Modules are the centralised method of accessing different software on an HPC cluster. By using a variety of modules you can quickly and easily access different versions of applications and create work flows that suit particular projects. The modules offered on Apocrita cover a wide range of applications but there will always be situations that require something unusual or a relatively niche version of a piece of software.
The ITSR support team often receive tickets from Anaconda users concerned that creating environments and installing packages is taking quite a long time. We recently installed Miniconda as a module on Apocrita, which enables users to install packages using the Mamba libsolver.
Sometimes you may find yourself needing to filter a large amount of output
grep command. However,
grep can sometimes struggle when you try
to filter files with an incredibly large number of lines, as it loads each line
into RAM line-by-line. This can mean you can quickly exhaust even large amounts
of requested RAM. There are a few ways around this.
Following up from part one of our R tutorial we'll be taking a look at the differences between R - the command-line language which can be loaded as a module and used in your Apocrita batch jobs - and Rstudio - the graphical development environment, accessed via a web server and provided via the OnDemand service.
The ITSR support team often receive tickets from
R users that cover similar
ground. So we thought we would collate our most frequent responses into some
"Top Tips"! The tips below apply equally to
Rscript but this article only
covers the interactive
You may wonder why some jobs start immediately but some wait in the queue for hours or days, even if your job is quite simple. If you notice your job has been queueing for a while, you may want to consider adjusting the requested resources to reduce queueing time and reduce any potential resource wastage as the job runs. Below, we outline two useful tools for you to check the resource usage of previous jobs.
In this tutorial we'll be showing you how to visualise HEALPix results using Jupyter Notebook in our OnDemand appliance on the Apocrita HPC cluster. We'll start with installing the required Python packages before demonstrating how to run the Healpy tutorial.
Information about running other components of HEALPix not covered in this tutorial can be found on our docs site.