Models

If you have some data and want to do machine learning on it, you probably want to head over to the Models plugins. This tutorial is for implementing a machine learning algorithm. Which is probably what you want if there’s not an existing plugin for your algorithm.

DFFML is not like PyTorch or TensorFlow. It’s higher level than those. However, that doesn’t mean it has to be higher level. You can use the lower level APIs of any library, or no library if you wanted.

These tutorials will show you two use existing DFFML models, implement your own machine learning algorithm or wrap an existing libraries implementation, and package it so others can use it easily.

Contents:

• Use a Model
  • Iris Dataset
  • Python Usage
  • Command Line Usage
• Load Models Dynamically
• Using Archives to Store Models
• Writing a Model
  • Imports
  • Math
  • Config
  • Class
  • Train
  • Predict
  • Python Usage
  • Command Line Usage
  • HTTP Server Usage
• Packaging a Model
  • Create the Package
  • Install the Package
  • Testing
  • Entrypoint Registration
  • Command Line Usage
  • Uploading to PyPi
• Documenting a Model
  • Python docstrings
  • Quick rST background
  • consoletest
  • Example Datasets
  • Example CLI Commands
  • Example Python Usage
  • Testing Examples