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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 »
  • MLOps Studio Trainer
  • DestinE Data Lake portal

MLOps Studio Trainer

  • MLOps Studio Trainer overview
    • TrainJob
    • Training Runtimes:
    • Custom PyTorch CPU runtime scenario
    • Scheduling
    • Distributed PyTorchJob scenario
    • Running the Trainer via kubectl
  • Scenarios for other frameworks
    • Overview
    • Prerequisites
    • Trainer launch scenario for TFJob
    • Scenario - launching Trainer for JAXJob
    • Scenario - launching Trainer for XGBoostJob
    • What to do next
  • Trainer error 400: failing to pull image
    • Overview
    • When this issue appears
    • How to fix the problem
    • Step-by-step fix
    • Expected result
    • What to do next
  • Trainer error 409: AlreadyExists
    • Overview
    • When this issue appears
    • Why it happens
    • How to fix the problem
    • Step-by-step fix
    • Expected result
    • What to do next
  • Trainer scenarios
    • Introduction
    • Prerequisites
    • Overview
    • Scenario - launching Trainer
    • Scenario - using a custom PyTorch CPU Runtime
    • Scenario – Trainer – missing required section pytorchReplicaSpecs
    • Scenario – Trainer – incorrect data type in the replicas field
    • Scenario – Trainer – incorrect container image (pod-level error – ErrImagePull)
    • Scenario - launching Trainer via kubectl
    • Scenario – error when launching via kubectl
    • Scenario – Invalid NCCL backend on a CPU cluster
    • Gloo on CPU
    • What to do next
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