Tuner base class.
BaseTuner(
oracle,
hypermodel,
directory = NULL,
project_name = NULL,
overwrite = FALSE
)Instance of Oracle class.
Instance of `HyperModel` class (or callable that takes hyperparameters and returns a `Model` instance). It is optional when `Tuner.run_trial()` is overriden and does not use `self.hypermodel`.
A string, the relative path to the working directory.
A string, the name to use as prefix for files saved by this Tuner.
Boolean, defaults to `FALSE`. If `FALSE`, reloads an existing project of the same name if one is found. Otherwise, overwrites the project. **kwargs: Arguments for backward compatibility.
base tuner object
`BaseTuner` is the super class of all `Tuner` classes. It defines the APIs for the `Tuner` classes and serves as a wrapper class for the internal logics. `BaseTuner` supports parallel tuning. In parallel tuning, the communication between `BaseTuner` and `Oracle` are all going through gRPC. There are multiple running instances of `BaseTuner` but only one `Oracle`. This design allows the user to run the same script on multiple machines to launch the parallel tuning. The `Oracle` instance should manage the life cycles of all the `Trial`s, while a `BaseTuner` is a worker for running the `Trial`s. `BaseTuner`s requests `Trial`s from the `Oracle`, run them, and report the results back to the `Oracle`. A `BaseTuner` also handles events happening during running the `Trial`, like saving the model, logging, error handling. Other than these responsibilities, a `BaseTuner` should avoid managing a `Trial` since the relevant contexts for a `Trial` are in the `Oracle`, which only accessible from gRPC. The `BaseTuner` should be a general tuner for all types of models and avoid any logic directly related to Keras. The Keras related logics should be handled by the `Tuner` class, which is a subclass of `BaseTuner`.
remaining_trials: Number of trials remaining, `NULL` if `max_trials` is not set. This is useful when resuming a previously stopped search.