Copyright | (c) 2013-2023 Brendan Hay |
---|---|
License | Mozilla Public License, v. 2.0. |
Maintainer | Brendan Hay |
Stability | auto-generated |
Portability | non-portable (GHC extensions) |
Safe Haskell | Safe-Inferred |
Language | Haskell2010 |
Documentation
data HyperbandStrategyConfig Source #
The configuration for Hyperband
, a multi-fidelity based hyperparameter
tuning strategy. Hyperband
uses the final and intermediate results of
a training job to dynamically allocate resources to utilized
hyperparameter configurations while automatically stopping
under-performing configurations. This parameter should be provided only
if Hyperband
is selected as the StrategyConfig
under the
HyperParameterTuningJobConfig
API.
See: newHyperbandStrategyConfig
smart constructor.
HyperbandStrategyConfig' | |
|
Instances
newHyperbandStrategyConfig :: HyperbandStrategyConfig Source #
Create a value of HyperbandStrategyConfig
with all optional fields omitted.
Use generic-lens or optics to modify other optional fields.
The following record fields are available, with the corresponding lenses provided for backwards compatibility:
$sel:maxResource:HyperbandStrategyConfig'
, hyperbandStrategyConfig_maxResource
- The maximum number of resources (such as epochs) that can be used by a
training job launched by a hyperparameter tuning job. Once a job reaches
the MaxResource
value, it is stopped. If a value for MaxResource
is
not provided, and Hyperband
is selected as the hyperparameter tuning
strategy, HyperbandTrainingJ
attempts to infer MaxResource
from the
following keys (if present) in
StaticsHyperParameters:
epochs
numepochs
n-epochs
n_epochs
num_epochs
If HyperbandStrategyConfig
is unable to infer a value for
MaxResource
, it generates a validation error. The maximum value is
20,000 epochs. All metrics that correspond to an objective metric are
used to derive
early stopping decisions.
For
distributive
training jobs, ensure that duplicate metrics are not printed in the logs
across the individual nodes in a training job. If multiple nodes are
publishing duplicate or incorrect metrics, training jobs may make an
incorrect stopping decision and stop the job prematurely.
$sel:minResource:HyperbandStrategyConfig'
, hyperbandStrategyConfig_minResource
- The minimum number of resources (such as epochs) that can be used by a
training job launched by a hyperparameter tuning job. If the value for
MinResource
has not been reached, the training job will not be stopped
by Hyperband
.
hyperbandStrategyConfig_maxResource :: Lens' HyperbandStrategyConfig (Maybe Natural) Source #
The maximum number of resources (such as epochs) that can be used by a
training job launched by a hyperparameter tuning job. Once a job reaches
the MaxResource
value, it is stopped. If a value for MaxResource
is
not provided, and Hyperband
is selected as the hyperparameter tuning
strategy, HyperbandTrainingJ
attempts to infer MaxResource
from the
following keys (if present) in
StaticsHyperParameters:
epochs
numepochs
n-epochs
n_epochs
num_epochs
If HyperbandStrategyConfig
is unable to infer a value for
MaxResource
, it generates a validation error. The maximum value is
20,000 epochs. All metrics that correspond to an objective metric are
used to derive
early stopping decisions.
For
distributive
training jobs, ensure that duplicate metrics are not printed in the logs
across the individual nodes in a training job. If multiple nodes are
publishing duplicate or incorrect metrics, training jobs may make an
incorrect stopping decision and stop the job prematurely.
hyperbandStrategyConfig_minResource :: Lens' HyperbandStrategyConfig (Maybe Natural) Source #
The minimum number of resources (such as epochs) that can be used by a
training job launched by a hyperparameter tuning job. If the value for
MinResource
has not been reached, the training job will not be stopped
by Hyperband
.