1(self.webpackChunk_N_E=self.webpackChunk_N_E||[]).push([[972],{9040:function(e,t,s){(window.__NEXT_P=window.__NEXT_P||[]).push(["/v1/examples/custom-data",function(){return s(945)}])},9369:function(e,t,s){"use strict";var o=s(5893);s(7294);var n=s(5675),a=s.n(n),i=s(3750),r=s(1163),l=s(2681);let c={logo:(0,o.jsxs)("span",{style:{display:"flex",alignItems:"center"},children:[(0,o.jsx)(a(),{alt:"logo",width:30,height:30,src:"/docs/logo-400x400-rounded.png"}),"\xa0",(0,o.jsx)("b",{children:"Pipeless"})]}),logoLink:"https://pipeless.ai",primaryHue:297,project:{link:"https://github.com/pipeless-ai/pipeless"},chat:{link:"https://discord.gg/K2qxQ8uedG",icon:(0,o.jsx)(i.KpZ,{size:25})},docsRepositoryBase:"https://github.com/pipeless-ai/docs/tree/main",footer:{text:(0,o.jsx)("p",{children:"Copyright \xa9 2023 Pipeless, Inc."})},search:{loading:"Loading..."},head:()=>{let{asPath:e,defaultLocale:t,locale:s}=(0,r.useRouter)(),{frontMatter:n}=(0,l.ZR)(),a="https://www.pipeless.ai"+(t===s?e:"/".concat(s).concat(e));return(0,o.jsxs)(o.Fragment,{children:[(0,o.jsx)("meta",{property:"og:url",content:a}),(0,o.jsx)("meta",{property:"og:title",content:n.title||"Pipeless Documentation"}),(0,o.jsx)("meta",{property:"og:description",content:n.description||"Documentation about Pipeless, the computer vision framework"}),(0,o.jsx)("meta",{property:"og:image",content:"https://www.pipeless.ai/docs/logo-400x400-rounded.png"})]})},useNextSeoProps:()=>({titleTemplate:"%s â Pipeless"}),banner:{dismissible:!1,key:"agents",text:(0,o.jsx)("a",{target:"blank",href:"https://agents.pipeless.ai",children:(0,o.jsxs)("p",{children:["\uD83D\uDE80 \xa0\xa0",(0,o.jsx)("b",{children:"Pipeless Agents is live!"}),"\xa0Create vision powered apps and automations effortlessly within seconds.\xa0",(0,o.jsx)("span",{style:{textDecoration:"underline"},children:"Click here to get started!"})]})})},sidebar:{defaultMenuCollapseLevel:1}};t.Z=c},945:function(e,t,s){"use strict";s.r(t),s.d(t,{__toc:function(){return l}});var o=s(5893),n=s(2673),a=s(2681),i=s(9369);s(9966);var r=s(2643);s(9013);let l=[{depth:2,value:"Example",id:"example"}];function c(e){let t=Object.assign({h1:"h1",p:"p",a:"a",h2:"h2",code:"code",ul:"ul",li:"li",pre:"pre",span:"span"},(0,r.a)(),e.components);return(0,o.jsxs)(o.Fragment,{children:[(0,o.jsx)(t.h1,{children:"Passing custom data (per frame) between hooks and stages"}),"\n","\n",(0,o.jsx)(t.p,{children:"This example contains boilerplate code that you can use to learn how to associate custom data to a frame. That custom data can be obtained back from other hooks of the same stage and even from hooks of subsequent stages."}),"\n",(0,o.jsxs)(t.p,{children:["Note this mechanism allows you to associate data to a single frame. If you need data that will be shared by all frames check the ",(0,o.jsx)(t.a,{href:"/v1/kvstore",children:"KV store documentation"}),"."]}),"\n",(0,o.jsx)(t.h2,{id:"example",children:"Example"}),"\n",(0,o.jsxs)(t.p,{children:["This example contains boilerplate code that you can find ",(0,o.jsx)(t.a,{href:"https://github.com/pipeless-ai/pipeless/tree/main/examples/custom-data",children:"here"}),"."]