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Replicable Python Environments with JupyterLab

Technical Reference Documentation
Getting Started
Brian Fromme, Technical Alliance Manager (SUSE)
Terry Smith, Partner Solutions Director (SUSE)
SUSE Linux Enterprise Server 15 SP4
Date: 2022-12-05
This document illustrates how to install and run JupyterLab, the web-based interactive development environment from Project Jupyter, on SUSE Linux Enterprise.
Documents published as part of the series SUSE Technical Reference Documentation have been contributed voluntarily by SUSE employees and third parties. They are meant to serve as examples of how particular actions can be performed. They have been compiled with utmost attention to detail. However, this does not guarantee complete accuracy. SUSE cannot verify that actions described in these documents do what is claimed or whether actions described have unintended consequences. SUSE LLC, its affiliates, the authors, and the translators may not be held liable for possible errors or the consequences thereof.

1 Introduction

1.1 Motivation

The Python programming ecosystem is popular for its simplicity, extensibility, cross-platform availability, and its large open source community of active developers and users. Developers in many domains benefit from and contribute to the large and growing Python ecosystem of open source tools and libraries. This has resulted in a rapid state of continuous development for the language and for the domains where Python is used.

The modern Python developer’s environment can be highly complex, leveraging a wealth of libraries, each with its own development life cycle and dependencies. Such complexity makes it difficult to faithfully replicate and share a development environment for collaboration, testing, and deployment. This is especially relevant in scientific computing, including the modern field of machine learning, where experimental replication is critical for validation but minor variations in the complex weave of library dependencies could invalidate test results or lead to unexpected behaviors in production.

1.2 Scope

This guide helps you take a first step to overcoming the challenge of creating and sharing replicable Python development environments.

Specifically, you get a brief introduction to the following concepts, systems, tools, and applications:


Many of the steps herein may be followed with little or no modification on the corresponding version of openSUSE Leap.

1.3 Audience

This guide is intended for researchers, engineers, developers, operations teams, and others interested in implementing replicable, ML development environments, featuring Python and computational documents with JupyterLab. The reader should have basic Linux command line skills.

2 Prerequisites

For this guide, you need:

  • SUSE Linux Enterprise Server 15 SP4 (SLES 15 SP4)

  • Python 3

    Python 3.6 is included with SLES 15 SP4 by default. It is a stable version, tested against the libraries and tools that are part of the operating system. This version is usually adequate for many use cases, but may not be new enough to support some workloads.

    This guide uses Python 3.10, which you install in the next section.

  • Internet access

    You need Internet access to download additional libraries and applications used in this guide.

3 Setting up your Python environment

Some disciplines, like machine learning, are in fast development and often have strict version dependencies. Installing dependent packages to support one project may thus break another project. Developers need a way to create isolated environments where they can install the packages they need without impacting other projects or system operations.

3.1 Addressing Python versions

Your application may have a hard dependency for a specific version of the Python interpreter itself. You can install a newer version of Python on your system. In fact, you can have multiple versions of Python. But care must be taken not to break dependencies for system tools or other applications.

At the time of this writing, Python 3.6 is the default version installed with SLES 15 SP4. And Python 3.10 is included in the standard repositories.

You should use the Python packages in the official SUSE repositories when possible. These packages have undergone extensive testing and are supported by SUSE. If you must use another version, you can build and install it from source code or use an implementation from a trusted provider (such as Anaconda).


SUSE mitigates the risk of Python version conflicts with explicit naming, ensuring that respective versions installed from the official repositories have unique file and directory names. For example, you will find the default Python interpreter is the executable file, /usr/bin/python3.6, but, if you have Python 3.10 installed, its interpreter is the file, /usr/bin/python3.10.

  1. Install Python 3.10 or the latest version available in the official repositories.

    sudo zypper install python310

    You can list the available Python 3 packages with sudo zypper search -d "Python 3 Interpreter" or sudo zypper search python3.

  2. Verify installation.

    /usr/bin/python3.10 --version

    or, with /usr/bin/ in your path:

    python3.10 --version

The default Python is still symbolically linked to /usr/bin/python3. Do not change this symbolic link, as this could break system tools that rely on the default version.


Tools like pyenv can help simplify installation and management of multiple Python environments, but this is out of scope for this document.

3.2 Creating a Python virtual environment

The Python venv module provides a mechanism for creating lightweight "virtual environments" and gives us a tool for isolating software dependencies. See the Python Enhancement Proposal 405 - Python Virtual Environments for more details on the venv module.

Creating a Python virtual environment is easy. Run the following commands as your user (root access is not required):

  1. Create a virtual environment, named 'myproj'.

    python3.10 -m venv $HOME/myproj

    This creates a new myproj/ subdirectory in your home directory and populates it with files, subdirectories, and symbolic links to enable the virtual environment to use the Python interpreter you specify with this command.

