Polyaxon makes it easy to start a JupyterLab session on your GPU cluster.
Polyaxon schedules JupyterLab sessions based on this component.
polyaxon run --hub jupyterlab
To provide a specific tag:
polyaxon run --hub jupyterlab:TAG_VERSION
To start the session on a different project
polyaxon run --hub jupyterlab -p project-name
Go to the UI under the
polyaxon ops dashboard [-uid] [-p]
Or to get to the service directly:
polyaxon ops service [-uid] [-p]
Or to get the service in full-screen mode:
polyaxon ops service --external [-uid] [-p]
You can provide more information before scheduling the service, like the queue, presets, …
polyaxon run --hub jupyterlab -q agent-name/queue-name --presets preset-name1,preset-name2
polyaxon run --hub jupyterlab -f path/to/preset.yaml
You can also provide a full operation manifest to customize the environment section, node selector, connections, initializers, resources requirements, …
version: 1.1 kind: operation hubRef: jupyterlab runPatch: init: - git: ... connections: [...] environment: ... container: resources: requests: memory: 300Mi
If you need to expose the
jupyterlab component with your predefined configuration without requiring end-users to create operations,
we suggest that you clone the component and customize it.
On Polyaxon CE, you will need to create a new
.yaml file where you will host the content of the component, and users can either start new sessions using:
polyaxon run -f my-custom-jupyterlab.yaml
polyaxon run --url https://path/to/my-custom-jupyterlab.yaml
On Polyaxon Cloud or Polyaxon EE, you just need to add a new component hub.
The end users will need to run with
org-name/jupyterlab instead of
polyaxon run --hub acme/jupyterlab
Note: In order to use
:tagyou need to name the version
jupyterlab versions can be found on the component hub
polyaxon hub ls -c jupyterlab