- 1.63.0 (latest)
- 1.62.0
- 1.60.0
- 1.59.0
- 1.58.0
- 1.57.0
- 1.56.0
- 1.55.0
- 1.54.1
- 1.53.0
- 1.52.0
- 1.51.0
- 1.50.0
- 1.49.0
- 1.48.0
- 1.47.0
- 1.46.0
- 1.45.0
- 1.44.0
- 1.43.0
- 1.39.0
- 1.38.1
- 1.37.0
- 1.36.4
- 1.35.0
- 1.34.0
- 1.33.1
- 1.32.0
- 1.31.1
- 1.30.1
- 1.29.0
- 1.28.1
- 1.27.1
- 1.26.1
- 1.25.0
- 1.24.1
- 1.23.0
- 1.22.1
- 1.21.0
- 1.20.0
- 1.19.1
- 1.18.3
- 1.17.1
- 1.16.1
- 1.15.1
- 1.14.0
- 1.13.1
- 1.12.1
- 1.11.0
- 1.10.0
- 1.9.0
- 1.8.1
- 1.7.1
- 1.6.2
- 1.5.0
- 1.4.3
- 1.3.0
- 1.2.0
- 1.1.1
- 1.0.1
- 0.9.0
- 0.8.0
- 0.7.1
- 0.6.0
- 0.5.1
- 0.4.0
- 0.3.1
InstanceConfig(mapping=None, *, ignore_unknown_fields=False, **kwargs)
Configuration defining how to transform batch prediction input instances to the instances that the Model accepts.
Attributes |
|
---|---|
Name | Description |
instance_type |
str
The format of the instance that the Model accepts. Vertex AI will convert compatible [batch prediction input instance formats][google.cloud.aiplatform.v1.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
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": , where 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": , where is the
Base64-encoded string of the content of the file.
|
key_field |
str
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.
|
included_fields |
MutableSequence[str]
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, BigQuery
or TfRecord.
|
excluded_fields |
MutableSequence[str]
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, BigQuery or TfRecord. |
Methods
InstanceConfig
InstanceConfig(mapping=None, *, ignore_unknown_fields=False, **kwargs)
Configuration defining how to transform batch prediction input instances to the instances that the Model accepts.