Jupyter Server

Jupyter is an Integrated Development Environment (IDE) that provides an interactive and collaborative environment for scientific computing. This interactive coding environment allows for immediate execution and visualization of code, facilitating on-the-fly data analysis and visualization. It supports over 40 programming languages (including Python, R, Julia, Java, and Scala) and seamlessly integrates with popular data science libraries. Its collaborative work environment simplifies sharing of results and workflows, and integrates well with version control systems like Git. With its extensibility and modularity, users can customize their workspace to fit specific needs. Finally, it offers rich text and Markdown support (for user-friendly project description and code documentation) and provides a suite of integrated tools (e.g., file browser or debugger), making it an all-in-one platform for data science projects.

The Jupyter Project offers a suite of open-source software tools, including:

• Jupyter Notebook for creating and sharing documents that contain live code, equations, and visualizations,
• JupyterHub for providing multi-user access to notebooks, and
• JupyterLab, a highly flexible and interactive web-based interface, which integrates notebooks, text editors, terminals, and custom components in a single unified environment.

Both JupyterLab and JupyterNotebook tools are currently in active use on SCINet infrastructure, including Ceres and Atlas clusters.

SCINet Options for Jupyter

Jupyter operates as a client/server application. When you run Jupyter, the application starts a server that serves the Jupyter application to your web browser.

• The server part of the application runs your Python (or other languages) code, manages your notebooks (files), and serves the Jupyter application to your browser. You can launch the Jupyter server on a high-performance computing cluster (Ceres or Atlas) via Open OnDemand service (online, no-installation) or on your own computing machine as a local (offline, requires Jupyter installation) server.
• The client part of the application (regardless of how it was started) runs in a web browser on your local machine and handles the interactive user interface (GUI) of Jupyter. This is where you will perform all your tasks.

SCINet users can use Jupyter in one of the following ways:

1. To run JupyterLab and access data on your local workstation, install the open-source JupyterLab tool.
   • Once installed, open the Terminal or Command Prompt window on your local workstation and execute the command:

       jupyter lab
     

     This will automatically open the JupyterLab interface in your default web browser.
     Manually you can also open it using the URL address: http://localhost:8888/lab

2. To run JupyterLab on and access data in Amazon Web Services, contact the SCINet VRSC.

3. NEW: Jupyter Server on Ceres in Open OnDemand (online, no-installation).

NOTE: Jupyter on Ceres through SCINet VPN or via ssh-tunnel is not possible, because Jupyter’s interface is designed to work in a web browser.

Jupyter Server in Open OnDemand

In your browser go to https://ceres-ood.scinet.usda.gov/, enter your SCINet credentials: Username and SCINet Password, followed by preferred multi-factor authentication (MFA Guide).

After logging into Open OnDemand, click on “Interactive Apps” in the menu on the top and select “Jupyter” from the dropdown.

This will redirect you automatically to the Jupyter Session setup page.

Modify default values if needed. Specifically, make sure:

• that your selected Account is correct
• to adjust the time needed (Number of hours) for your work in the Jupyter interface
• to choose the right tool for your task (typically Lab is preferred over Notebook as it provides a built-in file browser)
• to specify the Working Directory path, which is your $HOME by default, but you may want to work in the /project/<your-group> space instead.

Once you are done with the settings, click on the blue “Launch” button at the bottom of the page. It may take up to several minutes for the new session to start.

Note that once you click on the “Launch” button and a new session starts, requested resources will be allocated to your job and won’t be available to other users even if you don’t run anything in Jupyter. Please be considerate, request only resources that you need for your tasks, and remember to delete the session when done.

Once it starts a new button “Connect to Jupyter” will appear. Clicking on the button will open Jupyter IDE in a new browser tab, with the Python Console opened by default.

When done using Jupyter, return to the previous tab to stop the Jupyter session by clicking on the red “Delete” button.

Note that while the Jupyter session is running you can connect and disconnect to/from the Jupyter Server multiple times. Every click on the “Connect to Jupyter” button will open a new browser tab.

For tips and tricks refer to the following section and to the Jupyter Tutorials in the DataScience Workbook:

• Jupyter: Interactive Web-Based Multi-Kernel DE
• Getting Started with JupyterLab on a local machine
• Getting Started with Jupyter Notebook on HPC systems
• Jupyter Lab: create an interactive Python notebook

Interface of JupyterLab vs Jupyter Notebook

Jupyter offers two user interface options for creating and managing documents Jupyter Notebook and JupyterLab. Within the Open OnDemand (OOD) interface, under the Interactive Apps section, you have the option to set up a Jupyter session with the choice of either Jupyter Notebook or JupyterLab. You have separate Server options for them on Atlas, and on Ceres, you select one when setting up the Session parameters.

