Conda¶

Description¶

Conda and mamba provide package, dependency, and environment management for any language. Modules system sets up the following environment variables for this module:

Environment Modules¶

Run module spider conda to find out what environment modules are available for this application.

Environment Variables¶

HPC_CONDA_DIR - installation directory

Additional Usage Information¶

Background¶

Many projects that use Python code require careful management of the respective Python environments. Rapid changes in package dependencies, package version conflicts, deprecation of APIs (function calls) by individual projects, and obsolescence of system drivers and libraries make it virtually impossible to use an arbitrary set of packages or create one all-encompassing environment that will serve everyone's needs over long periods of time. The high velocity of changes in the popular ML/DL frameworks and packages and GPU computing exacerbates the problem.

Expand this section to view pip problems and how conda mends them.

The Problem with `pip install`¶

Most guides and project documentation for installing python packages recommend using pip install for package installation. While pip is easy to use and works for many use cases, there are some major drawbacks. There are a few issues with doing pip install on a supercomputer like HiPerGator:

Pip by default installs binary packages (wheels), which are often built on systems incompatible with HiPerGator. This can lead to importing errors, and its attempts to build from source will fail without additional configuration.
If you are pip installing a package that is/will be installed in an environment provided by UFIT-RC, your pip version will take precedence. Your dependencies eventually become incompatible causing errors.
Different packages may require different versions of a package as dependencies, leading to impossible to reconcile installation scenarios. This becomes a challenge to manage with pip as there isn't a method to swap active versions.
On its own, pip installs everything in one location: ~/.local/lib/python3.X/site-packages/.
In Jupyter Notebooks, even with a Kernel selected, pip installs in .local. While many tutorials out there will tell you to !pip install ____, this is poor advice that can cause lots of problems.

Conda to the Rescue!¶

conda solves some of these issues. Conda represents a higher level of packaging abstraction that can combine compiled packages, applications, and libraries as well as pip-installed python packages. Conda also allows easier management of project-specific environments and switching between environments as needed. Conda makes it much easier to report the exact configuration of packages in an environment, facilitating reproducibility. Moreover, conda environments don't even have to be activated to be used; in most cases adding the path to the conda environment's bin directory to the $PATH in the shell environment is sufficient for using them.

A Caveat¶

conda gets packages from channels, or repositories of prebuilt packages. While there are several available channels, like conda-forge or bioconda, not every Python package is available from such channel as they have to be packaged for conda first. You may still need to use pip to install some packages as noted later. However, conda still helps manage environment by installing packages into separate directory trees rather than trying to install all packages into a single folder that pip does.

Conda Configuration¶

Prior to usage, you may need to configure conda. The first time a HiPerGator user loads the conda environment module, a recommended configuration will be created. This configuration can be viewed and modified with the conda config command.

Conda storage locations¶

The settings for package and environment storage will be of particular interest to HiPerGator users. By default conda will store these files in a user's home directory, which can rapidly lead to the user's 40GB home storage quota being used up.

The first time a HiPerGator user loads the conda environment module, these values will be set to a location in /blue/groupname/username/. If a user's primary group does not have a blue storage allocation, these values will need to be set with conda config.

The conda storage locations are configured with the envs_dirs and pkgs_dirs settings which determine where environments and downloaded packages are stored.

To view your current setttings:

[username@login12 ~]$ conda config --show envs_dirs pkgs_dirs
envs_dirs:
  - /blue/groupname/username/.conda/envs
pkgs_dirs:
  - /blue/groupname/username/.conda/pkgs

These settings are lists of locations. To add new locations as the defaults, run these commands:

conda config --prepend envs_dirs /blue/othergroup/username/.conda/envs 
conda config --prepend pkgs_dirs /blue/othergroup/username/.conda/pkgs

Info

You do not need to manually create the folders that you setup in your conda configuration. conda will take care of that for you when you create environments.

Create and Activate a Conda Environment¶

The UFIT Research Computing Applications Team uses conda for many application installs behind the scenes. We are happy to install applications on request for you. However, if you would like to use conda to create multiple environments for your personal projects we encourage you to do so. Here are some recommendations for successful conda use on HiPerGator.

