Launching SageMath

Now we assume that you installed SageMath properly on your system. This section quickly explains how to start the Sage console and the Jupyter Notebook from the command line.

If you did install the Windows version or the macOS application you should have icons available on your desktops or launching menus. Otherwise you are strongly advised to create shortcuts for Sage as indicated in the part 6 of the “Installation steps” Section in Installation steps. Assuming that you have this shortcut, running

sage

in a console starts a Sage session. To quit the session enter quit and then press <Enter>.

To start a Jupyter Notebook instead of a Sage console, run the command

sage -n jupyter

instead of just sage. To quit the Jupyter Notebook press <Ctrl> + <c> twice in the console where you launched the command.

You can pass extra parameters to this command. For example,

sage -n jupyter --port 8899

will run the Jupyter server on a port different from the default (8888). In particular on WSL, this is very useful because Jupyter may not be able to detect whether the default port is already taken by another instance of Jupyter running in Windows.

Environment variables

Sage uses the following environment variables when it runs:

  • DOT_SAGE - this is the directory, to which the user has read and write access, where Sage stores a number of files. The default location is $HOME/.sage/.

  • SAGE_STARTUP_FILE - a file including commands to be executed every time Sage starts. The default value is $DOT_SAGE/init.sage.

  • BROWSER - on most platforms, Sage will detect the command to run a web browser, but if this doesn’t seem to work on your machine, set this variable to the appropriate command.

  • TMPDIR - this variable is used by Python, and hence by Sage; it gives the directory in which temporary files should be stored. This includes files used by the notebook. Some browsers have security settings which restrict the locations of files that they will access, and users may need to set this variable to handle this situation.

  • See https://docs.python.org/3/using/cmdline.html#environment-variables for more variables used by Python (not an exhaustive list). With Python 3.11 or later, a brief summary can also be obtained by running \(python3 --help-env\).

Using a Jupyter Notebook remotely

If Sage is installed on a remote machine to which you have ssh access, you can launch a Jupyter Notebook using a command such as

ssh -L localhost:8888:localhost:8888 -t USER@REMOTE sage -n jupyter --no-browser --port=8888

where USER@REMOTE needs to be replaced by the login details to the remote machine. This uses local port forwarding to connect your local machine to the remote one. The command will print a URL to the console which you can copy and paste in a web browser.

Note that this assumes that a firewall which might be present between server and client allows connections on port 8888. See details on port forwarding on the internet, e.g. https://www.ssh.com/ssh/tunneling/example.

WSL Post-installation steps

If you’ve installed SageMath from source on WSL, there are a couple of extra steps you can do to make your life easier:

Create a notebook launch script

If you plan to use JupyterLab, install that first.

Now create a script called ~/sage_nb.sh containing the following lines, and fill in the correct paths for your desired starting directory and SAGE_ROOT

#!/bin/bash
# Switch to desired windows directory
cd /mnt/c/path/to/desired/starting/directory
# Start the Jupyter notebook
SAGE_ROOT/sage --notebook
# Alternatively you can run JupyterLab - delete the line above, and uncomment the line below
#SAGE_ROOT/sage --notebook jupyterlab

Make it executable:

chmod ug+x ~/sage_nb.sh

Run it to test:

cd ~
./sage_nb.sh

The Jupyter(Lab) server should start in the terminal window, and you windows browser should open a page showing the Jupyter or JupyterLab starting page, at the directory you specified.

Create a shortcut

This is a final nicety that lets you start the Jupyter or JupyterLab server in one click:

  • Open Windows explorer, and type %APPDATA%\Microsoft\Windows\Start Menu\Programs in the address bar and press enter. This is the folder that contains you start menu shortcuts. If you want the sage shortcut somewhere else (like your desktop), open that folder instead.

  • Open a separate window and go to %LOCALAPPDATA%\Microsoft\WindowsApps\

  • Right-click-drag the ubuntu.exe icon from the second window into the first, then choose Create shortcuts here from the context menu when you drop it.

  • To customize this shortcut, right-click on it and choose properties.

    • On the General tab:

      • Change the name to whatever you want, e.g. “Sage 9.2 JupyterLab”

    • On the Shortcut tab:

      • Change Target to: ubuntu.exe run ~/sage_nb.sh

      • Change Start in to: %USERPROFILE%

      • Change Run to: Minimised

      • Change the icon if you want

Now hit the start button or key and type the name you gave it. it should appear in the list, and should load the server and fire up your browser when you click on it.

For further reading you can have a look at the other documents in the SageMath documentation at http://doc.sagemath.org/.

Setting up SageMath as a Jupyter kernel in an existing Jupyter notebook or JupyterLab installation

By default, SageMath installs itself as a Jupyter kernel in the same environment as the SageMath installation. This is the most convenient way to use SageMath in a Jupyter notebook. To check if the Sage kernel is available, start a Jupyter notebook and look for the kernel named sagemath in the list of available kernels. Alternatively, you can use the following command to check which kernels are available:

$ jupyter kernelspec list
Available kernels:
  python3     <path to env>/share/jupyter/kernels/python3
  sagemath    <path to env>/share/jupyter/kernels/sagemath

Note

The kernel is not automatically available if you have installed SageMath in editable mode (pip install -e). In that case, it is recommended to reinstall SageMath in a non-editable way.

