This page has information about the different architectural designs of core
pieces in the Jupyter ecosystem. Some of these are individual projects, and others
show the relationships between projects.
This section focuses on IPython and kernels.
When we discuss IPython, we talk about two fundamental roles:
Terminal IPython as the familiar REPL
The IPython kernel that provides computation and communication with the
frontend interfaces, like the notebook
When you type ipython, you get the original IPython interface, running in
the terminal. It does something like this:
code = input(">>> ")
Of course, it’s much more complex, because it has to deal with multi-line
code, tab completion using readline, magic commands, and so on. But the
model is like code example: prompt the user for some code, and when they’ve
entered it, execute it in the same process. This model is often called a
REPL, or Read-Eval-Print-Loop.
All the other interfaces —- the Notebook, the Qt console, ipython console
in the terminal, and third party interfaces —- use the IPython Kernel. The
IPython Kernel is a separate process which is responsible for running user
code, and things like computing possible completions. Frontends, like the
notebook or the Qt console, communicate with the IPython Kernel using JSON
messages sent over ZeroMQ sockets; the protocol used
between the frontends and the IPython Kernel is described in
Messaging in Jupyter.
The core execution machinery for the kernel is shared with terminal IPython:
A kernel process can be connected to more than one frontend simultaneously. In
this case, the different frontends will have access to the same variables.
This design was intended to allow easy development of different frontends
based on the same kernel, but it also made it possible to support new
languages in the same frontends, by developing kernels in those languages, and
we are refining IPython to make that more practical.
Today, there are two ways to develop a kernel for another language. Wrapper
kernels reuse the communications machinery from IPython, and implement only
the core execution part. Native kernels implement execution and communications
in the target language:
Wrapper kernels are easier to write quickly for languages that have good
Python wrappers, like octave_kernel,
or languages where it’s impractical to implement the communications machinery,
like bash_kernel. Native kernels
are likely to be better maintained by the community using them, like
Making kernels for Jupyter
Jupyter Notebooks are structured data that represent your code, metadata, content,
and outputs. When saved to disk, the notebook uses the extension .ipynb, and
uses a JSON structure. For more information about the notebook format structure
and specification, see the nbformat documentation.
Jupyter Notebook and its flexible interface extends the notebook beyond code
to visualization, multimedia, collaboration, and more. In addition to running your code,
it stores code and output, together with markdown notes, in an editable
document called a notebook. When you save it, this is sent from your browser
to the notebook server, which saves it on disk as a JSON file with a
The notebook server, not the kernel, is responsible for saving and loading
notebooks, so you can edit notebooks even if you don’t have the kernel for
that language—you just won’t be able to run code. The kernel doesn’t know
anything about the notebook document: it just gets sent cells of code to
execute when the user runs them.
The Nbconvert tool in Jupyter converts notebook files to other formats, such
as HTML, LaTeX, or reStructuredText. This conversion goes through a series of
Preprocessors modify the notebook in memory. E.g. ExecutePreprocessor runs
the code in the notebook and updates the output.
An exporter converts the notebook to another file format. Most of the
exporters use templates for this.
Postprocessors work on the file produced by exporting.
The nbviewer website uses nbconvert with the
HTML exporter. When you give it a URL, it fetches the notebook from that URL,
converts it to HTML, and serves that HTML to you.
IPython also includes a parallel computing framework,
allows you to control many individual engines, which are an extended version
of the IPython kernel described above.
JupyterHub is a multi-user Hub that spawns, manages, and proxies multiple instances of the
single-user Jupyter notebook server. This can be used to serve a variety of interfaces
and environments, and can be run on many kinds of infrastructure. JupyterHub on Kubernetes
is a Helm Chart for running JupyterHub on kubernetes infrastructure, and BinderHub is a
customized JupyterHub deployment for sharable, reproducible interactive computing environments.
The links below describe the architecture of JupyterHub and several distributions of
JupyterHub core architecture
JupyterHub for Kubernetes architecture
JupyterLab is a flexible, extensible interface for interactive computing. Below
are a few links that are useful for understanding the JupyterLab architecture.
JupyterLab document model
JupyterLab notebook model
Design patterns in JupyterLab
Below is a high level visual overview of project relationships. It is current as of