Overview

The supercomputer uses Mamba, a high-performance parallel package manager, to allow users to install the Python modules they need. It also plays a pivotal role in optimizing software environments on supercomputers. In the upcoming instructions, we'll explore the process of loading Mamba modules and delve into creating and loading new environments.

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Why Create a New Environment?

In a fresh terminal session, python or python3 points to a system-installed copy of Python (typically in /usr/bin). As the operating system heavily depends on this Python instance, the version is fixed and only the most basic, built-in libraries are available.

Creating a new environment allows you to have full control over the Python version, the selection of libraries, and the specific versions, too. Python environments can then be engaged and disengaged freely, enabling a wide-variety of specific uses including CPU compute and even GPU acceleration.

Load the Package and Environment Manager

Load the latest stable version of the mamba Mamba Python manager with:

module load mamba/latest

List Available Environments

Many Python suites, such as Pytorch or Qiime, are commonly-requested and thus are provided by Research Computing staff on the supercomputers already. These environments are version-fixed and read-only, so they may be used freely by any number of users simultaneously without any risk of the environment changing.

All global/admin-maintained python Python environments may be found under /rc/packages/envs:

$ ls /rc/packages/envs/
  pytorch-gpu-2.0.1/     scicomp/        tensorflow-gpu-2.12.1/  

User environments are by default installed to /home/asurite~/.conda/envs, and after running module load mamba/latest, all available environments may be listed with mamba info --envs.

[asureite@login1<asurite>@login1 ~]$ mamba info --envs

          mamba version : 1.5.1
# conda environments:
#
pytorchGPU               /home/dmccaff4<asurite>/.conda/envs/pytorchGPU
testing                  /home/dmccaff4<asurite>/.conda/envs/testing
updateTest               /home/dmccaff4<asurite>/.conda/envs/updateTest
base                     /rc/packages/apps/mamba/1.5.1
pytorch-gpu-2.0.1        /rc/packages/envs/pytorch-gpu-2.0.1
scicomp                  /rc/packages/envs/scicomp
tensorflow-gpu-2.12.1     /rc/packages/envs/tensorflow-gpu-2.12.1

Load Available Environments

Use the source activate command to load the environment you want.

$ module load mamba/latest
$ source activate gurobi-9.5.1pytorch-gpu-2.0.1

The name of the environment will appear to the left of the command prompt so that you know what environment is currently active.

(pytorch-gpu-2.0.1) $ python nobel_prize.py

Creating Environments

$ module load mamba/latest
$ mamba create -n <environment_name> -c conda-forge [-c <channel>] [packages]
$ source activate <environment_name>

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Adding dependencies to existing Environments

To install a new package to a mamba environment, first load the mamba module, then activate the existing environment, and finally install as many new dependencies as required.

$ module load mamba/latest
$ source activate <environment_name>
$ mamba install -c conda-forge [-c <channel>] <packages>

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Using environments in Jupyter

Once an environment is created, a kernel interface will need to be made to have that environment available in Jupyter. This is as easy as, mkjupy <env_name>. Please review Using Preparing Python Environments for Jupyterfor additional details.

ADVANCED: Building from GitHub repository

Many python packages are not necessarily available on available mamba channels. It is best to avoid these packages when possible. However, it is possible to integrate them into a workflow. First, clone the git repository into your home directory:

$ git clone <url of github repository>

This URL can be copied from GitHub repository. In the figure below, the blue line indicates the URL of the corresponding repository (repo) page.

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The cloned directory should include instructions for installing the Python package.

Once the environment is created and activated, and all dependencies installed, the new repository module may be installed as specified in the README, typically a pip install.

Additional Help

If you require further assistance on this topic, please don't hesitate to contact the Research Computing Team. To create a support ticket, kindly send an email to rtshelp@asu.edu. For quick inquiries, you're welcome to reach out via our #rc-support Slack Channel or attend our office hours for live assistance

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What if the Package I Need Isn’t in Conda-forge?

Conda-forge is the only pre-approved place to get Python modules right now. Other modules will need to go through an approval process with the KE Secure Cloud team before they can be used. Open a request at https://links.asu.edu/kesc-support to ask for a module.