@Generated(value="com.amazonaws:aws-java-sdk-code-generator") public class HyperbandStrategyConfig extends Object implements Serializable, Cloneable, StructuredPojo
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.
Constructor and Description |
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HyperbandStrategyConfig() |
Modifier and Type | Method and Description |
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HyperbandStrategyConfig |
clone() |
boolean |
equals(Object obj) |
Integer |
getMaxResource()
The maximum number of resources (such as epochs) that can be used by a training job launched by a hyperparameter
tuning job.
|
Integer |
getMinResource()
The minimum number of resources (such as epochs) that can be used by a training job launched by a hyperparameter
tuning job.
|
int |
hashCode() |
void |
marshall(ProtocolMarshaller protocolMarshaller)
Marshalls this structured data using the given
ProtocolMarshaller . |
void |
setMaxResource(Integer maxResource)
The maximum number of resources (such as epochs) that can be used by a training job launched by a hyperparameter
tuning job.
|
void |
setMinResource(Integer minResource)
The minimum number of resources (such as epochs) that can be used by a training job launched by a hyperparameter
tuning job.
|
String |
toString()
Returns a string representation of this object.
|
HyperbandStrategyConfig |
withMaxResource(Integer maxResource)
The maximum number of resources (such as epochs) that can be used by a training job launched by a hyperparameter
tuning job.
|
HyperbandStrategyConfig |
withMinResource(Integer minResource)
The minimum number of resources (such as epochs) that can be used by a training job launched by a hyperparameter
tuning job.
|
public void setMinResource(Integer 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 is not stopped by
Hyperband
.
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 is not stopped by Hyperband
.public Integer getMinResource()
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 is not stopped by
Hyperband
.
MinResource
has not been reached, the training
job is not stopped by Hyperband
.public HyperbandStrategyConfig withMinResource(Integer 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 is not stopped by
Hyperband
.
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 is not stopped by Hyperband
.public void setMaxResource(Integer 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.
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.
public Integer getMaxResource()
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.
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.
public HyperbandStrategyConfig withMaxResource(Integer 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.
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.
public String toString()
toString
in class Object
Object.toString()
public HyperbandStrategyConfig clone()
public void marshall(ProtocolMarshaller protocolMarshaller)
StructuredPojo
ProtocolMarshaller
.marshall
in interface StructuredPojo
protocolMarshaller
- Implementation of ProtocolMarshaller
used to marshall this object's data.