ROAR User Guide   »   Anaconda
Feedback [ + ]


Anaconda is a very useful package manager that is available on RC. Package managers simplify software package installation and manage dependency relationships while increasing both the repeatability and the portability of software. The user environment is modified by the package manager so the shell can access different software packages. Anaconda was originally created for Python, but it can package and distribute software for any language. It is usually very simple to create and manage new environments, install new packages, and import/export environments. Many packages are available for installation through Anaconda, and it enables retaining the environments in a silo to reduce cross-dependencies between different packages that may perturb environments.

Anaconda can be loaded from the software stack on RC with the following command:


$ module load anaconda

Installation Example

After loading the anaconda module, environments can be created and packages can be installed within those environments. When using the anaconda module for the first time on RC, the conda init bash command may be required to initialize anaconda, then a new session must be started for the change to take effect. In the new session, the command prompt will be prepended with (base) which denotes that the session is in the base anaconda environment.

To create an environment that contains both numpy and scipy, for example, run the following commands:


(base) $ conda create -n py_env
(base) $ conda activate py_env
(py_env) $ conda install numpy
(py_env) $ conda install scipy


Note that after the environment is entered, the leading item in the prompt changes to reflect the current environment.

Alternatively, the creation of an environment and package installation can be completed with a single line.


(base) $ conda create -n py_env numpy scipy


For more detailed information on usage, check out the Anaconda documentation.


Useful Anaconda Commands


Command Description
conda create –n <env_name> Creates a conda environment by name
conda create –p <env_path> Creates a conda environment by location
conda env list Lists all conda environments
conda env remove –n <env_name> Removes a conda environment by name
conda activate <env_name> Activates a conda environment by name
conda list Lists all packages within an active environment
conda deactivate Deactivates the active conda environment
conda install <package> Installs a package within an active environment
conda search <package> Searches for a package
conda env export > env_name.yml Exports active environment to a file
conda env create –f env_name.yml Loads environment from a file

Submission Script Usage

Slurm does not automatically source the ~/.bashrc file in your batch job, so Anaconda fails to be properly initialized within Slurm job submission scripts. Fortunately, the anaconda module intitializes the software so that the conda command is automatically available within the Slurm job submission script. If using a different anaconda installation, this issue can be resolved by directly sourcing the ~/.bashrc file in your job script before running any conda commands:


source ~/.bashrc


Alternatively, the environment can be activated using source instead of conda.


source activate <environment>


Another way to resolve this is to add the following shebang to the top of a slurm job script:


#!/usr/bin/env bash -l


Yet another option would be to put the following commands before activating the conda environment:


module load <custom anaconda module>
CONDAPATH=`which conda`
eval "$(${CONDAPATH} shell.bash hook)"


To reiterate, the anaconda module available on RC is configured such that the conda command is automatically available within a Slurm job submission sript. The above options are only necessary for other anaconda installations.


Using Conda Environments in Interactive Apps

Environments built with Anaconda can be used in Interactive Apps on the RC Portal as well. Typically the environment should be created and configured in an interactive compute session on RC, and then some additional steps are needed to make the environment available from within an Interactive App.


Jupyter Server

To access a conda environment within a Jupyter Server session, the ipykernel package must be installed within the environment. To do so, enter the environment and run the following commands:


(base) $ conda activate <environment>
(<environment>) $ conda install -y ipykernel
(<environment>) $ ipython kernel install --user --name=<environment>


After the ipykernel package is successfully installed within this environment, a Jupyter Server session can be launched via the RC Portal. When submitting the form to launch the session, under the Conda environment type field, select the Use custom text field option from the dropdown menu. Then enter the following into the Environment Setup text field:


module load anaconda


After launching and entering the session, the environment is displayed in the kernel list.



To launch an RStudio Interactive App session, RStudio must have access to an installation of R. R can either be installed within the conda environment itself, or it can be loaded from the software stack. Typically, R will be installed by default when installing R packages within a conda environment; therefore, it is recommended when using conda environments within RStudio to simply utilize the environment’s own R installation. To create an environment containing an R installation, run the following command:


(base) $ conda create -y -n <environment> r


Alternatively, R can simply be added to an existing environment by entering that environment and installing using the following command:


(<environment>) $ conda install r <plus any additional R packages>


R packages can installed directly via Anaconda within the environment as well. R packages available in Anaconda are usually named r-<package name> such as r-plot3dr-spatial, or r-ggplot.


After R and any necessary R packages are installed within the environment, an RStudio session can be launched via the RC Portal. When submitting the form to launch the session, under the Environment type field, select the Use custom text field option from the dropdown menu. Then enter the following into the Environment Setup text field:


module load anaconda
conda activate <environment>
export CONDAENVLIB=~/.conda/envs/<environment>/lib


Please note that the default location of conda environments is in ~/.conda/envs, which is why the CONDAENVLIB variable is being set to ~/.conda/envs/<environment>/lib. If the environment is instead installed a non-default location, then the CONDAENVLIB variable should be set accordingly. The two export commands in the block above are required because RStudio often has an issue loading some libraries while accessing the conda envrionment’s R installation. Explicitly adding the conda environment’s lib directory to the LD_LIBRARY_PATH variable seems to clear up this issue.