Package Management

5 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 miniconda2 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. miniconda2 - Basic Python 2 install (default)
  2. miniconda3 - Basic Python 3 install
  3. anaconda2 - Full Python 2 install
  4. anaconda3 - 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 miniconda2 #This is the default

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

Then create your Python 2 conda environment, like so:

conda create -n NameForNewEnv python=2.7.14 # Many Python versions are available

For Python 3, please use the miniconda3, like so:

module unload miniconda2
module load miniconda3
conda create -n NameForNewEnv python=3.6.4 # 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
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 using tunneling, or you can use the web portal Jupyter-Hub.

Virtual Environment

In order to enable your conda virtual environemnt within the Jupyter web portal you will need to do the following:

# Create a virtual environment, if you don't already have one
conda create -n ipykernel_py2 python=2 ipykernel

# Load the new environment
conda activate ipykernel_py2

# Install kernel
python -m ipykernel install --user --name myenv --display-name "JupyterPy2"

Now when you visit Jupyter-Hub you should see the option “JupyterPy2” when you click the “New” dropdown menu in the upper left corner of the home page.

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/4.1.0 and is NOT loaded automatically for you.

You will have to do this manually on your own, like so:

module load R/4.1.0_gcc-8.3.0

Or, you can rebase by loading the base/gcc/8.3.0 module, which will load the latest version of R and many other compatible modules:

module load base/gcc/8.3.0

If you wish to revert back to your previous modules, then you can simply unload the base module, like so:

module unload base

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

The older version of R/4.0.1 is loaded by default.

You can load other versions of R with the following:

module unload R
module load R/3.4.2

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")
Last modified August 10, 2022: Update package_manage.md (c170830d)