Struct google_api_proto::google::cloud::automl::v1beta1::ImageClassificationModelMetadata

pub struct ImageClassificationModelMetadata {
    pub base_model_id: String,
    pub train_budget: i64,
    pub train_cost: i64,
    pub stop_reason: String,
    pub model_type: String,
    pub node_qps: f64,
    pub node_count: i64,
}

Model metadata for image classification.

Fields

base_model_id: String

Optional. The ID of the base model. If it is specified, the new model will be created based on the base model. Otherwise, the new model will be created from scratch. The base model must be in the same project and location as the new model to create, and have the same model_type.

train_budget: i64

Required. The train budget of creating this model, expressed in hours. The actual train_cost will be equal or less than this value.

train_cost: i64

Output only. The actual train cost of creating this model, expressed in hours. If this model is created from a base model, the train cost used to create the base model are not included.

stop_reason: String

Output only. The reason that this create model operation stopped, e.g. BUDGET_REACHED, MODEL_CONVERGED.

model_type: String

Optional. Type of the model. The available values are:

• cloud - Model to be used via prediction calls to AutoML API. This is the default value.
• mobile-low-latency-1 - A model that, in addition to providing prediction via AutoML API, can also be exported (see [AutoMl.ExportModel][google.cloud.automl.v1beta1.AutoMl.ExportModel]) and used on a mobile or edge device with TensorFlow afterwards. Expected to have low latency, but may have lower prediction quality than other models.
• mobile-versatile-1 - A model that, in addition to providing prediction via AutoML API, can also be exported (see [AutoMl.ExportModel][google.cloud.automl.v1beta1.AutoMl.ExportModel]) and used on a mobile or edge device with TensorFlow afterwards.
• mobile-high-accuracy-1 - A model that, in addition to providing prediction via AutoML API, can also be exported (see [AutoMl.ExportModel][google.cloud.automl.v1beta1.AutoMl.ExportModel]) and used on a mobile or edge device with TensorFlow afterwards. Expected to have a higher latency, but should also have a higher prediction quality than other models.
• mobile-core-ml-low-latency-1 - A model that, in addition to providing prediction via AutoML API, can also be exported (see [AutoMl.ExportModel][google.cloud.automl.v1beta1.AutoMl.ExportModel]) and used on a mobile device with Core ML afterwards. Expected to have low latency, but may have lower prediction quality than other models.
• mobile-core-ml-versatile-1 - A model that, in addition to providing prediction via AutoML API, can also be exported (see [AutoMl.ExportModel][google.cloud.automl.v1beta1.AutoMl.ExportModel]) and used on a mobile device with Core ML afterwards.
• mobile-core-ml-high-accuracy-1 - A model that, in addition to providing prediction via AutoML API, can also be exported (see [AutoMl.ExportModel][google.cloud.automl.v1beta1.AutoMl.ExportModel]) and used on a mobile device with Core ML afterwards. Expected to have a higher latency, but should also have a higher prediction quality than other models.

node_qps: f64

Output only. An approximate number of online prediction QPS that can be supported by this model per each node on which it is deployed.

node_count: i64

Output only. The number of nodes this model is deployed on. A node is an abstraction of a machine resource, which can handle online prediction QPS as given in the node_qps field.

Trait Implementations

impl Clone for ImageClassificationModelMetadata

fn clone(&self) -> ImageClassificationModelMetadata

Returns a copy of the value.

fn clone_from(&mut self, source: &Self)

Performs copy-assignment from source.

impl Debug for ImageClassificationModelMetadata

fn fmt(&self, f: &mut Formatter<'_>) -> Result

Formats the value using the given formatter.

impl Default for ImageClassificationModelMetadata

fn default() -> Self

Returns the “default value” for a type.

impl Message for ImageClassificationModelMetadata

fn encoded_len(&self) -> usize

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

fn clear(&mut self)

Clears the message, resetting all fields to their default.

fn encode(&self, buf: &mut impl BufMut) -> Result<(), EncodeError>

where Self: Sized,

Encodes the message to a buffer.

fn encode_to_vec(&self) -> Vec<u8>

where Self: Sized,

Encodes the message to a newly allocated buffer.

fn encode_length_delimited( &self, buf: &mut impl BufMut, ) -> Result<(), EncodeError>

where Self: Sized,

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

fn encode_length_delimited_to_vec(&self) -> Vec<u8>

where Self: Sized,

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

fn decode(buf: impl Buf) -> Result<Self, DecodeError>

where Self: Default,

Decodes an instance of the message from a buffer.

fn decode_length_delimited(buf: impl Buf) -> Result<Self, DecodeError>

where Self: Default,

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

fn merge(&mut self, buf: impl Buf) -> Result<(), DecodeError>

where Self: Sized,

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

fn merge_length_delimited(&mut self, buf: impl Buf) -> Result<(), DecodeError>

where Self: Sized,

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

impl PartialEq for ImageClassificationModelMetadata

fn eq(&self, other: &ImageClassificationModelMetadata) -> bool

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

fn ne(&self, other: &Rhs) -> bool

This method tests for !=. The default implementation is almost always sufficient, and should not be overridden without very good reason.

impl StructuralPartialEq for ImageClassificationModelMetadata