Summary and Setup
This is a new lesson built with The Carpentries Workbench.
Welcome to the NextGen In A Box (NGIAB) 101 training module!
Source Code and Documentation
The source code for NextGen In A Box (NGIAB) is found at our GitHub repository. The documentation for NGIAB and its extensions are found on the CIROH docs.
To dive right into NGIAB as quickly as possible, follow the quick start guide on our GitHub repository, or head straight to our second episode, Installation. If you are interested in taking your time and learning in-depth about the features of NGIAB, follow the episodes in this module.
For all users, the Glossary may be a useful reference. The Advanced Topics episode is optional, but may contain useful content depending on your specific use case.
Note: This module requires you to use Bash scripting in your command line and Git. All commands will be given in this module. However, if you are interested in learning more, you can refer to this quick Bash tutorial by Ubuntu, this basic Git tutorial in the Git documentation, and this Git workflow tutorial by CIROH.
HPC Users
Are you planning on running NGIAB on an HPC? You can follow the episodes in order, but replace Episode 2: Installation and Setup with the HPC-specific instructions in the Advanced Topics module.
2i2c Users
Are you planning on running NGIAB through CIROH-2i2c JupyterHub? If so, you do not need to install Docker, NGIAB, or supporting software on your local machine. Instead, request access to CIROH-2i2c through the Infrastructure Access page, launch a CIROH Community NextGen Hub server, and follow the Jupyter notebook workflows provided in the cloud environment.
You may still find the Installation episode useful for understanding the NGIAB ecosystem, but local software installation steps can be skipped.
System Requirements
The Installation episode will walk you through the steps to install Windows Subsystem for Linux (WSL), Docker, NGIAB, and retrieve sample data sets. This page summarizes system requirements.
- Software: Docker, Git, WSL, Astral UV
- Recommended Minimum RAM: 8 GB
- Software: Docker, Git, Astral UV
- Recommended Minimum RAM: 8 GB
- Software: Docker, Git, Astral UV
- Recommended Minimum RAM: 8 GB