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JupyterLab

Revision as of 20:45, 12 November 2021 by Plstonge (talk | contribs) (Julia Kernel configuration procedure)
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Introduction

JupyterLab is now the recommended general-purpose user interface to use on a JupyterHub. From a JupyterLab server, you can manage your remote files and folders, and you can launch Jupyter applications like a terminal, (Python 3) notebooks, RStudio and a Linux desktop.

The subsection JupyterHub on clusters contains the list of available Jupyter hubs at Compute Canada.

Launching a JupyterLab server as a job

 
Server Options form on Béluga's JupyterHub

On Béluga and Hélios, once the authentication is done on JupyterHub, your Web browser is redirected to either a) a previously launched Jupyter server or b) a form that allows you to configure and submit a new interactive session on the cluster. In any case, the JupyterLab server is running on dedicated compute resources.

In the Server Options form, you can:

The JupyterLab Interface

When JupyterLab is ready to be used, the interface has multiple panels.

 
Default home tab when JupyterLab is loaded

Menu bar on top

  • In the File menu:
    • Hub Control Panel: if you want to manually stop the JupyterLab server and the corresponding job on the cluster. This is useful when you want to start a new JupyterLab server with more or less resources
    • Log Out: the JupyterHub session will end, which will also stop the JupyterLab server and the corresponding job on the cluster
  • Most other menu items are related to notebooks and Jupyter applications

Tool selector on left

  • File Browser (folder icon):
    • This is where you can browse in your home, project and scratch spaces
    • It is also possible to upload files
  • Running Terminals and Kernels (stop icon):
    • To stop kernel sessions and terminal sessions
  • Commands
  • Property Inspector
  • Open Tabs:
    • To navigate between application tabs
    • To close application tabs - the corresponding kernels remain active
 
Loaded modules and available modules
  • Softwares (blue diamond sign):
    • Compute Canada modules can be loaded and unloaded in the JupyterLab session. Depending on the module loaded, an icon directing to the Jupyter application will appear in the Launcher tab.
    • The search box can search for any available module and give the result in the Available Modules sub-panel. Note: some modules are hidden until their dependency is loaded - we recommend that you first look for a specific module with module spider module_name from a terminal.
    • The next sub-panel is the list of Loaded Modules in the whole JupyterLab session. Note: while python and ipython-kernel modules are loaded by default, additional modules must be loaded before launching some other applications or notebooks. For example: scipy-stack.
    • The last sub-panel is the list of Available modules, similar to the output of module avail. By clicking on a module's name, detailed information about the module is displayed. By clicking on the Load link, the module will be loaded and added to the Loaded Modules list.

Application area on right

Status bar at the bottom

  • By clicking on the icons, this brings you to the Running Terminals and Kernels tool.

Jupyter Applications

JupyterLab offers access to a terminal, an IDE (Desktop), a Python console and different options to create text and Markdown files. This section presents only the main supported Jupyter applications that work with the Compute Canada software stack.

Command Line Interpreters

 
Julia console launcher button
 
Python console launcher button
 
Terminal launcher button

Julia Console

To enable the Julia 1.x console launcher, an ijulia-kernel module needs to be loaded. When launched, a Julia interpreter is presented in a new JupyterLab tab.

Python Console

The Python 3.x console launcher is available by default in a new JupyterLab session. When launched, a Python 3 interpreter is presented in a new JupyterLab tab.

Terminal

This application launcher will open a terminal in a new JupyterLab tab:

  • The terminal runs a (Bash) shell on the remote compute node without the need of an SSH connection
    • Gives access to the remote filesystems (/home, /project, /scratch)
    • Allows running compute tasks
  • The terminal allows copy-and-paste operations of text:
    • Copy operation: select the text, then press Ctrl+C
      • Note: usually, Ctrl+C is used to send a SIGINT signal to a running process, or to cancel the current command. To get this behaviour in JupyterLab's terminal, click on the terminal to deselect any text before pressing Ctrl+C
    • Paste operation: press Ctrl+V

Available Notebook Kernels

Julia Notebook

To enable the Julia 1.x notebook launcher, an ijulia-kernel module needs to be loaded. When launched, a Julia notebook is presented in a new JupyterLab tab.

Python Notebook

 
Searching for scipy-stack modules

If any of the following scientific Python packages is required by your notebook, before you open this notebook, you must load the scipy-stack module from the JupyterLab Softwares tool:

  • ipython, ipython_genutils, ipykernel, ipyparallel
  • matplotlib
  • numpy
  • pandas
  • scipy
  • Other notable packages: Cycler, futures, jupyter_client, jupyter_core, mpmath, pathlib2, pexpect, pickleshare, ptyprocess, pyzmq, simplegeneric, sympy, tornado, traitlets
  • And many more (click on the scipy-stack module to see all Included extensions)

Note: you may also install needed packages by running for example the following command inside of a cell: !pip install --no-index numpy

  • For some packages (like plotly, for example), you may need to restart the notebook's kernel before importing the package.
  • The installation of packages in the default Python kernel environment is temporary to the lifetime of the JupyterLab session; you will have to reinstall these packages the next time you start a new JupyterLab session. For a persistent Python environment, you may configure a custom Python kernel.

