Empower your deep machine learning data science workflows with Kotlin’s type safety and expressiveness in Jupyter notebooks.
skainet 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.
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Full Kotlin language support in Jupyter notebooks
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Interactive data visualization capabilities
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Seamless integration with popular ML libraries
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Type-safe data manipulation
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Rich markdown and documentation support
You can create Kotlin notebooks directly in IntelliJ IDEA using one of these methods:
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Within a Project:
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Right-click on source root/folder in Project view
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Select
New
→Kotlin Notebook
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Scratch Notebook:
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Press kbd:[Cmd+Shift+N] (macOS) or kbd:[Ctrl+Alt+Shift+Insert] (Windows/Linux)
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Select
Kotlin Notebook
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From Welcome Screen:
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Navigate to
Kotlin Notebooks
tab -
Click
New Notebook
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Install Jupyter and Kotlin Kernel:
$ pip install jupyter $ jupyter kernelspec install kotlin
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Create a New Notebook:
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Launch Jupyter:
jupyter notebook
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Select "New" → "Kotlin"
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Basic Usage:
// Import skainet libraries import ai.tribit.skainet.* // Example code val data = DataFrame.readCsv("data.csv") data.head(5).show()
Here’s a concise instruction snippet for setting up dependencies in Gradle KTS to consume your open-source project:
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Configure repository Add the following to your project’s
build.gradle.kts
:
repositories {
maven {
url = uri("https://maven.pkg.github.com/sk-ai-net/skainet")
credentials {
username = providers.gradleProperty("gpr.user")
.orElse(System.getenv("GITHUB_ACTOR"))
.get()
password = providers.gradleProperty("gpr.token")
.orElse(System.getenv("GITHUB_TOKEN"))
.get()
}
}
}
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Set GitHub credentials Ensure you provide your GitHub username (
gpr.user
) and a personal access token (gpr.token
) with package read permission.