Using Python on Quest

Tips for using Python on Quest.


Python is available for use on Quest login and compute nodes via the command line and GUI IDEs. You can access the compute nodes via scheduled jobs.

Using Python on Login Nodes

Python scripts can be run on the login nodes with some limits. Users cannot use more than 4 cores or 4 GB of memory in total for all the applications they are running on a login node. These limits are implemented to ensure a smooth operation for all usersconnecting to Quest through login nodes.

All Quest nodes natively include Python (version 2.7.5). However, this Python version has no package support and users are strongly encouraged to use Python distributions provided by the module system. You can see the available versions of Python with the command

module spider python

The command output will include several python/<distribution> modules. Currently, only Anaconda distributions are actively supported on Quest. You can make a particular version of Python available to use by loading the corresponding module. For instance, if you would like to work with Anaconda distribution of Python 3.6, you should type the command

module load python/anaconda3.6

To start the interpreter, type python on the command line. You can also run your Python script directly by typing the following line on the command line

python <>

Scheduled Jobs: Basic Python Use

Python can be used interactively on login without scheduling jobs as discussed above. Note that, however, the login node use is for development and non-computationally intensive tests runs. Compute nodes can be accessed via scheduling jobs on Quest. There are two classes of jobs: interactive and batch.

Interactive Use on the Compute Nodes

To use Python interactively, please first submit an interactive job to obtain the cpu and memory resources you need from a compute node. Once your job starts, your command line will move to a compute node where you can proceed as if you are on the login node. If you are planning to do computationally intensive work while maintaining interactivity with the script or the interpreter, this type of use recommended. This is also the way to request resources to use an IDE such as Spyder (which is available as part of the Anaconda distributions of Python).

Batch Jobs

In submission scripts for batch jobs, you must include the command to load the version of Python that you want to run, then add the line where you can call Python:

python <>

which causes the output and errors to be written to the files for stdout and stderr, respectively, which are determined by your job submission parameters (see Submitting a Job on Quest).

See Example of Python Batch Jobs for Quest for template files you can adapt for your work.

Usage Notes

While module load python will load the current default version of Python, to ensure the future compatibility of your scripts with the Quest system, we highly recommend that you always load a specific version of Python rather than relying on the default version.

Python Package Management

Checking Available Packages Provided by Module

It is important to make sure that the required packages for a Python script are already installed in the Python module you have loaded. If the packages are not available your job will fail. The most reliable way to test the availability is to import the package within the Python interpreter. If you don't receive any errors after the import, that means the package is available. You can also inquire the version of the package with the Python command <package-name>.__version__ as most packages include this information. For instance if you would like to test Numpy's existence and query its version you could do the following:

$ module load python/anaconda3.6
$ python
>>> import numpy as np
>>> np.__version__

pip freeze will list packages in your current environment, but the list may be incomplete. It is best to test package availability by importing the package, as above.

Installing and using Python packages when module packages insufficient

Although you may find references to using pip install --user <mypackage> as a method to install external Python packages on shared computing systems like Quest, we strongly recommend that you utilize virtual environments instead. Using --user to install Python packages locally can often lead to having multiple, sometimes conflicting, versions of a package. For instance, you may want to use TensorFlow which relies on having one version of numpy installed, but also you want to use some other package which requires a version of numpy which is incompatible with that TensorFlow installation. The method --user cannot handle these conflicts the way that having separate isolated virtual environments can.

Anaconda Virtual Environments

You can use conda environments with the Anaconda Python modules on Quest, or virtual environments with any Python module. There are multiple ways to utilize virtual environments. First, you can use conda to create an environment with only Python installed in it. For example, the commands below will create an isolated environment in your HOME folder in the following location ~/.conda/envs/my-virtenv-py38 with only Python 3.8 installed.

$ module load python-miniconda3
$ conda create --name my-virtenv-py38 python=3.8 --yes
$ source (conda) activate my-virtenv-py38

Once the environment is activated, you have full control over what Python packages are installed into the environment. In many ways, you are the system administrator of this Python installation as you have full read/write/execute privileges inside of that folder in your HOME directory. At this point, you can use Python's native package manager, PyPi, to install whatever Python package(s) you want to as long as they are available via pip. For example, pip install numpy matplotlib scipy pandas.

In some instances, you may find it useful to install Python packages using anaconda itself instead of PyPi. An overwhelming majority of software (both Python and non-Python) is available via anaconda in one of three locations. These locations are called "channels", which can be thought of as the remote/cloud repository in which anaconda looks for the package. These three locations are


--channel=anaconda(a.k.a the default location)


If you do opt to install packages directly from anaconda (which can help insure that all the packages are compatible with each other), it is good practice to make sure they are all installed from the same remote repository/channel.

Finally, if you are need of C and/or C++ compilers you can install those directly into your environment via

conda install -c conda-forge gxx_linux-64 gcc_linux-64

To share an environment with other members of your allocation, you can create the environment in a specific directory.

With conda, use the --prefix option to create the environment in a specific location, such as in your /projects/<allocationID> directory. To use the environment (either interactively from the command line or as part of your job submission script), you then need to point to this directory when activating it.

For example, to create a conda environment called env1 in a subdirectory of a project directory (here p10000 as an example), with the package sqlalchemy included, run the following:

$ module load python-miniconda3
$ cd /projects/p10000
$ mkdir pythonenvs
$ conda create --prefix /projects/p10000/pythonenvs/env1 sqlalchemy python=3.8 --yes

To use this environment, specify the full path to the environment when you activate it:

$ module load python-miniconda3
$ source activate /projects/p10000/pythonenvs/env1

If you're activating a conda environment when submitting a Slurm job, please see the Using Anaconda in a Slurm Job section at the bottom of this article.

"conda activate" vs. "source activate":

Older conda versions used the source activate <environment name> syntax to activate an environment on Linux, which differed from how environments were activated on Windows. In order for conda to work consistently across platforms, its behavior changed slightly in version 4.6, and now conda activate <environment name> works on all platforms. To use the new conda, run these commands one time to set up your environment:

$ module load python-miniconda3
$ conda init bash

Then log out of Quest and log back in. Once you have done this, you can use conda activate instead of source activate, although the latter will still work, so existing batch scripts for example can continue to use source activate.

If you need help creating an anaconda virtual environment, please e-mail


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Article ID: 1672
Thu 5/12/22 12:39 PM
Fri 6/14/24 10:25 AM

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Northwestern IT provides support installing software for use on Quest, Northwestern’s high-performance computing (HPC) cluster.