Implementing a kernel¶
In most of the cases, the base kernel implementation is enough, and creating a kernel only means implementing the interpreter part.
The structure of your project should at least look like the following:
└── example/
├── src/
│ ├── custom_interpreter.cpp
│ ├── custom_interpreter.hpp
│ └── main.cpp
├── share/
│ └── jupyter/
│ └── kernels/
│ └── my_kernel/
│ └── kernel.json.in
└── CMakeLists.txt
The xeus-cookiecutter project provides a template for a xeus-based kernel, and includes the base structure for a xeus-based kernel.
Implementing the interpreter¶
Let’s start by editing the custom_interpreter.hpp
file, it should contain the declaration of your interpreter class:
Note
Almost all custom_interpreter
methods return a nl::json
instance. This is actually using nlohmann json which is a modern C++ implementation of a JSON datastructure.
In the following sessions we will see details about each one of the methods that need to be implemented in order to have a functional kernel. The user can opt for using the reply API that will appropriately create replies to send to the kernel, or create the replies themselves.
Code Execution¶
You can implement all the methods described here in the custom_interpreter.cpp
file. The main method is of
course the execute_request_impl
which executes the code whenever the client is sending an execute request.
The result and arguments of the execution request are described in the execute_request documentation.
Note
The other methods are all optional, but we encourage you to implement them in order to have a fully-featured kernel.
Within this method the use of create_error_reply_ and create_successful_reply_ might be useful.
Input request¶
For input request support, you would need to monkey-patch the language functions that prompt for a user input (input
and raw_input
in Python, io.read
in Lua etc) and call xeus::blocking_input_request
instead. The second parameter
should be set to False if what the user is typing should not be visible on the screen.
#include "xeus/xinput.hpp"
xeus::blocking_input_request("User name:", true);
xeus::blocking_input_request("Password:", false);
Configuration¶
The configure_impl
method allows you to perform some operations after the custom_interpreter
creation and before executing
any request. This is optional, but it can be useful, for example it is used in xeus-python
for initializing the auto-completion engine.
Code Completion¶
The complete_request_impl
method allows you to implement the auto-completion logic for your kernel.
The result and arguments of the completion request are described in the complete_request documentation.
Code Inspection¶
Allows the kernel user to inspect a variable/class/type in the code. It takes the code and the cursor position as arguments, it is up to the kernel author to extract the token at the given cursor position in the code in order to know for which name the user wants inspection.
The result and arguments of the inspection request are described in the inspect_request documentation and the create_inspect_reply_ might be useful to create a reply within specifications.
Code Completeness¶
This request is never called from the Notebook or from JupyterLab clients, but it is called from the Jupyter console client. It allows the client to know if the user finished typing his code, before sending an execute request. For example, in Python, the following code is not considered as complete:
def foo:
So the kernel should return “incomplete” with an indentation value of 4 for the next line.
The following code is considered as complete:
def foo:
print("bar")
So the kernel should return “complete”.
The result and arguments of the completness request are described in the is_complete_request documentation. Both create_default_complete_reply_ and create_is_complete_reply_ methods are recommended.
Kernel info¶
This request allows the client to get information about the kernel: language, language version, kernel version, etc.
The result and arguments of the kernel info request are described in the kernel_info_request documentation. The create_info_reply_ method will help you to provide complete information about your kernel.
Kernel shutdown¶
This allows you to perform some operations before shutting down the kernel.
Kernel replies¶
Error reply¶
Creates a default error reply to the kernel or allows custom input. The signature of the method is the following:
Where evalue
is exception value, ename
is exception name and trace_back
a vector of strings with the exception stack.
Successful reply¶
Creates a default success reply to the kernel or allows custom input. The signature of the method is the following:
Where payload
is a way to trigger frontend actions from the kernel (payloads are deprecated but since there are still no replecement for it you might need to use it) more information about the different kinds of payloads in the official docs_. data
is a dictionary which the keys is a MIME_type
(this is the type of data to be shown it must be a valid MIME type, for a list of the possibilities check MDN, note that you’re not limited by these types) and the values are the content of the information intended to be displayed in the frontend. And user_expressions
is a dictionary of strings of arbitrary code, more information about it on the official docs_.
Complete reply¶
Creates a custom completion reply to the kernel. The signature of the method is the following:
Where matches
the list of all matches to the completion request, it’s a mandatory argument. cursor_start
and cursor_end
mark the range of text that should be replaced by the above matches when a completion is accepted, typically cursor_end
is the same as cursor_pos
in the request and both these arguments are mandatory for the implementation of the method. metadata
a dictionary of strings that contains information that frontend plugins might use for extra display information about completions.
In case you do not wish to implement completion in your kernel, instead of creating a complete reply you can use the create_successful_reply
with its default arguments.
Is complete reply¶
Creates a default is complete reply to the kernel or allows custom input. The signature of the method is the following:
status
one of the following ‘complete’, ‘incomplete’, ‘invalid’, ‘unknown’. indent
if status is ‘incomplete’, indent should contain the characters to use to indent the next line. This is only a hint: frontends may ignore it and use their own autoindentation rules. For other statuses, this field does not exist.
Create info reply¶
Thorough information about the kernel’s infos variables can be found in the Jupyter kernel docs_.
Implementing the main entry¶
Now let’s edit the main.cpp
file which is the main entry for the kernel executable.
Kernel file¶
The kernel.json
file is a json
file used by Jupyter in order to retrieve all the available kernels.
It must be installed in the INSTALL_PREFIX/share/jupyter/kernels/my_kernel
directory, we will see how to
do that in the next chapter.
This json
file contains:
display_name
: the name that the Jupyter client should display in its interface (e.g. on the main JupyterLab page).
argv
: the command that the Jupyter client needs to run in order to start the kernel. You should leave this value unchanged if you are not sure what you are doing.
language
: the target language of your kernel.
You can edit the kernel.json.in
file as following. This file will be used by cmake for generating the actual kernel.json
file which will be installed.
Note
You can provide logos that will be used by the Jupyter client. Those logos should be in files named logo-32x32.png
and
logo-64x64.png
(32x32
and 64x64
being the size of the image in pixels), they should be placed next to the kernel.json.in
file.
Compiling and installing the kernel¶
Your CMakeLists.txt
file should look like the following:
Now you should be able to install your new kernel and use it with any Jupyter client.
For the installation you first need to install dependencies, the easier way is using conda
:
conda install -c conda-forge cmake jupyter xeus xtl nlohmann_json cppzmq
Then create a build
folder in the repository and build the kernel from there:
mkdir build
cd build
cmake -D CMAKE_INSTALL_PREFIX=$CONDA_PREFIX ..
make
make install
That’s it! Now if you run the Jupyter Notebook interface you should be able to create a new Notebook
selecting the my_kernel
kernel. Congrats!
Writing unit-tests for your kernel¶
For writing unit-tests for you kernel, you can use the jupyter_kernel_test Python library. It allows you to test the results of the requests you send to the kernel.