Kotlin kernel for IPython/Jupyter
Beta version. Tested with Jupyter Notebook 6.0.3, Jupyter Lab 1.2.6 and Jupyter Console 6.1.0 on Windows, Ubuntu Linux and macOS.
To start using Kotlin kernel for Jupyter take a look at introductory guide.
Example notebooks can be found in the samples folder
There are three ways to install the kernel:
If you have
conda installed, just run the following command to install stable package version:
conda install -c jetbrains kotlin-jupyter-kernel (package home)
To install conda package from the dev channel:
conda install -c jetbrains-dev kotlin-jupyter-kernel (package home)
conda remove kotlin-jupyter-kernel
You can also install this package through
pip install kotlin-jupyter-kernel (package home)
pip install -i https://test.pypi.org/simple/ kotlin-jupyter-kernel (package home)
pip uninstall kotlin-jupyter-kernel
git clone --recurse-submodules https://github.com/Kotlin/kotlin-jupyter.git cd kotlin-jupyter ./gradlew install
Default installation path is
To install to some other location use option
-PinstallPath=, but note that Jupyter
looks for the kernel specs files only in predefined places. For more detailed info
see Jupyter docs.
There could be a problem with kernel spec detection because of different python environments and installation modes. If you are using pip or conda to install the package, try running post-install fixup script:
python -m kotlin_kernel fix-kernelspec-location
This script replaces kernel specs to the "user" path where they are always detected. Don't forget to re-run this script on the kernel update.
jupyter console --kernel=kotlin
To start using
kotlin kernel inside Jupyter Notebook or JupyterLab create a new notebook with
The default kernel will use the JDK pointed to by the environment variable
JAVA_HOME if the first is not set.
JVM arguments will be set from the environment variable
JAVA_OPTS if the first is not set.
Additionally, arguments from
KOTLIN_JUPYTER_JAVA_OPTS_EXTRA will be added.
Arguments are parsed using
To create a kernel for a specific JDK, JVM arguments, and environment variables, you can use the
python -m kotlin_kernel add-kernel [--name name] [--jdk jdk_home_dir] [--set-jvm-args] [--jvm-arg arg]* [--env KEY VALUE]* [--force]
The command uses
@argfile (you will need to escape the
@ in powershell), and
--opt=value are all supported.
--jvm-arg=arg in particular
is needed when passing JVM arguments that start with
jdk not specified,
name is required. If
name is not specified but
jdk is the name will be
JDK $vendor $version detected from the JDK. Regardless, the actual name of the kernel will be
and the directory will be
kotlin_$name with the spaces in
name replaced by underscores
(so make sure it's compatible with your file system).
JVM arguments are joined with a
' ', so multiple JVM arguments in the same argument are supported.
The arguments will be added to existing ones (see above section) unless
--set-jvm-args is present, in which case they
will be set to
KOTLIN_JUPYTER_JAVA_OPTS. Note that both adding and setting work fine alongside
While jupyter kernel environment variable substitutions are supported in
env, note that if the used environment
variable doesn't exist, nothing will be replaced.
python -m kotlin_kernel add-kernel --name "JDK 15 Big 2 GPU" --jdk ~/.jdks/openjdk-15.0.2 --jvm-arg=-Xmx8G --env CUDA_VISIBLE_DEVICES 0,1
The following REPL commands are supported:
:help- display help
:classpath- show current classpath
:vars- get visible variables values
Dependencies resolving annotations
It is possible to add dynamic dependencies to the notebook using the following annotations:
@file:DependsOn(<coordinates>)- adds artifacts to classpath. Supports absolute and relative paths to class directories or jars, ivy and maven artifacts represented by the colon separated string
@file:Repository(<absolute-path>)- adds a directory for relative path resolution or ivy/maven repository. To specify Maven local, use
Note that dependencies in remote repositories are resolved via Ivy resolver.
Caches are stored in
~/.ivy2/cache folder by default. Sometimes, due to network
issues or running several artifacts resolutions in parallel, caches may get corrupted.
If you have some troubles with artifacts resolution, please remove caches, restart kernel
and try again.
The following maven repositories are included by default:
The following line magics are supported:
%use- injects code for supported libraries: artifact resolution, default imports, initialization code, type renderers. Usage example:
%use klaxon(5.5), lets-plot
%trackClasspath- logs any changes of current classpath. Useful for debugging artifact resolution failures. Usage example:
%trackExecution- logs pieces of code that are going to be executed. Useful for debugging of libraries support. Usage example:
%useLatestDescriptors- use latest versions of library descriptors available. By default, bundled descriptors are used. Usage example:
%output- output capturing settings. Usage example:
%output --max-cell-size=1000 --no-stdout --max-time=100 --max-buffer=400
%logLevel- set logging level. Usage example:
See detailed info about line magics here.
When a library is included with
%use keyword, the following functionality is added to the notebook:
- repositories to search for library artifacts
- artifact dependencies
- default imports
- library initialization code
- renderers for special types, e.g. charts and data frames
This behavior is defined by
json library descriptor. Descriptors for all supported libraries can be found in libraries repository.
A library descriptor may provide a set of properties with default values that can be overridden when library is included.
