Development/Conda
| -- Caution -- |
| The licensing situation with Anaconda is currently unclear. To be on the safe side, make sure to only use open source channels! |
Conda helps to manage software environments and packages. Installing software packages into independent environments improves programming flexibility and leads to a higher reproducibility of research results. A majority of the scientific software is available as conda package, which allows for convenient installations.
Installation and Usage
Before you can get started with creating conda environments, you need to set up conda. Some clusters provide a centrally installed conda module and others require you to install conda yourself. The following table provides an overview of the necessary initial steps depending on the cluster.
| Cluster | Description | Commands |
|---|---|---|
| bwUniCluster 2.0 | Load conda module and prepare the environment | module load devel/miniconda
|
| Helix | Load conda module and prepare the environment | module load devel/miniconda/3
source $MINICONDA_HOME/etc/profile.d/conda.sh
|
| NEMO | Load conda module | module load devel/conda
|
| Other | Install Miniconda in your home directory | see #Conda_Installation |
Conda Installation
 |
The installation will modify your .bashrc, which is loaded whenever you log in. Save the file and test changes with a seconds login to make sure you don't lock yourself out of the system → see .bashrc Do's and Don'ts |
If no conda module is available, you can install conda as follows:
# Download installer
$ wget https://repo.anaconda.com/miniconda/Miniconda3-latest-Linux-x86_64.sh
# Backup your .bashrc to a file with a current date
cp .bashrc bashrc-$(date --iso)
# Execute
$ sh Miniconda3-latest-Linux-x86_64.sh
# update .bashrc (have a second login shell available in case this fails)
$ source ~/.bashrc
You will need to add this line to your jobscripts such that the environments are available on the compute nodes:
source $HOME/miniconda3/etc/profile.d/conda.sh
conda activate <env_name>
Create Environments and Install Software
An environment is an isolated space that allows you to manage a custom constellation of software packages and versions.
If you want, you can set a specific installation directory for your environments, for example a workspace:
conda config --prepend envs_dirs /path/to/conda/envs
conda config --prepend pkgs_dirs /path/to/conda/pkgs
conda config --show envs_dirs
conda config --show pkgs_dirs
If you don't specify a new envs_dir, Conda will use ~/.conda/envs in your home directory as the default installation path (same applies to pkgs_dirs).
You can create python environments and install packages into these environments afterwards or add them already during the setup of the environment:
# Create an environment
conda create -n scipy
# Activate this environment
conda activate scipy
# Install software into this environment
(scipy) $ conda install scipy
Install packages and create a new environment:
conda create -n scipy scipy
conda activate scipy
Search for an exact version (see Versioning):
conda search scipy==1.7.3
Create a Python 2.7 environment:
conda create -n scipy-py27 scipy python=2.7
Activate/Deactivate/Delete Environments
In order to use the software in an environment you'll need to activate it first:
conda activate scipy
Deactivate this environment to be able to activate an environment with a different Python or software version instead. Or to work with software outside of an environment.
conda deactivate
Deleting Environments:
conda env remove -n scipy-1.7.3 --all
List Environments and Packages
List environments:
conda env list
In the output, the * is denoting the currently activated environment. The base environment is condas default environment. It is not advised to install software into the default environment and on some clusters this possibility is even disabled.
List packages of current environment:
conda list
List packages in given environment:
conda list -n scipy
Use Channels
Different channels enable the installation of different software packages. Some software packages require specific channels. We suggest to try the following channels:
conda-forge
bioconda
Search in default and extra channel:
conda search -c conda-forge scipy
You can add channel to your channels, but than you'll search and install automatically from this channel:
conda config --add channels bioconda
conda config --add channels conda-forge
conda config --show channels
conda config --remove channels bioconda # remove channel again
Use conda-forge Conda Packages
The full list of conda-forge Python packages can be found in the conda channel.
You can install the core conda-forge Python stack:
conda install -c conda-forge -n conda-forgepython3 conda-forgepython3_core
... with a "fuzzy" Python version (see Versioning):
conda install -c conda-forge -n conda-forgepython-3.9.10 conda-forgepython3_core python=3.9.10
... with an exact conda-forge OneApi version (see Versioning):
conda create -c conda-forge -n conda-forgepython-2022.1.0 conda-forgepython3_core==2022.1.0
... or the full conda-forge Python stack:
conda create -c conda-forge -n conda-forgepython-2022.1.0 conda-forgepython3_full==2022.1.0
... or just some conda-forge MKL optimized scientific software for the newest conda-forge OneAPI version 2022:
conda search -c conda-forge scipy
conda create -c conda-forge -n scipy-1.7.3 scipy=1.7.3=py39h5c0f66f_1
Reproducible Conda Environments
This section describes how to secure environments in a reproducible manner.
For a more detailed environments documentation refer to the conda documentation.
