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 change an optimizer attribute evenly at the begining of one training.
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.dump_graph Returns a 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.

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.TimeTrigger Trigger based on a fixed time interval.