Jupyter Notebook

Jupyter Notebook is the original interface which provides an environment for interactive computing. You can create and share documents that contain live code, equations, visualizations, and narrative text. It is best used for simple tasks and for learning purposes as its interface is less complex and easier to understand.

Jupyter Notebook initiates with a File Browser view, allowing you to navigate directories, and create or upload folders, text files, and Python or R notebook documents. Additionally, it provides the Terminal app for direct command line access to the file system, all within the Jupyter interface and accessible through separate browser tabs.

In the Jupyter Notebook interface, in the Files tab (top-left corner), click New button (top-right corner) and select the type of the app to be launched in a separate tab in your web browser (e.g., Python or R notebook, text file or Terminal).

Jupyter Lab

JupyterLab is the next-generation user interface for Project Jupyter. It offers all the familiar building blocks of the classic Jupyter Notebook (notebook, terminal, text editor, Python console, file browser, rich outputs, etc.) in a flexible and powerful user interface. A graphical File Manager (foldable, on the left) allows file upload/download from Ceres via a web browser.

In JupyterLab, if the Launcher tab is not currently visible, you can easily open a new one by navigating to the “File” menu at the top of the JupyterLab interface, then selecting “New Launcher”. This will open a new Launcher tab where you can start different types of sessions like notebooks, consoles, terminals, or other applications by clicking on them. Each application opens in a new tab within the JupyterLab working panel, adjacent to the foldable File Browser, providing a multi-tasking environment all within a single browser tab (e.g., Python notebook app).

JupyterLab also provides additional features not found in Jupyter Notebook, such as:

• Drag-and-drop cells within and between notebooks
• Enhanced file management (browse directories, open multiple files, etc.)
• A more modern and polished user interface
• Integrated markdown rendering
• Multiple views of a single document
• Ability to arrange multiple notebooks, text files, terminals, and output areas, all in separate panels of a single layout (in the same window)

This makes JupyterLab better suited for more complex tasks, larger projects, or when you want to work with several notebooks and documents at the same time.

Overview of the JupyterLab applications

Each application in JupyterLab has its own use case, and they can all be used in combination to provide a comprehensive environment for Python programming, data analysis, and scientific computing. JupyterLab offers Console, Notebook, and Script File applications for both Python and R, two of the most widely used programming languages, providing a versatile environment for data analysis and research.

Python and R applications in JupyterLab are essentially the same in terms of their user interface and functionality, but they are designed to work with different programming languages, Python and R respectively. The differences arise from the capabilities and features of the two languages themselves.

Note: If you want to deepen your knowledge of Python programming or coding in R, be sure to visit the DataScience Workbook. There you’ll find numerous practical tutorials with examples and tips specifically tailored for SCINet Users, offering a valuable resource to enhance your coding skills.

Console

The Console in JupyterLab is an interactive environment where you can input and execute individual lines of code, ideal for quick testing, debugging, and exploratory work. The primary differences between Python Console and R Console arise from the inherent characteristics and capabilities of the two languages.

Python Console

Python Console provides an interactive shell where you can execute Python commands one by one and see the output immediately. It’s great for quick experimentation and testing of Python code snippets.

The Python Console in JupyterLab consists of an input field for typing Python commands (command prompt), and an output area that displays the results, providing an interactive environment for executing and testing Python code line-by-line. In the Python console of JupyterLab, you execute commands by pressing Shift + Enter on your keyboard.

For example, to check the Python version in use, execute the command:

import sys
print(sys.version)

R Console

R Console is an interactive shell where you can execute R commands one by one and see the output immediately. It’s ideal for quickly testing R code snippets, data exploration, and running statistical analyses.

The R Console in JupyterLab consists of an input field for typing R commands (command prompt), and an output area that displays the results, providing an interactive environment for executing and testing R code. In the R console of JupyterLab, you execute commands by pressing Shift + Enter on your keyboard.

For example, to check the version of R in an R console, you can use the following command:

version

When you execute this command, it will print out information about the version of R that you are currently using, along with other details like the system it’s built for and the date of the build.

Notebook

The Notebook in JupyterLab is a versatile tool that combines live code, equations, visualizations, and narrative text, facilitating iterative and exploratory data analysis, modeling, and the creation of reproducible reports.

Python Notebook

Python Notebook is an interactive document that combines live Python code, equations, visualizations, and text rendered in Markdown. Notebooks are perfect for data analysis, statistical modeling, machine learning, and creating a shareable document of your work.

Here’s a simple step-by-step example of using a Python Notebook in JupyterLab:

1. Markdown Cell Rendering

• Create a new cell and change its type to Markdown using the dropdown menu at the top of the notebook.
• Type some text in markdown format: e.g., # This is an example Python Notebook
• Press Shift + Enter to render the markdown text into a formatted view.

