pub struct HyperParameterTuningJobWarmStartConfig {
pub parent_hyper_parameter_tuning_jobs: Vec<ParentHyperParameterTuningJob>,
pub warm_start_type: String,
}
Expand description
Specifies the configuration for a hyperparameter tuning job that uses one or more previous hyperparameter tuning jobs as a starting point. The results of previous tuning jobs are used to inform which combinations of hyperparameters to search over in the new tuning job.
All training jobs launched by the new hyperparameter tuning job are evaluated by using the objective metric, and the training job that performs the best is compared to the best training jobs from the parent tuning jobs. From these, the training job that performs the best as measured by the objective metric is returned as the overall best training job.
All training jobs launched by parent hyperparameter tuning jobs and the new hyperparameter tuning jobs count against the limit of training jobs for the tuning job.
Fields
parent_hyper_parameter_tuning_jobs: Vec<ParentHyperParameterTuningJob>
An array of hyperparameter tuning jobs that are used as the starting point for the new hyperparameter tuning job. For more information about warm starting a hyperparameter tuning job, see Using a Previous Hyperparameter Tuning Job as a Starting Point.
Hyperparameter tuning jobs created before October 1, 2018 cannot be used as parent jobs for warm start tuning jobs.
warm_start_type: String
Specifies one of the following:
- IDENTICALDATAANDALGORITHM
-
The new hyperparameter tuning job uses the same input data and training image as the parent tuning jobs. You can change the hyperparameter ranges to search and the maximum number of training jobs that the hyperparameter tuning job launches. You cannot use a new version of the training algorithm, unless the changes in the new version do not affect the algorithm itself. For example, changes that improve logging or adding support for a different data format are allowed. You can also change hyperparameters from tunable to static, and from static to tunable, but the total number of static plus tunable hyperparameters must remain the same as it is in all parent jobs. The objective metric for the new tuning job must be the same as for all parent jobs.
- TRANSFERLEARNING
-
The new hyperparameter tuning job can include input data, hyperparameter ranges, maximum number of concurrent training jobs, and maximum number of training jobs that are different than those of its parent hyperparameter tuning jobs. The training image can also be a different version from the version used in the parent hyperparameter tuning job. You can also change hyperparameters from tunable to static, and from static to tunable, but the total number of static plus tunable hyperparameters must remain the same as it is in all parent jobs. The objective metric for the new tuning job must be the same as for all parent jobs.
Trait Implementations
sourceimpl Clone for HyperParameterTuningJobWarmStartConfig
impl Clone for HyperParameterTuningJobWarmStartConfig
sourcefn clone(&self) -> HyperParameterTuningJobWarmStartConfig
fn clone(&self) -> HyperParameterTuningJobWarmStartConfig
Returns a copy of the value. Read more
1.0.0 · sourcefn clone_from(&mut self, source: &Self)
fn clone_from(&mut self, source: &Self)
Performs copy-assignment from source
. Read more
sourceimpl Default for HyperParameterTuningJobWarmStartConfig
impl Default for HyperParameterTuningJobWarmStartConfig
sourcefn default() -> HyperParameterTuningJobWarmStartConfig
fn default() -> HyperParameterTuningJobWarmStartConfig
Returns the “default value” for a type. Read more
sourceimpl<'de> Deserialize<'de> for HyperParameterTuningJobWarmStartConfig
impl<'de> Deserialize<'de> for HyperParameterTuningJobWarmStartConfig
sourcefn deserialize<__D>(__deserializer: __D) -> Result<Self, __D::Error> where
__D: Deserializer<'de>,
fn deserialize<__D>(__deserializer: __D) -> Result<Self, __D::Error> where
__D: Deserializer<'de>,
Deserialize this value from the given Serde deserializer. Read more
sourceimpl PartialEq<HyperParameterTuningJobWarmStartConfig> for HyperParameterTuningJobWarmStartConfig
impl PartialEq<HyperParameterTuningJobWarmStartConfig> for HyperParameterTuningJobWarmStartConfig
sourcefn eq(&self, other: &HyperParameterTuningJobWarmStartConfig) -> bool
fn eq(&self, other: &HyperParameterTuningJobWarmStartConfig) -> bool
This method tests for self
and other
values to be equal, and is used
by ==
. Read more
sourcefn ne(&self, other: &HyperParameterTuningJobWarmStartConfig) -> bool
fn ne(&self, other: &HyperParameterTuningJobWarmStartConfig) -> bool
This method tests for !=
.
impl StructuralPartialEq for HyperParameterTuningJobWarmStartConfig
Auto Trait Implementations
impl RefUnwindSafe for HyperParameterTuningJobWarmStartConfig
impl Send for HyperParameterTuningJobWarmStartConfig
impl Sync for HyperParameterTuningJobWarmStartConfig
impl Unpin for HyperParameterTuningJobWarmStartConfig
impl UnwindSafe for HyperParameterTuningJobWarmStartConfig
Blanket Implementations
sourceimpl<T> BorrowMut<T> for T where
T: ?Sized,
impl<T> BorrowMut<T> for T where
T: ?Sized,
const: unstable · sourcefn borrow_mut(&mut self) -> &mut T
fn borrow_mut(&mut self) -> &mut T
Mutably borrows from an owned value. Read more
sourceimpl<T> Instrument for T
impl<T> Instrument for T
sourcefn instrument(self, span: Span) -> Instrumented<Self>
fn instrument(self, span: Span) -> Instrumented<Self>
sourcefn in_current_span(self) -> Instrumented<Self>
fn in_current_span(self) -> Instrumented<Self>
sourceimpl<T> ToOwned for T where
T: Clone,
impl<T> ToOwned for T where
T: Clone,
type Owned = T
type Owned = T
The resulting type after obtaining ownership.
sourcefn clone_into(&self, target: &mut T)
fn clone_into(&self, target: &mut T)
toowned_clone_into
)Uses borrowed data to replace owned data, usually by cloning. Read more
sourceimpl<T> WithSubscriber for T
impl<T> WithSubscriber for T
sourcefn with_subscriber<S>(self, subscriber: S) -> WithDispatch<Self> where
S: Into<Dispatch>,
fn with_subscriber<S>(self, subscriber: S) -> WithDispatch<Self> where
S: Into<Dispatch>,
Attaches the provided Subscriber
to this type, returning a
WithDispatch
wrapper. Read more
sourcefn with_current_subscriber(self) -> WithDispatch<Self>
fn with_current_subscriber(self) -> WithDispatch<Self>
Attaches the current default Subscriber
to this type, returning a
WithDispatch
wrapper. Read more