The Jupyter Notebook Service

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Jupyter is a project for development of open-source software, standards, and services for interactive computation across multiple programming languages. The Platform comes preinstalled with the JupyterLab web-based user interface, including Jupyter Notebook and JupyterLab Terminals, which are available via a Jupyter Notebook user application service.

Jupyter Notebook is an open-source web application that allows users to create and share documents that contain live code, equations, visualizations, and narrative text; it's currently the leading industry tool for data exploration and training. Jupyter Notebook supports integration with all key analytics services, enabling users to perform all stages of the data science flow, from data collection to production, from a single interface using various APIs and tools to concurrently access the same data without having to move the data. Your Jupyter Notebook code can execute Spark jobs (for example, using Spark DataFrames); run SQL queries using Trino; define, deploy, and trigger Nuclio serverless functions; send web-API requests; use pandas and V3IO Frames DataFrames; use the Dask library to scale the use of pandas DataFrames; and more. You can use Conda and pip, which are available as part of the Jupyter Notebook service, to easily install Python packages such as Dask and machine-learning and computation packages. In addition, you can use Jupyter terminals to execute shell commands, such as file-system and installation commands. As part of the configuration of the platform's Jupyter Notebook service you select a specific Jupyter flavor and you can optionally define environment variables for the service.

Iguazio provides tutorial Jupyter notebooks with code examples ranging from getting-started examples to full end-to-end demo applications, including detailed documentation. Start out by reading the introductory welcome.ipynb notebook (available also as a Markdown file), which is similar to the introduction on the documentation site. Then, proceed to the getting-started tutorial.

Configuring the Service

Pod Priority

Pods (services, or jobs created by those services) can have priorities, which indicate the relative importance of one pod to the other pods on the node. The priority is used for scheduling: a lower priority pod can be evicted to allow scheduling of a higher priority pod. Pod priority is relevant for all pods created by the service.
Eviction uses these values to determine what to evict with conjunction to the pods priority. See more details in Interactions between Pod priority and quality of service.

Pod priority is specified through Priority classes, which map to a priority value. The priority values are: High, Medium, Low. The default is Medium.

Configure the default User functions default priority for a service, which is applied to the service itself or to all subsequently created user-jobs in the service's Common Parameters tab, User jobs defaults section, Priority class drop-down list.

Jupyter Flavors

You can set the custom Flavor parameter of the Jupyter Notebook service to one of the following flavors to install a matching Jupyter Docker image:

Jupyter Full Stack
A full version of Jupyter for execution over central processing units (CPUs).
Jupyter Full Stack with GPU
A full version of Jupyter for execution over graphics processing units (GPUs). This flavor is available only in environments with GPUs and is sometimes referred to in the documentation as the Jupyter "GPU flavor". For more information about the platform's GPU support, see Running Applications over GPUs.

This parameter is in the Custom Parameters tab of the service.