Datalayer 0.3.0 Black Snake
Datalayer 0.3.0 Black Snake is released on December 15, 2023.
- Users authenticate and run remote Kernels in Docker, Devcontainers or Datalayer Run.
- Devops manage SSH keys, virtual machines and Kubernetes clusters on OVHcloud and can SSH from JupyterLab.
- Developers have easier interaction with cloud resources, can inspect and expose JupyterLab components with React.js.
Black Snake Pantherophis alleghaniensis, commonly called the eastern rat snake, is a species of nonvenomous snake in the family Colubridae. The species is endemic to North America. Additional common names for P. alleghaniensis include black rat snake, pilot snake, pilot black snake, chicken snake; and in Florida, yellow rat snake and Everglades rat snake.
Datalayer is the base foundation package used by many other Datalayer packages.
It is also the meta package to get the other Datalayer packages installed.
It also ships a JupyterLab extension to inspect the JupyterLab's internal strutures like plugin graph, file types, models and widgets.
Clouder is a JupyterLab extension to interact with cloud services.
Devops can manage SSH Keys, virtual machines and Kubernetes clusters.
OVHcloud is supported for now. More cloud support is planned in subsequent Datalayer releases.
NbModel is a collaborative and extensible data model on top of Jupyter NbFormat.
It is used as foundation for Jupyter RTC.
Jupyter React is a set of React.js components that allow a frontend developer to build data products compatible with the Jupyter ecosystem. The user interface delivers executable notebooks and cells. This release provides:
- Better IPyWidgets support with bundled and externals props on the Notebook component.
- Support the display of Jupyter React components in a JupyterLabApp component providing out-of-the-box suppoort for any JupyterLab extension.
- More configuration to inform if the component is displayed in JupyterLab and/or JupyterHub.
Jupyter Viewer provides React.js components and a JupyterLab extension to render Notebooks without any Kernel.
It can be seen as the modern version of the existing NbViewer solution.
Developers can create static version of the Notebook. If needed, Users can then connect that static artifcat to a Kernel to make it even more interactive.
This release add support for IPyWidgets.
Jupyter Docker allows you to manage Docker from JupyterLab.
Users view the current Docker Images, Containers, Volumes, Networks and Secret, start a Container from an Image and stop running Containers.
Jupyter Containers allows Users to start and stop Jupyter Servers with various extensions and kernels in containers from JupyterLab.
This is useful if to offload your analysis from your local operating system to a container.
Jupyter Devcontainers brings the Visual Studio Code Devcontainers features to JupyterLab.
Jupyter Kubernetes is a JupyterLab that allows you to manage Kubernetes from Jupyter. Users can visually access the Kurbernetes objects.
It is like the official K8S Dashboard (developed with Angular.js) but as React.js components and JupyterLab extension.
Users can connect to remote servers with SSH from JupyterLab.
Jupyter IAM is responsible for the identity and access management to the Jupyter experience developed by Datalayer.
Jupyter Content allows Users to create, access and share code files (Notebooks, Markdown...) and datasets persisted in local drives, distributed file systems or cloud buckets.
Jupyter Environments allow Users to define the packages and libraries (Python...) that are used in their Kernels.
An Environment is also the place where Users define their Secrets and Environment Variables.
Jupyterpool is a pool of Jupyter Kernels that are pre-warmed with code and pre-mounted with content. It solves the cold-start Kernel issue.
Jupyterpool serves user interfaces or processes eager to consume code execution from Kernels.
A set of available Kernels is maintained on a Kubernetes cluster. Those Kernels can be configured to pre-run defined code before being available and have mounted local or remote drives. A defined number of available Kernels (min and max) is guaranteed by Jupyterpool.
Jupyter Kernels delivers fully managed Jupyter Kernels in the cloud.
Users create, start, pause, restart, and terminate their Kernels.
We plan to support Kernel culling, backup, restore, collaboration and teleport in subsequent Datalayer releases.