To open an existing Python notebook:

  • Go back to the File Browser
  • Browse to the location of the *.ipynb file
  • Double-click on the *.ipynb file:
    • This will open the Python notebook in a new JupyterLab tab
    • An IPython kernel will start running in background for this notebook

To open a new Python notebook in the current File Browser directory:

  • Click on the Python 3.x launcher under the Notebook section:
    • This will open a new Python 3 notebook in a new JupyterLab tab
    • A new IPython kernel will start running in background for this notebook

Other Applications

OpenRefine

 
OpenRefine launcher button

To enable the OpenRefine application launcher, an openrefine module needs to be loaded. Depending on the software environment version, the latest version of OpenRefine should be loaded:

  • With StdEnv/2020, load module: openrefine/3.4.1
  • With StdEnv/2018.3, load module: openrefine/3.3

This OpenRefine launcher will open or reopen an OpenRefine interface in a new Web browser tab:

  • It is possible to reopen an active OpenRefine session after the Web browser tab was closed
  • The OpenRefine session will end when the JupyterLab session will end

RStudio

 
RStudio launcher button

To enable the RStudio application launcher, the following three modules need to be loaded:

  1. gcc
  2. r
  3. rstudio-server

Depending on the software environment version, you should load the following two modules (r is loaded automatically):

  • With StdEnv/2020, it is not yet supported
  • With StdEnv/2018.3, load modules: gcc/7.3.0, rstudio-server/1.2.1335
  • With StdEnv/2016.4, load modules: gcc/7.3.0, rstudio-server/1.2.1335

This RStudio launcher will open or reopen an RStudio interface in a new Web browser tab:

  • It is possible to reopen an active RStudio session after the Web browser tab was closed
  • The RStudio session will end when the JupyterLab session will end

VS Code

 
VS Code launcher button

To enable the VS Code (Visual Studio Code) application launcher, a code-server module needs to be loaded. Depending on the software environment version, the latest version of VS Code should be loaded:

  • With StdEnv/2020, load module: code-server/3.5.0
  • With StdEnv/2018.3, load module: code-server/3.4.1

This VS Code launcher will open or reopen the VS Code interface in a new Web browser tab:

  • For a new session, the VS Code session can take up to 3 minutes to complete its startup.
  • It is possible to reopen an active VS Code session after the Web browser tab was closed
  • The VS Code session will end when the JupyterLab session will end

Desktop

 
Desktop launcher button

This Desktop launcher will open or reopen a remote Linux desktop interface in a new Web browser tab:

  • This is equivalent to running a VNC server on a compute node, then creating an SSH tunnel and finally using a VNC client, but you need nothing of all this with JupyterLab!
  • It is possible to reopen an active desktop session after the Web browser tab was closed
  • The desktop session will end when the JupyterLab session will end

Create your own Jupyter Application Kernel

It is possible to add kernels for other programming languages, for a different Python version or for a persistent virtual environment that has all required packages and libraries for your project. Refer to Making kernels for Jupyter to learn more.

The installation of a new kernel is done in two steps:

  1. Installation of the packages that will allow the language interpreter to communicate with the Jupyter Notebook.
  2. Creation of a file that will indicate to Jupyter Notebook how to initiate a communication channel with the language interpreter. This file is called a kernel spec file, and it will be saved in a sub-folder of ~/.local/share/jupyter/kernels.

In the following sections, we provide a few examples of the kernel installation procedure.

Julia Kernel

Prerequisites:

  1. The configuration of a Julia kernel depends on a Python virtual environment that already has all the Python Kernel dependencies. If you do not have such virtual environment, make sure to follow instructions listed in the next section.
  2. Since the installation of Julia packages requires an access to Internet, the configuration of a Julia kernel must be done in a remote shell session on a login node. For example, from a local terminal or any SSH client: 
    [my_computer ~] $ ssh username@cluster_address

Once you have a Python virtual environment available, you may configure the Julia kernel:

  1. Activate the Python virtual environment. 
    [name@server ~]$ source $HOME/jupyter_py3.8/bin/activate
  2. Load the Julia module. 
    (jupyter_py3.8) [name@server ~]$ module load julia
  3. Install IJulia. 
    (jupyter_py3.8) [name@server ~]$ echo -e 'using Pkg\nPkg.add("IJulia")' | julia
  4. Deactivate the virtual environment and close the remote session: 
    (jupyter_py3.8) [name@server ~]$ deactivate && exit
  5. Start or restart a new JupyterLab session.

