Python Environments with Conda

Conda is a popular package manager that is especially popular in data science and machine learning communities.

It is commonly used to handle complex requirements of Python and R packages.

Quick usage guide

First time setup

You can get conda by loading the miniconda-module:

$ module load miniconda

By default Conda stores installed packages and environments in your home directory. However, as your home directory has a lower quota, it is a good idea to tell conda to install packages and environments into your work directory:

$ mkdir $WRKDIR/.conda_pkgs
$ mkdir $WRKDIR/.conda_envs

$ conda config --append pkgs_dirs ~/.conda/pkgs
$ conda config --append envs_dirs ~/.conda/envs
$ conda config --prepend pkgs_dirs $WRKDIR/.conda_pkgs
$ conda config --prepend envs_dirs $WRKDIR/.conda_envs

Now you’re all set up to create your first environment.

Creating a simple environment with conda

One can install environments from the command line itself, but a better idea is to write an environment.yml-file that describes the environment.

Below we have a simple environment.yml:

name: conda-example
  - conda-forge
  - numpy
  - pandas

Now we can use the conda-command to create the environment:

$ module load miniconda
$ conda env create --file environment.yml

Once the environment is installed, you can activate it with:

$ source activate conda-example

conda init, conda activate, and source activate

We don’t recommend doing conda init like many sources recommend: this will permanently affect your .bashrc file and make hard-to-debug problems later. The main points of conda init are to a) automatically activate an environment (not good on a cluster: make it explicit so it can be more easily debugged) and b) make conda a shell function (not command) so that conda activate will work (source activate works as well in all cases, no confusion if others don’t.)

  • If you activate one environment from another, for example after loading an anaconda module, do source activate ENV_NAME like shown above (conda installation in the environment not needed).

  • If you make your own standalone conda environments, install the conda package in them, then…

  • Activate a standalone environment with conda installed in it by source PATH/TO/ENV_DIRECTORY/bin/activate (which incidentally activates just that one session for conda).

Resetting conda

Sometimes it is necessary to reset your Conda configuration. So here are instructions on how to wipe all of your conda settings and existing environments. To be able to do so first activate conda. On Triton, by loading the miniconda environment:

$ module load miniconda

First, check where conda stores your environments:

$ conda config --show envs_dirs
$ conda config --show pkgs_dirs

Delete the directories that are listed and start with /home/USERNAME (this could e.g. be /home/<username>/.conda/envs) and /scratch/ ( e.g. /scratch/work/USERNAME/conda_envs). You would delete these with rm -r DIRNAME, but be careful you use the right paths because there is no going back. This will clean up all packages and environments you have installed.

Next, clean up your .bashrc, .zshrc, .kshrc and .cshrc (whichever ones exist for you). Open these files in an editor (e.g. nano .bashrc) and search for the line # >>> conda initialize >>> delete everything between this line and the line # <<< conda initialize <<<. These lines automatically initilize conda upon login which can cause a lot of trouble on a cluster.

Finally delete the file .condarc from your home folder ( rm ~/.condarc) to reset your conda configuration. After this close the current connection to triton and reconnect in a new session.

Now you should have a system that doesn’t have any remains of conda, so you can now follow the initial steps as detailed here.

Understanding the environment file

Conda environment files are written using YAML syntax. In an environment file one usually defines the following:

  • name: Name of the desired environment.

  • channels: Which channels to use for packages.

  • dependencies: Which conda and pip packages to install.

Choosing conda channels

When an environment file is used to create an environment, conda looks up the list of channels (in descending priority) and it will try to find the needed packages.

Some of the most popular channels are:

  • conda-forge: An open-source channel with over 18k packages. Highly recommended for new environments. Most packages in anaconda-modules come from here.

  • defaults: A channel maintained by Anaconda Inc.. Free for non-commercial use. Default for anaconda distribution.

  • r: A channel of R packages maintained by Anaconda Inc.. Free for non-commercial use.

  • bioconda: A community maintained channel of bioinformatics packages.

  • pytorch: Official channel for PyTorch, a popular machine learning framework.

One can have multiple channels defined like in the following example:

name: pytorch-env
  - nvidia
  - pytorch
  - conda-forge
  - pytorch
  - pytorch-cuda=12.1
  - torchvision
  - torchaudio

Setting package dependencies

Packages in environment.yml can have version constraints and version wildcards. One can also specify pip packages to install after conda-packages have been installed.

For example, the following dependency-env.yml would install a numpy with version higher or equal than 1.10 using conda and scipy via pip:

name: dependency-env
  - conda-forge
  - numpy>=1.10.*
  - pip
  - pip:
    - scipy

Listing packages in an environment

To list packages installed in an environment, one can use:

$ conda list

Removing an environment

To remove an environment, one can use:

$ conda env remove --name environment_name

Do remember to deactivate the environment before trying to remove it.