}),"\n",(0,o.jsxs)(t.p,{children:["To assign custom data to a frame such as bounding boxes results, masks, or anything you like, you just need to set ",(0,o.jsx)(t.code,{children:"frame_data['user_data']"}),". It accepts any type as value. The only restriction is that, if you set a dictionary as value, al the keys must be strings. Some examples:"]}),"\n",(0,o.jsxs)(t.ul,{children:["\n",(0,o.jsxs)(t.li,{children:["Integers:","\n",(0,o.jsx)(t.pre,{"data-language":"python","data-theme":"default",hasCopyCode:!0,children:(0,o.jsx)(t.code,{"data-language":"python","data-theme":"default",children:(0,o.jsxs)(t.span,{className:"line",children:[(0,o.jsx)(t.span,{style:{color:"var(--shiki-color-text)"},children:"frame_data"}),(0,o.jsx)(t.span,{style:{color:"var(--shiki-token-punctuation)"},children:"["}),(0,o.jsx)(t.span,{style:{color:"var(--shiki-token-string-expression)"},children:"'user_data'"}),(0,o.jsx)(t.span,{style:{color:"var(--shiki-token-punctuation)"},children:"]"}),(0,o.jsx)(t.span,{style:{color:"var(--shiki-color-text)"},children:" "}),(0,o.jsx)(t.span,{style:{color:"var(--shiki-token-keyword)"},children:"="}),(0,o.jsx)(t.span,{style:{color:"var(--shiki-color-text)"},children:" "}),(0,o.jsx)(t.span,{style:{color:"var(--shiki-token-constant)"},children:"100"})]})})}),"\n"]}),"\n",(0,o.jsxs)(t.li,{children:["Floats:","\n",(0,o.jsx)(t.pre,{"data-language":"python","data-theme":"default",hasCopyCode:!0,children:(0,o.jsx)(t.code,{"data-language":"python","data-theme":"default",children:(0,o.jsxs)(t.span,{className:"line",children:[(0,o.jsx)(t.span,{style:{color:"var(--shiki-color-text)"},children:"frame_data"}),(0,o.jsx)(t.span,{style:{color:"var(--shiki-token-punctuation)"},children:"["}),(0,o.jsx)(t.span,{style:{color:"var(--shiki-token-string-expression)"},children:"'user_data'"}),(0,o.jsx)(t.span,{style:{color:"var(--shiki-token-punctuation)"},children:"]"}),(0,o.jsx)(t.span,{style:{color:"var(--shiki-color-text)"},children:" "}),(0,o.jsx)(t.span,{style:{color:"var(--shiki-token-keyword)"},children:"="}),(0,o.jsx)(t.span,{style:{color:"var(--shiki-color-text)"},children:" "}),(0,o.jsx)(t.span,{style:{color:"var(--shiki-token-constant)"},children:"100.5"})]})})}),"\n"]}),"\n",(0,o.jsxs)(t.li,{children:["Strings:","\n",(0,o.jsx)(t.pre,{"data-language":"python","data-theme":"default",hasCopyCode:!0,children:(0,o.jsx)(t.code,{"data-language":"python","data-theme":"default",children:(0,o.jsxs)(t.span,{className:"line",children:[(0,o.jsx)(t.span,{style:{color:"var(--shiki-color-text)"},children:"frame_data"}),(0,o.jsx)(t.span,{style:{color:"var(--shiki-token-punctuation)"},children:"["}),(0,o.jsx)(t.span,{style:{color:"var(--shiki-token-string-expression)"},children:"'user_data'"}),(0,o.jsx)(t.span,{style:{color:"var(--shiki-token-punctuation)"},children:"]"}),(0,o.jsx)(t.span,{style:{color:"var(--shiki-color-text)"},children:" "}),(0,o.jsx)(t.span,{style:{color:"var(--shiki-token-keyword)"},children:"="}),(0,o.jsx)(t.span,{style:{color:"var(--shiki-color-text)"},children:" "}),(0,o.jsx)(t.span,{style:{color:"var(--shiki-token-string-expression)"},children:'"Hello!"'