FluxTraining.jl

Docs (master)

A Julia package for using and writing powerful, extensible training loops for deep learning models.

What does it do?

• Implements a training loop to take the boilerplate out of training deep learning models

• Lets you add features to training loops through reusable callbacks

• Comes with callbacks for many common use cases like hyperparameter scheduling , metrics tracking and logging , checkpointing , early stopping , and more...

• Is extensible by creating custom, reusable callbacks or even custom training loops

When should you use FluxTraining.jl?

• You don't want to implement your own metrics tracking and hyperparameter scheduling or insert common training feature here for the 10th time

• You want to use composable and reusable components that enhance your training loop

• You want a simple training loop with reasonable defaults that can grow to the needs of your project

How do you use it?

Install like any other Julia package using the package manager:

			]add FluxTraining

After installation, import it, create a Learner from a Flux.jl model, data iterators, an optimizer, and a loss function. Finally train with fit!.

			
			
			
			using
			
			 
			FluxTraining
			

			

			
			learner
			 
			=
			 
			

	
			Learner
			(
			model
			,
			 
			lossfn
			)
			

			

	
			fit!
			(
			learner
			,
			 
			10
			,
			 
			
			(
			trainiter
			,
			 
			validiter
			)
			)

Next, you may want to read

• Getting started

• A full example training an image classifier on the MNIST dataset

• The documentation of FastAI.jl which features many end-to-end examples

Acknowledgements

The design of FluxTraining.jl's two-way callbacks is adapted from fastai 's training loop.