Struct google_api_proto::google::cloud::aiplatform::v1beta1::batch_prediction_job::InstanceConfig

pub struct InstanceConfig {
    pub instance_type: String,
    pub key_field: String,
    pub included_fields: Vec<String>,
    pub excluded_fields: Vec<String>,
}

Configuration defining how to transform batch prediction input instances to the instances that the Model accepts.

Fields

instance_type: String

The format of the instance that the Model accepts. Vertex AI will convert compatible [batch prediction input instance formats][google.cloud.aiplatform.v1beta1.BatchPredictionJob.InputConfig.instances_format] to the specified format.

Supported values are:

• object: Each input is converted to JSON object format.

  • For bigquery, each row is converted to an object.
  • For jsonl, each line of the JSONL input must be an object.
  • Does not apply to csv, file-list, tf-record, or tf-record-gzip.

• array: Each input is converted to JSON array format.

  • For bigquery, each row is converted to an array. The order of columns is determined by the BigQuery column order, unless [included_fields][google.cloud.aiplatform.v1beta1.BatchPredictionJob.InstanceConfig.included_fields] is populated. [included_fields][google.cloud.aiplatform.v1beta1.BatchPredictionJob.InstanceConfig.included_fields] must be populated for specifying field orders.
  • For jsonl, if each line of the JSONL input is an object, [included_fields][google.cloud.aiplatform.v1beta1.BatchPredictionJob.InstanceConfig.included_fields] must be populated for specifying field orders.
  • Does not apply to csv, file-list, tf-record, or tf-record-gzip.

If not specified, Vertex AI converts the batch prediction input as follows:

• For bigquery and csv, the behavior is the same as array. The order of columns is the same as defined in the file or table, unless [included_fields][google.cloud.aiplatform.v1beta1.BatchPredictionJob.InstanceConfig.included_fields] is populated.
• For jsonl, the prediction instance format is determined by each line of the input.
• For tf-record/tf-record-gzip, each record will be converted to an object in the format of {"b64": <value>}, where <value> is the Base64-encoded string of the content of the record.
• For file-list, each file in the list will be converted to an object in the format of {"b64": <value>}, where <value> is the Base64-encoded string of the content of the file.

key_field: String

The name of the field that is considered as a key.

The values identified by the key field is not included in the transformed instances that is sent to the Model. This is similar to specifying this name of the field in [excluded_fields][google.cloud.aiplatform.v1beta1.BatchPredictionJob.InstanceConfig.excluded_fields]. In addition, the batch prediction output will not include the instances. Instead the output will only include the value of the key field, in a field named key in the output:

• For jsonl output format, the output will have a key field instead of the instance field.
• For csv/bigquery output format, the output will have have a key column instead of the instance feature columns.

The input must be JSONL with objects at each line, CSV, BigQuery or TfRecord.

included_fields: Vec<String>

Fields that will be included in the prediction instance that is sent to the Model.

If [instance_type][google.cloud.aiplatform.v1beta1.BatchPredictionJob.InstanceConfig.instance_type] is array, the order of field names in included_fields also determines the order of the values in the array.

When included_fields is populated, [excluded_fields][google.cloud.aiplatform.v1beta1.BatchPredictionJob.InstanceConfig.excluded_fields] must be empty.

The input must be JSONL with objects at each line, BigQuery or TfRecord.

excluded_fields: Vec<String>

Fields that will be excluded in the prediction instance that is sent to the Model.

Excluded will be attached to the batch prediction output if [key_field][google.cloud.aiplatform.v1beta1.BatchPredictionJob.InstanceConfig.key_field] is not specified.

When excluded_fields is populated, [included_fields][google.cloud.aiplatform.v1beta1.BatchPredictionJob.InstanceConfig.included_fields] must be empty.

The input must be JSONL with objects at each line, BigQuery or TfRecord.

Trait Implementations

impl Clone for InstanceConfig

fn clone(&self) -> InstanceConfig

Returns a copy of the value.

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

Performs copy-assignment from source.

impl Debug for InstanceConfig

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

Formats the value using the given formatter.

impl Default for InstanceConfig

fn default() -> Self

Returns the “default value” for a type.

impl Message for InstanceConfig

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 InstanceConfig

fn eq(&self, other: &InstanceConfig) -> 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 InstanceConfig