OpenViSTAI · Agents · Blockchain · Crypto
Submit a project
Menu
AI Developer Tools & SDKs

Pipcook

Pipcook is an open-source JavaScript framework for machine-learning applications and engineering workflows. It combines a pipeline framework, management tools and a JavaScript runtime; pipelines use plugins for dataset handling, training, validation and deployment, and produce an npm package containing a trained model and JavaScript functions. It also describes a Boa bridge that lets Node.js code access Python packag

Explore the practical guide ↓

Independent OpenViST project profile, based on linked sources.

Pipcook — OpenViST project profile in AI Developer Tools & SDKs.
OpenViST project illustration. See the linked sources for product details.
Understand the project

Practical overview

When it fits

Use Pipcook when you want to build a machine-learning workflow from JavaScript and its pipeline model fits your task, such as training an image classifier and serving it locally. Consider its Boa bridge when a workflow needs Python packages from a Node.js environment.

Who it is for

  • Web and JavaScript engineers learning machine learning
  • Developers building, training and serving models
  • Teams seeking to evaluate or optimize models within JavaScript-oriented workflows

Documented uses

  • Author machine-learning pipelines from scripts and configurations
  • Train and validate models, including an example image-classification workflow
  • Package trained models with JavaScript functions for use in applications
  • Serve a trained model locally for browser-based interaction
  • Access named Python packages such as NumPy, scikit-learn, jieba and TensorFlow from Node.js through Boa
Before you choose

Requirements & limits

The project is public on GitHub and licensed under Apache-2.0. The documented CLI setup requires Node.js and npm.

  • Pipelines are modular and support plugins for datasets, training, validation and deployment.
  • A valid pipeline must output a trained model as its final stage.
  • Pipeline output is described as an npm package containing a model and JavaScript functions.
  • Boa provides access to Python packages through CPython interfaces using N-API.
  • The README specifies minimum Node.js and npm versions for CLI setup.

Check current requirements in the official documentation. Source-based guidance does not mean OpenViST has run or independently tested this project.

Source-based practical guide

Train and serve the example image classifier

Use Pipcook’s example pipeline to train an image classifier, predict a validation image’s category, and serve the resulting model.

Before you start

  • Node.js >= 12.17 or >= 14.0.0
  • npm >= 6.14.4

Steps

  1. Install the Pipcook CLI: the command below.

    Documented command
    npm install -g @pipcook/cli

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

  3. Predict the category of the example validation image: pipcook predict ./output/image-classification-mobilenet.json -s ./output/data/validation/blurBackground/71197_223__30.7_36.jpg.

  4. Serve the trained output with pipcook serve ./output, then open the reported local address in a browser.

Expected outcome

The README shows the example prediction classified the image as `blurBackground` and the server starting at `http://localhost:9091`.

Guide limitations

  • The documented example uses its supplied image-classification pipeline and validation image; other datasets or workflows are not specified here.
  • The pipeline output must be a trained model for the pipeline to be valid.
Documentation 1 ↗
Evidence and transparency

Sources & review

Official repository ↗
Source record details
Source availability
Active
Sources checked
10 October 2026
Original website title
alibaba/pipcook · Official homepage title · retained in its source language
How we review sourcesSuggest a correction