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Pipcook: A JavaScript Application Framework for Machine Learning

 A JavaScript application framework for machine learning and its engineering.  

Builds

Build TypesStatus
tests
documentation
docker

Why Pipcook

With the mission of enabling JavaScript engineers to utilize the power of machine learning without any prerequisites and the vision to lead front-end technical field to the intelligention. Pipcook is to become the JavaScript application framework for the cross-cutting area of machine learning and front-end interaction.

We are truly to design Pipcook's API for front-end and machine learning applications, and focusing on the front-end area and developed from the JavaScript engineers' view. With the principle of being friendly to JavaScript, we will push the whole area forward with the machine learning engineering. For this reason we opened an issue about machine-learning application APIs, and look forward to you get involved.

What's Pipcook

The project provides subprojects including machine learning pipeline framework, management tools, a JavaScript runtime for machine learning, and these can be also used as building blocks in conjunction with other projects.

Principles

Pipcook is an open-source project guided by strong principles, aiming to be modular and flexible on user experience. It is open to the community to help set its direction.

  • Modular the project includes some of projects that have well-defined functions and APIs that work together.
  • Swappable the project includes enough modules to build what Pipcook has done, but its modular architecture ensures that most of the modules can be swapped by different implementations.

Audience

Pipcook is intended for Web engineers looking to:

  • learn what's machine learning.
  • train their models and serve them.
  • optimize own models for better model evaluation results, like higher accuracy for image classification.

If you are in the above conditions, just try it via installation guide.

Subprojects

Pipcook Pipeline

It's used to represent ML pipelines consisting of Pipcook scripts. This layer ensures the stability and scalability of the whole system and uses a plug-in mechanism to support rich functions including dataset, training, validations, and deployment.

A Pipcook Pipeline is generally composed of lots of scripts. Through different scripts and configurations, the final output to us is an NPM package, which contains the trained model and JavaScript functions that can be used directly.

Note: In Pipcook, each pipeline has only one role, which is to output the above-trained model you need. That is to say, the last stage of each pipeline must be the output of the trained model, otherwise, this Pipeline is invalid.

Pipcook Bridge to Python

For JavaScript engineers, the most difficult part is the lack of a mature machine learning toolset in the ecosystem. In Pipcook, a module called [Boa][https://github.com/imgcook/boa], which provides access to Python packages by bridging the interface of CPython using N-API.

With it, developers can use packages such as numpy, scikit-learn, jieba, tensorflow, or any other Python ecology in the Node.js runtime through JavaScript.

Quick start

Setup

Prepare the following on your machine:

InstallerVersion Range
[Node.js][]>= 12.17 or >= 14.0.0
[npm][]>= 6.14.4

Install the command-line tool for managing Pipcook projects:

$ npm install -g @pipcook/cli

Then train from anyone of those pipelines, we take image classification as an example:

$ pipcook train https://cdn.jsdelivr.net/gh/alibaba/pipcook@main/example/pipelines/image-classification-mobilenet.json -o ./output

This dataset specfied by the pipeline includes 2 categories image: avatar and blurBackground. After training, we can predict the category of a image:

$ pipcook predict ./output/image-classification-mobilenet.json -s ./output/data/validation/blurBackground/71197_223__30.7_36.jpg
✔ Origin result:[{"id":1,"category":"blurBackground","score":0.9998120665550232}]

The input is a blurBackground image from the validation dataset. And the model determines that its category is blurBackground.

Want to deploy it?

$ pipcook serve ./output
ℹ preparing framework
ℹ preparing scripts
ℹ preparing artifact plugins
ℹ initializing framework packages
Pipcook has served at: http://localhost:9091

Then you can open the browser and try your image classification server.

Playground

If you are wondering what you can do in Pipcook and where you can check your training logs and models, you could start from Pipboard:

open https://pipboard.imgcook.com

You will see a web page prompt in your browser, and there is a MNIST showcase on the home page and play around there.

Pipelines

If you want to train a model to recognize MNIST handwritten digits by yourself, you could try the examples below.

