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Install the Library

Kinetix ML should be installed as a node module using the command below. 
yarn add kml-pipe-ts
There is a known issue with Tensorflow JS dependencies when installing using the npm command so please use yarn. They are for the most part interoperable.

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Initialization

First start by importing the module. 
import { KMLPipeline } from 'kml-pipe-ts';
Create the pipeline object using your API Key and project name. 
var pipeline = new KMLPipeline("[Your Project Name]", "[Your API Key]");
Initialize the pipeline. 
await pipeline.initialize();

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Execution

To execute the pipeline you need to run the execute method with the required input data. 
await pipeline.execute([]) // the [] should be replaced with your list of inputs

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Webcam Inference

The pipeline will need a data source for each of its inputs. A common input is image data for nodes such as PoseDetection2D. For these nodes you should first create a video source element. 
<video id="webcam" autoplay />
Now we need to make a function that we can call for every frame 
let outputs = []
let processing = false
async function runInference() {
  if (!processing) {
      processing = true;
      outputs = await pipe.execute([videoSource]);
      processing = false;
    }
  document.getElementById("webcam").requestVideoFrameCallback(runInference); // run inference on every frame
};
Then, to use your webcam, run the following function which will ask the user for webcam permission, start playing the video, and start inference. 
async function startWebcam() {
    const stream = await navigator.mediaDevices.getUserMedia({ video: true });
    document.getElementById("webcam").srcObject = stream;
    await document.getElementById("webcam").play();
    document.getElementById("webcam").requestVideoFrameCallback(runInference);
};