JupyterLab + SoS Suite setup

Operating OS requirement

The instructions on this page are tested and known to work for Linux and MacOS. 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. Here is a video made by a lab member to demonstrate installing WSL on a Windows machine.

If you use Debian based Linux desktop (Debian or Ubuntu) here are some recommendations on setting up your machine.

For Neurology HPC users, you need to add these commands below to your ~/.bashrc and then source it to set network proxy. Open a text editor to open ~/.bashrc, add to it:

export http_proxy=http://menloproxy.cumc.columbia.edu:8080
export https_proxy=http://menloproxy.cumc.columbia.edu:8080

and type source ~/.bashrc to load the changes.

Purge previous installations

This is an optional step only necessary for those who had installed various software previously and now would like to start from scratch.

First, remove the miniconda3 installation by:

rm -rf ~/miniconda3

Then make a back up of ~/.bashrc by:

mv ~/.bashrc ~/.bashrc_backup

and install a new copy of it:

cp /etc/skel/.bashrc ~/.bashrc

Make sure you add the http_proxy and https_proxy to this new file, see section above.

Finally if you want to purge your local R packages:

rm -rf ~/R/*

At this point, it is recommended to log out then log back in to refresh the computing environment.

Install miniconda3 the Python development environment

We recommend using miniconda over anaconda and customize your installation as needed after install this minimal version of conda. To install please follow instructions on this page. Please go for miniconda3.

You can download the installer via command tool if you are on a Linux server without graphical interface. For example:

wget https://repo.anaconda.com/miniconda/Miniconda3-latest-Linux-x86_64.sh

Or, download it and upload to the server using command tools such as scp. Then run:

bash Miniconda3-latest-Linux-x86_64.sh

to install. After following the prompts in the installation process you should find in ~/.bashrc or ~/.bash_profile a line like this:

export PATH=$HOME/miniconda3/bin:$PATH

and type source ~/.bashrc (or source ~/.bash_profile) to load the changes. To verify you’ve installed it successfully:

which python

It should show the path as $HOME/miniconda3/bin/python.

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 anaconda or 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 miniconda3.

Note It is of crucial importance that after the installation of conda, conda init command shall not be ran for reason outlined in issue #3.

conda vs pip for package installation

With 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 conda or pip but not both.

I wouldn’t discuss too much details on what each does and pros and cons. I’d just say that:

  1. it is recommended to consistently use either conda or pip and not a combination of them
  2. for those savvy in Python and in package management in general, I recommend using pip over conda.
  3. Do not use conda to 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 HOME directory.
  4. Do not use conda activate or conda init on the cluster, the codes introduced by it in .bashrc has proven to cause a wide array of problem including segmentation fault or compilation errors.

Note: if the installation commands below generates timeout errors on your cluster system, please check and make sure your system can access the internet and also pipy repo.

pip installation for SoS, JupyterLab and kernels

Base notebook

pip install notebook jupyterlab jupyter_contrib_nbextensions

SoS Suite

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

Bash kernel

pip install bash_kernel --no-cache-dir
python -m bash_kernel.install

Markdown kernel

pip install markdown-kernel --no-cache-dir
python -m markdown_kernel.install 

R kernel

You need to install R first. Here are some tips for Debian based Linux (possibly outdated). For MacOS you can download the R software installer from CRAN and install from there.

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.

Note On our cluster, R is pre-installed and specific version of R needs to be loaded, e.g. module load R/, you can check the avaliable modules with command module aval. After module loaded, enter the R interface and install the package needed.

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 transient-display-data extension, jupyterlab, sos-notebook, and 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.

Install Docker

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.