Introduction

PIC offers a service for running Jupyter notebooks on CPU or GPU resources. This service is primarily thought for code developing rather than massive data processing. The usage is similar to running notebooks on your personal computer but offers the advantage of developing and testing your code on different hardware configurations.

Since the service is strictly thought for development and small scale testing tasks, there is a shutdown policy in place: 1) Your session will be closed and removed after 2 hours of idle keyboard. 2) The maximum duration for a session (idle or active) is limited to 48h. In practice that means that you should estimate the test data volume that you work with during a session to be able to be processed in less than 48 hours.

How to connect to the service

Got to jupyter01.pic.es to see your login screen.

Sign in with your PIC user credentials. This will prompt you to the following screen.

Here you can choose the hardware configuration for your Jupyter session. After choosing a configuration and pressing start the next screen will show you the progress of the initialisation process. Keep in mind that a job containing your Jupyter session is actually sent to the HTCondor queuing system and waiting for available resources before being started. This can take a bit but shouldn't be more than a minute.

In the next screen you can choose the tool that you want to use for your work: a Python notebook, a Python console or a plain bash terminal.

Your python environments should appear under Notebook and Console headers. In a later section we will show you how to create a new environment and to remove an existing one.

Terminate your session and logout

It is important that before you log out you terminate your job. In order to do so, go to the top page menu "File -> Hub Control Panel" and you will see the following screen.

Here click on the Stop My Server button. After that you can log out by clicking the logout button in the right upper corner.

Virtual environments with conda

On the homepage of your Jupyter session, click on the terminal button on the session dashboard on the right to open a bash terminal.

Initialize conda

If you use conda for the first time in your home , you have to initialize conda.

Fiirst, let's go on and activate the base environment:

[neissner@td110 ~]$ eval "$(/path/to/anaconda/bin/conda shell.bash hook)"

Initialize conda:

(base) [neissner@td110 ~]$ conda init

This actually changes the .bashrc file in your home directory in order to activate the base environment on login. To avoid that the base environment is activated every time you log on to a node, run:

(base) [neissner@td110 ~]$ conda config --set auto_activate_base false

For now you can deactivate the base environment and exit the terminal.

(base) [neissner@td110 ~]$ conda deactivate
[neissner@td110 ~]$ exit

Bind existing environments to your Jupyter sessions

If you want to know which environments are available for your project, please contact your project liaison at PIC. Although, you might find instructions on how to create your own environments, we would like to encourage the use of the predefined environments. The main reason is the sheer size of a conda environment which reaches easily several GB.

Log into Jupyter, start a session. From the session dashboard choose the bash terminal.

Inside the terminal, activate your environment:

[neissner@td110 ~]$ conda activate /path/to/predefined/environment
(/path/to/predefined/environment) [neissner@td110 ~]$

Install the ipykernel package and link it to the environment (it might already be installed):

(/path/to/predefined/environment) [neissner@td110 ~]$ conda install --prefix=/path/to/predefined/environment ipykernel
Collecting package metadata (current_repodata.json): done
Solving environment: done

# All requested packages already installed.

(/path/to/predefined/environment) [neissner@td110 ~]$ ipython kernel install --user --name=whatever_kernel_name
Installed kernelspec whatever_kernel_name in /nfs/pic.es/user/n/neissner/.local/share/jupyter/kernels/whatever_kernel_name

Deactivate your environment:

(/path/to/predefined/environment) [neissner@td110 ~]$ conda deactivate

Now you can exit the terminal. After refreshing the Jupyter page your whatever_kernel_name appears in the dashboard. In this example test has been used for whatever_kernel_name

Remove environment/kernel from Jupyter

Log onto Jupyter, start a session and from the session dashboard choose the bash terminal. To remove your environment/kernel from Jupyter run:

[neissner@td110 ~]$ jupyter kernelspec uninstall whatever_kernel_name
Kernel specs to remove:
  whatever_kernel_name                  /nfs/pic.es/user/n/neissner/.local/share/jupyter/kernels/whatever_kernel_name
Remove 1 kernel specs [y/N]: y
[RemoveKernelSpec] Removed /nfs/pic.es/user/n/neissner/.local/share/jupyter/kernels/whatever_kernel_name

Keep in mind that, although not available in Jupyter anymore, the environment still exists. Whenever you need it, you can link it again.

JupyterHub

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