})]})})}),"\n"]}),"\n",(0,o.jsxs)(t.li,{children:["Heterogeneus arrays:","\n",(0,o.jsx)(t.pre,{"data-language":"python","data-theme":"default",hasCopyCode:!0,children:(0,o.jsx)(t.code,{"data-language":"python","data-theme":"default",children:(0,o.jsxs)(t.span,{className:"line",children:[(0,o.jsx)(t.span,{style:{color:"var(--shiki-color-text)"},children:"frame_data"}),(0,o.jsx)(t.span,{style:{color:"var(--shiki-token-punctuation)"},children:"["}),(0,o.jsx)(t.span,{style:{color:"var(--shiki-token-string-expression)"},children:"'user_data'"}),(0,o.jsx)(t.span,{style:{color:"var(--shiki-token-punctuation)"},children:"]"}),(0,o.jsx)(t.span,{style:{color:"var(--shiki-color-text)"},children:" "}),(0,o.jsx)(t.span,{style:{color:"var(--shiki-token-keyword)"},children:"="}),(0,o.jsx)(t.span,{style:{color:"var(--shiki-color-text)"},children:" ["}),(0,o.jsx)(t.span,{style:{color:"var(--shiki-token-string-expression)"},children:'"string"'}),(0,o.jsx)(t.span,{style:{color:"var(--shiki-token-punctuation)"},children:","}),(0,o.jsx)(t.span,{style:{color:"var(--shiki-color-text)"},children:" "}),(0,o.jsx)(t.span,{style:{color:"var(--shiki-token-constant)"},children:"13"}),(0,o.jsx)(t.span,{style:{color:"var(--shiki-token-punctuation)"},children:","}),(0,o.jsx)(t.span,{style:{color:"var(--shiki-color-text)"},children:" "}),(0,o.jsx)(t.span,{style:{color:"var(--shiki-token-constant)"},children:"34.6"}),(0,o.jsx)(t.span,{style:{color:"var(--shiki-color-text)"},children:"]"})]})})}),"\n"]}),"\n",(0,o.jsxs)(t.li,{children:["Heterogeneus Dictionaries (IMPORTANT: all keys must be strings):","\n",(0,o.jsx)(t.pre,{"data-language":"python","data-theme":"default",hasCopyCode:!0,children:(0,o.jsxs)(t.code,{"data-language":"python","data-theme":"default",children:[(0,o.jsxs)(t.span,{className:"line",children:[(0,o.jsx)(t.span,{style:{color:"var(--shiki-color-text)"},children:"frame_data"}),(0,o.jsx)(t.span,{style:{color:"var(--shiki-token-punctuation)"},children:"["}),(0,o.jsx)(t.span,{style:{color:"var(--shiki-token-string-expression)"},children:"'user_data'"}),(0,o.jsx)(t.span,{style:{color:"var(--shiki-token-punctuation)"},children:"]"}),(0,o.jsx)(t.span,{style:{color:"var(--shiki-color-text)"},children:" "}),(0,o.jsx)(t.span,{style:{color:"var(--shiki-token-keyword)"},children:"="}),(0,o.jsx)(t.span,{style:{color:"var(--shiki-color-text)"},children:" "}),(0,o.jsx)(t.span,{style:{color:"var(--shiki-token-punctuation)"},children:"{"})]}),"\n",(0,o.jsxs)(t.span,{className:"line",children:[(0,o.jsx)(t.span,{style:{color:"var(--shiki-color-text)"},children:" "}),(0,o.jsx)(t.span,{style:{color:"var(--shiki-token-string-expression)"}
