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Contents:

  • Destination Earth Data Lake Introduction
  • Discovery and Data Access
  • Edge Services - Big Data Processing Services
  • Interfaces (Endpoints)
  • DestinE Platform for DEDL users
  • My DataLake Services
  • Working with AI in the Data Lake
    • MLOps Studio
      • Access the MLOps Studio workspace
      • Create and manage a MLOps Studio namespace
      • Download and use kubeconfig for a MLOps Studio namespace
      • Use the MLOps Studio dashboard
      • Notebook Servers
      • Access a MLOps Studio notebook environment with Visual Studio Code Remote Tunnels
      • Access a MLOps Studio notebook environment with kubectl port-forward
      • Choose how to run Python workloads in MLOps Studio
      • Run Python code in a MLOps Studio notebook environment
      • Run Python code as a MLOps Studio PyTorchJob
      • Access MLOps Studio Pipelines API from outside the cluster
      • MLOps Studio Pipelines: Accessing Model Registry via Istio
      • Use DEDL standalone S3 object storage with KServe
      • Choosing Gloo or NCCL for Distributed PyTorch Training in PyTorchJob
      • Known limitations in MLOps Studio
      • MLOps Studio Scenarios
        • General MLOps Studio acceptance test scenarios
        • Katib scenarios
        • KServe
        • MLOps Studio Trainer
        • Model Registry
        • Notebooks
        • Pipelines
        • Security - preventing container escape on a shared Kubernetes / Kubeflow cluster
        • Spark
        • MLOps Studio TensorBoard PVC Smoke Test
  • FAQs
  • Privacy Notice
  • Terms and Conditions
Destination Earth Data Lake
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  • Working with AI in the Data Lake »
  • MLOps Studio »
  • MLOps Studio Scenarios »
  • Notebooks
  • DestinE Data Lake portal

Notebooks

The Notebooks section collects practical guidance for creating, configuring, and using notebook environments in MLOps Studio. These articles cover both pipeline workflows and persistence checks so that users can choose the right setup and verify its behavior.

  • Use notebooks with MLOps Studio Pipelines
    • Overview
    • Prerequisites
    • Access MLOps Studio
    • Notebook configuration variants
    • Create a notebook for MLOps Studio Pipelines
    • Upload a notebook file and run it
    • Install kfp when needed
    • Launch a MLOps Studio Pipeline from a notebook
    • Fix “Permission denied: ‘pipeline.yaml’”
    • Smoke test: notebook without Jupyter Enterprise Gateway
    • Test RStudio and Visual Studio Code notebooks
    • Test notebook volume persistence
    • What to do next
  • MLOps Studio notebook test: Conda environment and package persistence
    • Purpose
    • Test configuration
    • Preconditions
    • Test procedure
    • Default base environment test
    • Expected final result
    • Pass criteria
    • Troubleshooting
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