You may already have a global installation of Jupyter. For added convenience, it is possible to link your installation of SageMath into your Jupyter installation, adding it to the list of available kernels that can be selected in the notebook or JupyterLab interface.

Assuming that SageMath can be invoked by typing sage, you can use

sage -sh -c 'ls -d $SAGE_VENV/share/jupyter/kernels/sagemath'

to find the location of the SageMath kernel description. Alternatively, use jupyter kernelspec list from the same environment where SageMath is installed to find the location of the SageMath kernel.

Now pick a name for the kernel that identifies it clearly and uniquely.

For example, if you install Sage from source tarballs, you could decide to include the version number in the name, such as sagemath-9.6. If you build SageMath from a clone of the git repository, it is better to choose a name that identifies the directory, perhaps sagemath-dev or sagemath-teaching because the version will change.

Now assuming that the Jupyter notebook can be started by typing jupyter notebook, the following command will install SageMath as a new kernel named sagemath-dev.

jupyter kernelspec install --user $(sage -sh -c 'ls -d $SAGE_VENV/share/jupyter/kernels/sagemath') --name sagemath-dev

The jupyter kernelspec approach by default does lead to about 2Gb of SageMath documentation being copied into your personal jupyter configuration directory. You can avoid that by instead putting a symlink in the relevant spot and

jupyter --paths

to find valid data directories for your Jupyter installation. A command along the lines of

ln -s $(sage -sh -c 'ls -d $SAGE_VENV/share/jupyter/kernels/sagemath') $HOME/.local/share/jupyter/kernels/sagemath-dev

can then be used to create a symlink to the SageMath kernel description in a location where your own jupyter can find it.

If you have installed SageMath from source, the alternative command

ln -s $(sage -sh -c 'ls -d $SAGE_ROOT/venv/share/jupyter/kernels/sagemath') $HOME/.local/share/jupyter/kernels/sagemath-dev

creates a symlink that will stay current even if you switch to a different Python version later.

To get the full functionality of the SageMath kernel in your global Jupyter installation, the following Notebook Extension packages also need to be installed (or linked) in the environment from which the Jupyter installation runs.

You can check the presence of some of these packages using the command jupyter nbextension list.

  • For the Sage interacts, you will need the package widgetsnbextension installed in the Python environment of the Jupyter installation. If your Jupyter installation is coming from the system package manager, it is best to install widgetsnbextension in the same way. Otherwise, install it using pip.

    To verify that interacts work correctly, you can evaluate the following code in the notebook:

    @interact
def _(k=slider(vmin=-1.0, vmax= 3.0, step_size=0.1, default=0), auto_update=True):
plot([lambda u:u^2-1, lambda u:u+k], (-2,2),
     ymin=-1, ymax=3, fill={1:[0]}, fillalpha=0.5).show()

  • For 3D graphics using Three.js, by default, internet connectivity is needed, as SageMath’s custom build of the Javascript package Three.js is retrieved from a content delivery network.

    To verify that online 3D graphics with Three.js works correctly, you can evaluate the following code in the notebook:

    plot3d(lambda u,v:(u^2+v^2)/4-2,(-2,2),(-2,2)).show()

    However, it is possible to configure graphics with Three.js for offline use. In this case, the Three.js installation from the Sage distribution needs to be made available in the environment of the Jupyter installation. This can be done by copying or symlinking. The Three.js installation in the environment of the Jupyter installation must exactly match the version that comes from the Sage distribution. It is not supported to use several Jupyter kernels corresponding to different versions of the Sage distribution.

    To verify that offline 3D graphics with Three.js works correctly, you can evaluate the following code in the notebook:

    plot3d(lambda u,v:(u^2+v^2)/4-2,(-2,2),(-2,2), online=False).show()

  • For 3D graphics using jsmol, you will need the package jupyter-jsmol installed in the Python environment of the Jupyter installation. You can install it using pip. (Alternatively, you can copy or symlink it.)

    To verify that jsmol graphics work correctly, you can evaluate the following code in the notebook:

    plot3d(lambda u,v:(u^2+v^2)/4-2,(-2,2),(-2,2)).show(viewer="jmol")

Using Jupyter notebook through Visual Studio Code (VS Code) in WSL

If you have installed Sage on Windows using Windows Subsystem for Linux (WSL), it is convenient to use Visual Studio Code (VS Code) to interact with Sage.

Here are steps to use SageMath in a Jupyter notebook in VS Code:

  • Install and run VS Code in Windows.

  • Click the “Extension” icon on the left (or press Ctrl + Shift + X) to open a list of extensions. Install the “Remote - WSL” and “Jupyter” extensions.

  • In the command palette (Ctrl + Shift + P), enter “Remote-WSL: New Window”, and hit Enter.

  • In the command palette, enter “Create: New Jupyter Notebook”, and hit Enter.

  • Click “Select Kernel” on the right (or press Ctrl + Alt + Enter), select SageMath, and hit Enter.