Cleaning up conda cache

Conda uses a cache for downloaded and installed packages. This cache can get large or it can be corrupted by failed downloads.

In these situations one can use conda clean to clean up the cache.

  • conda clean -i cleans up the index cache that conda uses to find the packages.

  • conda clean -t cleans up downloaded package installers.

  • conda clean -p cleans up unused packages.

  • conda clean -a cleans up all of the above.

Installing new packages into an environment

Installing new packages into an existing environment can be done with conda install-command. The following command would install matplotlib from conda-forge into an environment.

$ conda install --freeze-installed --channel conda-forge matplotlib

Installing packages into an existing environment can be risky: conda uses channels given from the command line when it determines which channels it should use for the new packages.

This can cause a situation where installing a new package results in the removal and reinstallation of multiple packages. Adding the --freeze-installed-flags makes already installed packages safe and by giving explicitly the channels to use, one can make certain that the new packages come from the same source.

It is usually a better option to create a new environment with the new package set as an additional dependency in the environment.yml. This keeps the environment reproducible.

If you intend on installing packages to existing environment, adding default channels for the environment can also make installing packages easier.

Setting default channels for an environment

It is a good idea to store channels used when creating the environment into a configuration file that is stored within the environment. This makes it easier to install any missing packages.

For example, one could add conda-forge into the list of default channels with:

$ conda config --env --add channels conda-forge

We can check the contents of the configuration file with:

$ cat $CONDA_PREFIX/.condarc

Doing everything faster with mamba

mamba is a drop-in replacement for conda that does environment building and solving much faster than conda.

To use it, you either need to install mamba-package from conda-forge-channel or use the miniconda-module.

If you have mamba, you can just switch from using conda-command to using mamba and it should work in the same way, but faster.

For example, one could create an environment with:

$ mamba env create --file environment.yml

Motivation for using conda

When should you use conda?

If you need basic Python packages, you can use pre-installed anaconda-modules. See the Python-page for more information.

You should use conda when you need to create your own custom environment.

Why use conda? What are its advantages?

Quite often Python packages are installed with Pip from the Python Package Index (PyPI). These packages contain Python code and in many cases some compiled code as well.

However, there are three problems pip cannot solve without additional tools:

  1. How do you install multiple separate suites of packages for different use cases?

  2. How do you handle packages that depend on some external libraries?

  3. How do you make sure that all of the packages have are compatible with each other?

Conda tries to solve these problems with the following ways:

  1. Conda creates environments where packages are installed. Each environment can be activated separately.

  2. Conda installs library dependencies to the environment with the Python packages.

  3. Conda uses a solver engine to figure out whether packages are compatible with each other.

Conda also caches installed packages so doing copies of similar environments does not use additional space.

One can also use the environment files to make the installation procedure more reproducible.

Creating an environment with CUDA toolkit

NVIDIA’s CUDA-toolkit is needed for working with NVIDIA’s GPUs. Many Python frameworks that work on GPUs need to have a supported CUDA toolkit installed.

Conda is often used to provide the CUDA toolkit and additional libraries such as cuDNN. However, one should choose the version of the CUDA toolkit based on what the software requires.

If the package is installed from a conda channel such as conda-forge, conda will automatically retreive the correct version of CUDA toolkit.

In other cases one can use an environment file like this cuda-env.yml:

name: cuda-env
  - conda-forge
  - cudatoolkit


During installation conda will try to verify what is the maximum version of CUDA installed graphics cards can support and it will install non-CUDA enabled versions by default if none are found (as is the case on the login node, where environments are normally built). This can be usually overcome by setting explicitly that the packages should be the CUDA-enabled ones. It might however happen, that the environment creation process aborts with a message similar to:

nothing provides __cuda needed by tensorflow-2.9.1-cuda112py310he87a039_0

In this instance it might be necessary to override the CUDA settings used by conda/mamba. To do this, prefix your environment creation command with CONDA_OVERRIDE_CUDA=CUDAVERSION, where CUDAVERSION is the CUDA toolkit version you intend to use as in:

CONDA_OVERRIDE_CUDA="11.2" mamba env create -f cuda-env.yml

This will allow conda to assume that the respective CUDA libraries will be present at a later point and so it will skip those requirements during installation.

For more information, see this helpful post in Conda-Forge’s documentation.

Creating an environment with GPU enabled Tensorflow

To create an environment with GPU enabled Tensorflow you can use an environment file like this tensorflow-env.yml:

name: tensorflow-env
  - conda-forge
  - tensorflow=*=*cuda*

Here we install the latest tensorflow from conda-forge-channel with an additional requirement that the build version of the tensorflow-package must contain a reference to a CUDA toolkit. For a specific version replace the =*=*cuda* with e.g. =2.8.1=*cuda* for version 2.8.1.

