GitHub - emacs-jupyter/jupyter: An interface to communicate with Jupyter kernels. (original) (raw)

An interface to communicate with Jupyter kernels in Emacs.

Table of Contents

What does this package do?

How do I install this package?

Using MELPA

NOTE: This package relies on the emacs-zmq package which means your Emacs needs to have been built with module support. See the README of that package for more information.

You can install this package with any package manager that allows you to install MELPA packages. For Emacs’s built-in package manager:

  1. Ensure MELPA is in package-archives
    (add-to-list 'package-archives '("melpa" . "https://melpa.org/packages/"))
  2. Ensure the latest versions of MELPA packages are available
    M-x package-refresh-contents RET
  3. Install Jupyter
    M-x package-install RET jupyter RET

Manual installation

For a manual installation you can add the repository directory to yourload-path and ensure the following dependencies are installed:

markdown-mode (optional)

https://jblevins.org/projects/markdown-mode/

company-mode (optional)

http://company-mode.github.io/

emacs-websocket

https://github.com/ahyatt/emacs-websocket

simple-httpd

https://github.com/skeeto/emacs-web-server

zmq

http://github.com/nnicandro/emacs-zmq

(add-to-list 'load-path "~/path/to/jupyter") (require 'jupyter)

Building the widget support (EXPERIMENTAL)

There is limited support for interacting with Jupyter widgets through an external browser. In this case, Emacs acts as a relay for passing messages between the kernel and the browser.

To try it out, install node (https://nodejs.org/en/) then run the following shell command from the top-level directory of this project.

After, launch Emacs, connect to a kernel (e.g. through a REPL), and run some code that creates a widget.

How do I run the tests?

You must have Eldev installed to be able to run the tests. Once Eldev is installed, then in the top level directory of this project you can run one of the following from the command line

Run the whole set of tests

make test

Run tests tagged with org

make test TAGS=org

Run tests tagged with org and babel

make test TAGS=org,babel

Run tests whose name match a pattern

make test PATTERN=font-lock

ob-ipython

The org-mode source block frontend in emacs-jupyter is similar to what is offered by ob-ipython (and also the scimax version).

emacs-ipython-notebook (ein)

ein is a complete Jupyter notebook interface in Emacs with many powerful features for Python kernels. There is some overlap in the features provided byemacs-jupyter and ein, but I have never used ein so I cannot speak very much about any similarities/differences.

How do I use the built-in frontends?

REPL

M-x jupyter-run-repl launches a new local kernel and displays a REPL buffer.

M-x jupyter-connect-repl connects to an existing kernel using the kernel’s connection file, which is supplied by the user, and displays a REPL buffer.

The REPL supports some of the rich output that a kernel may send to a client, e.g. images, LaTeX, and HTML.

Rich kernel output

Below is a table of the supported output mimetypes and their dependencies. If a dependency is not available for a particular mimetype, a mimetype of lower priority gets displayed instead.

For widgets, before attempting to open one, you also need to run the shell command make widgets in the top-level directory of this project to build some JavaScript files.

Mimetype Dependencies
application/vnd.jupyter.widget-view+json websocket, simple-httpd
text/html Emacs built with libxml2
text/markdown markdown-mode
text/latex org-mode
image/svg+xml Emacs built with librsvg2
image/png none
text/plain none

Inspection

To inspect the code around point press M-i.

Completion

Completion is implemented through the completion-at-point interface and should just work.

In addition to completing symbols in the REPL buffer, completion also works in buffers associated with a REPL. For org-mode users, there is even completion in the org-mode buffer when editing the contents of a Jupyter source code block.

REPL history

To navigate the REPL history: M-n and M-p.

To search the REPL history: C-s and C-s C-r.

Associating buffers with a REPL (jupyter-repl-interaction-mode)

M-x jupyter-repl-associate-buffer sets the jupyter-current-client of the current buffer to an existing REPL client and enables jupyter-repl-interaction-mode, allowing you to, for example, send the current line for evaluation by the client’s kernel.

