Package ai.djl.training

Interface TrainingConfig

  • All Known Implementing Classes:
    DefaultTrainingConfig
    public interface TrainingConfig
    An interface that is responsible for holding the configuration required by Trainer.

    A trainer requires different information to facilitate the training process. This information is passed by using this configuration.

    The required options for the configuration are:

    • Required Loss - A loss function is used to measure how well a model matches the dataset. Because the lower value of the function is better, it is called the "loss" function. This is the only required configuration.
    • Evaluator - An evaluator is used to measure how well a model matches the dataset. Unlike the loss, they are only there for people to look at and are not used for optimization. Since many losses are not as intuitive, adding other evaluators can help to understand how the model is doing. We recommend adding as many as possible.
    • Device - The device is what hardware should be used to train your model on. Typically, this is either GPU or GPU. The default is to use a single GPU if it is available or CPU if not.
    • Initializer - The initializer is used to set the initial values of the model's parameters before training. This can usually be left as the default initializer.
    • Optimizer - The optimizer is the algorithm that updates the model parameters to minimize the loss function. There are a variety of optimizers, most of which are variants of stochastic gradient descent. When you are just starting, you can use the default optimizer. Later on, customizing the optimizer can result in faster training.
    • ExecutorService - The executorService is used for parallelization when training batches on multiple GPUs or loading data from the dataset. If none is provided, all operations with be sequential.
    • TrainingListener - The training listeners add additional functionality to the training process through a listener interface. This can include showing training progress, stopping early if the training fails, or recording performance metrics. We offer several easy sets of TrainingListener.Defaults.

    • Method Detail

      • getDevices

        Device[] getDevices()
        Gets the Device that are available for computation.

        This is necessary for a Trainer as it needs to know what kind of device it is running on, and how many devices it is running on.

        Returns:
        an array of Device

      • getInitializers

        ai.djl.util.PairList<Initializer,​java.util.function.Predicate<Parameter>> getInitializers()
        Gets a list of Initializer and Predicate to initialize the parameters of the model.
        Returns:
        an Initializer

      • getLossFunction

        Loss getLossFunction()
        Gets the Loss function to compute the loss against.
        Returns:
        a Loss function

      • getExecutorService

        java.util.concurrent.ExecutorService getExecutorService()
        Gets the ExecutorService for parallelization.
        Returns:
        an ExecutorService

      • getEvaluators

        java.util.List<Evaluator> getEvaluators()
        Returns the list of Evaluators that should be computed during training.
        Returns:
        a list of Evaluators