NameDescriptionOpen in Colab
mnist-image-classificationpipeline for classific MNIST image classification problem.N/A
databinding-image-classificationpipeline example to train the image classification task which is 
to classify imgcook databinding pictures.
Open In Colab
object-detectionpipeline example to train object detection task which is for component recognition 
used by imgcook.
Open In Colab
text-bayes-classificationpipeline example to train text classification task with bayesN/A

See here for complete list, and it's easy and quick to run these examples. For example, to do a MNIST image classification, just run the following to start the pipeline:

$ pipcook run https://cdn.jsdelivr.net/gh/alibaba/pipcook@main/example/pipelines/image-classification-mobilenet.json -o output

After the above pipeline is completed, you have already trained a model at the current output/model directory, it's a tensorflow.js model.

Developers

Clone this repository:

$ git clone git@github.com:alibaba/pipcook.git

Install dependencies, e.g. via npm:

$ npm install

After the above, now build the project:

$ npm run build

Community

DingTalk

Or searched via the group number: 30624012.

Download DingTalk (an all-in-one free communication and collaboration platform) here: English | 中文

Gitter Room

 

Who's using it


Author: alibaba
Source code: https://github.com/alibaba/pipcook
License: Apache-2.0 license

#javascript #typescript 

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Pipcook: A JavaScript Application Framework for Machine Learning

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Machine learning is one of the technologies that have already begun their promising marks in the transportation industry.Autonomous Vehicles,Smartphone Apps,Traffic Management Solutions,Law Enforcement,Passenger Transportation etc are the applications of AI and ML in the transportation industry.Following challenges in the transportation industry can be solved by machine learning and Artificial Intelligence.

  • ML and AI can offer high security in the transportation industry.
  • It offers high reliability of their services or vehicles.
  • The adoption of this technology in the transportation industry can increase the efficiency of the service.
  • In the transportation industry ML helps scientists and engineers come up with far more environmentally sustainable methods for powering and operating vehicles and machinery for travel and transport.

Healthcare industry

Technology-enabled smart healthcare is the latest trend in the healthcare industry. Different areas of healthcare, such as patient care, medical records, billing, alternative models of staffing, IP capitalization, smart healthcare, and administrative and supply cost reduction. Hire dedicated machine learning developers for any of the following applications.

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  • Drug Discovery and Manufacturing
  • Medical Imaging Diagnosis
  • Personalized Medicine
  • Machine Learning-based Behavioral Modification
  • Smart Health Records
  • Clinical Trial and Research
  • Better Radiotherapy
  • Crowdsourced Data Collection
  • Outbreak Prediction

**
Finance industry**

In financial industries organizations like banks, fintech, regulators and insurance are Adopting machine learning to improve their facilities.Following are the use cases of machine learning in finance.

  • Fraud prevention
  • Risk management
  • Investment predictions
  • Customer service
  • Digital assistants
  • Marketing
  • Network security
  • Loan underwriting
  • Algorithmic trading
  • Process automation
  • Document interpretation
  • Content creation
  • Trade settlements
  • Money-laundering prevention
  • Custom machine learning solutions

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Education industry is one of the industries which is investing in machine learning as it offers more efficient and easierlearning.AdaptiveLearning,IncreasingEfficiency,Learning Analytics,Predictive Analytics,Personalized Learning,Evaluating Assessments etc are the applications of machine learning in the education industry.

Outsource your machine learning solution to India,India is the best outsourcing destination offering best in class high performing tasks at an affordable price.Business** hire dedicated machine learning developers in India for making your machine learning app idea into reality.
**
Future of machine learning

Continuous technological advances are bound to hit the field of machine learning, which will shape the future of machine learning as an intensively evolving language.

  • Improved Unsupervised Algorithms
  • Increased Adoption of Quantum Computing
  • Enhanced Personalization
  • Improved Cognitive Services
  • Rise of Robots

**Conclusion
**
Today most of the business from different industries are hire machine learning developers in India and achieve their business goals. This technology may have multiple applications, and, interestingly, it hasn’t even started yet but having taken such a massive leap, it also opens up so many possibilities in the existing business models in such a short period of time. There is no question that the increase of machine learning also brings the demand for mobile apps, so most companies and agencies employ Android developers and hire iOS developers to incorporate machine learning features into them.

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