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# Making kernels for Jupyter

A ‘kernel’ is a program that runs and introspects the user’s code. IPython includes a kernel for Python code, and people have written kernels for several other languages.

At kernel startup, Jupyter passes the kernel a connection file. This specifies how to set up communications with the frontend.

There are three options for writing a kernel:

1. You can reuse the IPython kernel machinery to handle the communications, and just describe how to execute your code. This is much simpler if the target language can be driven from Python. See Making simple Python wrapper kernels for details.

2. You can implement the kernel machinery in your target language. This is more work initially, but the people using your kernel might be more likely to contribute to it if it’s in the language they know.

3. You can use the xeus library that is a C++ implementation of the Jupyter kernel protocol. Kernel authors only need to implement the language-specific logic in their implementation (execute code, auto-completion…). This is the simplest solution if your target language can be driven from C or C++: e.g. if it has a C-API like most scripting languages. Check out the xeus documentation for more details. Examples of kernels based on xeus include:

   - xeus-cling

   - xeus-python

   - JuniperKernel

## Connection files

Your kernel will be given the path to a connection file when it starts (see Kernel specs for how to specify the command line arguments for your kernel). This file, which is accessible only to the current user, will contain a JSON dictionary looking something like this:

    {
      "control_port": 50160,
      "shell_port": 57503,
      "transport": "tcp",
      "signature_scheme": "hmac-sha256",
      "stdin_port": 52597,
      "hb_port": 42540,
      "ip": "127.0.0.1",
      "iopub_port": 40885,
      "key": "a0436f6c-1916-498b-8eb9-e81ab9368e84"
    }

The `transport`, `ip` and five `_port` fields specify five ports which the kernel should bind to using ZeroMQ. For instance, the address of the shell socket in the example above would be:

    tcp://127.0.0.1:57503

New ports are chosen at random for each kernel started.

`signature_scheme` and `key` are used to cryptographically sign messages, so that other users on the system can’t send code to run in this kernel. See The Wire Protocol for the details of how this signature is calculated.

## Handling messages

After reading the connection file and binding to the necessary sockets, the kernel should go into an event loop, listening on the hb (heartbeat), control and shell sockets.

Heartbeat messages should be echoed back immediately on the same socket - the frontend uses this to check that the kernel is still alive.

Messages on the control and shell sockets should be parsed, and their signature validated. See The Wire Protocol for how to do this.

The kernel will send messages on the iopub socket to display output, and on the stdin socket to prompt the user for textual input.

See also

Messaging in Jupyter

  Details of the different sockets and the messages that come over them

Creating Language Kernels for IPython

  A blog post by the author of IHaskell, a Haskell kernel

simple_kernel

  A simple example implementation of the kernel machinery in Python

## Kernel specs

A kernel identifies itself to IPython by creating a directory, the name of which is used as an identifier for the kernel. These may be created in a number of locations:

| Unix | Windows
--- | --- | ---
System | `/usr/share/jupyter/kernels` `/usr/local/share/jupyter/kernels` | `%PROGRAMDATA%\jupyter\kernels`
Env | `{sys.prefix}/share/jupyter/kernels`
User | `~/.local/share/jupyter/kernels` (Linux) `~/Library/Jupyter/kernels` (Mac) | `%APPDATA%\jupyter\kernels`

The user location takes priority over the system locations, and the case of the names is ignored, so selecting kernels works the same way whether or not the filesystem is case sensitive. Since kernelspecs show up in URLs and other places, a kernelspec is required to have a simple name, only containing ASCII letters, ASCII numbers, and the simple separators: `-` hyphen, `.` period, `_` underscore.

Other locations may also be searched if the `JUPYTER_PATH` environment variable is set.

Inside the kernel directory, three types of files are presently used: `kernel.json`, `kernel.js`, and logo image files. Currently, no other files are used, but this may change in the future.

Inside the directory, the most important file is kernel.json. This should be a JSON serialised dictionary containing the following keys and values:

- argv: A list of command line arguments used to start the kernel. The text `{connection_file}` in any argument will be replaced with the path to the connection file.

- display_name: The kernel’s name as it should be displayed in the UI. Unlike the kernel name used in the API, this can contain arbitrary unicode characters.

- language: The name of the language of the kernel. When loading notebooks, if no matching kernelspec key (may differ across machines) is found, a kernel with a matching `language` will be used. This allows a notebook written on any Python or Julia kernel to be properly associated with the user’s Python or Julia kernel, even if they aren’t listed under the same name as the author’s.

- interrupt_mode (optional): May be either `signal` or `message` and specifies how a client is supposed to interrupt cell execution on this kernel, either by sending an interrupt `signal` via the operating system’s signalling facilities (e.g. `SIGINT` on POSIX systems), or by sending an `interrupt_request` message on the control channel (see Kernel interrupt). If this is not specified the client will default to `signal` mode.

- env (optional): A dictionary of environment variables to set for the kernel. These will be added to the current environment variables before the kernel is started. Existing environment variables can be referenced using `${<ENV_VAR>}` and will be substituted with the corresponding value. Administrators should note that use of `${<ENV_VAR>}` can expose sensitive variables and should use only in controlled circumstances.

- metadata (optional): A dictionary of additional attributes about this kernel; used by clients to aid in kernel selection. Metadata added here should be namespaced for the tool reading and writing that metadata.

For example, the kernel.json file for IPython looks like this:

    {
     "argv": ["python3", "-m", "IPython.kernel",
              "-f", "{connection_file}"],
     "display_name": "Python 3",
     "language": "python"
    }

To see the available kernel specs, run:

    jupyter kernelspec list

To start the terminal console or the Qt console with a specific kernel:

    jupyter console --kernel bash
    jupyter qtconsole --kernel bash

The notebook offers you the available kernels in a dropdown menu from the ‘New’ button.

## Packaging

To release your kernel as a Python package, we recommend following the pattern used in the echo_kernel, which uses the hatch build backend and a build file that creates the kernel directory with the `kernel.json` and kernel icons, which is included as `shared-data`, ending up in the `share/jupyter/kernels/` folder in the user’s installed environment. See pyproject.toml and hatch_build.py for more details.