If you encounter errors related to CUDA while creating the environment, do note this hint on overriding CUDA during installation.

Creating an environment with GPU enabled PyTorch

To create an environment with GPU enabled PyTorch you can use an environment file like this pytorch-env.yml:

name: pytorch-env
  - nvidia
  - pytorch
  - conda-forge
  - pytorch
  - pytorch-cuda=12.1
  - torchvision
  - torchaudio

Here we install the latest pytorch version from pytorch-channel and the pytorch-cuda-metapackage that makes certain that the

Additional packages required by pytorch are installed from conda-forge-channel.

If you encounter errors related to CUDA while creating the environment, do note this hint on overriding CUDA during installation.

Installing numpy with Intel MKL enabled BLAS

NumPy and other mathematical libaries utilize BLAS (Basic Linear Algebra Subprograms) implementation for speeding up many operations. Intel provides their own fast BLAS implementation in Intel MKL (Math Kernel Library). When using Intel CPUs, this library can give a significant performance boost to mathematical calculations.

One can install this library as the default BLAS by specifying blas * mkl as a requirement in the dependencies like in this mkl-env.yml:

name: mkl-env
  - conda-forge
  - blas * mkl
  - numpy

Advanced usage

Finding available packages

Because conda tries to make certain that all packages in an environment are compatible with each other, there are usually tens of different versions of a single package.

One can search for a package from a channel with the following command:

$ mamba search --channel conda-forge tensorflow

This will return a long list of packages where each line looks something like this:

tensorflow                     2.8.1 cuda112py39h01bd6f0_0  conda-forge

Here we have:

  • The package name (tensorflow).

  • Version of the package (2.8.1).

  • Package build version. This version often contains information on:

    • Python version needed by the package (py39 or Python 3.9).

    • Other libraries used by the package (cuda112 or CUDA 11.2).

  • Channel where the package comes from (conda-forge).

Checking package dependencies

One can check package dependencies by adding the --info-flag to the search command. This can give a lot of output, so it is a good idea to limit the search to one specific package:

$ mamba search --info --channel conda-forge tensorflow=2.8.1=cuda112py39h01bd6f0_0

The output looks something like this:

tensorflow 2.8.1 cuda112py39h01bd6f0_0
file name   : tensorflow-2.8.1-cuda112py39h01bd6f0_0.tar.bz2
name        : tensorflow
version     : 2.8.1
build       : cuda112py39h01bd6f0_0
build number: 0
size        : 26 KB
license     : Apache-2.0
subdir      : linux-64
url         :
md5         : 35716504c8ce6f685ae66a1d9b084fc7
timestamp   : 2022-05-21 09:09:53 UTC
  - __cuda
  - python >=3.9,<3.10.0a0
  - python_abi 3.9.* *_cp39
  - tensorflow-base 2.8.1 cuda112py39he716a45_0
  - tensorflow-estimator 2.8.1 cuda112py39hd320b7a_0

Packages with underscores are meta-packages that should not be added to conda environment specifications. They will be solved by conda automatically.

Here we can see more info on the package, including its dependencies.

When using mamba, one can also use mamba repoquery depends to see the dependencies:

$ mamba repoquery depends --channel conda-forge tensorflow=2.8.1=cuda112py39h01bd6f0_0

Output looks something like this:

 Name                     Version Build                 Channel
 tensorflow               2.8.1   cuda112py39h01bd6f0_0 conda-forge/linux-64
 __cuda >>> NOT FOUND <<<
 python                   3.9.9   h62f1059_0_cpython    conda-forge/linux-64
 python_abi               3.9     2_cp39                conda-forge/linux-64
 tensorflow-base          2.8.1   cuda112py39he716a45_0 conda-forge/linux-64
 tensorflow-estimator     2.8.1   cuda112py39hd320b7a_0 conda-forge/linux-64

One can also print the full dependency list with mamba repoquery depends --tree. This will produce a really long output.

$ mamba repoquery depends --channel conda-forge tensorflow=2.8.1=cuda112py39h01bd6f0_0

Fixing conflicts between packages

Usually first step of fixing conflicts between packages is to write a new environment file and list all required packages in the file as dependencies. A fresh solve of the environment can often result in a working environment.

Sometimes there is a case where a single package does not have support for a specific version of Python or specific version of CUDA toolkit. In these cases it is usually beneficial to give more flexibility to the solver by limiting the number of specified versions.

One can also use the search commands provided by mamba to see what dependencies individual packages have.