  2. Change to the new directory.

    cd $HOME/myproj
  3. Activate the new virtual environment.

    source ./bin/activate

    The activate script cannot be run on its own and must be "sourced" to update current environment variables in support of the isolated environment.


    When your virtual environment is active, your command prompt is prefixed with the environment name. In the present example, you should see '(myproj)' before your normal prompt.

  4. Deactivate your active virtual environment.


    When you are done working in this virtual environment, you can use the deactivate command to reset all the environment changes (including your prompt). This does not delete the virtual environment. To reactivate it, simply source the activate script again.

You can use the Python venv module to create as many virtual environments as you need, each in its own working directory.


If you no longer need a virtual environment and want to delete it, simply:

  1. deactivate the environment.

  2. delete its directory.

4 Managing Python packages

The SUSE Linux Enterprise Server repositories include many Python packages, tested against the included version of Python. These can be installed system-wide and are fine for many development projects. Sometimes your project may require a different version of one of these packages or a package not available in the standard repositories. In these cases, you may choose to use the Python Package Index (PyPI). This is a public repository of open source licensed packages made available by a community of developers from around the world.

There are many ways to install Python packages from PyPI, but the preferred method is with pip. pip is a powerful tool for managing Python packages from PyPI, but it can also install from a Git repository or from local distribution files.

Many Python packages are dependent on other packages. But manually identifying and installing all dependencies can be grueling, especially when each package may have additional dependencies of its own. Fortunately, pip automatically performs dependency resolution to ensure installation of all required packages and alerts you if resolution fails. See the pip documentation for details.


Your Python virtual environment has its own pip executable and symbolic links to the Python interpreter you used to create it. These are located in the bin/ subdirectory of your working directory. After activating your virtual environment, use python --version and pip --version to verify the versions of these commands.

4.1 Performing basic package management

With the following commands, you will perform some basic operations with the pip package installer.

  1. Activate your virtual environment.

    cd $HOME/myproj
    source ./bin/activate
  2. Upgrade pip itself to the latest version.

    pip install --upgrade pip
  3. Install a specific version of the numpy package.

    pip install numpy==1.18.5

    Be sure to use two equal signs ('==') between the package name and the desired version.

  4. Upgrade to the latest compatible version of numpy.

    pip install --upgrade numpy

    You may not actually get the latest version of a package. Instead, you get the latest version that is compatible with your Python environment.

  5. Uninstall numpy.

    pip uninstall numpy
  6. Install multiple packages (numpy, pandas, and matplotlib) together.

    pip install numpy pandas matplotlib
    Collecting numpy
      Using cached numpy-1.19.5-cp36-cp36m-manylinux2010_x86_64.whl (14.8 MB)
    Collecting pandas
      Using cached pandas-1.1.5-cp36-cp36m-manylinux1_x86_64.whl (9.5 MB)
    Collecting matplotlib
      Using cached matplotlib-3.3.4-cp36-cp36m-manylinux1_x86_64.whl (11.5 MB)
    Collecting pytz>=2017.2
      Using cached pytz-2022.2.1-py2.py3-none-any.whl (500 kB)
    Collecting python-dateutil>=2.7.3
      Using cached python_dateutil-2.8.2-py2.py3-none-any.whl (247 kB)
    Collecting pyparsing!=2.0.4,!=2.1.2,!=2.1.6,>=2.0.3
      Using cached pyparsing-3.0.9-py3-none-any.whl (98 kB)
    Collecting cycler>=0.10
      Using cached cycler-0.11.0-py3-none-any.whl (6.4 kB)
    Collecting pillow>=6.2.0
      Using cached Pillow-8.4.0-cp36-cp36m-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (3.1 MB)
    Collecting kiwisolver>=1.0.1
      Using cached kiwisolver-1.3.1-cp36-cp36m-manylinux1_x86_64.whl (1.1 MB)
    Collecting six>=1.5
      Using cached six-1.16.0-py2.py3-none-any.whl (11 kB)
    Installing collected packages: six, pytz, python-dateutil, pyparsing, pillow, numpy, kiwisolver, cycler, pandas, matplotlib
    Successfully installed cycler-0.11.0 kiwisolver-1.3.1 matplotlib-3.3.4 numpy-1.19.5 pandas-1.1.5 pillow-8.4.0 pyparsing-3.0.9 python-dateutil-2.8.2 pytz-2022.2.1 six-1.16.0

    Because some additional packages are required by those requested, pip automatically downloads and installs them.

4.2 Creating replicable experiments

Repeatability is key to many disciplines, but particularly in the sciences. Ensuring that your Python environment can be recreated the same way with the same packages and the same versions of those packages can be a challenge.

