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HPE Machine Learning Development Environment Documentation
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version 0.20.1
  • Welcome

Getting Started

  • Try Determined
    • Run Your First Experiment
    • PyTorch MNIST Tutorial
    • PyTorch Porting Tutorial
    • TensorFlow Keras Fashion MNIST Tutorial
  • How It Works
    • Introduction to HPE Machine Learning Development Environment
    • System Architecture
  • Quickstart for Model Developers
  • Examples
  • Model Hub Library
    • Huggingface Trainsformers
      • Tutorial
      • Examples
    • MMDetection

Set Up Determined

  • Set Up Guide
    • Deploy on Prem
      • Installation Requirements
      • Install Determined Using Docker
      • Install Determined Using det deploy
      • Install Determined Using Linux Packages
      • Install Determined Using Homebrew (macOS)
    • Deploy on AWS
      • Install Determined
      • Deploy Determined with Dynamic Agents
      • Use Spot Instances
    • Deploy on GCP
      • Install Determined
      • Deploy Determined with Dynamic Agents
    • Deploy on Kubernetes
      • Install Determined on Kubernetes
      • Set up and Manage an Azure Kubernetes Service (AKS) Cluster
      • Set up and Manage an AWS Kubernetes (EKS) Cluster
      • Set up and Manage a Google Kubernetes Engine (GKE) Cluster
      • Development Guide
      • Customize a Pod
      • Helm and Kubectl Command Examples
      • Troubleshooting
    • Deploy on Slurm/PBS
      • Installation Requirements
      • Install Determined on Slurm/PBS
      • Provide a Container Image Cache
      • HPC Launching Architecture
      • Known Issues
  • Basic Setup
  • Security
    • OAuth 2.0 Configuration
    • Transport Layer Security
    • OpenID Connect Integration
    • SAML Integration
    • SCIM Integration
    • RBAC
  • User Accounts
  • Workspaces and Projects
  • Logging and Elasticsearch
  • Cluster Usage History
  • Monitor Experiment Through Webhooks
    • Through Zapier
    • Through Slack
  • Upgrade
  • Troubleshooting

Model Developer Guide

  • Overview
  • Distributed Training
  • Prepare Container Environment
    • Set Environment Images
    • Customize Environment
  • Prepare Data
  • Training API Guides
    • Core API
    • PyTorch API
    • PyTorch Lightning API
    • Keras API
    • DeepSpeed API
      • Usage Guide
      • Advanced Usage
      • PyTorchTrial to DeepSpeedTrial
    • Estimator API
  • Hyperparameter Tuning
    • Configure Hyperparameter Ranges
    • Hyperparameter Search Constraints
    • Instrument Model Code
    • Handle Trial Errors and Early Stopping Requests
    • Search Methods
      • Adaptive (Asynchronous) Method
      • Grid Method
      • Random Method
      • Single Search Method
      • Custom Search Methods
  • Submit Experiment
  • How to Debug Models
  • Model Management
    • Checkpoints
    • Organize Models in the Model Registry
  • Best Practices

Reference

  • Overview
  • Python SDK
  • REST API
  • Training Reference
    • Core API Reference
    • PyTorch API Reference
    • PyTorch Lightning API Reference
    • Keras API Reference
    • DeepSpeed API Reference
    • Estimator API Reference
    • Experiment Configuration
  • Model Hub Reference
    • MMDetection API
    • Transformers API
  • Deployment Reference
    • Common Configuration Options
    • Master Configuration Reference
    • Agent Configuration Reference
    • Helm Chart Configuration Reference
  • Job Configuration Reference
  • Custom Searcher Reference

Tools

  • Commands and Shells
  • WebUI Interface
  • Jupyter Notebooks
  • TensorBoards
  • Exposing Custom Ports

Integrations

  • Works with Determined
  • IDE Integration
  • Prometheus and Grafana
  • Open Source Licenses

Set Up

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Reference

Welcome to HPE Machine Learning Development Environment!ΒΆ

You can quickly train almost any deep learning model using HPE Machine Learning Development Environment.

Try It

Learn the basics of working with Determined and how to port your existing code to the Determined environment.

How It Works

Learn about core concepts, key features, and system architecture.

Set Up Determined

Set up an on-premise or cloud-based cluster, including AWS, GCP, and Azure.

Model Developer Quickstart

Learn the basic steps needed to set up a Determined environment and train models.

Model Developer Guide

Learn how to work with Training APIs and configure your distributed training experiments.

Reference

Explore API libraries and configuration settings.


New features, upgrades, deprecation notices, known issues, and bug fixes: Release Notes

Next
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  • Welcome to HPE Machine Learning Development Environment!