Package Management

6 minute read

Python

The scope of this manual is a brief introduction on how to manage Python packages.

Python Versions

Different Python versions do not play nice with each other. It is best to only load one Python module at any given time. The miniconda3 module for Python is the default version. This will enable users to leverage the conda installer, but with as few Python packages pre-installed as possible. This is to avoid conflicts with future needs of individuals.

Conda

We have several Conda software modules:

1. miniconda3 - Basic Python 3 install (Default)
2. anaconda - Full Python 3 install For more information regarding our module system please refer to Environment Modules.

The miniconda modules are very basic installs, however users can choose to unload this basic install for a fuller one (anaconda), like so:

module load miniconda3

After loading anaconda, you will see that there are many more Python packages installed (ie. numpy, scipy, pandas, jupyter, etc…). For a list of installed Python packages try the following:

pip list

Virtual Environments

Sometimes it is best to create your own environment in which you have full control over package installs. Conda allows you to do this through virtual environments.

Initialize

Conda will now auto initialize when you load the corresponding module. No need to run the conda init or make any modifications to your ~/.bashrc file.

Configure

Installing many packages can consume a large (ie. >20GB) amount of disk space, thus it is recommended to store conda environments under your bigdata space. If you have bigdata, create the .condarc file (otherwise conda environments will be created under your home directory).

Create the file .condarc in your home, with the following content:

channels:
  - defaults
pkgs_dirs:
  - ~/bigdata/.conda/pkgs
envs_dirs:
  - ~/bigdata/.conda/envs
auto_activate_base: false

After changing the configuration, environments can be moved to the new bigdata location using conda rename -n NAME NAME_tmp, then conda rename -n NAME_tmp NAME to return it to it’s original name. Replacing NAME with the name of the environment you wish to move. If you receive an error while trying to rename, try activting the base conda environment using conda activate base and running the conda rename commands again.

It’s also recommended to clean your old conda packages using conda clean -a. Note that this command can take a while (»1 hour) if there are a lot of downloaded packages.

Create a Python 3.10 conda environment, like so:

module load miniconda3  # Should already be auto-loaded during login
conda create -n NameForNewEnv python=3.10  # Many Python versions are available

Activating

Once your virtual environment has been created, you need to activate it before you can use it:

conda activate NameForNewEnv

With more modules being added as conda environments, it’s sometimes requried to “stack” user environments on top of module-provided environments. Running conda activate will deactivate the current environment before activating the new environment.. To counter this, the --stack flag can be used to effectively “combine” environments. For example conda activate --stack NameForNewEnv. Please see the conda page on Nested Activation for more details.

Deactivating

In order to exit from your virtual environment, do the following:

conda deactivate

Installing packages

Before installing your packages, make sure you are on a computer node. This ensures your downloads to be done quickly and with less chance of running out of memory. This can be done using the following command:

srun -p short -c 4 --mem=10g --pty bash -l          # Adjust the resource request as needed

Here is a simple example for installing packages under your Python virtual environment via conda:

conda install -n NameForNewEnv PackageName

You may need to enable an additional channel to install the package (refer to your package’s documentation):

conda install -n NameForNewEnv -c ChannelName PackageName

Cloning

It is possible for you to copy an existing environment into a new environment:

conda create --name AnotherNameForNewEnv --clone NameForNewEnv

Listing Environments

Run the following to get a list of currently installed conda evironments:

conda env list

Removing

If you wish to remove a conda environment run the following:

conda env remove --name myenv

More Info

For more information regarding conda please visit Conda Docs.

Jupyter

You can run jupyter as an interactive job or you can use the web instance, see Jupyter Usage for details.

Virtual Environments (Kernels)

In order to use a custom Python/Conda virtual environment within Jupyter, it must be configured as a kernel. You will need to do the following:

# Create a virtual environment named "ipykernel_py3", if you don't already have one
# It can be named whatever you like, "ipykernel_py3" is just an example.
# You can also indicate a more specific version of Python here. Otherwise you'll get
# the latest version provided by Anaconda.
conda create -n ipykernel_py3 python=3 ipykernel

# Load the new environment
conda activate ipykernel_py3

# Install kernel
# --name is used to define the internal name used by Jupyter, and should not contain spaces.
# --display-name is the name you will see in the Jupyter web interface, should be descriptive.
python -m ipykernel install --user --name ipykernel_py3 --display-name "IPyKernel (Python 3)"

Now when you visit the notebook you should see the option “JupyterPy3” when you click the “New” dropdown menu in the upper left corner of the home page.

To remove an unwanted kernel, use the following commands:

jupyter kernelspec list  # List available kernels
jupyter kernelspec uninstall UNWANTEDKERNEL

Replace UNWANTEDKERNEL with the name of the kernel you wish to remove

Further reading: Installing the IPython kernel

R

For instructions on how to configure your R environment please visit IRkernel. Since we should already have IRkernel install in the latest version of R, you would only need to do the following within R:

IRkernel::installspec(name = 'ir44', displayname = 'R 4.0.1')

R

This section is regarding how to manage R packages.

Current R Version

NOTE: Please be aware that this version of R is built with GCC/8.3.0, which means that previously compiled modules may be incompatible.

Currently the default version of R is R/4.3.0 and is loaded automatically for you.

When a new release of R is available, you should reinstall any local R packages, however keep in mind of the following:

• Remove redundantly installed local R packages with the RdupCheck command.
• Newer version of R packages are not backward compatible, once installed they only work for that specific version of R.

Older R Versions

You can load other versions of R with the following:

module unload R
module avail R
module load R/VERSION

Installing R Packages

The default version of R has many of the most popular R packages already installed and available. It is also possible for you to install additional R packages in your local environment.

Only install packages if they are not already available, this will minimize issues later. You can check the current version of R from the command line, like so:

Rscript -e "library('some-package-name')"

Or you can check from within R, like so:

library('some-package-name')

If the package is not available, then proceed with installation.

Bioconductor Packages

To install from Bioconductor you can use the following method:

BiocManager::install(c("package-to-install", "another-packages-to-install"))
Update all/some/none? [a/s/n]: n

For more information please visit Bioconductor Install Page.

GitHub Packages

# Load devtools
library(devtools)

# Replace name with the GitHub account/repo
install_github("duncantl/RGoogleDocs")

Local Packages

# Replace URL with your URL or local path to your .tar.gz file
install.packages("http://hartleys.github.io/QoRTs/QoRTs_LATEST.tar.gz",repos=NULL,type="source")