---
title: Jupyterhub on Merlin
#tags:
#keywords:
last_updated: 31 July 2019
#summary: ""
sidebar: merlin6_sidebar
permalink: /merlin6/jupyterhub.html
---

Jupyterhub provides [jupyter notebooks](https://jupyter.org/) that are launched on
cluster nodes of merlin and can be accessed through a web portal.

## Accessing Jupyterhub and launching a session

The service is available inside of PSI (or through a VPN connection) at


**<https://merlin-jupyter.psi.ch:8000>**


   1. **Login**: You will be presented with a **Login** web page for
      authenticating with your PSI account.
   1. **Spawn job**: The **Spawner Options** page allows you to
      specify the properties (Slurm partition, running time,...) of
      the batch jobs that will be running your jupyter notebook. Once
      you click on the `Spawn` button, your job will be sent to the
      Slurm batch system. If the cluster is not currently overloaded
      and the resources you requested are available, your job will
      usually start within 30 seconds.
   






## Jupyter software environments - running different kernels

Your notebooks can run within different software environments which are offered by a number of available **Jupyter kernels**.

E.g. in this test installation we provide two environments targeted at data science
  * **tensorflow-1.13.1_py37**: contains Tensorflow, Keras, scikit-learn, Pandas, numpy, dask, and dependencies. Stable
  * **talos_py36**: also contains the Talos package. This
    environment is experimental and subject to updates and changes.

When you create a new notebook you will be asked to specify which kernel you want to use. It is also possible to switch the kernel of a running notebook, but you will lose the state of the current kernel, so you will have to recalculate the notebook cells with this new kernel.

These environments are also available for standard work in a shell session. You can activate an environment in a normal merlin terminal session by using the `module` (q.v. [using Pmodules](using-modules.html)) command to load anaconda python, and from there using the `conda` command to switch to the desired environment

```
module use unstable
module load anaconda/2019.07
conda activate tensorflow-1.13.1_py36
```

When the `anaconda` module has been loaded, you can list the available environments by executing

```
conda info -e
```

You can get more info on the use of the `conda` package management tool at its official [https://conda.io/projects/conda/en/latest/commands.html](documentation site).

## Using your own custom made environments with jupyterhub
Python environments can take up a lot of space due to the many dependencies that will be installed. You should always install your extra environments to the data area belonging to your account, e.g. `/data/user/${YOUR-USERNAME}/conda-envs` 

In order for jupyterhub (and jupyter in general) to recognize the provided environment as a valid kernel, make sure that you include the `nb_conda_kernels` package in your environment. This package provides the necessary activation and the dependencies.

Example:
```
conda create -c conda-forge -p /data/user/${USER}/conda-envs/my-test-env python=3.7 nb_conda_kernels

```

After this, your new kernel will be visible as `my-test-env` inside of your jupyterhub session.



## Troubleshooting

   In case of problems or request, please either submit a **[PSI Service Now](https://psi.service-now.com/psisp)** incident containing *"Merlin Jupyterhub"* as part of the subject, or contact us by mail through <merlin-admins@lists.psi.ch>.