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Conda environments

Aim: Provide information about how to install software without root privileges and/or need to install a custom kernel for a Jupyter Notebook.

Target audience: Users who need to specific software environments.


Conda is an open source package and environment manager. It helps find and install packages without needing root privileges (i.e., with a simple conda install...). Conda environments can be extremely useful when building software. It is part of the Anaconda package which is available on all CPU and GPU interactive nodes]. Anaconda is a software distribution tool for Python packages.1

Conda is also useful when adding customized kernels to JupyterLab sessions (the next-generation of a Jupyter Notebook) and Jupyter Notebooks, which can be used from the Nikhef JupyterHub service.


Note on software toolchains

Software toolchains (of which many LHC experiments and non-LHC experiments have their own toolchains) consist of a number of pieces of software required to build other software. This generally comprises the C/C++ libraries; a C/C++ compiler; tools to handle, load and execute binary files and a set of kernel headers.

For example the default toolchain available on machines running CentOS7 consists of:

gcc 4.8.5
glibc 2.17
binutils 2.27
kernel-headers 3.10.0
If support for C++11 or later is required, this toolchain is too old, and a newer one needs to be used.

To keep up with demands on the toolchain of various dependencies of python packages, Anaconda ships its own (modern) toolchain.

Using Conda on your laptop

To install anaconda, the Miniforge version is recommended, which sets conda-forge as the default channel. Download an installer from this page, and install it on your laptop; for example:

> wget

> bash -b -p /somewhere/on/your/laptop

The final step of the conda installation will modify your shell startup scripts to automatically make the conda command available. You will have to log out and back in to see these changes.

Creating a virtual environment with Conda

Because the software environment can be regenerated, we recommend creating a virtual environment (venv) in your group directory on /data instead of /project or your home directory.

The python version you need can also be specified when creating the venv:

> conda create --prefix /data/your_project/your_username/my_venv python=3.9
It may happen that some of the packages that you would like to install into the environment are only available for certain python versions, so pay attention when you create the venv.

Once the virtual environment has been created, you can activate it using:

> conda activate /data/your_project/your_username/my_venv

Installing Python packages inside the conda environment

Once the virtual environment has been created and activated, it is ready to be used. The first step is usually to install additional software into the environment. There are two ways of doing this: with conda and with pip. The rule of thumb is: try with conda first and if a package is not available, try with pip.

The main reason for this ordering is that for some packages pip uses the C/C++/Fortran toolchain to build libraries that are then loaded into python. It, however, doesn’t know about the (newer) toolchain installed by conda and will use the one on the (CentOS 7) host instead. For quite a few packages this toolchain is too old and the build will fail. When packages are installed with conda, they have been built using the conda toolchain and will therefore work anyway.

Installing packages via conda

To install additional software into the virtual environment using conda itself: activate it and install:

> conda activate /data/your_project/your_username/my_venv
> conda install root
Packages available in conda can be searched using:
> conda search root

Installing packages via pip

To install additional software into the virtual environment with pip: activate it, install pip itself and then use it to install other software; e.g.:

> conda activate /data/your_project/your_username/my_venv
> conda install pip
> pip install keras
> pip install tensorflow-gpu

Using the software in the new venv

Once things are installed, they can be used directly:

> python
Python 3.9.7 (default, Sep 16 2021, 13:09:58)
[GCC 7.5.0] :: Anaconda, Inc. on linux
Type "help", "copyright", "credits" or "license" for more information.
>>> from ROOT import gROOT
>>> gROOT.GetVersion()


  • Email for help setting up virtual or conda environments.

  1. The distribution happens through so-called "channels". The default channel is administered by the developers of Anaconda: Anaconda Inc. There is also a community-managed channel called conda-forge and anybody can create and use their own channel.