When jupyter-repl-interaction-mode is enabled, the following keybindings are available

Key binding Command
C-M-x jupyter-eval-defun
M-i jupyter-inspect-at-point
C-c C-b jupyter-eval-buffer
C-c C-c jupyter-eval-line-or-region
C-c C-i jupyter-repl-interrupt-kernel
C-c C-r jupyter-repl-restart-kernel
C-c C-s jupyter-repl-scratch-buffer
C-c C-o jupyter-eval-remove-overlays
C-c M-: jupyter-eval-string

Integration with emacsclient

If emacsclient is set as the EDITOR and evaluated code opens a file in a major-mode compatible with the client that sent the code, the opened file will automatically be associated with the client and havejupyter-repl-interaction-mode enabled.

This feature probably wont work correctly when there are multiple competing clients sending requests to their underlying kernels that want to open files or if the underlying kernel takes longer than jupyter-long-timeout seconds to open a file.

See jupyter-server-mode-set-client for more details.

jupyter-repl-maximum-size

A variable that determines the maximum number of lines a REPL buffer can have before being truncated.

jupyter-repl-allow-RET-when-busy

A variable that determines whether to allow insertion of newlines in a REPL cell when a kernel is busy or not. See the variable documentation for more details.

jupyter-repl-without-is-complete

A variable that determines whether or not an is_complete_request is sent to the kernel to determine the syntactical correctness, i.e. when it is safe to send it for evaluation. When it is non-nil, no is_complete_request is used and the main way to send code to kernels for evaluation is a press of S-RET, RET would insert a newline and indent in this case. When the variable is nil, RET sends an is_complete_request to determine if the code is valid to send for execution or not and sends it if it is or inserts a newline and indents if it isn’t.

See also jupyter-repl-use-builtin-is-complete for another method of sending code to kernels without the use of an is_complete_request.

jupyter-repl-echo-eval-p

A variable that determines whether code evaluated with the jupyter-eval-* commands gets copied over to a REPL input cell or not. You can set this variable to t if you prefer having the history of all evaluated code visible in the REPL.

org-mode source blocks

To enable support for Jupyter based source code blocks, add jupyter to org-babel-load-languages. Ensure the jupyter entry is added last since loading ob-jupyter depends on the value of variables such as org-src-lang-modes and org-babel-tangle-lang-exts.

(org-babel-do-load-languages 'org-babel-load-languages '((emacs-lisp . t) (julia . t) (python . t) (jupyter . t)))

After loading, source code blocks with names like jupyter-LANG will be available for use. LANG can be any one of the kernel languages found on your system. See jupyter-available-kernelspecs.

#+BEGIN_SRC jupyter-python :session py
x = 'foo'
y = 'bar'
x + ' ' + y
#+END_SRC

#+BEGIN_SRC jupyter-python :session py :async yes
x = 'foo'
y = 'bar'
x + ' ' + y
#+END_SRC

#+BEGIN_SRC jupyter-python :session py :async yes :kernel python2
x = 'foo'
y = 'bar'
x + ' ' + y
#+END_SRC

Note, the same session name can be used for different values of :kernel since the underlying REPL buffer’s name is based on both :session and :kernel.

Note on the language name provided by a kernelspec

Some kernelspecs use spaces in the name of the kernel language. Those get replaced by dashes in the language name you need to use for the corresponding source blocks, e.g. Wolfram Language has the source block language jupyter-Wolfram-Language.

Integration with ob-async

If you have ob-async installed and are getting errors when your source block specifies the :async header argument, try putting something like the following in your configuration:

(setq ob-async-no-async-languages-alist '("jupyter-python" "jupyter-julia"))

See ob-async-no-async-languages-alist for more details.

