SKaiNET Kotlin Notebook

Empower your deep machine learning data science workflows with Kotlin’s type safety and expressiveness in Kotlin Notebooks.

Overview

SKaiNET project is an open-source deep learning framework written in Kotlin, designed with developers in mind to enable the creation of modern AI-powered applications with ease. It seamlessly integrates with Jupyter notebooks, providing a powerful environment for interactive data analysis, machine learning experimentation, and model development.

Features

• Full Kotlin language support in Kotlin Notebooks

• Interactive data visualization capabilities

• Seamless integration with popular ML libraries

• Type-safe data manipulation

• Rich markdown and documentation support

Using with Jupyter Notebook

IntelliJ IDEA

You can create Kotlin notebooks directly in IntelliJ IDEA using one of these methods:

Within a Project

• Right-click on source root/folder in Project view

• Select New → Kotlin Notebook

Scratch Notebook

• Press kbd:[Cmd+Shift+N] (macOS) or kbd:[Ctrl+Alt+Shift+Insert] (Windows/Linux)

• Select Kotlin Notebook

SKaiNET notebook dependency

Use SKaiNET directly in a Kotlin Notebook via Maven Central.

@file:DependsOn("sk.ainet.app:kotlin-notebook:0.5.0")

code

val input = data<FP32, Float>(ctx) {
    tensor<FP32, Float>() {
        shape(5) {
            fromArray(
                floatArrayOf(1f,2f,3f,4f,5f)
            )
        }
    }
}

Inline image display (Quick Start)

You can convert tensors to images and show them in a Kotlin notebook using our utils:

import sk.ainet.app.notebook.tools.Layout
import sk.ainet.app.notebook.tools.toImage

imageTensor.toImage(Layout.CHW)