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Showing content from https://github.com/alibaba/pipcook/issues/33 below:

the application framework for machine learning · Issue #33 · alibaba/pipcook · GitHub

The vision of Pipcook is to take the JavaScript developers and engineers into the world of machine learning quickly and seamlessly, then we're responsible for creating easy enough APIs.

In the Pipcook stack, the pipcook-app is to be defined the ML application, which abstracts some duplicated stuffs and hides low-level algorithm implementation which requires a learning curve for every ML rookie.

Every module represents a type of dataset, and basically we provide some different methods for developers.

This module is to create machine learning functions, it provides the core abilities to represent your machine learning application in an intuitive way.

To hide the ML details as possible, Pipcook lets your declare your functions for machine learning purpose in a specific type ml.Function, you can create a ml.Function via the following create() function.

Internally, the Pipcook compiler parses the applications, then generates the training code via the ml.Function instances, and replaces these slots with model generated inferences.

This interface is to describe the internal machine learning internals for applications, and it accepts an argument in data.MLType as the input, however the output's type is not required.

This is to create the above ml.Function with a ml.FunctionImpl object.

const mlfunc: ml.Function = ml.create((input: ml.ImageType) => {
  // call other ML Application APIs here and return
});

// ...
mlfunc(new ml.ImageType(...)); // call this function anywhere.

This module is to declare all types for your application's I/O.

It's the base interface to tell the Pipcook compiler a type for ML.

It represents the image type for given ml.Function I/O.

It represents the text type for given ml.Function I/O.

This module provides vision-related functions like image classification and object detection.

it represents the position in 2d for object detections:

• label {string} the label string represents the object's type.
• left {number} the left of detected object in pixel.
• top {number} the top of detected object in pixel.
• height {number} the height of detected object.
• width {number} the width of detected object.

It recognizes the type of image, and returns the type string.

ml.create((img: data.ImageType) => {
  const label = vision.classify(img); // returns the label
});

It detects target from a single image, and returns the position and label of detected objects.

ml.create((img: data.ImageType) => {
  const objects = vision.detect(img);
  objects.forEach((o) => {
    console.log(o.label, o.left, o.top); // prints the label, left and top.
  });
});

This module provides NLP-related functions like text classification and clustering.

• label {string} the label for this cluster.
• items {string[]} the strings in this cluster.

• clusters {nlp.Cluster[]} all grouped clusters, and each is an object of nlp.Cluster.
• noises {string[]} all labeled noises strings.

it recognizes the type of text, and returns the type string.

it clusters all types of given inputs, and returns the result in nlp.ClusteringResult.

The anti-API means the API must be hidden under the application user, there is a list here:

• hide the training workflow, therefore some interfaces to train and predict should be invisible.
• hide the dataset workflow, in the future, developer uses a tool for dataset processing and validation.
• hide the model-related APIs: graph structure, parameters and model validation.
• hide the serving implementation, every ML application should be serve-able in pipcook-app, thus we don't any other APIs for serving models specially.

// example.ts
import { ml, vision, data } from '@pipcook/pipcook-app';

class MyImage extends data.ImageType {
  constructor(x, y, buffer) {
    super(x, y, buffer, 100, 100);
  }
}

const listAvatars: ml.Function = ml.create((img: MyImage) => {
  const components = vision.recognizeComponent(img);
  if (!component)
    return false;

  components.map((item: UIView) => {
    const img = item.toImage() as UIImage;
    return vision.detectFace(img);
  }).filter((avatar: data.FaceType) => {
    return avatar !== null;
  });
});

// use the listAvatars function for your use
const app = express();
app.get('/', (req, res) => {
  const img = new MyImage(req.body.x, req.body.y, req.body.buffer);
  res.json(listAvatars(img).toJSON());
});

Then run the following commands to train:

$ pipcook train example.ts --epoch=5 --no-validation
generated the model at example.ts.im

And run your ML application:

$ pipcook try example.ts
$ pipcook deploy example.ts --eas=xxx

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