Issues with ob-ipython

If both ob-ipython and this package are installed, you may experience issues such as this one, causing Search failed errors. To avoid such errors, remove ipython from org-babel-do-load-languages and restart your Emacs.

Overriding built-in src-block languages

Instead of having to specify jupyter-LANG as a source block name, you can have LANG source blocks use the Jupyter machinery. To do so, place a call to org-babel-jupyter-override-src-block somewhere in your config (after the call to org-babel-do-load-languages).

(org-babel-jupyter-override-src-block "python")

After calling the above function, all python source blocks are effectively aliases of jupyter-python source blocks and the variableorg-babel-default-header-args:python will be set to the value oforg-babel-default-header-args:jupyter-python.

Note, org-babel-default-header-args:python will not be an alias of org-babel-default-header-args:jupyter-python, the value of the former is merely set to the value of the latter after calling org-babel-jupyter-override-src-block.

You can restore the original behavior by calling org-babel-jupyter-restore-src-block.

(org-babel-jupyter-restore-src-block "python")

Rich kernel output

The supported display mimetypes ordered by priority are:

A note on using the :results header argument

There are some cases where the normal result insertion mechanism may not be wanted. To control result insertion somewhat, use the :results header argument:

Insert unwrapped LaTeX

Normally LaTeX results are wrapped in aBEGIN_EXPORT block, in order to insert LaTeX unwrapped, specify:results raw.

Suppress table creation

Whenever a result can be converted into anorg-mode table, e.g. when it look like [1, 2 , 3], it is automatically converted into a table. To suppress this behavior you can specify:results scalar.

Fixing the file name of images with the :file argument

Whenever an image result is returned, a random image file name is generated and the image is written to org-babel-jupyter-resource-directory. To specify your own file name for the image, set the :file header argument.

If no file extension is specified in the provided :file, then one will be inferred from the returned output. This can be useful in scenarios where the file resulting from the src-block can have different types depeneding on the code, e.g. if the image type returned can be either png or svg depending on certain settings, you can specify :file = output which will be converted into output.png or output.svg depending on the MIME type return by the executed src-block.

Changing the mime-type priority with the :display argument

The priority of mimetypes used to display results can be overwritten using the:display option. If instead of displaying HTML results we’d wish to display plain text, the argument :display text/plain text/html would prioritize plain text results over html ones. The following example displays plain text instead of HTML:

#+BEGIN_SRC jupyter-python :session py :display plain import pandas as pd data = [[1, 2], [3, 4]] pd.DataFrame(data, columns=["Foo", "Bar"]) #+END_SRC

Image output without the :file header argument

For images sent by the kernel, if no :file parameter is provided to the code block, a file name is automatically generated based on the image data and the image is written to file in org-babel-jupyter-resource-directory. This is great for quickly generating throw-away plots while you are working on your code. Once you are happy with your results you can specify the :file parameter to fix the file name.

org-babel-jupyter-resource-directory

This variable is similar to org-preview-latex-image-directory but solely for any files created when Jupyter code blocks are run, e.g. automatically generated image file names.

Deletion of generated image files

Whenever you run a code block multiple times and replace its results, before the results are replaced, any generated files will be deleted to reduce the clutter in org-babel-jupyter-resource-directory.

Convert rich kernel output with the :pandoc header argument

By default html, markdown, and latex results are wrapped in a BEGIN_EXPORT block. If the header argument :pandoc t is set, they are instead converted to org-mode format with pandoc. You can control which outputs get converted with the custom variable jupyter-org-pandoc-convertable.

Editing the contents of a code block

When editing a Jupyter code block’s contents, i.e. by pressing C-c ' when at a code block, jupyter-repl-interaction-mode is automatically enabled in the edit buffer and the buffer will be associated with the REPL session of the code block (see jupyter-repl-associate-buffer).

You may also bind the command org-babel-jupyter-scratch-buffer to an appropriate key in org-mode to display a scratch buffer in the code block’smajor-mode and connected to the code block’s session.

