Jupyter Notebooks
It is extremely common for data scientists and researchers to use Jupyter notebooks for exploratory data analysis. Rafay GPU PaaS provides a turnkey experience for users with a "Notebook as a Service" type experience.
Rafay also provides a number of profiles for notebooks that allow users to spin up environments preloaded with the required libraries and frameworks.
Create Notebook¶
To create a notebook, access the Developer Hub and navigate to the home page. The page provides options to create and manage notebooks, which are predefined configurations designed to deliver interactive environments for tasks such as data analysis, visualization, and machine learning development. On the Developer Hub home page, users can either click on View All to access the Notebooks page or click on New Notebook to create a new notebook. Users can also click on the Notebooks menu on the left to directly access the Notebooks page.
New Notebook¶
To create a new notebook,
- Select Notebooks from the menu on the left of the console
- Click on New notebook
- Select a suitable notebook service profile that suits your requirements
- Provide a name for the notebook with an optional description
- Select the compute instance from the drop down you would like to deploy the notebook to
- Provide the other details and click on Deploy
It can take a few minutes for the notebook and associated software components to be deployed and ready for use.
Info
Users can deploy multiple notebooks on an instance. The only constraint is whether the underlying instance has the resources required for all the notebooks.
- The notebook initially displays a status of In Progress. Upon successful deployment, the status updates to Success
Notebook Profiles¶
A notebook profile maps to a notebook pre-installed and pre-configured with the required frameworks, libraries and software add-ons that match the profile. Profiles allow the data scientist to start using the notebook right away instead of wasting time trying to install and configure all the software on top of the notebook.
| Profile | Description |
|---|---|
| Minimal | Basic libraries and frameworks only |
| Data Science | With common libraries for data science |
| Spark | With libraries required for Spark |
| Tensorflow | With Tensorflow libraries |
| Tensorflow with CUDA | With Tensorflow and CUDA libraries |
| PyTorch | With PyTorch libraries |
| PyTorch with CUDA | With PyTorch and CUDA libraries |
Use Notebook¶
Once a notebook has been successfully deployed onto a compute instance, the user is presented with the URL for the notebook and a token (authentication credentials) to securely access the notebook. To access the notebook, the user can either click on the URL or copy/paste it into a web browser.
They need to then provide the access token as a credential before they can access the Jupyter notebook.
View Notebook¶
Clicking on the notebooks menu will list of all the notebooks the user has access to. Note that notebooks may span different workspaces and different instances. To view details about a specific notebook, users just need to click on the name of the notebook.
Delete Notebook¶
To delete a notebook, users should click on the ellipses on the far right of the selected notebook and select delete.
Info
Once deletion has been initiated, it cannot be stopped or reversed. Users can create a new notebook if required.