Development/Conda

Install and use Conda

Conda can be used to easily install missing Python packages on your own into different Python environments with different versions in your own work spaces.

There are two possibilities of using Conda. One to install Conda itself, the other is to load a central installation if possible.

(A) Installing Conda into a Workspace

We suggest using workspaces for Conda installation. Otherwise $HOME/.conda will be used as default Conda path.

Create workspace:

ws_allocate conda <days>   # e.g. 100
cd $( ws_find conda )

Download script and install conda:

wget https://repo.continuum.io/miniconda/Miniconda3-latest-Linux-x86_64.sh -O miniconda.sh
bash miniconda.sh -b -p $( ws_find conda )/conda

Source variables for conda:

source $( ws_find conda )/conda/etc/profile.d/conda.sh

Optional Steps

Optional: Add source path in your local ~/.bashrc (edit file):

source $( ws_find conda )/conda/etc/profile.d/conda.sh

Optional: Update conda (usually base enviroment):

conda update -n base conda

Optional: Removing installation script:

rm miniconda.sh

(B) Use a centrally installed Conda Module

If a Conda Module is provided you can just load it and create environments a workspace.

Load conda module:

module avail tools/conda   # list Conda modules
module load tools/conda    # load module

Create workspace:

ws_allocate conda <days>   # e.g. 100

The last step defines the newly created workspace as the download and installation path for your environments:

conda config --append envs_dirs $( ws_find conda )/conda/envs
conda config --append pkgs_dirs $( ws_find conda )/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).

Install Packages into Environments

You can create python environments and install packages into these environments or create them during install:

conda create -n scipy
source activate scipy
conda install scipy

Install packages and create a new environment:

conda create -n scipy scipy
source activate scipy

Search a special verson:

conda search scipy==1.1.0

Create a Python 2.7 environment:

conda create -n scipy_py27 scipy python=2.7
source activate scipy_py27

Activating Environments

In order to use the software in an environment you'll need to activate it first:

source activate scipy

Deactivate to use different Python or software version:

source deactivate

List packages and Environments

List packages of current environment:

conda list

List packages in given environment:

conda list -n scipy

List environments:

conda env list

Use Channels

Add channels to get more software. We suggest to try the following channels:

conda-forge
intel
bioconda

Search in default and extra channel:

conda search -c intel scipy

You can add channel to your channels, but than you'll search and install automatically from this channel:

conda config --add channels intel
conda config --show channels
conda config --remove channels intel   # remove again

Use Intel Conda Packages

You can find the full list of Intel Python packages on the Intel web site.

You can install the core Intel Python stack:

conda install -c intel -n intelpython3 intelpython3_core

... with a special Python version:

conda install -c intel -n intelpython-3.6.5 intelpython3_core python=3.6.5

... with a Intel update version:

conda create -c intel -n intelpython-2018.0.3 intelpython3_core==2018.0.3

... or the full Intel Python stack:

conda create -c intel -n intelpython-2018.0.3 intelpython3_full==2018.0.3

... or just some Intel MKL optimized scientific software for the newest Intel 2019 version:

# installs scipy-1.1.0-np115py36_6
conda create -c intel -n scipy-1.1.0-np115py36_6 intelpython3_core=2019

Create Reproducible Conda Environments

For a more detailed environments documentation refer to the conda documentation.

Create an environment file for re-creation:

conda env export -n scipy-1.1.0-np115py36_6 -f scipy-1.1.0-np115py36_6.yml

Re-create saved environment:

conda env create -f scipy-1.1.0-np115py36_6.yml

Create a file with full URL for re-installation of packages:

conda list --explicit -n scipy-1.1.0-np115py36_6 >scipy-1.1.0-np115py36_6.txt

Install requirements file into environment:

conda create --name scipy-1.1.0 --file scipy-1.1.0-np115py36_6.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 delete an environment:

conda env remove -n scipy-1.1.0

To clone an existing environment:

conda create --name scipy-1.1.0 --clone scipy-1.1.0-np115py36_6

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==1.1.0
fuzzy version	scipy=1.1
greater equal	"scipy>=1.1"

For more information see cheat sheet below.

Example:

conda create -c intel -n scipy-1.1.0 scipy==1.1.0=np115py36_6

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-1.1.0-np115py36_6 --export >$( ws_find conda )/conda/envs/scipy-1.1.0-np115py36_6/conda-meta/pinned

Cheat Sheet

Conda official cheat sheet