Connecting to an existing kernel

To connect to an existing kernel, pass the kernel’s connection file as the value of the :session parameter. The name of the file must have a .json suffix for this to work.

Remote kernels

If the connection file is a remote file name, i.e. has a prefix like/method:host:, the kernel’s ports are assumed to live on host. Before attempting to connect to the kernel, ssh tunnels for the connection are created. So if you had a remote kernel on a host named ec2 whose connection file is /run/user/1000/jupyter/kernel-julia-0.6.json on that host, you could specify the :session like

#+BEGIN_SRC jupyter-julia :session /ssh:ec2:/run/user/1000/jupyter/kernel-julia-0.6.json ... #+END_SRC

Note, the kernel on the remote host needs to have the ZMQ socket ports exposed. This means that starting a kernel using

jupyter notebook --no-browser

currently doesn’t work since the notebook server does not allow communication with a kernel using ZMQ sockets. You will have to use the connection file created from using something like

jupyter kernel --kernel=python

Password handling for remote connections

Currently there is no password handling, so if your ssh connection requires a password I suggest you instead use key-based authentication. Or if you are connecting to a server using a pem file add something like

Host ec2 User HostName IdentityFile .pem

to your ~/.ssh/config file.

Starting a remote kernel

If :session is a remote file name that doesn’t end in .json, e.g./ssh:ec2:jl, then a kernel on the remote host /ssh:ec2: is started using the jupyter kernel command on the host. The local part of the session name serves to distinguish different remote sessions on the same host.

Communicating with kernel (notebook) servers

If :session is a TRAMP file name like /jpy:localhost#8888:NAME it is interpreted as corresponding to a connection to a kernel through a Jupyter notebook server located at http://localhost:8888.

If NAME is a kernel ID corresponding to an existing kernel on a server, e.g. /jpy::161b2318-180c-497a-b4bf-de76176061d9, then a connection to an existing kernel with the corresponding ID will be made. Otherwise, a new kernel will be launched on the server and NAME will be used as an identifier for the session.

When a new kernel is launched, NAME will also be associated with the kernel’s ID, see jupyter-server-kernel-names. This is useful to distinguish Org mode :session kernels from other ones in the buffer shown by jupyter-server-list-kernels.

When connecting to an existing kernel, i.e. when NAME is the ID of a kernel, the :kernel header argument must match the name of the kernel’s kernelspec.

To connect to a kernel behind an HTTPS connection, use a TRAMP file name that looks like /jpys:... instead.

Standard output, displayed data, and code block results

In contrast to non-Jupyter code blocks, the kernel of Jupyter code block can request extra data, other than stdout or a code block’s result, be displayed (see display_data messages).

To account for this, Jupyter code blocks do not go through the normal org-mode result insertion mechanism (see org-babel-insert-result), instead providing its own result insertion. The downside is that, compared to normal org-mode code blocks, only a small subset of the header arguments are supported. The upside is that all forms of results produced by a kernel can be inserted into the buffer similar to a Jupyter notebook.

jupyter-org-interaction-mode

A minor mode that enables completion and custom keybindings when point is inside a Jupyter code block. This mode is enabled by default in org-mode buffers, but only has an effect when point is inside a Jupyter code block.

Custom keybindings inside Jupyter code blocks

You can define new keybindings that are enabled when point is inside a Jupyter code block by using the function jupyter-org-define-key. These bindings are added to jupyter-org-interaction-mode-map and are only active when jupyter-org-interaction-mode is enabled.

By default the following keybindings from jupyter-repl-interaction-mode are available when jupyter-org-interaction-mode is enabled

Key binding Command
C-M-x jupyter-eval-defun
M-i jupyter-inspect-at-point
C-x C-e jupyter-eval-line-or-region
C-c C-i jupyter-repl-interrupt-kernel
C-c C-r jupyter-repl-restart-kernel

Disable automatic connections to a source block session

When typing into the region of a Jupyter source block, under certain conditions, an attempt at connecting to the source block’s session is made if not already connected.

