Build and Deploy ML Models with Pipcook 2.0 in Under 20 Seconds

Pipcook 1.0 allowed web developers to start machine learning with a low barrier, but users reported difficult installation, low success rates, and long setup times (often over three minutes). Pipcook 2.0 addresses these issues with a complete rewrite and optimization.

Installation time drops from several minutes to less than 20 seconds, and the daemon no longer needs to be installed via

pipcook init

. The lighter package size yields a near‑100% installation success rate.

Model training and service launch for a text‑classification task now take only about 20 seconds.

Pipeline Introduction

In Pipcook, a Pipeline defines the workflow of a model. Four pipelines are currently provided:

image classification MobileNet

image classification ResNet

text classification Bayes

object detection YOLO

The article demonstrates the

text classification Bayes

pipeline.

A Pipeline is described with JSON that specifies the datasource, dataflow, model script, artifacts, and options.

<code>{
  "specVersion": "2.0",
  "type": "ImageClassification",
  "datasource": "https://cdn.jsdelivr.net/gh/imgcook/pipcook-script@5ec4cdf/scripts/image-classification/build/datasource.js?url=http://ai-sample.oss-cn-hangzhou.aliyuncs.com/image_classification/datasets/imageclass-test.zip",
  "dataflow": [
    "https://cdn.jsdelivr.net/gh/imgcook/pipcook-script@5ec4cdf/scripts/image-classification/build/dataflow.js?size=224&size=224"
  ],
  "model": "https://cdn.jsdelivr.net/gh/imgcook/pipcook-script@5ec4cdf/scripts/image-classification/build/model.js",
  "artifacts": [],
  "options": {
    "framework": "[email protected]",
    "train": {
      "epochs": 10
    }
  }
}
</code>

The JSON fields include

datasource

,

dataflow

,

model

,

artifacts

, and

options

. Supported pipeline types are

ImageClassification

,

TextClassification

, and

ObjectDetection

, with more to be added in future releases.

Running a Pipeline

Installation

Operating system: macOS or Linux (Windows has limited support)

Node.js v12.17.0 or v14.0.0 and above (v13 not supported)

Install the CLI globally:

<code>$ npm install @pipcook/cli -g</code>

Training

Save the pipeline definition as

image-classification.json

and run:

<code>$ pipcook train ./image-classification.json -o my-pipcook</code>

Sample log output shows framework preparation, script download, dataset extraction, and training progress across 10 epochs, ending with the model saved in the workspace.

<code>ℹ preparing framework
ℹ preparing scripts
ℹ preparing artifact plugins
ℹ initializing framework packages
ℹ running datasource script
... (truncated log) ...
ℹ pipeline finished, the model has been saved at /Users/pipcook-playground/my-pipcook/model</code>

The workspace layout after training:

<code>my-pipcook
    ├── cache
    ├── data
    ├── framework -> /Users/pipcook-playground/.pipcook/framework/... 
    ├── image-classification.json
    ├── model
    └── scripts
</code>

Prediction

Place an image (e.g.,

avatar.jpg

) and run:

<code>$ pipcook predict ./my-pipcook -s ./avatar.jpg</code>

The result shows the category

avatar

with a confidence of 0.99999.

Deployment

Serve the trained model:

<code>$ pipcook serve ./my-pipcook/image-classification.json</code>

The service starts at

http://localhost:9091

(default port, configurable with

-p

). A web UI allows users to upload images and obtain predictions.

Conclusion

Pipcook 2.0 dramatically reduces the friction of building, training, and deploying machine‑learning models for web developers. The project welcomes stars, issues, pull requests, and community participation via its GitHub repositories.

Pipcook repository: https://github.com/alibaba/pipcook Script repository: https://github.com/imgcook/pipcook-script