Using Python with Anaconda

To support the diverse python workflows and high levels of customization Research Computing users require, Anaconda is installed on the CURC system. Anaconda is an open-source python and R distribution that uses the conda package manager to easily install software and packages. The following documentation describes how to activate the CURC Anaconda distribution and our default environments, as well as how to create and activate your own custom Anaconda environments. Additional documentation on the CURC JupyterHub is available for users desiring to interact with their custom environments via Jupyter notebooks.

Note: CURC also hosts several python modules for those users who prefer modules over Anaconda. Type module spider python for a list of available python versions. Each module employs the Intel python distribution and has numerous pre-installed packages which can be queried by typing pip freeze.

Using the CURC Anaconda environment

Follow these steps from a Research Computing terminal session.

Before you use conda for the first time:

Modify your ~/.condarc file so that packages are downloaded to your /projects directory

Your /home/$USER directory (also denoted with “~”) is small – only 2 GB. By default, conda downloads packages to your home directory when creating a new environment, and it will quickly become full. The steps here modify the conda configration file, called ~/.condarc, to change the default location of pkgs_dirs so that the packages are downloaed to your (much bigger) /projects directory.

Open your ~/.condarc file in your favorite text editor (e.g., nano): (note: this file may not exist yet – if not, just create a new file with this name)

[johndoe@shas0137]$ nano ~/.condarc

…and add the following two lines:

  - /projects/$USER/.conda_pkgs

…then save and exit the file. You won’t need to perform this step again – it’s permanent unless you change pkgs_dirs by editing ~/.condarc again.

Note that there are lots of other things you can customize using the ~/.condarc file.

Activate the CURC Anaconda environment

For python2:

[johndoe@shas0137 ~]$ source /curc/sw/anaconda2/2019.03/bin/activate
(base) [johndoe@shas0137 ~]$ conda activate idp

For python3:

[johndoe@shas0137 ~]$ source /curc/sw/anaconda3/2019.03/bin/activate
(base) [johndoe@shas0137 ~]$ conda activate idp

The first command activates the “base” python2 or python3 environment, which uses the Anaconda python distribution. You will know that you have properly activated the environment because you should see (base) in front of your prompt. E.g.:

(base) [johndoe@shas0137 ~]$

The second command (conda activate idp) activates the Intel python distribution (idp), which is optimized for many mathematics functions and will run more efficiently on the Intel architecture of Summit and Blanca. You will know that you have properly activated the environment because you should see (idp) in front of your prompt. E.g.:

(idp) [johndoe@shas0137 ~]$

*We strongly recommend using the Intel python distribution on Summit.

Using python in Anaconda

To list the packages currently installed in the environment:

(idp) [johndoe@shas0137 ~]$ conda list

To add a new package named “foo” to the environment:

(idp) [johndoe@shas0137 ~]$ conda install foo 

To list the conda environments currently available:

(idp) [johndoe@shas0137 ~]$ conda env list

To deactivate an environment:

(idp) [johndoe@shas0137 ~]$ conda deactivate

To create a new environment in a predetermined location in your /projects directory.

*Note: In the examples below the environment is created in /projects/$USER/software/anaconda/envs. This assumes that the software, anaconda, and envs directories already exist in /projects/$USER. Environments can be installed in any writable location the user chooses.

1a Activate the conda environment if you haven’t already done so.
[johndoe@shas0137 ~]$ source /curc/sw/anaconda3/2019.03/bin/activate
(base) [johndoe@shas0137 ~]$ conda activate idp
2a. Create a custom environment “from scratch”: Here we create a new environment called mycustomenv:
(idp) [johndoe@shas0137 ~]$ conda create --prefix /projects/$USER/software/anaconda/envs/mycustomenv

or if you want a specific version of python other than the default installed in the CURC Anaconda base environment:

(idp) [johndoe@shas0137 ~]$ conda create --prefix /projects/$USER/software/anaconda/envs/mycustomenv python==2.7.16
2b. Create a custom environment by cloning a preexisting environment: Here we clone the preexisting Intel Python3 distribution in the CURC Anaconda environment, creating a new environment called mycustomenv:
(idp) [johndoe@shas0137 ~]$ conda create --clone idp --prefix /projects/$USER/software/anaconda/envs/mycustomenv
3. Activate your new environment
(idp) [johndoe@shas0137 ~]$ conda activate /projects/$USER/software/anaconda/envs/mycustomenv
Notes on creating environments:
  • You can create an environment in any directory location you prefer (as long as you have access to that directory). We recommend using your /projects directory because it is much larger than your /home directory).
  • Although we don’t show it here, it is expected that you will be installing whatever software and packages you need in this environment, as you normally would with conda).
  • We strongly recommend cloning the Intel Python distribution if you will be doing any computationally-intensive work, or work that requires parallelization. The Intel Python distribution will run more efficiently on our Intel architecture than other python distributions.


If you are having trouble loading a package, you can use conda list or pip freeze to list the available packages and their version numbers in your current conda environment. Use conda install <packagname> to add a new package or conda install <packagename==version> for a specific verison; e.g., conda install numpy=1.16.2.

Sometimes conda environments can “break” if two packages in the environment require different versions of the same shared library. In these cases you try a couple of things.

  • Reinstall the packages all within the same install command (e.g., conda install <package1> <package2>). This forces conda to attempt to resolve shared library conflicts.
  • Create a new environment and reinstall the packages you need (preferably installing all with the same conda install command, rather than one-at-a-time, in order to resolve the conflicts).