This behavior can be suppressed by setting jupyter-org-auto-connect to nil. In this case, a connection is attempted upon executing a source block, for example.

Enable client-side queuing of requests

If the customizable variable jupyter-org-queue-requests is non-nil, then perform client side queuing of source block execute requests. This means that when multiple requests are made, for example by executing a subtree, the requests are queued locally in Emacs instead of sending all the requests immediately to the kernel as would happen when :async yes is specified on all the source blocks. It is only when one request finishes that the next is sent. In addition, if any request fails all the queued requests that are meant to come after it are aborted and do not get sent to the kernel.

To turn client side queuing on or off you can M-x jupyter-org-toggle-request-queuing.

Kernel/notebook server

Managing live kernels

The main entry point for working with a kernel server is thejupyter-server-list-kernels command which shows a list of all live kernels from the server URL that you provide when first calling the command. Any subsequent calls to the command will use the same URL as the first call. To change server URLs give a prefix argument, C-u M-x jupyter-server-list-kernels. This will then set the current server URL for future calls to the one you provide. See the jupyter-current-server command for more details.

From the buffer shown by jupyter-server-list-kernels you can launch new kernels (C-RET), connect a REPL to an existing kernel (RET), interrupt a kernel (C-c TAB), kill a kernel (C-c C-d or d), refresh the list of kernels (g) etc. See the jupyter-server-kernel-list-mode for all the available key bindings.

Note, the default-directory of the jupyter-server-kernel-list-mode buffer will be the root directory of the kernel server (so that dired-jump will show a dired listing of the directory). See the section on TRAMP integration below.

Naming kernels

From the jupyter-server-list-kernels buffer one can also name (or rename) a kernel (R) so that it has an identifier other than its ID. Naming a kernel adds the name to the jupyter-server-kernel-names global variable in a form suitable for persisting across Emacs sessions. See its documentation for more details about persisting its value.

TRAMP integration

There is also integration with the Jupyter notebook contents API in the form of a TRAMP backend. This means that reading/writing the contents of directories the notebook server has access to can be done using normal Emacs file operations using file names with TRAMP syntax. Two new TRAMP file name methods are defined, jpy for HTTP connections and jpys for HTTPS connections. So suppose you have a local notebook server at http://localhost:8888, then to access its directory contents you can type

M-x dired RET /jpy:localhost#8888:/

Note localhost is the default host and 8888 is the default port so /jpy:: is equivalent to /jpy:localhost#8888:. You can change the defaults by modifying the jpy or jpys methods in the variable tramp-methods andtramp-default-host-alist.

jupyter-api-authentication-method

Authentication method used for new notebook server connections. By default, when connecting to a new notebook server you will be asked if either a password or a token should be used for authentication. If you only use tokens for authentication you can change this variable to avoid being asked on every new connection.

Customizable variables available for all frontends

jupyter-eval-use-overlays

When non-nil, display the text/plain representation of evaluation results inline using overlays. All other representations are displayed in the usual way. This only works with the jupyter-eval-* commands like jupyter-eval-line-or-region.

You can control the appearance of the overlay, see jupyter-eval-overlay-prefix and the jupyter-eval-overlay face.

To clear all overlays from the buffer, bind jupyter-eval-remove-overlays to some key. Its bound to C-c C-o when jupyter-repl-interaction-mode is enabled. Individual overlays are removed whenever the text in the region that was evaluated is modified.

For multi-line overlays you can fold/unfold the overlay by pressing S-RET when point is inside the region of code that caused the overlay to be created. See jupyter-eval-overlay-keymap.

jupyter-eval-short-result-max-lines

If the number of lines of an evaluation result is smaller than this variable, the function stored in jupyter-eval-short-result-display-function is used to display a result.