Kubeflow Notebooks

What it does

Access control

Notebook access control is managed by Kubeflow RBAC, which the docs say enables notebook sharing across an organization.checked 9 Oct 2026

Container images

Official example images include JupyterLab with PyTorch or TensorFlow, RStudio with Tidyverse, and code-server with Conda Python.checked 9 Oct 2026

Custom image requirements

A compatible custom image must expose HTTP on port 8888, run as user jovyan with home directory /home/jovyan and UID 1000, and start with an empty PVC mounted there.checked 9 Oct 2026

Deployment

Kubeflow Notebooks can be deployed as a standalone subproject or as part of a Kubeflow distribution.checked 9 Oct 2026

Image requirements

Custom notebook images must expose an HTTP interface on port 8888, run as user jovyan with UID 1000, and start with an empty PVC mounted at /home/jovyan.checked 8 Oct 2026

In-cluster environments

Users can create notebook containers directly in the cluster instead of running them locally on their workstations.checked 8 Oct 2026

Intended users

Kubeflow describes its broader platform as serving AI practitioners, platform administrators, and decision-makers with tools for data, AI/ML, and HPC workloads.checked 8 Oct 2026

Maturity

The installation guide lists Kubeflow Notebooks among graduated projects, which it describes as stable and ready for general availability.checked 9 Oct 2026

Notebook interfaces

It natively supports JupyterLab, RStudio, and Visual Studio Code through code-server.checked 8 Oct 2026

Organization images

Administrators can provide standard notebook images with required packages pre-installed, and users can also select a custom Docker image.checked 9 Oct 2026

Package persistence

Packages installed after a notebook starts last only for the lifetime of its pod unless installed in a PVC-backed directory; custom images are another way to preserve packages across pod restarts.checked 9 Oct 2026

Project governance

Kubeflow is part of the Cloud Native Computing Foundation and follows its governance structure.checked 8 Oct 2026

Purpose

Kubeflow Notebooks runs interactive development environments for AI, ML, and data workloads on Kubernetes.checked 9 Oct 2026

Related beta project

Kubeflow Workspaces is described as the next generation of Notebooks (Notebooks v2) and is beta software intended for staging and development clusters.checked 9 Oct 2026

Resource configuration

Users can set CPU and RAM requests, mount workspace and data volumes, and optionally request GPUs when creating a notebook server.checked 8 Oct 2026

Resources and storage

When creating a notebook server, users can specify CPU, RAM, a workspace volume, optional data volumes, and optional GPU requests.checked 9 Oct 2026

Runs in cluster

Notebook containers run directly in a Kubernetes cluster rather than on users’ workstations.checked 9 Oct 2026

Secrets configuration

Notebook configurations can match PodDefault resources in a profile namespace, including a documented example for mounting a GCP credential secret.checked 8 Oct 2026

Support

The project’s troubleshooting guide recommends checking Notebook and Pod events, Pod YAML, and Pod logs when a notebook does not start.checked 9 Oct 2026

Supported IDEs

It natively supports JupyterLab, RStudio, and Visual Studio Code through code-server; the docs say other web-based IDEs should also work.checked 9 Oct 2026