Operators Guide#
These pages are targeted at operators that need to deploy and configure a Jupyter Enterprise Gateway instance.
Use cases
As an operator, I want to fix the bottleneck on the Jupyter Kernel Gateway server due to large number of kernels running on it and the size of each kernel (spark driver) process, by deploying the Enterprise Gateway, such that kernels can be launched as managed resources within a Hadoop YARN cluster, distributing the resource-intensive driver processes across the cluster, while still allowing the multiple data analysts to leverage the compute power of a large cluster.
As an operator, I want to constrain applications to specific port ranges so I can more easily identify issues and manage network configurations that adhere to my corporate policy.
As an operator, I want to constrain the number of active kernels that each of my users can have at any given time.
Deploying Enterprise Gateway#
The deployment of Enterprise Gateway consists of several items, depending on the nature of the target environment. Because this topic differs depending on whether the runtime environment is targeting containers or traditional servers, we’ve separated the discussions accordingly.
Container-based deployments#
Enterprise Gateway includes support for two forms of container-based environments, Kubernetes and Docker.
Server-based deployments#
Tasks for traditional server deployments are nearly identical with respect to Enterprise Gateway’s installation and invocation, differing slightly with how the kernel specifications are configured. As a result, we marked those topics as “common” relative to the others.
Configuring Enterprise Gateway#
Jupyter Enterprise Gateway adheres to Jupyter’s common configuration approach . You can configure an instance of Enterprise Gateway using a configuration file (recommended), via command-line parameters, or by setting the corresponding environment variables.