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Conda best practices

NOTE: Do not install Conda environments directly on the shared file system

CSC has deprecated the use of Conda environments that are installed directly on the shared file system on Puhti/Mahti due to performance issues. We strongly recommend users to either containerize their Conda-based installations or consider alternative means of installation. Read more on our separate Conda deprecation page. See also the Tykky container wrapper tool to easily containerize your installations.

What is conda

Conda is

  1. a software install tool that can manage software dependencies, and
  2. a user (shell) environment management tool.

It is a bit similar to yum or apt, plus python virtual environments, if you are familiar with those. Conda

  • packages can contain software written in any language
  • works the same in Linux, Mac OS and Windows
  • works the same in machines from laptops to large clusters
  • packages contain pre-compiled binaries, and the recipe they we built with
  • does not require administrator privileges to run, unlike yum and apt
  • can install software into multiple install roots, Conda environments
  • is primarily designed for single user usage

Note, the term "Conda environment" relates to two somewhat separate concepts. It can refer to either or both of

  1. one of the user's conda software install root directories, and
  2. the user's shell environment that has been modified with conda activate ... command.

When to use Conda and what kind of software you should install with it?

Conda is well suited for installing desktop type software, and complex, possibly conflicting, package dependencies in Python, LaTex, or R, for example. It is designed to be used on personal software installs. Naturally, the software installed with it can be made available to others, too. The configuration files, environment.yaml, for the conda software environments can be easily shared with others.

When to yum, apt, or brew

If you are running on a personal Linux machine, such as laptop, have administrator privileges, and intend to run the program only on your personal machine, using yum, apt or homebrew may be more convenient.

When to build from the sources

If you are installing a MPI parallel and/or performance optimized application on a HPC cluster, follow the instructions of the computing center about building software from the sources. The software dependencies in HPC environments are usually handled using environment module system. For development work, using a laptop and possibly conda or yum/apt/homebrew for installing dependencies and development tools, is still likely more convenient.

There are package and management tools for building and installing HPC software from sources, such as Spack. Those are not as widely used as Conda, and often require some knowledge of the software build process, but they do compile and optimize the software for the particular architecture.

Conda channels (package repositories)

Conda channels are similar to Linux distributions' repositories, such as Ubuntu, CentOS or Debian. The most popular Conda channels are commercially maintained Anaconda, and community maintained Conda-forge.

How to install Conda

Install Conda by downloading the suitable installer script from Miniconda, see detailed instructions in the Example chapter below. Miniconda contains only a minimal set of packages, that allow you to run conda commands and install additional packages from different channels.

Python versions in the install scripts

You can use python 3 version to install environments with python 2 interpreter, and vice versa. We recommend downloading python 3 version, which by default installs python 3 in Conda's base environment.

Miniconda or Anaconda

On a personal workstation you can also install Anaconda distribution, which in addition to minimal set of packages, installs also a large number of packages from Anaconda channel. You can also install the same packages later from Anaconda channel if you start with Miniconda. The only practical difference is just in what is installed by default.

How to find if there is a conda package for a software?

First, google conda <name>, where <name> is the name of the package. Similar to using yum, apt or homebrew, guessing the name of the package that contains particular application may be the most difficult task. If there is a Conda package for the software, Google hits usually contain the exact names of the packages and in which channel (repository) they can be installed from.

What could possibly go wrong?

Running different conda than you think

If you follow the example instructions below, conda command is actually set up as a shell function, that refers to an environment variable CONDA_EXE. You can see how it is defined with command type conda. It is possible to have other conda command active by accident, too. They can sneak into shell environment from an earlier miniconda or anaconda installs, or from some module load ... command. Commands which conda and echo $CONDA_EXE may reveal those. The lesson here is that make sure you are using only a single conda setup at a time.

Shell initialization and other configuration files modifying user's shell environment

Many software install documentations and scripts, including Miniconda, give an option of adding setup lines into user's shell initialization scripts, .bashrc, .profile, etc, which modify user's shell environment so that a particular software is automatically set up for each new shell or login. This is convenient, but may lead to conflicts that are hard to find later. A safer practice is to put all these setup commands in separate scripts, let's say into user's ~/setup_scripts/ folder, and then explicitly source them only when necessary.

Messed up environment variables

There are many environment variables that are used to override or extend the defaults in how commands and libraries are searched, or which directories particular applications use. Examples of such environment variables are PATH, LD_LIBRARY_PATH, CONDA_*, and PYTHONPATH.

As a general safe practice, try to rely on these environment variables as little as possible. Some environment module systems, such as the one in, do extensively depend on modifying the values of environment variables. In it might be a good idea to run

module purge

before starting to use Conda.

Mixing packages from different channels, or simply outdated packages

Please note that installing packages from different channels to a single Conda environment does not always work. That is a bit similar to trying to mix packages from Ubuntu and Debian. The solution is to simply set up separate Conda environments for different tasks or projects if in doubt about the compatibility.

Some of the smaller channels are not always up-to-date or properly maintained, and packages from those may break your Conda environment. Fortunately you can do rollbacks on Conda environments, or simply try new packages in testing/staging environments before including them into your favorite environments.

The best friends to sort out conda configuration or shell environment related problems are the following commands:

# Are you using the version you think you are?
conda --version

# The single most useful command to check configuration settings?
conda info

# Is there something extra in the command or library search paths?

# You may need to unload some modules?
module list

# Are some environment variables overriding the default conda configuration?
env | grep ^CONDA_

# Is something set up by default at every login or new shell?
cat ~/.bashrc ~/.bash_profile ~/.profile

Sorting out unmaintained or otherwise broken packages

If you encounter a broken package, a package that does not have the feature you need, or an outdated package, it is possible to re-build the binary package from source by yourself. The details of this are slightly out of the scope of this document, but building Conda packages is perfectly doable. Basically, you need to install conda-build Conda package, modify the files meta.yaml and in the <condaroot>/pkgs/<package>/info/recipe sub-folder, rebuild the package, and install it into a local channel.


