Class BatchPredictionJob.InstanceConfig.Builder (3.7.0)

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

Protobuf type google.cloud.aiplatform.v1beta1.BatchPredictionJob.InstanceConfig

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 is not specified. When excluded_fields is populated, included_fields must be empty. The input must be JSONL with objects at each line, CSV, BigQuery or TfRecord.

repeated string excluded_fields = 4;

Fields that will be included in the prediction instance that is sent to the Model. If 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 must be empty. The input must be JSONL with objects at each line, CSV, BigQuery or TfRecord.

repeated string included_fields = 3;

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 is not specified. When excluded_fields is populated, included_fields must be empty. The input must be JSONL with objects at each line, CSV, BigQuery or TfRecord.

repeated string excluded_fields = 4;

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 is not specified. When excluded_fields is populated, included_fields must be empty. The input must be JSONL with objects at each line, CSV, BigQuery or TfRecord.

repeated string excluded_fields = 4;

Fields that will be included in the prediction instance that is sent to the Model. If 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 must be empty. The input must be JSONL with objects at each line, CSV, BigQuery or TfRecord.

repeated string included_fields = 3;

Fields that will be included in the prediction instance that is sent to the Model. If 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 must be empty. The input must be JSONL with objects at each line, CSV, BigQuery or TfRecord.

repeated string included_fields = 3;

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 is not specified. When excluded_fields is populated, included_fields must be empty. The input must be JSONL with objects at each line, CSV, BigQuery or TfRecord.

repeated string excluded_fields = 4;

Fields that will be included in the prediction instance that is sent to the Model. If 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 must be empty. The input must be JSONL with objects at each line, CSV, BigQuery or TfRecord.

repeated string included_fields = 3;

The format of the instance that the Model accepts. Vertex AI will convert compatible batch prediction input instance formats 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 is populated. included_fields must be populated for specifying field orders.
  • For jsonl, if each line of the JSONL input is an object, 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 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.

string instance_type = 1;

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. 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.

string key_field = 2;

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 is not specified. When excluded_fields is populated, included_fields must be empty. The input must be JSONL with objects at each line, CSV, BigQuery or TfRecord.

repeated string excluded_fields = 4;

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 is not specified. When excluded_fields is populated, included_fields must be empty. The input must be JSONL with objects at each line, CSV, BigQuery or TfRecord.

repeated string excluded_fields = 4;

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 is not specified. When excluded_fields is populated, included_fields must be empty. The input must be JSONL with objects at each line, CSV, BigQuery or TfRecord.

repeated string excluded_fields = 4;

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 is not specified. When excluded_fields is populated, included_fields must be empty. The input must be JSONL with objects at each line, CSV, BigQuery or TfRecord.

repeated string excluded_fields = 4;

Fields that will be included in the prediction instance that is sent to the Model. If 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 must be empty. The input must be JSONL with objects at each line, CSV, BigQuery or TfRecord.

repeated string included_fields = 3;

Fields that will be included in the prediction instance that is sent to the Model. If 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 must be empty. The input must be JSONL with objects at each line, CSV, BigQuery or TfRecord.

repeated string included_fields = 3;

Fields that will be included in the prediction instance that is sent to the Model. If 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 must be empty. The input must be JSONL with objects at each line, CSV, BigQuery or TfRecord.

repeated string included_fields = 3;

Fields that will be included in the prediction instance that is sent to the Model. If 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 must be empty. The input must be JSONL with objects at each line, CSV, BigQuery or TfRecord.

repeated string included_fields = 3;

The format of the instance that the Model accepts. Vertex AI will convert compatible batch prediction input instance formats 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 is populated. included_fields must be populated for specifying field orders.
  • For jsonl, if each line of the JSONL input is an object, 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 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.

string instance_type = 1;

The format of the instance that the Model accepts. Vertex AI will convert compatible batch prediction input instance formats 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 is populated. included_fields must be populated for specifying field orders.
  • For jsonl, if each line of the JSONL input is an object, 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 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.

string instance_type = 1;

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. 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.

string key_field = 2;

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. 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.

string key_field = 2;

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 is not specified. When excluded_fields is populated, included_fields must be empty. The input must be JSONL with objects at each line, CSV, BigQuery or TfRecord.

repeated string excluded_fields = 4;

Fields that will be included in the prediction instance that is sent to the Model. If 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 must be empty. The input must be JSONL with objects at each line, CSV, BigQuery or TfRecord.

repeated string included_fields = 3;

The format of the instance that the Model accepts. Vertex AI will convert compatible batch prediction input instance formats 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 is populated. included_fields must be populated for specifying field orders.
  • For jsonl, if each line of the JSONL input is an object, 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 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.

string instance_type = 1;

The format of the instance that the Model accepts. Vertex AI will convert compatible batch prediction input instance formats 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 is populated. included_fields must be populated for specifying field orders.
  • For jsonl, if each line of the JSONL input is an object, 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 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.

string instance_type = 1;

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. 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.

string key_field = 2;

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. 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.

string key_field = 2;