See the Conda project's documentation on managing conda environments.
We recommend creating environments by 'path' in /blue. The resulting environment should be located in the project(s) directory tree in /blue for better tracking of installs and better filesystem performance compared to home.

If you plan on using a GPU

To make sure your code will run on GPUs install a recent cudatoolkit package that works with the NVIDIA drivers on HPG (currently 12.x, but older versions are still supported) alongside the pytorch or tensorflow package(s).

See the UFIT-RC provided tensorflow or pytorch installs for examples if needed.

Conda can detect if there is a GPU available on the computer, so the easiest approach is to run the conda install command in a GPU session. Alternatively, you can run conda install on any node, or if a cpu-only pytorch package was already installed, by explicitly requiring a GPU version of pytorch when running conda install.

Load the `conda` Module¶

Before we can run conda on HiPerGator, we need to load the conda module:

module load conda

Create Your Environment¶

Create a Name Based Environment¶

To create your first name based (see path based instructions below) conda environment, run the following command. For example, to create an environment named my_env:

conda create -n my_env

Expand to see example command and output

[username@login7 ~]$ conda create -n my_env

Looking for: []

Preparing transaction: done
Verifying transaction: done
Executing transaction: done

To activate this environment, use

    $ conda activate my_env

To deactivate an active environment, use

    $ conda deactivate

[username@login7 ~]$

Tip

When creating a Conda environment you can also install Conda packages as needed at the same time. i.e: conda create -n another_env python=3.11 pytorch numpy=2.22

Create a Path Based Environment¶

To create a path based conda environment use the -p argument. For example, to create a path based enviroment at /blue/mygroup/share/project42/conda/envs/hfrl/

conda create -p /blue/mygroup/share/project42/conda/envs/another_env/

Expand to see example command and output

[username@login7 ~]$ conda create -p /blue/mygroup/share/project42/conda/envs/another_env/
Looking for: []
Preparing transaction: done
Verifying transaction: done
Executing transaction: done
To activate this environment, use
    $ conda activate /blue/mygroup/share/project42/conda/envs/another_env/
To deactivate an active environment, use
    $ conda deactivate
[username@login7 ~]$

Activate the new environment¶

To activate our environment:

For name based environments: conda activate my_env
For path based environments: conda activate /blue/mygroup/share/project42/conda/envs/another_env

Success

Notice that your command prompt changes when you activate an environment to indicate which environment is active, showing that in parentheses before the other information:

(myenv) [username@c0907a-s23 ~]$

Tip

Activation of your environment is really only needed for package installation. For using the environment just add the path to its bin directory to $PATH in your job script.

E.g. If you conda environment at /blue/mygroup/$USER/conda/envs/project1/, add the following into your job script before executing any commands

export PATH=/blue/mygroup/$USER/conda/envs/project1/bin:$PATH

Adjust the path as needed for your environment. The path should include the bin folder as shown above.

Once you are done installing packages inside the environment you can use

conda deactivate

Export or import an environment¶

Expand to see how to export your environment to an environment.yml file

Now that you have your environment working, you may want to document its contents and/or share it with others. The environment.yml file defines the environment and can be used to build a new environment with the same setup.

To export an environment file from an existing environment, run:

conda env export > my_env.yml

You can inspect the contents of this file with cat my_env.yml. This file defines the packages and versions that make up the environment as it is at this point in time. Note that it also includes packages that were installed via pip.

Expand to see how to create an environment from a yaml file

If you share the environment yaml file created above with another user, they can create a copy of your environment using the command:

conda env create --file my_env.yml

They may need to edit the last line to change the location to match where they want their environment created.

Group environments¶

It is possible to create a shared environment accessed by a group on HiPerGator, storing the environment in, for example, /blue/group/share/conda. In general, this works best if only one user has write access to the environment. All installs should be made by that one user and should be communicated with the other users in the group. It is recommended that user's umask configuration is set to group friendly permissions, such as umask 007. See Sharing Within A Cluster.

For More Information¶

There is additional information on adding conda environments as Jupyter Kernels on the Managing Conda Environments page.

Categories¶

programming