For more information, see the IJulia documentation.

Python Kernel

In a terminal with an active session on the remote server, you may configure a Python virtual environment with all the required Python modules and a custom Python kernel for JupyterLab. Here are the initial steps for the simplest Jupyter configuration in a new Python virtual environment:

  1. Start from a clean Bash environment (this is required if you are using the Jupyter Terminal): 
    [name@server ~]$ env -i HOME=$HOME bash -l
  2. Load a Python module: 
    [name@server ~]$ module load python/3.8
  3. Create a new Python virtual environment: 
    [name@server ~]$ virtualenv --no-download $HOME/jupyter_py3.8
  4. Activate your newly created Python virtual environment: 
    [name@server ~]$ source $HOME/jupyter_py3.8/bin/activate
  5. Install the ipykernel library: 
    (jupyter_py3.8) [name@server ~]$ pip install --no-index ipykernel
  6. Create the common kernels folder: 
    (jupyter_py3.8) [name@server ~]$ mkdir -p ~/.local/share/jupyter/kernels
  7. Generate the kernel spec file. Substitute <unique_name> by a name that will uniquely identify your kernel: 
    (jupyter_py3.8) [name@server ~]$ python -m ipykernel install --user --name <unique_name> --display-name "Python 3.8 Kernel"
  8. Deactivate the virtual environment and exit the temporary Bash session: 
    (jupyter_py3.8) [name@server ~]$ deactivate && exit
  9. Start or restart a new JupyterLab session.

For more information, see the ipykernel documentation.

Installing more Python libraries

Based on the Python virtual environment created in the previous section:

  1. Start from a clean Bash environment (this is required if you are using the Jupyter Terminal): 
    [name@server ~]$ env -i HOME=$HOME bash -l
  2. Activate the virtual environment: 
    [name@server ~]$ source $HOME/jupyter_py3.8/bin/activate
  3. Install any required library (for example: numpy): 
    (jupyter_py3.8) [name@server ~]$ pip install --no-index numpy
  4. Deactivate the virtual environment and exit the temporary Bash session: 
    (jupyter_py3.8) [name@server ~]$ deactivate && exit
  5. The newly installed Python libraries can now be imported in any notebook using the Python 3.8 Kernel.

R Kernel

Prerequisites:

  1. Configuring an R kernel still depends on a Python virtual environment that already has all the Python Kernel dependencies. If you do not have such virtual environment, make sure to follow instructions listed in the previous section.
  2. Since the installation of R packages requires an access to CRAN, the configuration of an R kernel must be done in a remote shell session on a login node. For example, from a local terminal or any SSH client: 
    [my_computer ~] $ ssh username@cluster_address

Once you have a Python virtual environment available, you may configure the R kernel:

  1. Activate the Python virtual environment. 
    [name@server ~]$ source $HOME/jupyter_py3.8/bin/activate
  2. Load an R module. 
    (jupyter_py3.8) [name@server ~]$ module load r/4.1
  3. Install the R kernel dependencies - this will take up to 10 minutes, and packages should be installed in a local directory like ~/R/x86_64-pc-linux-gnu-library/4.1. 
    (jupyter_py3.8) [name@server ~]$ R --no-save

    > install.packages(c('crayon', 'pbdZMQ', 'devtools'), repos='http://cran.us.r-project.org')
  4. Install the R kernel. 
    > devtools::install_github(paste0('IRkernel/', c('repr', 'IRdisplay', 'IRkernel')))
  5. Install the R kernel spec file. 
    > IRkernel::installspec()
  6. Quit R (with q()), deactivate the virtual environment and close the remote session: 
    (jupyter_py3.8) [name@server ~]$ deactivate && exit
  7. Start or restart a new JupyterLab session.

For more information, see the IRKernel documentation.

Installing more R packages

The installation of R packages cannot be done from Notebooks because there is no access to CRAN. As in the above installation procedure, it is required to install R packages from a login node:

  1. From a local terminal or any SSH client, connect to a login node of the cluster hosting JupyterHub.
  2. Load the same R module. 
    [name@server ~]$ module load r/4.1
  3. Start the R shell and install any required package. For example doParallel. 
    [name@server ~]$ R --no-save

    > install.packages('doParallel', repos='http://cran.us.r-project.org')
  4. Quit R (with q()) and close the remote session
  5. The newly installed R packages should already be usable in a Notebook executed by the R kernel.

Possible error messages

  • A "time-out" error message when starting a JupyterLab session:
    • Just like any interactive job on any cluster, the maximum time request should be under three (3) hours in order to avoid issues with the scheduler or a longer than usual wait time.
    • There may be no available interactive node at the moment. You should then try at another moment.
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