The examples below should work without modifications in The basic usage is the same in other machines, other clusters, laptops, etc.

Installing conda

If you are planning to install your own Conda, instead of using system applications that are installed with Conda by someone else, like bioconda or geoconda in, we recommend installing a project specific copy of Miniconda3.

As Conda packages may take significant storage space it is not recommended to to install Miniconda3 to your home directory. Instead you should install it to the ProjAppl directory of your Puhti project and preferably containerize the environment (see Tykky).

To get an overview of your directories in Puhti, run command:


You can pick the path of your ProjAppl directory from the output of the command above or if you are mostly using just one project in Puhti, you can set the environment variables $SCRATCH and $PROJAPPL to point to the scratch and projappl directories of a CSC project. This setting can be done with command:

csc-workspaces set <project>

Below we assume that $PROJAPPL has been defined. After that the actual installation can be done with commands:

bash -b -p $PROJAPPL/miniconda3

All conda files will be installed under the chosen Conda root install directory, here $PROJAPPL/miniconda3, with the exception of .condarc, which will be in the user's home directory. By default, which is also a recommended practice, all files installed subsequently with conda go under the same install root.

The option -b simply skips some questions and adding the automatic initialization lines into user\'s .bash_profile.

The Conda install root directory contains basically the following subdirectories:

  • bin, lib, ... the usual Linux directories for the Conda base environment
  • envs where all Conda environments will reside
  • pkgs Conda package cache

Activating conda tool

If you installed Conda into directory $PROJAPPL/miniconda3, you can activate conda tool with the initialization script:

source $PROJAPPL/miniconda3/etc/profile.d/

This simply sets couple of shell environment variables, and conda command as a shell function. If you allowed the install script to modify your .bashrc, this step is unnecessary.

When activating a new conda install first time, it's a good idea to run

conda info

to verify that the Conda configuration is ok.

Installing packages, named environments, environment.yaml

We recommend

  1. installing all conda packages into named environments, which go under the envs subdirectory, instead of installing them into the base environment directly under conda install root, and
  2. using environment.yaml configuration files instead of adding packages to environments directly from the command line with conda install ... commands.

In practice, you only need to create a single environment.yaml file for each your environments, see examples below, and then use a single conda command

conda env create -f <envname>.yaml

to create the whole environment.

Updating the packages, or adding new packages to an existing environment is done by modifying the environment.yaml file, and then running

conda env update -f <envname>.yaml

Activating conda environment

Activating Conda environment is done simply by

conda activate <envname>

This prepends the path to the Conda environment's bin directory to your shell environment's PATH environment variable, so that different commands are first searched from the Conda environment, and modifies the prompt so that it shows the name of the currently active Conda environment. This command also sources the activation hooks for this Conda environment in directory <envroot>/etc/conda/activate.d/, created by the installed packages to set application specific environment variables.

In supercomputer environments similar tasks for system software are often done using environment module system, and module load ... commands.

Examples of environment.yaml files

As the first example, let's use the environment.yaml file conda-docs-env.yaml, that can be used to install mkdocs with couple of plugins:

name: docs
  - conda-forge
  - python
  - pip
  - pip:
      - mkdocs
      - pymdown-extensions
      - mkdocs-windmill

The first field, name, simply defines the name of the Conda environment. The second field, channels, list from which channels the packages are pulled from to this environment. Field dependencies lists the actual Conda packages that are installed into the environment. Note, that Conda integrates nicely with Python pip, and you can also include pip packages, that are installed using pip, into the Conda environment (Sometimes you need to clean pip caches that are not under Conda's control).

Conda uses a "channel priority" for determining where to install packages from, i.e. it tries first to install packages from the first listed channel. If no package versions are specified, Conda always installs the latest versions.

As a second, more complex example, let's look at an environment for C program development, defined in file c-ide.yaml

name: c-ide
  - /projappl/project_123456/conda/channels/csc-forge-based
  - conda-forge
  - defaults
  - anaconda
  - git
  - font-ttf-source-code-pro
  - emacs
  - global
  - ctags
  - clangdev
  - cmake
  - make

The first listed channel is in a local directory. In this case it is used as a repository for a self created package, here GNU Global, which does not have an existing Conda package in Anaconda or Conda-forge repositories. Naturally this environment can only be created in machine, if package global is included.

Adding the environment.yaml file to the source repository of your project is probably an excellent idea. This allows an easy way to replicate the same environment in multiple machines. For example, you can do development conveniently in a local machine and then copy the environment to production platform. Also, you can easily share the environment with other developers.

Removing unused packages

Conda, as other software packaging solutions that install also all the dependencies, tends to eat up disc space. If running out of space, it is quite easy to remove old and unused packages.


conda env list
conda env remove -n <envname>
conda clean -a

list the created environments, remove the named environment <envname>, and remove unused packages from the local package cache pkgs, respectively.

Creating environments so that other user's can access them

Giving other users access to your Conda environment is as easy as giving them read access to the directory containing the environment, in principle. If you use $PROJAPPL in Puhti this is the default setting.

In other environments, like a local server or a Virtual Machine it is very easy to update packages, and then forget to give read access to the updated files. Also, some additional considerations need to be made, if multiple persons are maintaining the environment, and accidental overwrites and other mistakes are to be avoided.

Probability of these mistakes can be minimized by creating a separate project/Unix group and user accounts for environment maintainers, and then performing the environment maintenance task within a special shell environment. Some ideas for the shell environment setup can be found in file

Last update: June 27, 2022