JupyterLab + SoS Suite setup
Operating OS requirement
The instructions on this page are tested and known to work for Linux and MacOS. It has not been tested on Windows. Although with some efforts it might work for Windows, using Windows your every day computational biology research is discouraged. If you don’t have access to other types of OS, an alternative is to set up a Linux OS under your Windows OS using Windows Subsystem for Linux.
Install miniconda3 the Python development environment
We recommend using
anaconda and customize your installation as needed after install this minimal version of
To install please follow instructions on this page. Please go for
You can download the installer via command tool if you are on a Linux server without graphical interface. For example:
Or, download it and upload to the server using command tools such as
scp. Then run:
to install. After following the prompts in the installation process you should find in
~/.bash_profile a line like this:
source ~/.bashrc (or
source ~/.bash_profile) to load the changes. To verify you’ve installed it successfully:
It should show the path as
After you successfully installed the latest version of
miniconda3, please follow prompts below to setup
a JupyterLab + SoS Suite environment for daily computing.
Note: maybe you already have a version of
miniconda on your computer. If you are very familiar with
conda then please try to work with your existing version by either upgrading or create separate
env under it to install additional packages. You might also want to start afresh and retire your older version (but keep the installation around for a while just in case). A simple approach is to rename your
miniconda3 folder to, say
miniconda3_bak, and install the new
pip for package installation
miniconda there are two ways to install Python packages: either using
conda install or
pip install. We will provide instructions for both methods below but you only have to choose one approach: either
pip but not both.
I wouldn’t discuss too much details on what each does and pros and cons. I’d just say that:
- it is recommended to consistently use either
pipand not a combination of them
- for those savvy in Python and in package management in general, I recommend using
conda. For novices perhaps
- Do not use
condato install R and R packages: from my experience, this is not recommended — it creates more issues than convenience at least to me. On a cluster you can try to load the R software that the cluster system has already installed, then install packages to your home directory. You should be asked to set or confirm the path to install R packages to in your
Note: if the installation commands below generates timeout errors on your cluster system,
- On Columbia CUMC cluster, you need to run the commands below to set network proxy:
export http_proxy=http://bcp3.cumc.columbia.edu:8080 export https_proxy=http://bcp3.cumc.columbia.edu:8080
- If you are in China you might need to try a different mirror, depending on your location. For example use a mirror at Tsinghua University,
pip install -i https://pypi.tuna.tsinghua.edu.cn/simple ..., may help. You can also configure
pypito use alternative mirrors by default.
pip installation for SoS, JupyterLab and kernels
pip install notebook jupyterlab jupyter_contrib_nbextensions
pip install docker markdown wand graphviz imageio pillow nbformat feather-format --no-cache-dir pip install sos sos-notebook sos-r sos-pbs sos-python sos-bash -U --no-cache-dir python -m sos_notebook.install pip install jupyterlab-sos -U --no-cache-dir
pip install bash_kernel --no-cache-dir python -m bash_kernel.install
pip install markdown-kernel --no-cache-dir python -m markdown_kernel.install
I recommand against installing R via
conda unless you are familiar with the setup – in short, (as of 2019) the default configuration can cause various issues for other packages.
To install R kernel for Jupyter after you installed R,
R --slave -e "IRkernel::installspec()"
If you get a complaint that
IRkernel package is not available, please install it in R, eg
install.packages('IRkernel'), before you run the command above.
nbdime to work with git
This will override the default
git diff and display better the changes to IPython notebooks
pip install nbdime nbdime config-git --enable --global
conda installation for SoS, JupyterLab and kernels
You can ignore this section if you already installed everything using
pip as shown above
You can install JupyterLab with SoS using commands below. It will automatically install the
sos if needed.
conda install jupyterlab-sos -c conda-forge
You will need to install nodejs>=12.0.0 to upgrade JupyterLab extensions. To install a specific version just type:
conda install nodejs==15.12.0 -c conda-forge
To install the kernels, type:
conda install sos-r sos-python sos-pbs sos-bash markdown-kernel -c conda-forge
Finally upgrade your extensions to the latest version by typing:
jupyter labextension update --all
At this point everything you need should be installed.
What if Jupyter kernels keep dying?
This happened to us several times, and solution on this ticket was the rescue.
Notice: docker cannot be installed on many HPC cluster environments due to security reasons. Please skip this step if you are on the cluster. We may use
singularity instead of
docker to run some applications on cluster. But still having docker configured on your laptop or desktop computer can be useful.
We use Docker a lot running various software that are hard to install. SoS also provides an interface to run Docker images.
To install Docker on Linux,
- Run commands below:
curl -fsSL get.docker.com -o get-docker.sh sudo sh get-docker.sh sudo usermod -aG docker $USER
- Log out and log back in (no need to reboot computer)
To install it on MacOS, visit https://www.docker.com/products/docker-desktop and download & install the Docker Desktop installer.