Fortunately, with the pip freeze command, you can generate a package requirements file, containing a list of the packages and versions that should be installed. See https://pip.pypa.io/en/stable/cli/pip_freeze/ for more information.

Create a requirements file for your Python virtual environment by following these steps:

  1. Activate your virtual environment (if not already active).

  2. Generate a requirements file with pip freeze.

    pip freeze > requirements.txt
  3. Take a look at the requirements file.

    cat requirements.txt

When you have a requirements file, you can share it with another developer or use it yourself to make sure a new environment contains all the same packages.

  1. If you are in an active virtual environment, deactivate it and leave that working directory.

    cd ..
  2. Create a new Python virtual environment.

    python3 -m venv myproj2
  3. Enter the working directory and activate the new environment.

    cd myproj2
    source ./bin/activate
  4. Upgrade pip.

    pip install --upgrade pip
  5. Install packages with the requirements file.

    pip install -r ../myproj/requirements.txt
  6. Verify correct packages are installed.

    pip freeze

    pip freeze excludes pip and a few other packages. To see a complete list of packages, use pip list.

5 Project Jupyter and JuypterLab

Project Jupyter was born out of the IPython Project in 2014 as it evolved to support interactive data science and scientific computing across more programming languages.

Under the Jupyter umbrella are:

  • Jupyter Notebook: the original Web application that allows data scientists to create and share computational documents, called notebooks.

  • JupyterLab: the latest generation of Jupyter Notebook with a modular design to allow users to customize the interface to support workflows in data science, scientific computing, computational journalism, and machine learning.

  • JupyterHub: the multiuser version of Jupyter Notebook that enables companies, classrooms, and research labs to deliver computational environments and resources to users while managing them centrally.

  • Voilà: a tool that transforms Jupyter notebooks into secure, stand-alone Web applications.


The Jupyter team maintains the IPython project and includes the IPython kernel (IPyKernel) by default to enable interactive Python in JupyterLab. A variety of community maintained kernels are available to enable use of other programming languages, such as R, Julia, and many others.

5.1 Getting JupyterLab up and running

JupyterLab is a Web application, so you need a Web browser to access it.

  1. Install JupyterLab.

    JupyterLab is easy to install right from PyPI. In your active virtual environment, issue the command:

    pip install jupyterlab
  2. Update your requirements file so JupyterLab will be included.

    pip freeze > requirements.txt

    Update your requirements file each time you install packages.

  3. Launch JupyterLab.

    Once JupyterLab is installed, launch it with the command:


    In your terminal window, you see something like the following:

    [I 2022-08-22 13:14:26.394 ServerApp] Jupyter Server 1.13.1 is running at:
    [I 2022-08-22 13:14:26.394 ServerApp] http://localhost:8888/lab?token=4baab78a6959bbc8b9992572b8faf7d2416741c2d00768f7
    [I 2022-08-22 13:14:26.394 ServerApp]  or
    [I 2022-08-22 13:14:26.394 ServerApp] Use Control-C to stop this server and shut down all kernels (twice to skip confirmation).
    [C 2022-08-22 13:14:26.398 ServerApp]
        To access the server, open this file in a browser:
        Or copy and paste one of these URLs:
  4. Access the JupyterLab user interface (UI).

    JupyterLab will attempt to open your default Web browser to the UI. If this fails, you can manually open your browser to one of the URLs provided in your terminal window.


    JupyterLab uses an access token to authenticate you to the Web UI. This token is unique to each instance of the environment.


    If your environment is on a remote, headless host, you can use SSH port forwarding to gain access.

    1. Log in to the remote host with a local port forward.

      ssh -L8888:localhost:8888 USER@REMOTE-IP

      where USER is your user name on the remote host and REMOTE-IP is its IP address or domain name.

    2. Activate your virtual environment.

    3. Launch JupyterLab.

    4. Open your local Web browser to the URL (with your access token) specified in the command terminal.

  5. Shut down JupyterLab.

    Shutting down JupyterLab means stopping running extensions, kernels, terminals, and the UI.

    Shut down JupyterLab directly from the UI by selecting Shutdown under the File menu.


    If you select Logout from the File menu or close your browser, the JupyterLab environment continues to run. You can shut it down from the command terminal by issuing 'CTRL-C'. You will be prompted to confirm that you want to shut down the Jupyter server. Bypass confirmation by issuing 'CTRL-C' twice.

5.2 Exploring JupyterLab

JupyterLab has many of the features of a traditional integrated development environment (IDE), but it focuses on interactive and exploratory computing.

The main JupyterLab interface consists of a menu at the top, a collapsible sidebar on the left, and a main work area in the middle.

JupyterLab UI Launcher

The menu provides access to some familiar functions, like file management, editing functions, UI view and settings, and help. It also includes functions specific to working in an interactive computing environment, like running code and managing compute kernels.