1,children:'"key1"'}),(0,o.jsx)(t.span,{style:{color:"var(--shiki-token-punctuation)"},children:":"}),(0,o.jsx)(t.span,{style:{color:"var(--shiki-color-text)"},children:" "}),(0,o.jsx)(t.span,{style:{color:"var(--shiki-token-constant)"},children:"0"}),(0,o.jsx)(t.span,{style:{color:"var(--shiki-token-punctuation)"},children:","})]}),"\n",(0,o.jsxs)(t.span,{className:"line",children:[(0,o.jsx)(t.span,{style:{color:"var(--shiki-color-text)"},children:" "}),(0,o.jsx)(t.span,{style:{color:"var(--shiki-token-string-expression)"},children:'"key2"'}),(0,o.jsx)(t.span,{style:{color:"var(--shiki-token-punctuation)"},children:":"}),(0,o.jsx)(t.span,{style:{color:"var(--shiki-color-text)"},children:" ["}),(0,o.jsx)(t.span,{style:{color:"var(--shiki-token-constant)"},children:"1"}),(0,o.jsx)(t.span,{style:{color:"var(--shiki-token-punctuation)"},children:","}),(0,o.jsx)(t.span,{style:{color:"var(--shiki-color-text)"},children:" "}),(0,o.jsx)(t.span,{style:{color:"var(--shiki-token-string-expression)"},children:'"3"'}),(0,o.jsx)(t.span,{style:{color:"var(--shiki-color-text)"},children:"]"}),(0,o.jsx)(t.span,{style:{color:"var(--shiki-token-punctuation)"},children:","})]}),"\n",(0,o.jsxs)(t.span,{className:"line",children:[(0,o.jsx)(t.span,{style:{color:"var(--shiki-color-text)"},children:" "}),(0,o.jsx)(t.span,{style:{color:"var(--shiki-token-string-expression)"}
1,children:'"key3"'}),(0,o.jsx)(t.span,{style:{color:"var(--shiki-token-punctuation)"},children:":"}),(0,o.jsx)(t.span,{style:{color:"var(--shiki-color-text)"},children:" "}),(0,o.jsx)(t.span,{style:{color:"var(--shiki-token-punctuation)"},children:"{"}),(0,o.jsx)(t.span,{style:{color:"var(--shiki-color-text)"},children:" "}),(0,o.jsx)(t.span,{style:{color:"var(--shiki-token-string-expression)"},children:'"inner1"'}),(0,o.jsx)(t.span,{style:{color:"var(--shiki-token-punctuation)"},children:":"}),(0,o.jsx)(t.span,{style:{color:"var(--shiki-color-text)"},children:" "}),(0,o.jsx)(t.span,{style:{color:"var(--shiki-token-string-expression)"},children:'"hola"'}),(0,o.jsx)(t.span,{style:{color:"var(--shiki-color-text)"},children:" "}),(0,o.jsx)(t.span,{style:{color:"var(--shiki-token-punctuation)"},children:"}"})]}),"\n",(0,o.jsx)(t.span,{className:"line",children:(0,o.jsx)(t.span,{style:{color:"var(--shiki-token-punctuation)"},children:"}"})})]})}),"\n"]}),"\n"]}),"\n",(0,o.jsx)(t.p,{children:"With that, in later hooks (either from the same stage or other) you can obtain back the data just accessing that field:"}),"\n",(0,o.jsx)(t.pre,{"data-language":"python","data-theme":"default",hasCopyCode:!0,children:(0,o.jsx)(t.code,{"data-language":"python","data-theme":"default",children:(0,o.jsxs)(t.span,{className:"line",children:[(0,o.jsx)(t.span,{style:{color:"var(--shiki-color-text)"},children:"my_data "}),(0,o.jsx)(t.span,{style:{color:"var(--shiki-token-keyword)"},children:"="}),(0,o.jsx)(t.span,{style:{color:"var(--shiki-color-text)"},children:" frame_data"}),(0,o.jsx)(t.span,{style:{color:"var(--shiki-token-punctuation)"},children:"["}),(0,o.jsx)(t.span,{style:{color:"var(--shiki-token-string-expression)"},children:"'user_data'"}),(0,o.jsx)(t.span,{style:{color:"var(--shiki-token-punctuation)"},children:"]"})]})})}),"\n",(0,o.jsxs)(t.p,{children:["In order to connect two or more stages, simply pass the list of stage names to the ",(0,o.jsx)(t.code,{children:"add stream"})," command under the ",(0,o.jsx)(t.code,{children:"frame-path"})," option. For