2. Python Code

• Create another new cell, ensuring it’s set to Code in the dropdown menu.
• Type in some Python code: e.g.,

  # create a list of numbers
  numbers = [1, 2, 3, 4, 5]
  # calculate the square of each number and print it
  for number in numbers:
    print(f'The square of {number} is {number ** 2}.')
  

• Press Shift + Enter to execute the code. Note that lines marked with # are comments in Python, and are not executed.

3. Visual Output

In a Python notebook, you can readily display visual outputs, including image previews and generated graphs, all within the same environment. This ability to interleave code with its outputs, including visuals, facilitates a more intuitive and interactive approach to data analysis.

Image Preview
For image previews, Python notebook can handle it directly with modules included in the standard library. You can use the IPython.display.Image module to display images right away in your notebook.

• For example, use the following code snippet to read an image file from your file system on Ceres and display it in the Jupyter notebook:

  from IPython.display import Image
  # Let's display an image
  Image(filename="path_to/your_image_file.jpg")
  

  

Finding the correct path of an image or any other file is crucial when you’re working with file operations in a programming environment. To find the correct path of an image or any file in JupyterLab, you can either right-click on the file in the File Browser and select ‘Copy Path’, or use the Terminal app to navigate to the file and copy its full path from the command line.

Generate Graphs and Plots
To generate graphs, charts, or any other complex visualizations from your data in Python Notebook, you’ll need to use external Python libraries like matplotlib, seaborn, or plotly. These libraries are not part of the Python standard library, so you’ll need to install them before you can use them in your notebook. Before installing these libraries, it’s a good practice to set up a virtual environment using a tool like Conda.

• Navigate to section Conda package and environment manager to learn how to create the Conda environment and install the Python graphing libraries.
• Once you completed the steps required for setting up a new Conda environment:
  • Select the new kernel in your Python notebook: Open or create a new notebook, click on the Kernel menu, then Change kernel, and finally select your new environment from the list.
• Use the following Python code snippet to create a simple graph using matplotlib library:

  import matplotlib.pyplot as plt
  x = [1, 2, 3, 4, 5]
  y = [1, 4, 9, 16, 25]
  plt.plot(x, y)
  

R Notebook

An R Notebook is an interactive document that combines live R code with narrative text, images, and visualizations. This is especially useful for data analysis, statistical modeling, creating reproducible research documents, and sharing your work.

Files and scripts

JupyterLab supports different types of files: Script Files are text files for writing executable scripts, Markdown Files allow for creating text documents with simple formatting syntax, and Raw Text Files are plain text files used for writing notes or storing text-like data.

Python Script File

Python Script File is a text file where you can write a Python script and save it with a .py extension. You can run this script in a Python console or terminal. This is useful for more extensive coding projects that go beyond the exploratory phase.

Here’s a simple Python script that demonstrates Python’s syntax, data structures, and standard library functions like sum, min, and max. The script defines a function that takes a list of integers and returns a dictionary containing the sum, average, minimum, and maximum of the numbers.

python_script.py :

def summarize_numbers(numbers):
    if not numbers:                     # Check if the input is not empty
        return "Input list is empty."

    total = sum(numbers)                # Calculate the sum of the numbers
    average = total / len(numbers)      # Calculate the average of the numbers
    minimum = min(numbers)              # Find the minimum numbers
    maximum = max(numbers)              # Find the maximum numbers

    # Return a dictionary with the results
    return {
        'total': total,
        'average': average,
        'minimum': minimum,
        'maximum': maximum
    }

# Example usage
numbers = [10, 20, 30, 40, 50]
summary = summarize_numbers(numbers)

# Print the summary
for key, value in summary.items():
    print(f"{key}: {value}")

and execute the Python script in the terminal window:

python python_script.py

R Script File

R Script File is a text file in which you can write an R script and save it with a .R extension. You can run this script in an R console or terminal. This is typically used for larger coding projects or when you need to run a sequence of R commands repeatedly.

Here’s a simple R script that demonstrates R’s syntax, data structures (like vectors and lists), and base functions. This script defines a function that takes a vector of numbers and returns a list containing the sum, average, minimum, and maximum of the numbers.

R_script.r

summarize_numbers <- function(numbers) {
# Check if the input is not empty
    if(length(numbers) == 0) {
        return("Input vector is empty.")
    }
# Calculate the sum of the numbers
    total <- sum(numbers)
# Calculate the average of the numbers
    average <- mean(numbers)
# Find the minimum and maximum numbers
    minimum <- min(numbers)
    maximum <- max(numbers)
# Return a list with the results
    return(list(
        total = total,
        average = average,
        minimum = minimum,
        maximum = maximum
    ))
}

# Example usage
numbers <- c(10, 20, 30, 40, 50)
summary <- summarize_numbers(numbers)

# Print the summary
for(key in names(summary)) {
    print(paste(key, ": ", summary[[key]]))
}

The script can be run directly in the R console or in the Terminal with the Rscript command.

