Create a virtual environment to specify Python and R

Many applications require specific versions of R and Python or a combination of additional modules. As there might be conflicts between those modules, we recommend to use virtual environments to manage and use those packages.  


Individual steps

Log in to Scientific Computing (SciC) Linux Clusters:

#$ssh erisone

then load the anaconda module:

$module load anaconda/3-5.0.1

then you can create the conda environment:

$conda create --name env_name 

you can already add requirements here like:

$conda create --name env_name python=3.7 R=3.6

execution will take a while. Note that you do not need to specify a version. Do not specify it when you do not use R or Python. 


To activate the environment you type:

$conda activate env_name

If you have never used conda, it will give you an error:

CommandNotFoundError: Your shell has not been properly configured to use 'conda activate'.

If your shell is Bash or a Bourne variant, enable conda for the current user with

$ echo ". /apps/software-2.12/Anaconda3/5.2.0/etc/profile.d/" >> ~/.bashrc

Do as the error message suggests:

$ echo ". /apps/software-2.12/Anaconda3/5.2.0/etc/profile.d/" >> ~/.bashrc


 It will add conda to your path so you can activate the environment. for the changes to be active you need to log out and log in again: 

 $source ~/.bashrc

After this or a login you can do:

$conda activate env_name

in this virtual environment, you can then install packages with conda install package_name (e.g.) :

$conda install package 

Note that you need to add:


conda activate env_name

To your lsf scripts. 



Please read more about conda environments :


Go to KB0036593 in the IS Service Desk

Related articles

Analytics Enclave

IDEA Platform