A JavaScript application framework for machine learning and its engineering.
| Build Types | Status |
|---|---|
| tests |  |
| pipeline |  |
| release |  |
| documentation |  |
| docker |  |
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.
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.
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.
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.
Pipcook Pipeline
It's used to represent ML pipelines consisting of Pipcook plugins. 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 plugins. Through different plugins 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 numpy, scikit-learn, jieba, tensorflow, or any other Python ecology in the Node.js runtime through JavaScript.
Prepare the following on your machine:
| Installer | Version Range |
|---|---|
| Node.js | >= 12.19 |
| npm | >= 6.1 |
Install the command-line tool for managing Pipcook projects:
$ npm install -g @pipcook/pipcook-cli
$ pipcook init
$ pipcook daemon startOccuring the download problems? We use tuna mirror to address this issue:
$ pipcook init --tunaIf want to specify the version of daemon and pipboard, we can use:
$ pipcook init 1.1.0Or directly use option --beta to specify the beta version:
$ pipcook init --betaThen run a pipeline:
$ pipcook run https://raw.githubusercontent.com/alibaba/pipcook/master/example/pipelines/text-bayes-classification.jsonIf 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/#/tutorialYou will see a web page prompt in your browser, and there is a MNIST showcase on the home page and play around there.
If you want to train a model to recognize MNIST handwritten digits by yourself, you could try the examples below.
| Name | Description | Open in Colab |
|---|---|---|
| mnist-image-classification | pipeline for classific MNIST image classification problem. | N/A |
| databinding-image-classification | pipeline example to train the image classification task which is to classify imgcook databinding pictures. |
 |
| object-detection | pipeline example to train object detection task which is for component recognition used by imgcook. |
|
| text-bayes-classification | pipeline example to train text classification task with bayes | N/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://raw.githubusercontent.com/alibaba/pipcook/master/example/pipelines/mnist-image-classification.jsonAfter the above pipeline is completed, you have already trained a model at the current output directory, it's an independent NPM package and can be easily integrated in your existing system.
Clone this repository:
$ git clone [email protected]:alibaba/pipcook.gitInstall dependencies, e.g. via yarn:
$ yarnAfter the above, now build the project:
$ yarn build && yarn init-dev- Developer Documentation English | 中文
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