The sidebar offers a file browser, process controls, extension management, and more.

JupyterLab opens with the main work area displaying the Launcher tab. From the Launcher, you can start a notebook, console, terminal window, and more. These appear as additional tab in the main work area.


If you accidentally close the Launcher, you can open a new one from the File menu.

Try creating a notebook.

  1. Start a notebook by clicking Python 3 in the Notebook area of the Launcher.

    Empty Jupyter notebook
  2. In the first command cell of your notebook, enter:

    from sys import version

    Press 'CTRL-ENTER' at the end of the line to execute it.


    You can enter multiple lines by pressing 'ENTER' at the end of each line. The code will not execute until you press 'CTRL-ENTER'.

  3. In the second command cell, enter:

    Jupyter notebook with two command examples
  4. Save your notebook by selecting Save Notebook As…​ from the File menu.

    Enter a name when prompted, such as mypythonversion.ipynb.

  5. Close your notebook by clicking the 'X' in the notebook tab.

  6. Reopen your notebook by double-clicking mypythonversion.ipynb in the sidebar file browser.

5.3 Collaborating with JupyterLab

JupyterLab provides a great environment for interactive experimentation, and the notebook format offers an easy mechanism for sharing your code with others. But a Jupyter notebook by itself may not be sufficient, as your colleague’s development environment may be different.

When you are ready to share your work, you should include your:

  • notebook

  • data

  • requirements file

With these three items, your colleague can recreate your development environment and run your code against your data.

6 Summary

Whether you are getting started on your Python development journey or already looking to scale to production, start from a capable, adaptable, and supported foundation with SUSE Linux Enterprise Server.

In this guide, you learned to:

  • isolate your Python projects in virtual environments.

  • manage Python packages.

  • simplify replicability and sharing.

  • deploy JupyterLab for a self-documenting, interactive development environment.

Take your next steps with JupyterLab and Python with the following resources:

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If the Cover Text requirement of section 3 is applicable to these copies of the Document, then if the Document is less than one half of the entire aggregate, the Document’s Cover Texts may be placed on covers that bracket the Document within the aggregate, or the electronic equivalent of covers if the Document is in electronic form. Otherwise they must appear on printed covers that bracket the whole aggregate.


Translation is considered a kind of modification, so you may distribute translations of the Document under the terms of section 4. Replacing Invariant Sections with translations requires special permission from their copyright holders, but you may include translations of some or all Invariant Sections in addition to the original versions of these Invariant Sections. You may include a translation of this License, and all the license notices in the Document, and any Warranty Disclaimers, provided that you also include the original English version of this License and the original versions of those notices and disclaimers. In case of a disagreement between the translation and the original version of this License or a notice or disclaimer, the original version will prevail.

If a section in the Document is Entitled "Acknowledgements", "Dedications", or "History", the requirement (section 4) to Preserve its Title (section 1) will typically require changing the actual title.


You may not copy, modify, sublicense, or distribute the Document except as expressly provided for under this License. Any other attempt to copy, modify, sublicense or distribute the Document is void, and will automatically terminate your rights under this License. However, parties who have received copies, or rights, from you under this License will not have their licenses terminated so long as such parties remain in full compliance.


The Free Software Foundation may publish new, revised versions of the GNU Free Documentation License from time to time. Such new versions will be similar in spirit to the present version, but may differ in detail to address new problems or concerns. See http://www.gnu.org/copyleft/.

Each version of the License is given a distinguishing version number. If the Document specifies that a particular numbered version of this License "or any later version" applies to it, you have the option of following the terms and conditions either of that specified version or of any later version that has been published (not as a draft) by the Free Software Foundation. If the Document does not specify a version number of this License, you may choose any version ever published (not as a draft) by the Free Software Foundation.

ADDENDUM: How to use this License for your documents

Copyright (c) YEAR YOUR NAME.
   Permission is granted to copy, distribute and/or modify this document
   under the terms of the GNU Free Documentation License, Version 1.2
   or any later version published by the Free Software Foundation;
   with no Invariant Sections, no Front-Cover Texts, and no Back-Cover Texts.
   A copy of the license is included in the section entitled “GNU
   Free Documentation License”.

If you have Invariant Sections, Front-Cover Texts and Back-Cover Texts, replace the “ with…​Texts.” line with this:

with the Invariant Sections being LIST THEIR TITLES, with the
   Front-Cover Texts being LIST, and with the Back-Cover Texts being LIST.

If you have Invariant Sections without Cover Texts, or some other combination of the three, merge those two alternatives to suit the situation.

If your document contains nontrivial examples of program code, we recommend releasing these examples in parallel under your choice of free software license, such as the GNU General Public License, to permit their use in free software.