Pipcook can be divided into the following 3 layers from top to bottom.
It defines flexible and intuitive APIs to build machine-learning application, even though you don't know the details of algorithm.
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.
Pipcook Bridge to Python
Prepare the following on your machine:
Install the command-line tool for managing Pipcook projects:
$ npm install -g @pipcook/pipcook-cli
Initialize a project:
$ mkdir pipcook-example && cd pipcook-example $ pipcook init
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
$ pipcook board
You 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.
- pipeline-mnist-image-classification: pipeline for classific Mnist image classification problem.
- pipeline-databinding-image-classification: pipeline example to train the iamge classification task which is to classifify imgcook databinding pictures.
- pipeline-object-detection: pipeline example to train object detection task which is for component recognition used by imgcook.
- python-keras: example to use Python Keras library to train deep leraning network in js syntax and runtime.
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:
$ node examples/pipeline/pipeline-mnist-image-classification.js
Clone this repository:
$ git clone email@example.com:alibaba/pipcook.git
$ lerna -v $ tsc -v
or install via npm:
$ npm install
After the above, now build the project:
$ npm run build