• In the R console:

  source("R_script.r")
  

• In the Terminal with Rscript:

  Rscript R_script.r
  

Markdown File

Markdown File is a text file where you can write content using Markdown, a lightweight markup language that you can use to format and render your text in a human-friendly way. This is useful for writing notes, describing project pipeline, and creating documentation for your code.

Markdown in Jupyter notebooks lets you create rich text with different styles, lists, links, images, and even HTML elements. Here’s an example illustrating some of these components.

# Jupyter Notebook Markdown Example

This is an **example** of using *markdown* in Jupyter notebooks.

## Text styles

- **Bold text** is created using double asterisks: `**Bold text**`
- *Italic text* is created using single asterisks or underscores: `*Italic text*` or `_Italic text_`
- ~~Strikethrough text~~ is created using double tildes: `~~Strikethrough text~~`

## Lists

You can create bulleted lists:

- Item 1
- Item 2
- Item 3

Or numbered lists:

1. First item
2. Second item
3. Third item

## Links and Images

You can create links: [SCINet](https://scinet.usda.gov)

And display images: ![SCINet logo](https://scinet.usda.gov/assets/img/site/usda-logo-color.svg)

## HTML

You can use HTML for more complex formatting. For example, to color text and backgrounds:

<p style="color:red;">This is a text in red.</p>
<p style="background-color:lightblue;">This is a text with a light blue background.</p>

The provided Markdown example can be utilized in two ways within JupyterLab:

• Markdown cell in a notebook
  If you’re working within a Jupyter notebook (a .ipynb file), you can paste this example directly into a cell that is set to Markdown mode. You can change a cell’s mode in the toolbar at the top of the notebook. After pasting the example, hit Alt + Enter or use the Run button to render the Markdown syntax into formatted text.

• Markdown file
  If you’re working with a separate .md (Markdown) file in JupyterLab, you can paste this example directly into the file.

  • To see how your Markdown renders, you can right-click in the text editor and select “Show Markdown Preview”.
  • This will open a new tab where you can see your rendered Markdown that updates in real-time as you make changes to your .md file.

Text File

Text File is a standard, plain/raw text file where you can write and edit text. This is useful for writing notes or any other kind of text content, as well as storing text-like or numerical data.

For example, you can create a simple comma-separated values (CSV) text file, which can be viewed and edited as a Text File in JupyterLab. It could look something like this:

name,age,city
John Doe,35,New York
Jane Doe,32,Los Angeles
Jim Smith,28,Chicago
Jill Smith,30,Boston

This simple text data file represents a table with three columns (name, age, city) and four rows of data. In JupyterLab, you can create such a file using the Text File app in the JupyterLab Launcher. Next time, you can open this file by navigating to its location in the file browser and clicking on it. The file will open in a new tab where you can view and edit the text.

NOTE: Please note that while you can view and edit this file as a Text File in JupyterLab, if you want to perform data analysis tasks on this data, you’d likely want to open this CSV file in a Jupyter Notebook app using a data manipulation library like pandas in Python or read.csv in R.

Terminal

The terminal provides command-line access to your system, similar to the terminal application on your computer. This is useful for running system commands, installing packages, managing files, and running scripts. When working in the Terminal in Jupyter on Ceres via Open OnDemand you have immediate access to your locations in a cluster file system (the same as when logged in via SSH protocol).

Executing Bash commands and Python/R scripts

With the Terminal in JupyterLab, you have the ability to immediately execute and test Python and R script files you are actively creating, providing a seamless, integrated environment for coding and testing.

Use a Terminal in Jupyter like when you log in via SSH:

• execute Bash commands:

  pwd
  ls
  

• execute Python scripts:

  python python_script.py
  

• execute R scripts:

  Rscript R_script.r
  

• load modules and run software, for example use the preinstalled samtools tool

  Samtools is a suite of utilities for interacting with and post-processing short DNA sequence read alignments in the SAM, BAM and CRAM formats, including indexing, format conversion and basic statistics.

  • Find a preinstalled software on a list of modules:

    module avail samtools
    

  • Load a selected module:

    module load samtools/1.9
    

  • Next, you would run your Samtools command. For example, to get basic statistics about alignments in a BAM file, you can use the samtools flagstat command:

    samtools flagstat example.bam
    

    Replace example.bam with the path to your own BAM file.
    This will print to the terminal a summary of the read alignments in the BAM file, such as the total number of reads, the number of mapped reads, the number of paired-end reads, and so on.

