wattermarks",cats:"Detecting Cats on a Video","tf-pose":"Pose detection with TensorFlow",yolov8:"Object detection - YOLOv8 Python lib","onnx-candy":"ONXX Runtime - Candy filter","onnx-yolo":"ONNX Runtime - Object detection with YOLO","gaze-piano":"Play a piano with your eyes by looking to the notes"}},{kind:"MdxPage",name:"cats",route:"/v1/examples/cats",frontMatter:{title:"Cats recognition example",description:"A Pipeless example to recognize and detect cats in a video stream"}},{kind:"MdxPage",name:"gaze-piano",route:"/v1/examples/gaze-piano",frontMatter:{title:"Play a piano with your eyes - Gaze estimation",description:"A Pipeless example that allows you play a virtual piano with your eyes by looking to the note you want to play"}},{kind:"MdxPage",name:"onnx-candy",route:"/v1/examples/onnx-candy",frontMatter:{title:"Candy filter with ONNX runtime",description:"A Pipeless example using the ONNX Runtime to apply a candy filter to a video stream"}},{kind:"MdxPage",name:"onnx-yolo",route:"/v1/examples/onnx-yolo",frontMatter:{title:"Object detection with ONNX Runtime",description:"A Pipeless example that runs inference using the ONNX Runtime and YOLO to detect objects in a video stream"}},{kind:"MdxPage",name:"tf-pose",route:"/v1/examples/tf-pose",frontMatter:{title:"Pose estimation/detection with TensorFlow",description:"A Pipeless example that detects the pose with 17 keypoints in a video stream"}},{kind:"MdxPage",name:"wattermark",route:"/v1/examples/wattermark",frontMatter:{title:"Adding wattermarks to a video",description:"The most basic Pipeless example adding a wattermark to a video"}},{kind:"MdxPage",name:"yolov8",route:"/v1/examples/yolov8",frontMatter:{title:"Object detection with YOLOv8 and Ultralytics SDK",description:"A Pipeless example to detect objects in a video stream using YOLOv8 via the Ultralytics Python SDK"}}]},{kind:"MdxPage",name:"examples",route:"/v1/examples",frontMatter:{title:"Examples",description:"A list of fully functional examples to play around with the Pipeless computer vision framework."}},{kind:"MdxPage",name:"formats",route:"/v1/formats",frontMatter:{title:"Supported stream formats and protocols",description:"Learn the supported formats and protocols that can be used to provide video to your Pipeless application"}},{kind:"Folder",name:"getting-started",route:"/v1/getting-started",children:[{kind:"Meta",data:{installation:"\uD83D\uDEE0️ Installation","project-structure":"\uD83D\uDCC2 Project Structure"}},{kind:"MdxPage",name:"installation",route:"/v1/getting-started/installation",frontMatter:{title:"Installation guide",description:"Learn to install Pipeless, the computer vision framework"}},{kind:"MdxPage",name:"project-structure",route:"/v1/getting-started/project-structure",frontMatter:{title:"Project structure",description:"Learn how a Pipeless project is structured in the filesystem"}}]},{kind:"MdxPage",name:"getting-started",route:"/v1/getting-started",frontMatter:{title:"Getting started",description:"Get started with the Pipeless computer vision framework and enjoy the development experience of serverless frameworks for embedded computer vision."}},{kind:"MdxPage",name:"inference",route:"/v1/inference",frontMatter:{title:"Inference runtimes",description:"Learn to how to load a model into one of the embedded inference runtimes to perform your computer vision tasks in CPU, or GPUs. Use CUDA, TensorRT, OpenVINO, CoreML in any kind of GPU, including Nvidia GPUs."}},{kind:"MdxPage",name:"kvstore",route:"/v1/kvstore",frontMatter:{title:"Key-Value Store usage",description:"Learn to how use the included key value store to communicate data between the processing of different frames and the execution of different hooks"}},{kind:"Folder",name:"models",route:"/v1/models",children:[{kind:"Folder",name:"tensorflow",route:"/v1/models/tensorflow",children:[{kind:"MdxPage",name:"multi-pose-estimation",route:"/v1/models/tensorflow/multi-pose-estimation",frontMatter:{title:"Multi-pose estimation Tensorflow Model",description:"A ready to use TensorFlow model for multipose estimation in Python"}},{kind:"Meta",data:{"multi-pose-estimation":"Multi-pose estimation Tensorflow Model"}}]},{kind:"MdxPage",name:"tensorflow",route:"/v1/models/tensorflow"},{kind:"Meta",data:{tensorflow:"Tensorflow"}}]},{kind:"MdxPage",name:"models",route:"/v1/models",frontMatter:{title:"Ready to use computer vision models",description:"A list of ready to use computer vision models with Python APIs"}},{kind:"MdxPage",name:"rest-api",route:"/v1/rest-api",frontMatter:{title:"Pipeless REST API",description:"Documentation about the endpoints exposed by a running Pipeless instance"}},{kind:"MdxPage",name:"restart-policy",route:"/v1/restart-policy",frontMatter:{title:"Processing Restart Policy",description:"Documentation about the restart policy for stream processing"}},{kind:"MdxPage",name:"troubleshooting",route:"/v1/troubleshooting",frontMatter:{title:"Troubleshooting your Pipeless application",description:"General guidance and common issues when running your Pipeless application"}}]},{kind:"MdxPage",name:"v1",route:"/v1",frontMatter:{title:"Pipeless v1.x",description:"Documentation for Pipeless ver
1sions 1.x. The open-source computer vision framework"}}],flexsearch:{codeblocks:!0},title:"Introduction",headings:c},pageNextRoute:"/",nextraLayout:s.ZP,themeConfig:r.Z};t.default=(0,i.j)(u)}},function(e){e.O(0,[13,996,411,774,888,179],function(){return e(e.s=1464)}),_N_E=e.O()}]);

Line numbers count LF bytes from the start of the resource, as the search results do. Vendor segments are library code the classifier recognised; they are stored but not indexed. Bytes are shown as Latin1 characters, one per byte.