How To Run A Python Notebook#

Introduction#

Jupyter is provided as a service in Hopsworks, providing the same user experience and features as if run on your laptop.

Supports JupyterLab and the classic Jupyter front-end
Configured with Python and PySpark kernels

Step 1: Jupyter dashboard#

The image below shows the Jupyter service page in Hopsworks and is accessed by clicking Jupyter in the sidebar.

From this page, you can configure various options and settings to start Jupyter with as described in the sections below.

Step 2 (Optional): Configure resources#

Next step is to configure Jupyter, Click edit configuration to get to the configuration page and select Python.

Container cores: Number of cores to allocate for the Jupyter instance
Container memory: Number of MBs to allocate for the Jupyter instance

Configured resource pool is shared by all running kernels. If a kernel crashes while executing a cell, try increasing the Container memory.

Resource configuration for the Python kernel

Click Save to save the new configuration.

Step 3 (Optional): Configure environment, root folder and automatic shutdown#

Before starting the server there are three additional configurations that can be set next to the Run Jupyter button.

The environment that Jupyter should run in needs to be configured. Select the environment that contains the necessary dependencies for your code.

The runtime of the Jupyter instance can be configured, this is useful to ensure that idle instances will not be hanging around and keep allocating resources. If a limited runtime is not desirable, this can be disabled by setting no limit.

The root path from which to start the Jupyter instance can be configured. By default it starts by setting the /Jupyter folder as the root.

Step 4: Start Jupyter#

Start the Jupyter instance by clicking the Run Jupyter button.

Starting Jupyter and running a Python notebook

Going Further#

You can learn how to install a library so that it can be used in a notebook.