NOTE: For computationally intensive tasks, it is advisable to enclose your commands within a SLURM script and submit it to the queue in the (also from Jupyter’s Terminal app), as your JupyterLab session (and Terminal app within it) may not have sufficient resources.

Create Python environments and Jupyter kernels

A virtual environment is a self-contained directory tree that contains a Python installation for a particular version of Python, plus custom packages, each in a selected version. This provides an isolated environment for each project and makes it easy to handle project-specific dependencies.
NOTE: On SCINet clusters, users can freely install additional packages in their conda OR venv virtual environment(s),but they are NOT permitted to make system-wide package installations.

Best Practices:

• Always use virtual environments for your Python projects to ensure dependency isolation.
• Create conda or venv-based environment first and then convert it to a Jupyter kernel, if you want to use it in Jupyter Notebook app.
• If you work in Jupyter Terminal app (e.g., to execute your Python scripts), first activate your selected virtual environment.
• Every time you want to use custom kernel in JupyterLab, you don’t need to activate the virtual environment from the terminal. JupyterLab will handle it for you when you select that kernel in a notebook.
  • However, if you’re installing additional packages that you want available in that JupyterLab kernel, you’ll need to activate the virtual environment first, install the packages, and then they’ll be accessible in the JupyterLab notebook using that kernel.
• It’s a good idea to add the name of your virtual environments (e.g., myenv/) to your .gitignore file if you’re using Git, to prevent accidentally committing it.
• Some developers prefer conda for managing environments and dependencies, as it offers more features than venv.

Conda package and environment manager

Conda is a package and environment manager that makes it easy to install and manage software packages and their dependencies. Using Conda, you can create a virtual environment, a self-contained workspace with its own installed packages, that won’t interfere with your system or other projects. This is particularly helpful when different projects require different versions of the same package.

On Ceres, Conda is preinstalled and available as a module.

• To activate Conda manager open Terminal app in JupyterLab interface.
• Use the following command to display available Conda modules:

  module avail conda
  

• Select the Conda module, for example, miniconda/4.12.0, and load it with a command:

  module load miniconda/4.12.0
  

  From now on, you can use conda commands to create and manage Python environments and install required dependencies.

• Create a new Conda environment for Python-based interactive graphing:

  conda create -n graphing Python=3.9 matplotlib seaborn plotly
  

  This command will create a new virtual environment called ‘graphing’ in your user space on Ceres, so it will be accessible only to you. It will initialize the coding environment with Python in version 3.9 and install the specified libraries: matplotlib, seaborn, and plotly along with their dependencies. The process will take some time and you will be prompted with “Proceed ([y]/n)?” where you should type “y” and approve with enter to proceed with the installation.

The installation process will end with the printed message:

# To activate this environment, use
#
#     $ conda activate graphing
#
# To deactivate an active environment, use
#
#     $ conda deactivate

NOTE: On Ceres, while using miniconda you should activate the environment using source command instead of conda.

• Once the installation is finished, you can activate your new environment with a command:

  source activate graphing
  

• To be able to import these libraries in a Jupyter notebook you need also to install ipykernel module. This will make your Conda environment available as a Jupyter kernel. While in your Conda environment, you can install ipykernel using:

  conda install ipykernel
  

  • You also need to create a new kernel for your environment. Still within your environment, run the following command:

    python -m ipykernel install --user --name=graphing
    

    If successful, you should get a message in your terminal: “Installed kernelspec graphing in /home/{user}/.local/share/jupyter/kernels/graphing”

• Now, you can return to JupyterLab.
  • In the Python Notebook app, click on the “Kernel” menu, then “Change kernel”, and finally select your new environment from the list.
    NOTE: If a newly added kernel is not visible on the list, you may need to restart your JupyterLab server. Then, open new or existing Python Notebook to repeat the procedure of selecting the kernel.

Now, your Jupyter notebook is linked to your Conda environment, and you can import any libraries you installed in this environment.

Python built-in venv package and environment manager

venv is a module that comes with the standard library in Python to allow the creation of isolated virtual environments. Each environment has its own installation directories and doesn’t share libraries with other environments. This is useful when different projects have different requirements and can prevent conflicts between versions.

On Ceres, venv is automatically available with the loaded Python version, enabling isolated environments without additional setup.

• To activate venv manager open Terminal app in JupyterLab interface.
• Use the following command to display available Conda modules:

  module avail python
  

• Select the Python module, for example python_3/3.11.1, and load it with a command:

  module load python_3/3.11.1
  

  From now on, you can use venv with python command to create and manage Python environments and install required dependencies.

