The login node provides access to a wide variety of scientific software tools that users can access and use via the module system. These software tools were compiled and optimized for use on SCINet by members of the Virtual Research Support Core (VRSC) team. Most users will find the software tools they need for their research among the provided packages and thus will not need to compile their own software packages.
The popular R, Perl, and Python languages have many packages/modules available. Some of the packages are installed on Ceres and are available with the r/perl/python_2/python_3 modules. To see the list of installed packages, visit the Preinstalled Software List page or use module help <module_name> command. If users need packages that are not available, they can either request VRSC to add packages, or they can download and install packages in their home/project directories. We recommend installing packages in the project directories since collaborators on the same project most probably would need the same packages. In addition, home quotas are much lower than project directories quotas. See the Guide to Installing R, Python, and Perl Packages for instructions and examples on how to add packages/modules for these languages.
If users would like to compile their own software with GNU compilers, they will need to load the gcc module. It is recommended to compile on compute nodes and not on the login node. However, before embarking on compiling their own software packages we strongly encourage users to contact the VRSC team to ensure that their required tool(s) might not be better distributed as a shared package within the official software modules tree. All new software needs to be approved by SOC committee before being centrally installed on the system. To request a new software package to be installed, visit the Request Software page.
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User-installed R, Python, and Perl Packages
The popular R, Perl and Python languages have many packages/modules available. Some of the packages are installed on Ceres and are available with the r/perl/python_2/python_3 modules. To see the list of installed packages, visit the Preinstalled Software List page or use
module help <module_name>command. If users need packages that are not available, they can either request VRSC to add packages, or they can download and install packages in their home/project directories. We recommend installing packages in the project directories since collaborators on the same project most probably would need same packages. In addition, home quotas are much lower than quotas for project directories. -
Preinstalled Software
Each SCINet cluster has software preinstalled on it. Some general software is available in the global environment but most specialized scientific software is managed by the Module system.
This guide includes information about command-line software, as well as information on graphical software such as Galaxy, CLC, Geneious, RStudio, and Juptyer. -
User-Installed Software with Conda
Conda is a software package manager for data science that allows unprivileged (non-administrative) Linux or MacOS users to search, fetch, install, upgrade, use, and manage supported open-source software packages and programming languages/libraries/environments (primarily Python and R, but also others such as Perl, Java, and Julia) in a directory they have write access to. Conda allows SCINet users to create reproducible scientific software environments (including outside of Ceres) without requiring the submission of a SCINet software request form for new software, or contacting the VRSC to upgrade existing software.
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Environment Modules
The Environment Modules package provides dynamic modification of your shell environment. This also allows a single system to accommodate multiple versions of the same software application and for the user to select the version they want to use. Module commands set, change, or delete environment variables, typically in support of a particular application.
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Singularity/Apptainer Containers
Some software packages may not be available for the version of Linux running on the HPC cluster. In this case, users may want to run containers. Containers are self-contained application execution environments that contain all necessary software to run an application or workflow, so users don’t need to worry about installing all the dependencies. There are many pre-built container images for scientific applications available for download and use.
Apptainer (formerly Singularity) https://apptainer.org is an application for running containers on an HPC cluster. Containers are self-contained application execution environments that contain all necessary software to run an application or workflow, so you don’t need to worry about installing all the dependencies. There are many pre-built container images for scientific applications available for download and use, see section Container Images.