Struct gapi_grpc::google::cloud::aiplatform::v1beta1::schema::trainingjob::definition::AutoMlImageClassificationInputs[][src]

pub struct AutoMlImageClassificationInputs {
    pub model_type: i32,
    pub base_model_id: String,
    pub budget_milli_node_hours: i64,
    pub disable_early_stopping: bool,
    pub multi_label: bool,
}

Fields

model_type: i32base_model_id: String

The ID of the base model. If it is specified, the new model will be trained based on the base model. Otherwise, the new model will be trained from scratch. The base model must be in the same Project and Location as the new Model to train, and have the same modelType.

budget_milli_node_hours: i64

The training budget of creating this model, expressed in milli node hours i.e. 1,000 value in this field means 1 node hour. The actual metadata.costMilliNodeHours will be equal or less than this value. If further model training ceases to provide any improvements, it will stop without using the full budget and the metadata.successfulStopReason will be model-converged. Note, node_hour = actual_hour * number_of_nodes_involved. For modelType cloud(default), the budget must be between 8,000 and 800,000 milli node hours, inclusive. The default value is 192,000 which represents one day in wall time, considering 8 nodes are used. For model types mobile-tf-low-latency-1, mobile-tf-versatile-1, mobile-tf-high-accuracy-1, the training budget must be between 1,000 and 100,000 milli node hours, inclusive. The default value is 24,000 which represents one day in wall time on a single node that is used.

disable_early_stopping: bool

Use the entire training budget. This disables the early stopping feature. When false the early stopping feature is enabled, which means that AutoML Image Classification might stop training before the entire training budget has been used.

multi_label: bool

If false, a single-label (multi-class) Model will be trained (i.e. assuming that for each image just up to one annotation may be applicable). If true, a multi-label Model will be trained (i.e. assuming that for each image multiple annotations may be applicable).

Implementations

impl AutoMlImageClassificationInputs[src]

pub fn model_type(&self) -> ModelType[src]

Returns the enum value of model_type, or the default if the field is set to an invalid enum value.

pub fn set_model_type(&mut self, value: ModelType)[src]

Sets model_type to the provided enum value.

Trait Implementations

impl Clone for AutoMlImageClassificationInputs[src]

impl Debug for AutoMlImageClassificationInputs[src]

impl Default for AutoMlImageClassificationInputs[src]

impl Message for AutoMlImageClassificationInputs[src]

impl PartialEq<AutoMlImageClassificationInputs> for AutoMlImageClassificationInputs[src]

impl StructuralPartialEq for AutoMlImageClassificationInputs[src]

Auto Trait Implementations

impl RefUnwindSafe for AutoMlImageClassificationInputs

impl Send for AutoMlImageClassificationInputs

impl Sync for AutoMlImageClassificationInputs

impl Unpin for AutoMlImageClassificationInputs

impl UnwindSafe for AutoMlImageClassificationInputs

Blanket Implementations

impl<T> Any for T where
    T: 'static + ?Sized
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impl<T> Borrow<T> for T where
    T: ?Sized
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impl<T> BorrowMut<T> for T where
    T: ?Sized
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impl<T> From<T> for T[src]

impl<T> Instrument for T[src]

impl<T> Instrument for T[src]

impl<T, U> Into<U> for T where
    U: From<T>, 
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impl<T> IntoRequest<T> for T[src]

impl<T> ToOwned for T where
    T: Clone
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type Owned = T

The resulting type after obtaining ownership.

impl<T, U> TryFrom<U> for T where
    U: Into<T>, 
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type Error = Infallible

The type returned in the event of a conversion error.

impl<T, U> TryInto<U> for T where
    U: TryFrom<T>, 
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type Error = <U as TryFrom<T>>::Error

The type returned in the event of a conversion error.

impl<V, T> VZip<V> for T where
    V: MultiLane<T>, 
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impl<T> WithSubscriber for T[src]