• Navigate to your project directory (or wherever you want the virtual environment to live).
  • You can create a venv-based environment in any location on the system where you have write access. There is no common hidden directory in your home location (such as .conda) where the venv environments will be stored. You can create each virtual environment in the directory of a given project or you can create my_venvs directory on the /project path in a cluster, and create all your future environments there.
  • Once a virtual environment is activated, the Python interpreter and any scripts or commands you run will use the libraries and settings from that virtual environment, regardless of your current directory in the command line.
    • You don’t need to create a soft link to your home directory to use a venv-based virtual environment.
    • However, to activate the virtual environment, you’ll generally need to source the activate script from its specific location (or provide the full path). See sections below.
• Create a new venv environment for Python-based data manipulation:

  python -m venv data_processing
  

  This creates a new directory called “data_processing” (you can name it anything you like) which contains all the necessary files for the virtual environment.

• Once you’ve created the virtual environment, you need to activate it by executing the activate file located in the bin subfolder of a newly created virtual environment:
  NOTE: This file must be used with “source bin/activate” from bash; you cannot run it directly!

  source data_processing/bin/activate
  

  When the virtual environment is activated, you’ll typically see its name appear at the start of your command prompt, indicating that you are working within that environment.

• Once activated, you can install packages using pip and they will be installed specifically within the virtual environment, not system-wide.
  For example, install numpy and pandas libraries for data manipulation:

  pip install numpy pandas
  

  

• To be able to import these libraries in a Jupyter notebook you need also to install ipykernel module. This will make your venv environment available as a Jupyter kernel. While in your virtual environment, you can install ipykernel using:

  pip install ipykernel
  

  • You also need to create a new kernel for your environment. Still within your environment, run the following command:

    python -m ipykernel install --user --name=data_processing
    

    If successful, you should get a message in your terminal: “Installed kernelspec data_processing in /home/{user}/.local/share/jupyter/kernels/data_processing”

• Now, you can return to JupyterLab.
  • Once JupyterLab session is running, open File → New Launcher to see a new kernel option for Python Notebook and Python Console.
  • When in existing Python Notebook, click on the “Kernel” menu, then “Change kernel”, and finally select your new environment from the list to switch to the kernel you just created.
    NOTE: If a newly added kernel is not visible on the list, you may need to restart your JupyterLab server. Then, open new or existing Python Notebook to repeat the procedure of selecting the kernel.

• When you’re done working in the virtual environment in the Terminal app and want to return to the global Python environment or switch to another project, you can deactivate the current virtual environment:

  deactivate
  

• If you no longer need a specific virtual environment, you can simply deactivate it and delete its directory:

  rm -r data_processing
  

QIIME2 on Ceres via Jupyter

qiime2 is primarily known as a collection of open-source bioinformatics pipelines designed for microbiome analysis from raw DNA sequencing data. It offers a suite of tools to perform tasks ranging from quality control and feature extraction to statistical analysis and data visualization.

NOTE: QIIME 2 introduces a system of data storage and interoperability through the concept of Artifacts (.qza format) and Visualizations (.qzv format). These data formats are designed to enhance reproducibility and data provenance.

• Because QIIME 2 uses its own formats for data storage, importing external data into QIIME 2’s system is an explicit step. This means you need to transform your raw data (like a FASTQ file) into a QIIME 2 artifact.
• Both artifacts and visualizations in QIIME 2 are associated with a semantic type. The semantic type conveys what the data is representing. For example, raw sequence data might have the semantic type SampleData[SequencesWithQuality]. Semantic types help ensure that only appropriate data types are used with specific QIIME 2 methods.
• Make sure to specify appropriate output paths for these files in your commands.

On the Ceres cluster, qiime2 is accessible in two primary ways through JupyterLab interface as a module and as a Jupyter kernel. Within a JupyterLab session:

• You can load the qiime2 module in the Terminal app to utilize its command-line features with qiime commands. Learn more from the official QIIME 2 documentation: QIIME 2 command-line interface (q2cli)

• Alternatively, by selecting the qiime2 kernel, you can craft custom Python scripts harnessing the qiime2 library, either in the Python console or in a Python Notebook app. Learn more from the official QIIME 2 documentation: Artifact API (using QIIME 2 with Python)
  • NOTE: Using QIIME 2 with Python offers the advantage of interoperability with other Python libraries, such as Pandas, allowing for efficient manipulation and management of data structures, e.g., inputs pre-processing.
• Combining interfaces [folowing the official QIIME2 docs]

  Another powerful feature of QIIME 2 is that you can combine interfaces. For example, you could develop a Python script that automatically processes files for you to generate results, and then perform analysis of those files using the command line interface.

QIIME2 ready-made kernel for Jupyter Notebook

The pre-configured qiime2-2023.2 kernel provides an immediate environment to conduct your analysis using tools from the qiime2 Python package without the need of additional installation. By selecting either the Python console or Python notebook in the JupyterLab launcher, the related environment activates seamlessly.