example:"]}),"\n",(0,o.jsx)(t.pre,{"data-language":"bash","data-theme":"default",hasCopyCode:!0,children:(0,o.jsx)(t.code,{"data-language":"bash","data-theme":"default",children:(0,o.jsxs)(t.span,{className:"line",children:[(0,o.jsx)(t.span,{style:{color:"var(--shiki-token-function)"},children:"pipeless"}),(0,o.jsx)(t.span,{style:{color:"var(--shiki-color-text)"},children:" "}),(0,o.jsx)(t.span,{style:{color:"var(--shiki-token-string)"},children:"add"}),(0,o.jsx)(t.span,{style:{color:"var(--shiki-color-text)"},children:" "}),(0,o.jsx)(t.span,{style:{color:"var(--shiki-token-string)"},children:"stream"}),(0,o.jsx)(t.span,{style:{color:"var(--shiki-color-text)"},children:" "}),(0,o.jsx)(t.span,{style:{color:"var(--shiki-token-string)"},children:"--input-uri"}),(0,o.jsx)(t.span,{style:{color:"var(--shiki-color-text)"},children:" "}),(0,o.jsx)(t.span,{style:{color:"var(--shiki-token-string-expression)"},children:'"file:///home/user/my/path.mp4"'}),(0,o.jsx)(t.span,{style:{color:"var(--shiki-color-text)"},children:" "}),(0,o.jsx)(t.span,{style:{color:"var(--shiki-token-string)"},children:"--output-uri"}),(0,o.jsx)(t.span,{style:{color:"var(--shiki-color-text)"},children:" "}),(0,o.jsx)(t.span,{style:{color:"var(--shiki-token-string-expression)"},children:'"screen"'}),(0,o.jsx)(t.span,{style:{color:"var(--shiki-color-text)"},children:" "}),(0,o.jsx)(t.span,{style:{color:"var(--shiki-token-string)"},children:"--frame-path"}),(0,o.jsx)(t.span,{style:{color:"var(--shiki-color-text)"},children:" "}),(0,o.jsx)(t.span,{style:{color:"var(--shiki-token-string-expression)"},children:'"stage1,stage2"'})]})})}),"\n",(0,o.jsxs)(t.p,{children:["Using the above command, if you added data to the frame ",(0,o.jsx)(t.code,{children:"user_data"})," field in a hook of ",(0,o.jsx)(t.code,{children:"stage1"})," you will be able to use it in the hooks of ",(0,o.jsx)(t.code,{children:"stage2"}),"."]})]})}let d={MDXContent:function(){let e=arguments.length>0&&void 0!==arguments[0]?arguments[0]:{},{wrapper:t}=Object.assign({},(0,r.a)(),e.components);return t?(0,o.jsx)(t,{...e,children:(0,o.jsx)(c,{...e})}):c(e)},pageOpts:{filePath:"pages/v1/examples/custom-data.mdx",route:"/v1/examples/custom-data",frontMatter:{title:"Passing data between stages",description:"A Pipeless example to show how you can easily add custom data to a frame, making it available for subsequent hooks."},timestamp:1706709986e3,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 â",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","custom-data":"Passing data between hooks and stages",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:"custom-data",route:"/v1/examples/custom-data",frontMatter:{title:"Passing data between stages",description:"A Pipeless example to show how you can easily add custom data to a frame, making it available for subsequent hooks."}},{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\uDEE0ï¸ 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:"Passing data between stages",headings:l},pageNextRoute:"/v1/examples/custom-data",nextraLayout:a.ZP,themeConfig:i.Z};t.default=(0,n.j)(d)}},function(e){e.O(0,[13,996,774,888,179],function(){return e(e.s=9040)}),_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.