Access and Setup
The JupyterHub service enables the interactive execution of JupyterLab on Daint.Alps on a single compute node.
The service is accessed at https://jupyter-daint.cscs.ch.
Once logged in, you will be redirected to the JupyterHub Spawner Options form, where typical job configuration options can be selected in order to allocate resources. These options might include the type and number of compute nodes, the wall time limit, and your project account.
Single-node notebooks are launched in a dedicated queue, minimizing queueing time. For these notebooks, servers should be up and running within a few minutes. The maximum waiting time for a server to be running is 5 minutes, after which the job will be cancelled and you will be redirected back to the spawner options page. If your single-node server is not spawned within 5 minutes we encourage you to contact us.
When resources are granted the page redirects to the JupyterLab session, where you can browse, open and execute notebooks on the compute nodes. A new notebook with Python 3 kernel can be created with the menu new and then Python 3 . Under new it is also possible to create new text files and folders, as well as to open a terminal session on the allocated compute node.
Debugging
The log file of a JupyterLab server session is saved on $SCRATCH in a file named jupyterhub_<jobid>.log. If you encounter problems with your JupyterLab session, the contents of this file can be a good first clue to debug the issue.
If you receive the error message Unexpected error while saving file: disk I/O error. it is possible that you have run out of disk quota. Quotas can be checked by issuing the command quota.
Accessing file systems
The Jupyter sessions are started in your $HOME folder. All non-hidden files and folders in $HOME are visible and accessible through the JupyterLab file browser. However, you can not browse directly to folders above $HOME. To enable access your $SCRATCH folder, it is therefore necessary to create a symbolic link to your $SCRATCH folder. This can be done by issuing the following command in a terminal from your $HOME directory:
ln -s $SCRATCH scratch
Alternatively, you can issue the following command directly in a notebook cell: !ln -s $SCRATCH scratch.
Creating Jupyter kernels for Python
A kernel, in the context of Jupyter, is a program that runs the user code within the Jupyter notebooks. Jupyter kernels make it possible to access virtual environments, custom python installations like anaconda/miniconda or any custom python setting, from Jupyter notebooks.
A kernel can be created from an active Python virtual environment with ipykernel :
. /myenv/bin/activate python -m ipykernel install --user --name=<your_env_name> --display-name "Python (<your_env_name>)"
Jupyter kernels are powered by ipykernel. As a result, ipykernel must be installed in every environment that will be used as a kernel. That could be done with pip install ipykernel.
Ending your interactive session and logging out
The Jupyter servers can be shut down through the Hub. To end a JupyterLab session, please select Control Panel under the File menu and then Stop My Server. By contrast, clicking Logout will log you out of the server, but the server will continue to run until the Slurm job reaches its maximum wall time.
MPI in the Notebook via IPyParallel and mpi4py
MPI for Python provides bindings of the Message Passing Interface (MPI) standard for Python, allowing any Python program to exploit multiple processors.
MPI can be made available on Jupyter notebooks through IPyParallel. This is a Python package and collection of CLI scripts for controlling clusters for Jupyter: A set of servers that act as a cluster, called engines, is created and the code in the notebook's cells will be executed within them.
We provide the python package ipcmagic to make easier the mangement of IPyParallel clusters. ipcmagic can be installed by the user with
pip install ipcmagic-cscs
The engines and another server that moderates the cluster, called the controller, can be started an stopped with the magic %ipcluster start -n <num-engines> and %ipcluster stop, respectively. Before running the command, the python package ipcmagic must be imported
import ipcmagic
Information about the command, can be obtained with %ipcluster --help.
In order to execute MPI code on JupyterLab, it is necessary to indicate that the cells have to be run on the IPyParallel engines. This is done by adding the IPyParallel magic command %%px to the first line of each cell.
There are two important points to keep in mind when using IPyParallel. The first one is that the code executed on IPyParallel engines has no effect on non-%%px cells. For instance, a variable created on a %%px-cell will not exist on a non-%%px-cell. The opposite is also true. A variable created on a regular cell, will be unknown to the IPyParallel engines. The second one is that the IPyParallel engines are common for all the user's notebooks. This means that variables created on a %%px cell of one notebook can be accessed or modified by a different notebook.
The magic command %autopx can be used to make all the cells of the notebook %%px-cells. %autopx acts like a switch: running it once, activates the %%px and running it again deactivates it. If %autopx is used, then there are no regular cells and all the code will be run on the IPyParallel engines.
This notebook shows a simple example of the usage of ipcmagic. It can be run in one or multiple nodes.
Further details on IPyParallel: https://ipyparallel.readthedocs.io/en/latest/
Examples of notebooks with ipcmagic can be found here.
Further details on MPI for Python (mpi4py): https://mpi4py.readthedocs.io/en/stable/