Simply import qiime2 in your chosen interface to start leveraging its features. Let’s use Notebook app as an example:

import qiime2

To easily check if qiime2 has been imported correctly, you can use the __version__ attribute to verify its version:

print(qiime2.__version__)

PRO TIP: Jupyter notebooks allow for executing both Python code and shell (bash) commands within code cells. The primary language is determined by the kernel you’re using (e.g., Python), but you can also run shell commands in a Python kernel by prefixing the command with an exclamation point (!).

• Jupyter uses IPython as its underlying interpreter for the Python kernel, and IPython has some built-in automations and conveniences. For instance, certain commands like ls might be automatically interpreted as shell commands, especially if they don’t collide with any Python commands.
• Utilizing bash commands directly in a Jupyter notebook, alongside Python-based qiime2 analysis, facilitates recording the entire workflow, from setting up the project directory with mkdir, fetching data using wget or curl, other preprocessing steps, to analysis and visualization, ensuring comprehensive documentation of the entire process is captured within a single file.

If qiime2 is imported without any errors and its version is displayed, you can start your custom analysis using qiime2 functionality. For example, having the feature table file, you can create a QIIME2 Artifact object and use rarefy method (from qiime2.plugins module) to randomly subsample without replacement (i.e., select a set number of features from each sample) a feature table to standardize the sample depths. This is typically done to avoid biases introduced by different sequencing depths across samples.

# download the example file: NOTE: change the '2023.2' to version used in your kernel
!wget https://docs.qiime2.org/2023.2/data/tutorials/moving-pictures/table.qza

# imports from qiime modules
from qiime2.plugins import feature_table
from qiime2 import Artifact

# perform operations using qiime in Python
unrarefied_table = Artifact.load('table.qza')
rarefy_result = feature_table.methods.rarefy(table=unrarefied_table, sampling_depth=100)
rarefied_table = rarefy_result.rarefied_table

# preview the data with Pandas
import pandas as pd
df = rarefied_table.view(pd.DataFrame)
df.head()

To get started, follow the official QIIME 2 guide for Artifact API (using QIIME 2 with Python).
To discover the specific functionality or analysis tools you need, browse through QIIME 2’s list of available plugins. Once you navigate to the documentation page for a particular utility, such as alignment, you’ll find an Artifact API Import section that provides the Python import statement you’ll need, along with usage instructions of available methods.

QIIME2 module in Jupyter Terminal

The qiime2 module, pre-installed on Ceres, comes with a rich command-line interface (q2cli), which provides numerous sub-commands for different types of analyses. By choosing to work in this interface you will be able to use the qiime command directly in your terminal (including Jupyter Terminal app).

Once looged in to Ceres cluster via OOD, launch the Jupyter session and open the Terminal app. Then, check available qiime modules and load the selected one.

module avail qiime

module load qiime2/2023.7

To quickly check if the qiime command-line tool is available, you can run:

qiime --help

This should provide a list of available commands and options.
For example, version qiime2/2023.7 returns all the plugins and tools available:

Usage: qiime [OPTIONS] COMMAND [ARGS]...

  QIIME 2 command-line interface (q2cli)
  --------------------------------------

  To get help with QIIME 2, visit https://qiime2.org.

  To enable tab completion in Bash, run the following command or add it to
  your .bashrc/.bash_profile:

      source tab-qiime

  To enable tab completion in ZSH, run the following commands or add them to
  your .zshrc:

      autoload -Uz compinit && compinit
      autoload bashcompinit && bashcompinit
      source tab-qiime

Options:
  --version   Show the version and exit.
  --help      Show this message and exit.

Commands:
  info                Display information about current deployment.
  tools               Tools for working with QIIME 2 files.
  dev                 Utilities for developers and advanced users.
  alignment           Plugin for generating and manipulating alignments.
  composition         Plugin for compositional data analysis.
  cutadapt            Plugin for removing adapter sequences, primers, and
                      other unwanted sequence from sequence data.
  dada2               Plugin for sequence quality control with DADA2.
  deblur              Plugin for sequence quality control with Deblur.
  demux               Plugin for demultiplexing & viewing sequence quality.
  diversity           Plugin for exploring community diversity.
  diversity-lib       Plugin for computing community diversity.
  emperor             Plugin for ordination plotting with Emperor.
  feature-classifier  Plugin for taxonomic classification.
  feature-table       Plugin for working with sample by feature tables.
  fragment-insertion  Plugin for extending phylogenies.
  gneiss              Plugin for building compositional models.
  longitudinal        Plugin for paired sample and time series analyses.
  metadata            Plugin for working with Metadata.
  phylogeny           Plugin for generating and manipulating phylogenies.
  quality-control     Plugin for quality control of feature and sequence data.
  quality-filter      Plugin for PHRED-based filtering and trimming.
  sample-classifier   Plugin for machine learning prediction of sample
                      metadata.
  taxa                Plugin for working with feature taxonomy annotations.
  vsearch             Plugin for clustering and dereplicating with vsearch.

