pipcook

alibaba/pipcook is ranked 8,491 among these 8 peers; median-research-group/LibMTL is ranked 8,492.

A JavaScript application framework for machine learning and its engineering. 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. 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, which provides access to Python packages

by bridging the interface of CPython using N-API. With it, developers can use packages such as , , , , or any other Python ecology in the Node. 4 | Install the command-line tool for managing Pipcook projects: Then train from anyone of those pipelines, we take image classification as an example: This dataset specfied by the pipeline includes 2 categories image: avatar and blurBackground. After training, we can predict the category of a image: The input is a image from the validation

dataset. And the model determines that its category is . Then you can open the browser and try your image classification