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A Jupyter kernel for executing Java code.
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buildSrc Include dependency report in the zip as well as the new kernel config… Jul 29, 2018
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gradle/wrapper Bump gradle wrapper to 4.8.1. See #18 Jun 30, 2018
.gitignore Bump version to 1.2.0 release Nov 1, 2018
build.gradle Support specifying repos in the maven magic as well as verbosity level. Dec 21, 2018
gradlew Base kernel implementation Oct 1, 2017


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A Jupyter kernel for executing Java code. The kernel executes code via the new JShell tool. Some of the additional commands should be supported in the future via a syntax similar to the ipython magics.

The kernel is fully functional. Check out the list of features further down in the README. Any requests for new ones or prioritizing current requests are welcomed in the issues along with bug requests, installation help, or other questions.

If you are interested in building your own kernel that runs on the JVM check out the related project that this kernel is build on, jupyter-jvm-basekernel.

For Maven dependency resolution, the kernel is using ShrinkWrap resolvers.


Try Online

Clicking on the badge badge badges at the top (or right here) will spawn a jupyter server running this kernel. The binder base is the ijava-binder project.


Currently the kernel supports

  • Code execution. output
  • Autocompletion (TAB in Jupyter notebook). autocompletion
  • Code inspection (Shift-TAB up to 4 times in Jupyter notebook). code-inspection
  • Colored, friendly, error message displays. compilation-error incomplete-src-error runtime-error
  • Add maven dependencies at runtime (See also and Try the example Binder). maven-pom-dep
  • Display rich output (See also and maven magic). Chart library in the demo photo is XChart with the sample code taken from their README. (Try the example Binder) display-img
  • eval function. Note: the signature is Object eval(String) throws Exception. This evaluates the expression (a cell) in the user scope and returns the actual evaluation result instead of a serialized one. eval
  • Configurable evaluation timeout timeout


  1. Java JDK >= 9. Not the JRE. Java 11 is the current release and should be considered if selecting a version but if a java 9 or 10 build is installed, everything should still be working fine.

    1. Ensure that the java command is in the PATH and is using version 9. For example:

      > java -version
      java version "9"
      Java(TM) SE Runtime Environment (build 9+181)
      Java HotSpot(TM) 64-Bit Server VM (build 9+181, mixed mode)
    2. Next ensure that java is in a location where the jdk was installed and not just the jre. Use the java --list-modules command to do this. The list should contain jdk.jshell.

      • On *nix java --list-modules | grep "jdk.jshell"
      • On windows java --list-modules | findstr "jdk.jshell"

      Both should output jdk.jshell@ followed by your java version.

    If the kernel cannot start with an error along the lines of

    Exception in thread "main" java.lang.NoClassDefFoundError: jdk/jshell/JShellException
    Caused by: java.lang.ClassNotFoundException: jdk.jshell.JShellException

    then double check that java is referring to the command for the jdk and not the jre.

  2. Some jupyter-like environment to use the kernel in.

    A non-exhaustive list of options:


After meeting the requirements, the kernel can be installed locally. Any time you wish to remove a kernel you may use jupyter kernelspec remove java. If you have installed the kernel to multiple directories, this command may need to be run multiple times as it might only remove 1 installation at a time.

Install pre-built binary

Get the latest release of the software with no compilation needed. See Install from source for building the the latest commit.

Note: if you have an old installation or a debug one from running gradlew installKernel it is suggested that it is first removed via jupyter kernelspec remove java.

  1. Download the release from the releases tab. A prepackaged distribution will be in an artifact named ijava-$

  2. Unzip it into a temporary location. It should have at least the and java folder extracted in there.

  3. Run the installer with the same python command used to install jupyter. The installer is a python script and has the same options as jupyter kernelspec install but additionally supports configuring some of the kernel properties mentioned further below in the README.

    # Pass the -h option to see the help page
    > python3 -h
    # Otherwise a common install command is
    > python3 --sys-prefix
  4. Check that it installed with jupyter kernelspec list which should contain java.