PRO TIP: QIIME 2’s CLI is rich and extensive. Regularly referring to the --help option will guide you through its functionalities and ensure you’re using the tools effectively.

If you want to explore a specific plugin, e.g., alignment, you can type:

qiime alignment --help

This will display all the sub-commands and methods available under the alignment plugin.

Once you’ve found a specific method or tool you’d like to use, get detailed usage information with:

qiime alignment {sub-command} --help

Replace {sub-command} with the specific tool or method you’re interested in (e.g., mafft).

After understanding the required parameters for a specific sub-command, you can proceed with your analysis, such as:

# download example input data: Expected an artifact of type FeatureData[Sequence]
wget https://docs.qiime2.org/2023.7/data/tutorials/moving-pictures/rep-seqs-deblur.qza
mv rep-seqs-deblur.qza input.gza

qiime alignment mafft --i-sequences input.qza --o-alignment aligned.qza

The qiime alignment mafft command in QIIME 2 is used to perform sequence alignment using the MAFFT software. Sequence alignment is a fundamental step in many bioinformatics workflows, especially in phylogenetic analyses where aligned sequences are used to reconstruct evolutionary relationships. Once sequences are aligned using this method, they are often used for subsequent steps like masking/filtering and phylogenetic tree construction.

To get started, follow the official QIIME 2 guide for QIIME 2 command-line interface (q2cli).
The comprehensive list of tutorials can be accessed at https://docs.qiime2.org/.
A user-friendly tutorial for QIIME2 is available in the Bioinformatics Workbook, guiding Ceres users through the complete qiime2 pipeline with detailed comments and explanations for each step.

A few Ceres-specific notes:

Default working directory

In JupyterLab, the working directory is set during the setup of the Jupyter Session, before clicking the blue “Connect to Jupyter” button. You can create and save new files and notebooks in this directory and its subdirectories, but you cannot save above it in the file tree. By default, your Home directory is set as the working directory, but you can choose a different location during the setup of your Jupyter session. For more details, please refer to section Jupyter Server in Open OnDemand. You have the ability to access and load files from any location on the cluster in your notebooks, and you can freely navigate the file system in the Terminal app built into Jupyter. However, the built-in graphical file browser in Jupyter Lab restricts navigation outside your chosen working directory.

Data access

Running Jupyter Server on Ceres allows SCINet users to access any data on Ceres that they can access from the command line (SSH), including home location and your /project space. Within the JupyterLab interface, your newly created files and notebooks can only be saved in the selected working directory, however, you can utilize the built-in Terminal app to easily move them to any location on the cluster, allowing you a versatile file management capability.

Python Package installation

When you’re running JupyterLab on a cluster via the Open OnDemand service, you’re generally operating within a specific environment set up by administrators. Installing new libraries directly might not be allowed, or could conflict with existing software versions.

The best practice recommended is to create a personal Python virtual environment where you can install and manage your own Python packages. This can be done with conda package and environment manager. Once you’ve created and activated your environment, you can install new packages there without affecting the wider system. For details, please follow the guide provided in section Conda package and environment manager.

Before you start using Conda:

Note: Before starting to use Conda, it’s advisable to change the default location (your home directory) where Conda will install your customized libraries. Installing a lot of Python libraries may contribute to the default 5G soft limit quota on your home directory being surpassed. To overcome this issue you can move .conda directory from your home directory to your project directory and create a symbolic link to the new location. In JupyterLab open the “Terminal” app and enter the following commands substituting your values:

cd ~
mkdir /project/<your_project_dir>/<account_name>
mv .conda /project/<your_project_dir>/<account_name>/
chgrp -R proj-<your_project_dir> /project/<your_project_dir>/<account_name>/.conda
chmod -R g+s /project/<your_project_dir>/<account_name>/.conda
ln -s /project/<your_project_dir>/<account_name>/.conda .conda

The mv and chgrp commands may take longer time depending on how much data you have in the .conda directory. You will need to restart Jupyter for this new location to take effect.

Stopping Jupyter Server

When you are finished working in the Jupyter interface, ensure you have saved all changes in your open files, then you can simply close the browser tab. However, if you still have the Ceres Open OnDemand interface open (in a separate tab in a browser), remember to delete the Jupyter session that’s no longer in use. This action will free up resources for other users, promoting efficient use of the shared computing environment.