Training Tools

Chainer provides a standard implementation of the training loops under the chainer.training module. It is built on top of many other core features of Chainer, including Variable and Function, Link/Chain/ChainList, Optimizer, Dataset, and Reporter/Summary. Compared to the training loop abstraction of other machine learning tool kits, Chainer’s training framework aims at maximal flexibility, while keeps the simplicity for the typical usages. Most components are pluggable, and users can overwrite the definition.

The core of the training loop abstraction is Trainer, which implements the training loop itself. The training loop consists of two parts: one is Updater, which actually updates the parameters to train, and the other is Extension for arbitrary functionalities other than the parameter update.

Updater and some extensions use chainer.dataset and Iterator to scan the datasets and load mini-batches. The trainer also uses Reporter to collect the observed values, and some extensions use DictSummary to accumulate them and computes the statistics.

You can find many examples for the usage of this training utilities from the official examples. You can also search the extension implementations from Extensions.

Trainer

chainer.training.Trainer

The standard training loop in Chainer.

Updaters

chainer.training.Updater

Interface of updater objects for trainers.

chainer.training.updaters.StandardUpdater

Standard implementation of Updater.

chainer.training.updaters.ParallelUpdater

Implementation of a parallel GPU Updater.

chainer.training.updaters.MultiprocessParallelUpdater

Implementation of a multiprocess parallel GPU Updater.

We have two kinds of updaters for multi-gpus training. The pros/cons for the updaters are as follows:

ParallelUpdater:

  • (+) Can use the same iterator for any number of GPUs

  • (-) No parallelism at CPU side

  • (-) GPUs used later may be blocked due to the limit of kernel-launch queue size

MultiprocessParallelUpdater:

  • (+) Parallelism at CPU side

  • (+) No degrade due to kernel launch queue size

  • (-) Need per-process data iterator

  • (-) Reporter cannot collect data except for one of the devices

Extensions

An extension is a callable object that can perform arbitrary actions during the training loop. Extensions can be registered to Trainer by using Trainer.extend() method, and they are invoked when the Trigger condition is satisfied.

In addition to the built-in extensions listed below, you can define your own extension by implementing Extension or using the make_extension() decorator. See Trainer Extensions for details.

Common

chainer.training.Extension

Base class of trainer extensions.

chainer.training.make_extension

Decorator to make given functions into trainer extensions.

Evaluation and Metrics Collection

These extensions provide features to collect additional metrics. The typical use case is to use Evaluator to perform evaluation with a validation dataset to compute validation loss/accuracy.

chainer.training.extensions.Evaluator

Trainer extension to evaluate models on a validation set.

chainer.training.extensions.MicroAverage

Calculates micro-average ratio.

chainer.training.extensions.FailOnNonNumber

Trainer extension to raise RuntimeError if parameters contain NaN or Inf.

chainer.training.extensions.ParameterStatistics

Trainer extension to report parameter statistics.

chainer.training.extensions.observe_lr

Returns a trainer extension to record the learning rate.

chainer.training.extensions.observe_value

Returns a trainer extension to continuously record a value.

Optimizer Behavior Control

These extensions provide features to adjust optimizer behavior. The typical use case is to change the learning rate of the optimizer over time.

chainer.training.extensions.ExponentialShift

Trainer extension to exponentially shift an optimizer attribute.

chainer.training.extensions.InverseShift

Trainer extension to shift an optimizer attribute.

chainer.training.extensions.LinearShift

Trainer extension to change an optimizer attribute linearly.

chainer.training.extensions.MultistepShift

Trainer extension to shift an optimizer attribute in several steps.

chainer.training.extensions.PolynomialShift

Trainer extension to polynomially shift an optimizer attribute.

chainer.training.extensions.WarmupShift

Trainer extension to gradually initialize an optimizer attribute.

chainer.training.extensions.StepShift

Trainer extension to shift an optimizer attribute in "steps".

Reporting

These extensions provide features to perform reporting of metrics and various statistics to the console or files.

chainer.training.extensions.PrintReport

Trainer extension to print the accumulated results.

chainer.training.extensions.ProgressBar

Trainer extension to print a progress bar and recent training status.

chainer.training.extensions.LogReport

Trainer extension to output the accumulated results to a log file.

chainer.training.extensions.PlotReport

Trainer extension to output plots.

chainer.training.extensions.VariableStatisticsPlot

Trainer extension to plot statistics for Variables.

chainer.training.extensions.DumpGraph

Trainer extension to dump a computational graph.

Snapshot

These extensions provide features to take snapshots of models.

chainer.training.extensions.snapshot

Returns a trainer extension to take snapshots of the trainer.

chainer.training.extensions.snapshot_object

Returns a trainer extension to take snapshots of a given object.

Memory Release

These extensions provide features to release memories.

chainer.training.extensions.unchain_variables

Trainer extension to unchain all comptational graphs.

Triggers

A trigger is a callable object to decide when to process some specific event within the training loop. It takes a Trainer object as the argument, and returns True if some event should be fired.

It is mainly used to determine when to call an extension. It is also used to determine when to quit the training loop.

chainer.training.get_trigger

Gets a trigger object.

chainer.training.triggers.BestValueTrigger

Trigger invoked when specific value becomes best.

chainer.training.triggers.EarlyStoppingTrigger

Trigger for Early Stopping

chainer.training.triggers.IntervalTrigger

Trigger based on a fixed interval.

chainer.training.triggers.ManualScheduleTrigger

Trigger invoked at specified point(s) of iterations or epochs.

chainer.training.triggers.MaxValueTrigger

Trigger invoked when specific value becomes maximum.

chainer.training.triggers.MinValueTrigger

Trigger invoked when specific value becomes minimum.

chainer.training.triggers.OnceTrigger

Trigger based on the starting point of the iteration.

chainer.training.triggers.TimeTrigger

Trigger based on a fixed time interval.