AWS-Logo_White-Color
  • AutoPilot Workshop
    • How Amazon SageMaker Autopilot works
    • Getting Started
    • Load Autopilot Example
    • Run Autopilot Example
    • Workshop Clean-up
  • ML Training Workshop
    • Getting Started
      • Setup
      • Clone the Repository
    • Training with Built-in Algo
      • Built-in Algorithms
      • House Price Prediction
      • House Price Prediction Experiment
      • Image Classification
    • Training with your own script
      • Training with your own script
      • House Price Prediction
      • House Price Prediction-HPO
      • Img Classification
      • Img Classification-Pipe Mode
      • Img Classification-Experiment
      • Img Classificaiton-Distributed
      • Img Classificaiton-Spot training
      • Img Classification-Debugger
    • Training with your own container
      • Bring Your Own Container
      • House Price Prediction
      • Image Classification
  • SageMaker Operators for Kubernetes Workshop
    • Getting Started
      • Overview
      • Prerequisites
      • Install Eksctl
      • Create Cluster
      • Install Kubeflow
      • Install SageMaker Operators
    • Dashboard
      • Dashboard
    • Notebook
      • Create a notebook
      • Data Preparation
    • Training and Inference
      • Training
      • Inference
      • Batch Inference
      • Hyper Parameter Optimization
    • Improving the TensorFlow Model
      • Network Improvements
      • Data Preparation
      • Training Script
      • Training and Inference
    • Next Steps
      • Next Steps
      • Cleanup
  • SageMaker Components for Kubeflow Pipelines Workshop
    • Getting Started
      • Overview
      • Prerequisites
    • Creating the Pipeline
      • S3 Bucket
      • SageMaker Job Role
      • Compile the Pipeline Definition
      • Register the Pipeline
    • Running the Pipeline
      • Run the Pipeline
      • Check Pipeline Status
      • Batch Transform Output
      • Real Time Inference
    • Deep Learning Pipeline
      • Create a Jupyter Notebook
      • Create and Run Pipeline
    • Next Steps
  • Resources
    • Use Existing Code
      • Use Existing Tensorflow Code
      • Use Existing Pytorch Code
      • Use Existing MxNet Code
      • Use Existing Scikit Learn Code
      • Use Existing R Code
      • Using SageMaker Spark
    • Advanced Topics
      • Reinforcement Learning
      • Graph Neural Networks
      • Automatically Stopping Training jobs
      • Generative AI
      • Iterative Model Pruning
      • Model specific real-time analysis
      • Visualization related
    • Sample SageMaker Use Cases
      • Structured data
      • Time Series
      • Computer Vision
      • Natural Language Processing
      • SageMaker Marketplace Examples on SageMaker
    • Specific SageMaker Features
      • Prepare Datasets
      • Build, Train and Deploy models
      • Use Advanced Training capabilities
      • Use Advanced Deployment capabilities
      • Optimize Costs

More

  • More Resources
  • Authors
Privacy | Site Terms | © 2020, Amazon Web Services, Inc. or its affiliates. All rights reserved.
Edit this page
Amazon SageMaker Workshop > Resources > Advanced Topics > Automatically Stopping Training jobs

Automatically Stopping Training jobs

  1. Tensorflow stop training job based on rule
  2. Stop notebook instance based on idle CPU cycles
  3. Other lifecycle configuration scripts for SageMaker