Create an environment file for re-creation:
conda env export -n scipy-1.7.3 -f scipy-1.7.3.yml
Re-create saved environment:
conda env create -f scipy-1.7.3.yml
Create a file with full URL for re-installation of packages:
conda list --explicit -n scipy-1.7.3 >scipy-1.7-3.txt
Install requirements file into environment:
conda create --name scipy-1.7.3 --file scipy-1.7.3.txt
The first backup option is from the conda-env command and tries to reproduce the environment by name and version. The second option comes from the conda command itself and specifies the location of the file, as well. You can install the identical packages into a newly created environment. Please verify the architecture first.
To clone an existing environment:
conda create --name scipy-1.7.3-clone --clone scipy-1.7.3
Backup via Local Channels
Usually packages are cached in your Conda directory inside pkgs/ unless you run conda clean. Otherwise the environment will be reproduced from the channels' packages. If you want to be independent of other channels you can create your own local channel and backup every file you have used for creating your environments.
Install package conda-build:
conda install conda-build
Create local channel directory for linux-64:
mkdir -p $( ws_find conda )/conda/channel/linux-64
Create dependency file list and copy files to channel:
conda list --explicit -n scipy-1.7.3 >scipy-1.7.3.txt
for f in $( grep -E '^http|^file' scipy-1.7.3.txt ); do
cp $( ws_find conda )/conda/pkgs/$( basename $f ) $( ws_find conda )/conda/channel/linux-64/;
done
Optional: If packages are missing in the cache download them:
for f in $( grep -E '^http|^file' scipy-1.7.3.txt ); do
wget $f -O $( ws_find conda )/conda/channel/linux-64/$( basename $f );
done
Initialize channel:
conda index $( ws_find conda )/conda/channel/
Add channel to the channels list:
conda config --add channels file://$( ws_find conda )/conda/channel/
Alternative use -c file://$( ws_find conda )/conda/channel/ when installing.
Backup whole Environments
Alternatively you can create a package of your environment and unpack it again when needed.
Install conda-pack:
conda install -c conda-forge conda-pack
Pack activated environment:
conda activate scipy-1.7.3
(scipy-1.7.3) $ conda pack
(scipy-1.7.3) $ conda deactivate
Pack environment located at an explicit path:
conda pack -p $( ws_find conda )/conda/envs/scipy-1.7.3
The easiest way is to unpack the package into an existing Conda installation.
Just create a directory and unpack the package:
mkdir -p external_conda_path/envs/scipy-1.7.3
tar -xf scipy-1.7.3.tar.gz -C external_conda_path/envs/scipy-1.7.3
conda activate scipy-1.7.3
# Cleanup prefixes from in the active environment
(scipy-1.7.3) $ conda-unpack
(scipy-1.7.3) $ conda deactivate
Versioning
Please keep in mind that modifying, updating and installing new packages into existing environments can modify the outcome of your results. We strongly encourage researchers to creating new environments (or cloning) before installing or updating packages. Consider using meaningful names for your environments using version numbers and dependencies.
| Constraint | Specification |
|---|---|
| exact version | scipy==1.7.3 |
| fuzzy version | scipy=1.7 |
| greater equal | "scipy>=1.7" |
For more information see the #Cheat_Sheet.
Example:
conda create -c conda-forge -n scipy-1.7.3 scipy==1.7.3=py39h5c0f66f_1
Pinning
Pin versions if you don't want them to be updated accidentally (see documentation).
Example:
echo 'scipy==1.1.0=np115py36_6' >> $( ws_find conda )/conda/envs/scipy-1.1.0-np115py36_6/conda-meta/pinned
You can easily pin your whole environment:
conda list -n scipy-1.7.3 --export >$( ws_find conda )/conda/envs/scipy-1.7.3/conda-meta/pinned
Using Singularity Containers
Using Singularity Containers can create more robust software environments.
Build the container on your local machine!
This is Singularity recipe example for a CentOS image with a Conda environment:
cat << EOF >scipy-1.7.3.def
Bootstrap: docker
From: rockylinux:8
OSVersion: 8
# Alternative:
# From: almalinux:8
%runscript
echo "This is what happens when you run the container..."
source /conda/etc/profile.d/conda.sh
conda activate scipy-1.7.3
eval "$@"
%post
yum -y install vim wget
wget https://repo.anaconda.com/miniconda/Miniconda3-latest-Linux-x86_64.sh -O miniconda.sh
bash miniconda.sh -b -p conda
source /conda/etc/profile.d/conda.sh
conda update -y -n base conda
conda create -y -c conda-forge -n scipy-1.7.3 scipy=1.7.3=py39h5c0f66f_1
rm miniconda.sh -f
EOF
Build container (on local machine):
singularity build scipy-1.7.3.sif scipy-1.7.3.def
Copy the container on the cluster and start it:
singularity run scipy-1.7.3.sif python -V
Example for interactive usage:
singularity shell scipy-1.7.3.sif
Apptainer> source /conda/etc/profile.d/conda.sh
Apptainer> conda activate scipy-1.7.3
(scipy-1.7.3) Apptainer> python -V
See Singularity user documentation for more information on containers.