The major use case for library properties is to specify a particular version of library. If descriptor has only one property, it can be
defined without naming:
If library descriptor defines more than one property, property names should be used:
%use spark(scala=2.11.10, spark=2.4.2)
Several libraries can be included in single
%use statement, separated by
%use lets-plot, krangl, mysql(8.0.15)
You can also specify the source of library descriptor. By default, it's taken from the libraries repository. If you want to try descriptor from another revision, use the following syntax:
// Specify some git tag from this repository %use email@example.com // Specify commit sha, with more verbose syntax %use lets-plot@ref[24a040fe22335648885b106e2f4ddd63b4d49469] // Specify git ref along with library arguments %use krangl@dev(0.10)
Other options are resolving library descriptor from a local file or from remote URL:
// Load library from file %use mylib@file[/home/user/lib.json] // Load library from file: kernel will guess it's a file actually %use @/home/user/libs/lib.json // Or use another approach: specify a directory and file name without // extension (it should be JSON in such case) before it %use lib@/home/user/libs // Load library descriptor from a remote URL %use herlib@url[https://site.com/lib.json] // If your URL responds with 200(OK), you may skip `url` part: %use @https://site.com/lib.json // You may omit library name for file and URL resolution: %use @file[lib.json]
List of supported libraries:
- biokotlin - BioKotlin aims to be a high-performance bioinformatics library that brings the power and speed of compiled programming languages to scripting and big data environments.
- combinatoricskt - A combinatorics library for Kotlin
- coroutines - Asynchronous programming and reactive streams support
- dataframe - Kotlin framework for structured data processing
- deeplearning4j - Deep learning library for the JVM
- deeplearning4j-cuda - Deep learning library for the JVM (CUDA support)
- default - Default imports: dataframe and Lets-Plot libraries
- exposed - Kotlin SQL framework
- fuel - HTTP networking library
- ggdsl - Lets-plot and Dataframe ggdsl
- ggdsl-dataframe - Kotlin Dataframe integration for ggdsl
- ggdsl-echarts - Kotlin plotting DSL for Apache ECharts
- ggdsl-lets-plot - Kotlin plotting DSL for Lets-Plot
- gral - Java library for displaying plots
- jdsp - Java library for signal processing
- kalasim - Discrete event simulator
- kaliningraph - Graph library with a DSL for constructing graphs and visualizing the behavior of graph algorithms
- khttp - HTTP networking library
- klaxon - JSON parser for Kotlin
- kmath - Experimental Kotlin algebra-based mathematical library
- kotlin-dl - KotlinDL library which provides Keras-like API for deep learning
- kotlin-statistics - Idiomatic statistical operators for Kotlin
- krangl - Kotlin DSL for data wrangling
- kraphviz - Graphviz wrapper for JVM
- kravis - Kotlin grammar for data visualization
- lets-plot - ggplot-like interactive visualization for Kotlin
- lets-plot-dataframe - A bridge between Lets-Plot and dataframe libraries
- lets-plot-gt - Lets-Plot visualisation for GeoTools toolkit
- lib-ext - Extended functionality for Jupyter kernel
- londogard-nlp-toolkit - A Natural Language Processing (NLP) toolkit for Kotlin on the JVM
- multik - Multidimensional array library for Kotlin
- mysql - MySql JDBC Connector
- plotly - [beta] Plotly.kt jupyter integration for static plots.
- plotly-server - [beta] Plotly.kt jupyter integration for dynamic plots.
- rdkit - Open-Source Cheminformatics Software
- reflection - Imports for Kotlin Reflection
- roboquant - Algorithmic trading platform for Kotlin
- serialization - Kotlin multi-format reflection-less serialization
- smile - Statistical Machine Intelligence and Learning Engine
- spark - Kotlin API for Apache Spark: unified analytics engine for large-scale data processing
- spark-streaming - Kotlin API for Apache Spark Streaming: scalable, high-throughput, fault-tolerant stream processing of live data streams
By default, the return values from REPL statements are displayed in the text form. To use richer representations, e.g.
to display graphics or html, it is possible to send MIME-encoded result to the client using the
MIME helper function:
fun MIME(vararg mimeToData: Pair<String, String>): MimeTypedResult
MIME("text/html" to "<p>Some <em>HTML</em></p>", "text/plain" to "No HTML for text clients")
HTML outputs can also be rendered with
HTML helper function:
fun HTML(text: String): MimeTypedResult
TAB to get the list of suggested items for completion. In Jupyter Notebook, you don't need to press
completion is requested automatically. Completion works for all globally defined symbols and for local symbols
which were loaded into notebook during cells evaluation.
If you use Jupyter Notebook as Jupyter client, you will also see that compilation errors and warnings are underlined in red and in yellow correspondingly. This is achieved by kernel-level extension of Jupyter notebook which sends error-analysis requests to kernel and renders their results. If you hover the cursor over underlined text, you will get an error message which can help you to fix the error.
./gradlew installDebug. Debugger port is selected automatically. Default port is 1044, consequent ports will be used if it's in use. If you want an exact port, specify
jupyter notebook, open the desired notebook.
- Attach a remote debugger to JVM with corresponding port (debug port number will be printed in terminal on kernel startup).
Adding new libraries
Read this article if you want to support new
JVM library in the kernel.
There is a site with rendered KDoc comments from the codebase.
If you are a library author you may be interested in
(see adding new libraries). There is also a
lib module which contains entities
available from the Notebook cells and
shared-compiler module which may be used for Jupyter REPL integration
into standalone application or IDEA plugin.