Install from source

Get the latest version of the kernel but possibly run into some issues with installing. This is also the route to take if you wish to contribute to the kernel.

  1. Download the project.

    > git clone
    > cd IJava/
  2. Build and install the kernel.

    On *nix chmod u+x gradlew && ./gradlew installKernel

    On windows gradlew installKernel

    See all available options for configuring the install path with gradlew -q help --task installKernel. Pass the --default, --user, --sys-prefix, --prefix, --path, or --legacy options to change the install location. Also use the --param flag (repeatedly) to set (or add) parameter values with the parameter names (not environment variable) specified in the configuration section below. For example --param classpath:/my/classpath/root to append to the classpath list.


Configuring the kernel can be done via environment variables. These can be set on the system or inside the kernel.json. The configuration can be done at install time, which may be repeated as often as desired. The parameters are listed with python3 -h as well as below in the list of options. Configuration done via the installer (or gradlew installKernel --param ...:...) should use the names in the Parameter name column.

List of options

Environment variable Parameter name Default Description
IJAVA_COMPILER_OPTS comp-opts "" A space delimited list of command line options that would be passed to the javac command when compiling a project. For example -parameters to enable retaining parameter names for reflection.
IJAVA_TIMEOUT timeout "-1" A duration specifying a timeout (in milliseconds by default) for a single top level statement. If less than 1 then there is no timeout. If desired a time may be specified with a TimeUnit may be given following the duration number (ex "30 SECONDS").
IJAVA_CLASSPATH classpath "" A file path separator delimited list of classpath entries that should be available to the user code. Important: no matter what OS, this should use forward slash "/" as the file separator. Also each path may actually be a simple glob.
IJAVA_STARTUP_SCRIPTS_PATH startup-scripts-path "" A file path seperator delimited list of .jshell scripts to run on startup. This includes ijava-jshell-init.jshell and ijava-display-init.jshell. Important: no matter what OS, this should use forward slash "/" as the file separator. Also each path may actually be a simple glob.
IJAVA_STARTUP_SCRIPT startup-script "" A block of java code to run when the kernel starts up. This may be something like import my.utils; to setup some default imports or even void sleep(long time) { try {Thread.sleep(time); } catch (InterruptedException e) { throw new RuntimeException(e); }} to declare a default utility method to use in the notebook.
Simple glob syntax

Options that support this glob syntax may reference a set of files with a single path-like string. Basic glob queries are supported including:

  • * to match 0 or more characters up to the next path boundary /
  • ? to match a single character
  • A path ending in / implicitly adds a * to match all files in the resolved directory

Any relative paths are resolved from the notebook server's working directory. For example the glob *.jar will match all jars is the directory that the jupyter notebook command was run.

Note: users on any OS should use / as a path separator.

Changing VM/compiler options

See the List of options section for all of the configuration options.

To change compiler options use the IJAVA_COMPILER_OPTS environment variable (or --comp-opts parameter during installation) with a string of flags as if running the javac command.

The kernel VM parameters must currently be assigned in the kernel.json by adding/editing a JSON dictionary at the env key and changing the argv list. To find where the kernel is installed run

> jupyter kernelspec list
Available kernels:
  java           .../kernels/java
  python3        .../python35/share/jupyter/kernels/python3

and the kernel.json file will be in the given directory.

For example to enable assertions, set a limit on the heap size to 128m.

- "argv": [ "java", "-jar", "{connection_file}"],
+ "argv": [ "java", "-ea", "-Xmx128m", "-jar", "{connection_file}"],
  "display_name": "Java",
  "language": "java",
  "interrupt_mode": "message",
  "env": {


This is where the documentation diverges, each environment has it's own way of selecting a kernel. To test from command line with Jupyter's console application run:

jupyter console --kernel=java

Then at the prompt try:

In [1]: String helloWorld = "Hello world!"

In [2]: helloWorld
Out[2]: "Hello world!"
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