/tag/jupyter
Clear OOD Files
To clear OOD Session files, Rivanna will need to be accessed via a terminal. See documentation for information on how to access via SSH.
You can find the session files and logs for all Open on Demand apps at:
~/ondemand/data/sys/dashboard/batch_connect/sys Under this directory you will see subdirectories for the Open on Demand applications that you have used before. Under each sub directory you can find the files that are created when you launch a new session.
To quickly clear all session files for OnDemand from your /home directory run:
rm -rf ondemand Other directories related to Open on Demand such as .
Ivy Secure Environment
Ivy is a secure computing environment for researchers consisting of virtual machines (Linux and Windows). Researchers can use Ivy to process and store sensitive data with the confidence that the environment is secure and meets HIPAA, FERPA, CUI or ITAR requirements.
Facilities Statement - Are you submitting a grant proposal and need standard information about UVA research computing environments? Get it here. Overview Ivy consists of both virtual computing environments and secure storage. In order to obtain access to either system, users must
Submit an account request, Complete the Information Security Awareness Training, and Ensure their personal computer meets all High Security VPN requirements.
Converting a Jupyter Notebook to a Python Script
Sometimes it may be useful to convert a Jupyter notebook into a Python executable script. Once your notebook is opened in OOD you can select File > Export Notebook As … > Export Notebook to Executable Script:
This will download a Python executable with a ‘.py’ extension into your local computer’s Downloads folder. Your notebook may also show “Download as” instead of “Export Notebook As …”. Either of these selections will allow you to download a Python executable.
This script can be copied to Rivanna in the working directory where JupyterLab was accessing the notebook. Information on transferring files to and from Rivanna can be found here.
Converting a Jupyter Notebook to a PDF
Users cannot load modules inside the OpenOnDemand App for JupyterLab. Therefore it is not possible to convert a Jupyter Notebook to a PDF directly inside the JupyterLab Interactive App on OpenOnDemand.
There are 2 ways to convert a Jupyter Notebook to a PDF:
Directly from the command line. ssh from your terminal and type the following: module load anaconda/2020 texlive jupyter nbconvert –to pdf you_script.ipynb If you want to use GUI, please request a desktop session.
Fill out the form as you normally would for JupyterLab. After you get to a desktop, open a terminal (black box next to Firefox in the top bar) and type these commands: module load anaconda/2020 texlive jupyter notebook This will pull up JupyterLab.
Loading Module in Jupyter
Users cannot load modules inside a JupyterLab session. If you need access to modules, please request a desktop session instead of JupyterLab. Fill out the form as you normally would for JupyterLab. After you get to a desktop, open a terminal (next to Firefox in the top bar) and type these commands:
module load anaconda module load … # your modules here jupyter-lab This should start up Firefox shortly. If you accidentally close the window, right click on the link in the terminal and choose “open link” to restart.
An example of using LaTeX inside a JupyterLab session is shown in the screenshot below.
Custom Jupyter Kernels
You can create custom kernels from an Anaconda environment or an Apptainer container.
In both cases you’ll need to install the ipykernel package.
Jupyter kernel based on Anaconda environment To create a custom kernel of the Anaconda environment myenv that uses Python 3.7:
module load anaconda conda create -n myenv python=3.7 ipykernel <other_packages> source activate myenv python -m ipykernel install –user –name myenv –display-name "My Env" Note:
You can customize the display name for your kernel. It is shown when you hover over a tile in JupyterLab. If you do not specify a display name, the default Python [conda env:<ENV_NAME>] will be shown.
