This module provides base classes for Ivory.
Base— Base class for an entity class such as
Creator— Creator class to create
Callback— Callback class for the Ivory callback system.</>
CallbackCaller— Callback caller class.</>
Experiment— Experimet class is one of the main classes of Ivory library.</>
This module provides the Ivory Client class that is one of the main classes of Ivory library.
To create an
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')
Ivory uses four classes for data presentation:
Basically, you only need to define a class that is a subclass of
and use original
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
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
DataLoaders is used internally by
ivory.nnabla.trainer.Trainer classes to yield a minibatch in training loop.
'def' key for
dataset instead of
This module provides the
Run class that is one of the main classes of
Ivory library. In addition,
Study classes are defined, which manages
multiple runs for cross validation, hyperparameter tuning, and so on.
To create an
import ivory client = ivory.create_run('example')
example is an experiment name in which the created run is included.
Ivory assumes that
example.yaml file exists under the client's
You can comfirm the client's working directory by:
One you got a
Run instance. call
Run.start() to start training. For test,
Run.start('test') instead. Also, you can perform traing and test by one step