Bases: pydantic.BaseModel
Configuration options for the hyperparameter search.
-
method: Literal['optuna', 'hyperopt', 'random'] | None = None
The search method to use.
-
max_points: int | None = None
The maximum number of points to try.
-
default_points: int = 60
The default number of search points, if not limited by a maximum configuration.
-
max_epochs: int = 100
The maximum number of epochs to use in iterative training.
-
min_epochs: int = 3
The minimum number of epochs for iterative training.
-
metric: str | None = None
The metric to use.
-
list_length: int | None = None
The length of recommendation lists to use.
-
num_cpus: int | Literal['threads', 'backend-threads', 'all-threads'] = 'threads'
The number of CPUs to request from Ray Tune.
-
num_gpus: int | float = 0
The number of GPUs to requrest from Ray Tune.
-
checkpoint_iters: int = 2
The frequency for saving checkpoints.
-
update_max_points(n)
Limit the search points to a new maximum, if it exceeds the current maximum.
- Parameters:
n (int | None)
-
num_search_points()
Get the number of search points to use.
- Return type:
int
