Introduction to Pipcook
Pipcook is a comprehensive JavaScript-based machine learning and artificial intelligence development platform. It simplifies the process of creating, training, and deploying ML models with easy-to-use APIs. This guide introduces Pipcook’s powerful APIs with practical code snippets and a sample application.
API Examples
Pipcook.createPipeline()
This function allows you to create a pipeline for executing machine learning processes.
const pipcook = require('@pipcook/core'); pipcook.createPipeline({ plugins: [ { package: '@pipcook/plugins-csv-data-collect', params: { url: 'https://example.com/dataset.csv' } }, { package: '@pipcook/plugins-bayesian-classify-model-define' }, { package: '@pipcook/plugins-model-train' }, { package: '@pipcook/plugins-model-evaluate' } ] });
Pipcook.runPipeline()
This function runs the created pipeline and trains the model based on the dataset.
pipcook.runPipeline('path/to/your/pipeline/config').then(result => { console.log(result); });
Pipcook.loadDataset()
This function loads a dataset for training the model.
pipcook.loadDataset('path/to/your/dataset').then(dataset => { console.log(dataset); });
Pipcook.trainModel()
This function trains the model using the specified dataset.
pipcook.trainModel(model, dataset).then(trainedModel => { console.log(trainedModel); });
Sample Application
The following is an example application utilizing Pipcook APIs to create and train a machine learning model for image classification:
const pipcook = require('@pipcook/core'); async function createImageClassificationModel() { // Create a pipeline await pipcook.createPipeline({ plugins: [ { package: '@pipcook/plugins-image-classification-data-collect', params: { url: 'https://example.com/image-dataset.zip' } }, { package: '@pipcook/plugins-image-classification-data-process' }, { package: '@pipcook/plugins-image-classification-model-define' }, { package: '@pipcook/plugins-model-train' }, { package: '@pipcook/plugins-model-evaluate' } ] }); // Run the pipeline, train the model, and get the result const result = await pipcook.runPipeline('path/to/your/pipeline/config'); console.log(result); } // Execute the function createImageClassificationModel();
Hash: 549be0179e18739dfd5562b717112136caa9e09b8742cecda8af4e84f8a2a212