Traditionally we have recommend that users use a
Python virtualenv
or Conda environment
to manage personal package installations via pip install and mamba install
commands. But a new contender has entered the fray:
uv, an extremely fast Python package and
project manager, written in Rust.
The Apocrita cluster has been upgraded from CentOS 7 to Rocky 9 recently. There are some important things Python users need to know, such as how to migrate your existing environments to Rocky 9, as well as how to tackle some common problems during the process.
Regular expressions, or regex, are patterns used to match strings of text. They can be very useful for searching, validating, or manipulating text efficiently. This guide will introduce the basics of regex with easy-to-follow examples.
In this tutorial we'll be showing you how to create a new Git project within RStudio using either a new or existing GitHub repository
Whilst most Apocrita users will want to use the R module or RStudio via OnDemand for R workflows, it is also possible to use R inside of Conda via Miniforge.
Files quickly proliferate and need to be kept tidy. It is important that the correct people can access the files, and file systems are well-structured for easy navigation.
Installing packages into a personal R library can sometimes take quite a long time, but it doesn't always have to be this way.
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 Conda users concerned that creating environments and installing packages is taking quite a long time. Now we have moved to Miniforge exclusively for all Conda usage on Apocrita, the Mamba libsolver is available for all package installs.
Sometimes you may find yourself needing to filter a large amount of output
using the 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.