DFFML
82843d30b

Introduction

  • About
  • Installation
  • Concepts

Usage

  • Quickstart
  • Tutorials
    • Models
    • Sources
    • Scorers
    • DataFlows
    • Neural Networks
    • Double Context Entry
  • Examples

Reference

  • Plugins
  • Command Line
  • HTTP API
  • Base
  • Noasync
  • Log
  • Version
  • Record
  • Plugins
  • Cli
  • Tuner
  • Operation
  • Util
  • Configloader
  • Db
  • Service
  • Df
  • Model
  • High_Level
  • Source
  • Port
  • Accuracy
  • Feature
  • Secret
  • Troubleshooting
  • Architecture

Subprojects

  • shouldi

Community

  • GitHub
  • Master Branch Docs
  • Publications
  • Contact Us
  • Contributing
  • News
  • Changelog
DFFML
  • »
  • Tutorials
  • Edit on GitHub

Tutorials

Here you’ll find some tutorials on how you can create new data sources, models, or operations.

Tutorials are usually best approached by first creating an empty directory and installing any prerequisite packages to a virtual environment before beginning. See Create a new Virtual Environment for more details.

Contents:

  • Models
    • Use a Model
    • Load Models Dynamically
    • Using Archives to Store Models
    • Writing a Model
    • Packaging a Model
    • Documenting a Model
  • Sources
    • Simple source for new file types
    • Example SQLite source
  • Scorers
    • Accuracy Scorer
  • DataFlows
    • Using Locks In DataFlow
    • Prediction Using IO Operations
    • Gitter ML Inference Chatbot
    • Using NLP Operations
  • Neural Networks
    • Rock Paper Scissors Hand Pose Classification
    • Command Line
    • Python API
  • Double Context Entry
    • Rules of the double context entry pattern
    • Benefits of using this pattern
    • Example - Model
Previous Next

© Copyright 2017 - 2022, Intel.

Built with Sphinx using a theme provided by Read the Docs.