Ivory
PACKAGE

IVORY.CORE

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MODULE

IVORY.CORE.BASE

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This module provides base classes for Ivory.

Classes
  • Base — Base class for an entity class such as Client, Experiment, and Run.</>
  • Creator — Creator class to create Run instances.</>
  • Callback — Callback class for the Ivory callback system.</>
  • CallbackCaller — Callback caller class.</>
  • Experiment — Experimet class is one of the main classes of Ivory library.</>
MODULE

IVORY.CORE.CLIENT

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This module provides the Ivory Client class that is one of the main classes of Ivory library.

To create an Client instance:

import ivory

client = ivory.create_client()

Here, the current directory becomes the working directory in which experiment YAML files exist. If you want to refer other directory, use:

client = ivory.create_client('path/to/working_directory')
Classes
Functions
MODULE

IVORY.CORE.DATA

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Ivory uses four classes for data presentation: Data, Dataset, Datasets, and DataLoaders.

Basically, you only need to define a class that is a subclass of Data and use original Dataset and Datasets. An example parameter YAML file is:

datasets:
  data:
    class: your.Data  # a subclass of ivory.core.data.Data
  dataset:
  fold: 0

But if you need, you can define your Dataset and/or Datasets.

datasets:
  class: your.Datasets  # a subclass of ivory.core.data.Datasets
  data:
    class: your.Data  # a subclass of ivory.core.data.Data
  dataset:
    def: your.Dataset  # a subclass of ivory.core.data.Dataset
  fold: 0

The DataLoaders is used internally by ivory.torch.trainer.Trainer or ivory.nnabla.trainer.Trainer classes to yield a minibatch in training loop.

Note

Use a 'def' key for dataset instead of 'class'. See Tutorial

Classes
  • Data — Base class to provide data to a Dataset instance.</>
  • Dataset — Dataset class represents a set of data for a mode and fold.</>
  • Datasets — Dataset class represents a collection of Dataset for a fold.</>
  • DataLoaders — DataLoaders class represents a collection of DataLoader.</>
MODULE

IVORY.CORE.RUN

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This module provides the Run class that is one of the main classes of Ivory library. In addition, Task and Study classes are defined, which manages multiple runs for cross validation, hyperparameter tuning, and so on.

To create an Run instance:

import ivory

client = ivory.create_run('example')

The argument example is an experiment name in which the created run is included. Ivory assumes that example.yml or example.yaml file exists under the client's working directory.

You can comfirm the client's working directory by:

os.path.dirname(client.source_name)

One you got a Run instance. call Run.start() to start training. For test, call Run.start('test') instead. Also, you can perform traing and test by one step with Run.start('both').

Classes
  • Run — Run class is one of the main classes of Ivory library.</>
  • Task — Task class creates a parent run that generates multiple runs.</>
  • Study — Study class create a parent run to manage hyperparameter tuning.</>