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Package, dependency and environment management for anylanguage—Python, R, Ruby, Lua, Scala, Java, JavaScript, C/ C++,FORTRAN, and more.

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Conda is an open source package management system and environmentmanagement system that runs on Windows, macOS and Linux. Condaquickly installs, runs and updates packages and their dependencies.Conda easily creates, saves, loads and switches between environmentson your local computer. It was created for Python programs, but itcan package and distribute software for any language.

A conda-forge distribution. Contribute to conda-forge/miniforge development by creating an account on GitHub. Conda create -n myenv python=3.7 Here myenv is simply the name of the environment so you can replace it with anything. Also python 3.7 is the latest python version as of the time of this writing but may need to be changed with later version in the future.

Conda as a package manager helps you find and install packages.If you need a package that requires a different version ofPython, you do not need to switch to a different environmentmanager, because conda is also an environment manager. With justa few commands, you can set up a totally separate environment torun that different version of Python, while continuing to runyour usual version of Python in your normal environment.

In its default configuration, conda can install and manage thethousand packages at that are built, reviewedand maintained by Anaconda®.

Conda can be combined with continuous integration systems suchas Travis CI and AppVeyor to provide frequent, automated testingof your code.

The conda package and environment manager is included in all versions ofAnaconda and Miniconda.

Conda is also included in Anaconda Enterprise, which provides on-site enterprisepackage and environment management for Python, R, Node.js, Java and otherapplication stacks. Conda is also available on conda-forge,a community channel. You may also get conda on PyPI,but that approach may not be as up to date.

Conda is a package, dependency, and environment manager. It allows you to maintain different, often incompatible, sets of applications side-by-side. It has become a popular choice for managing pipelines that involve several tools, especially when multiple languages are involved. These sets of applications and their dependencies are kept in Conda environments, which you can switch between as your work dictates. Compared to the modules that we provide, there are often newer and more varied packages available that you can manage yourself, but they may not be as well optimized for the clusters. See Conda's official command-line reference and the offical docs for managing environments for detailed instructions. Here we present essential instructions and site-specific info.


Mixing modules and conda-managed software is almost never a good idea. When constructing an environment for your work you should load either modules or a conda environment. If you get stuck, you can always ask us for help.

The Miniconda Module


For your convenience, we provide a relatively recent version of Miniconda as a module. This is a read-only environment from which you can create your own. We set some defaults for you in this module, and we keep it relatively up-to-date so you don't have to. If you are using Conda-installed packages, this should be the only module you load in your jobs.

Note: If you are on Milgram and run out of space in your home directory for Conda, you can either reinstall your environment in your project space (see below) or contact us for help with your home quota.

Defaults We Set

On all clusters, we set the CONDA_ENVS_PATH and CONDA_PKGS_DIRS environment variables to conda_envs and conda_pkgs in your project directory where there is more quota available. Conda will install to and search in these directories for environments and cached packages.

Starting with minconda module version 4.8.3 we set the default channels (the sources to find packages) to conda-forge and bioconda, which provide a wider array of packages than the default channels do. We have found it saves a lot of typing. If you would like to override these defaults, see the Conda docs on managing channels. Below is the .condarc for the miniconda module.

Setup Your Environment

Load the miniconda Module

You can save this to your default module collection by using module save. See our module documentation for more details.

Create a conda Environment

To create an environment use the conda create command. Environment files are saved to the first path in $CONDA_ENVS_PATH, or where you specify with the --prefix option. You should give your environments names that are meaningful to you, so you can more easily keep track of their purposes.

Because dependency resolution is hard and messy, we find specifying as many packages as possible at environment creation time can help minimize broken dependencies. Although sometimes unavoidable for Python, we recommend against heavily mixing the use of conda and pip to install applications. If needed, try to get as much installed with conda, then use pip to get the rest of the way to your desired environment.


For added reproducibility and control, specify versions of packages to be installed using conda with packagename=version syntax. E.g. numpy=1.14

For example, if you have a legacy application that needs Python 2 and OpenBLAS:

If you want a good starting point for interactive data science in R/Python Jupyter Notebooks:

Conda Channels

Community-lead collections of packages that you can install with conda are provided with channels. Some labs will provide their own software using this method. A few popular examples are Conda Forge and Bioconda, which we set for you by default. See the Conda docs for more info about managing channels.

You can create a new environment called brian2 (specified with the -n option) and install Brian2 into it with the following:

You can also install packages from Bioconda, for example:

Conda Create Environment

Use Your Environment

To use the applications in your environment, run the following:


We recommend against putting source activate or conda activate commands in your ~/.bashrc file. This can lead to issues in interactive or batch jobs. If you have issues with an environment, trying re-loading the environment by calling conda deactivate before rerunning conda activate env_name.


Your Conda environments will not follow you into job allocations. Make sure to activate them after your interactive job begins.

In a Job Script

To make sure that you are running in your project environment in a submission script, make sure to include the following lines in your submission script before running any other commands or scripts (but after your Slurm directives):

Find and Install Additional Packages

You can search Anaconda Cloud or use conda search to find the names of packages you would like to install:

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Conda version doesn't match the module loaded

If you have run conda init in the past, you may be locked to an old version of conda. You can run the following to fix this:

Permission Denied

If you get a permission denied error when running conda install or pip install for a package, make sure you have created an environment and activated it or activated an existing one first.

bash: conda: No such file or directory

If you get the above error, it is likely that you don't have the necessary module file loaded. Try loading the minconda module and rerunning your conda activate env_name command.

Could not find environment

This error means that the version of miniconda you have loaded doesn't recognize the environment name you have supplied. Make sure you have the miniconda module loaded (and not a different Python module) and have previously created this environment. You can see a list of previously created environments by running:

Additional Conda Commands

List Installed Packages

Delete a Conda Environment

Save and Export Environments

If you want to share or back up a Conda environment, you can export it to a file. To do so you need to run the following, replacing env_name with the desired environment.

Last update: July 9, 2020