Struct googapis::google::cloud::aiplatform::v1beta1::study_spec::ConvexStopConfig[][src]

pub struct ConvexStopConfig {
    pub max_num_steps: i64,
    pub min_num_steps: i64,
    pub autoregressive_order: i64,
    pub learning_rate_parameter_name: String,
    pub use_seconds: bool,
}
Expand description

Configuration for ConvexStopPolicy.

Fields

max_num_steps: i64

Steps used in predicting the final objective for early stopped trials. In general, it’s set to be the same as the defined steps in training / tuning. When use_steps is false, this field is set to the maximum elapsed seconds.

min_num_steps: i64

Minimum number of steps for a trial to complete. Trials which do not have a measurement with num_steps > min_num_steps won’t be considered for early stopping. It’s ok to set it to 0, and a trial can be early stopped at any stage. By default, min_num_steps is set to be one-tenth of the max_num_steps. When use_steps is false, this field is set to the minimum elapsed seconds.

autoregressive_order: i64

The number of Trial measurements used in autoregressive model for value prediction. A trial won’t be considered early stopping if has fewer measurement points.

learning_rate_parameter_name: String

The hyper-parameter name used in the tuning job that stands for learning rate. Leave it blank if learning rate is not in a parameter in tuning. The learning_rate is used to estimate the objective value of the ongoing trial.

use_seconds: bool

This bool determines whether or not the rule is applied based on elapsed_secs or steps. If use_seconds==false, the early stopping decision is made according to the predicted objective values according to the target steps. If use_seconds==true, elapsed_secs is used instead of steps. Also, in this case, the parameters max_num_steps and min_num_steps are overloaded to contain max_elapsed_seconds and min_elapsed_seconds.

Trait Implementations

Returns a copy of the value. Read more

Performs copy-assignment from source. Read more

Formats the value using the given formatter. Read more

Returns the “default value” for a type. Read more

Returns the encoded length of the message without a length delimiter.

Clears the message, resetting all fields to their default.

Encodes the message to a buffer. Read more

Encodes the message to a newly allocated buffer.

Encodes the message with a length-delimiter to a buffer. Read more

Encodes the message with a length-delimiter to a newly allocated buffer.

Decodes an instance of the message from a buffer. Read more

Decodes a length-delimited instance of the message from the buffer.

Decodes an instance of the message from a buffer, and merges it into self. Read more

Decodes a length-delimited instance of the message from buffer, and merges it into self. Read more

This method tests for self and other values to be equal, and is used by ==. Read more

This method tests for !=.

Auto Trait Implementations

Blanket Implementations

Gets the TypeId of self. Read more

Immutably borrows from an owned value. Read more

Mutably borrows from an owned value. Read more

Performs the conversion.

Instruments this type with the provided Span, returning an Instrumented wrapper. Read more

Instruments this type with the current Span, returning an Instrumented wrapper. Read more

Performs the conversion.

Wrap the input message T in a tonic::Request

The resulting type after obtaining ownership.

Creates owned data from borrowed data, usually by cloning. Read more

🔬 This is a nightly-only experimental API. (toowned_clone_into)

recently added

Uses borrowed data to replace owned data, usually by cloning. Read more

The type returned in the event of a conversion error.

Performs the conversion.

The type returned in the event of a conversion error.

Performs the conversion.

Attaches the provided Subscriber to this type, returning a WithDispatch wrapper. Read more

Attaches the current default Subscriber to this type, returning a WithDispatch wrapper. Read more