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Kubeflow 0.6 Document
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Training

 来源:谷歌 浏览 618 扫码 分享 2020-04-19 09:42:23
  • Training
    • Chainer Training
    • MPI Training
    • MXNet Training
    • PyTorch Training
    • TensorFlow Training (TFJob)

    Training

    Training of ML models in Kubeflow

    Chainer Training

    Instructions for using Chainer for training

    MPI Training

    Instructions for using MPI for training

    MXNet Training

    Instructions for using MXNet

    PyTorch Training

    Instructions for using PyTorch

    TensorFlow Training (TFJob)
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    • About
      • Kubeflow
      • Contributing to Kubeflow
      • Community
      • Events Calendar
      • Docs
      • Style Guide for the Kubeflow Docs
    • Getting Started
      • Getting Started with Kubeflow
      • Cloud Installation
        • AWS for Kubeflow
        • Azure for Kubeflow
        • Google Cloud for Kubeflow
        • IBM Cloud Private for Kubeflow
      • Kubernetes Installation
        • Deploying Kubeflow on Existing Clusters
        • Kubeflow Deployment with kfctl_k8s_istio
        • Multi-user, auth-enabled Kubeflow with kfctl_existing_arrikto
      • Workstation Installation
        • Kubeflow on Linux
        • Kubeflow on MacOS
        • Kubeflow on Windows
    • Use Cases
      • GitOps For Kubeflow Using Argo CD
      • Job Scheduling
    • Jupyter Notebooks
      • Overview of Jupyter Notebooks in Kubeflow
      • Set Up Your Notebooks
      • Create a Custom Jupyter Image
      • Submit Kubernetes Resources
      • Build a Docker Image on GCP
      • Troubleshooting Guide
    • Pipelines
      • Pipelines Quickstart
      • Understanding Pipelines
        • Overview of Kubeflow Pipelines
        • Introduction to the Pipelines Interfaces
        • Concepts
        • Pipeline
          • Component
          • Graph
          • Experiment
          • Run and Recurring Run
          • Run Trigger
          • Step
          • Output Artifact
      • Building Pipelines with the SDK
        • Introduction to the Pipelines SDK
        • Install the Kubeflow Pipelines SDK
        • Build Components and Pipelines
        • Manipulate Kubernetes Resources
        • Build Reusable Components
        • Build Lightweight Python Components
        • Best Practices for Designing Components
        • Pipeline Parameters
        • Visualize Results in the Pipelines UI
        • Pipeline Metrics
        • DSL Static Type Checking
        • DSL Recursion
        • GCP-specific Uses of the SDK
        • Enable GPU and TPU
          • Using Preemptible VMs and GPUs on GCP
          • Upgrading and Reinstalling
      • Samples and Tutorials
        • Experiment with the Pipelines Samples
        • Run a Cloud-specific Pipelines Tutorial
      • Reference
        • Component Specification
        • Pipelines API Reference
        • Pipelines SDK Reference
    • Fairing
      • Overview of Kubeflow Fairing
      • Install Kubeflow Fairing
      • Configure Kubeflow Fairing
        • Fairing on GCP
        • Configure Kubeflow Fairing with Access to GCP
      • GCP Samples and Tutorials
      • Train and Deploy on GCP from a Local Notebook
        • Train and Deploy on GCP from a Kubeflow Notebook
          • Tutorials
          • Other Samples and Tutorials
    • Kubeflow on Azure
      • Deployment
      • Install Kubeflow
        • Initial cluster setup for existing cluster
        • Uninstall Kubeflow
        • End-to-End Pipeline Example on Azure
        • Access Control for Azure Deployment
        • Troubleshooting Deployments on Azure AKS
    • Kubeflow on AWS
      • Deployment
      • Install Kubeflow
        • Uninstall Kubeflow
        • Customizing Kubeflow on AWS
        • Logging
        • Private Access
        • Authentication and TLS Support
        • Storage Options
        • Troubleshooting Deployments on Amazon EKS
        • Kubeflow on AWS Features
    • Kubeflow on GCP
      • Deploying Kubeflow
      • Set up a GCP Project
        • Set up OAuth for Cloud IAP
        • Deploy using UI
        • Deploy using CLI
        • Monitor Cloud IAP Setup
        • Delete using CLI
        • Delete using GCP Console
        • Features of Kubeflow on GCP
        • Customizing Kubeflow on GKE
        • Using Your Own Domain
        • Authenticating Kubeflow to GCP
        • Using Cloud Filestore
        • Securing Your Clusters
        • Troubleshooting Deployments on GKE
        • End-to-end Kubeflow on GCP
        • Pipelines End-to-end on GCP
        • Logging and monitoring
    • Components of Kubeflow
      • Jupyter Notebooks
      • Hyperparameter Tuning
      • Hyperparameter Tuning (Katib)
      • Pipelines
      • Pipelines
      • Serving
      • Istio Integration (for TF Serving)
        • Seldon Serving
        • NVIDIA TensorRT Inference Server
        • TensorFlow Serving
        • TensorFlow Batch Predict
        • PyTorch Serving
      • Training
      • Chainer Training
        • MPI Training
        • MXNet Training
        • PyTorch Training
        • TensorFlow Training (TFJob)
      • Miscellaneous
      • Metadata
        • Nuclio functions
    • Tutorials, Samples, and Shared Resources
      • Kubeflow Samples
      • Codelabs, Workshops, and Tutorials
      • Blog Posts
      • Videos
      • Shared Resources and Components
    • Further Setup and Troubleshooting
      • Accessing Kubeflow UIs
      • Virtual Developer Environments
      • Microk8s for Kubeflow
        • MiniKF
        • Minikube for Kubeflow
        • Configuring Kubeflow with kfctl and kustomize
        • Kubeflow On-prem in a Multi-node Kubernetes Cluster
        • Usage Reporting
        • Multi-user Isolation
        • Troubleshooting
    • Upgrading Kubeflow
      • Upgrading a Kubeflow Deployment
    • Reference
      • Reference Overview
      • PyTorchJob Reference
        • PyTorchJob v1
        • PyTorchJob
        • PyTorchJob v1beta2
        • PyTorchJob
      • TFJob Reference
        • TFJob v1
        • TFJob Common
          • TFJob TensorFlow
        • TFJob v1beta2
        • TFJob Common
          • TFJob TensorFlow
      • Dockerfile Locations
      • Metadata API Reference
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