public final class InputDataConfig extends GeneratedMessageV3 implements InputDataConfigOrBuilder
Specifies Vertex AI owned input data to be used for training, and
possibly evaluating, the Model.
Protobuf type google.cloud.aiplatform.v1.InputDataConfig
Fields
public static final int ANNOTATIONS_FILTER_FIELD_NUMBER
Field Value
public static final int ANNOTATION_SCHEMA_URI_FIELD_NUMBER
Field Value
public static final int BIGQUERY_DESTINATION_FIELD_NUMBER
Field Value
public static final int DATASET_ID_FIELD_NUMBER
Field Value
public static final int FILTER_SPLIT_FIELD_NUMBER
Field Value
public static final int FRACTION_SPLIT_FIELD_NUMBER
Field Value
public static final int GCS_DESTINATION_FIELD_NUMBER
Field Value
public static final int PREDEFINED_SPLIT_FIELD_NUMBER
Field Value
public static final int TIMESTAMP_SPLIT_FIELD_NUMBER
Field Value
Methods
public boolean equals(Object obj)
Parameter
Returns
Overrides
public String getAnnotationSchemaUri()
Applicable only to custom training with Datasets that have DataItems and
Annotations.
Cloud Storage URI that points to a YAML file describing the annotation
schema. The schema is defined as an OpenAPI 3.0.2 Schema
Object.
The schema files that can be used here are found in
gs://google-cloud-aiplatform/schema/dataset/annotation/ , note that the
chosen schema must be consistent with
metadata of the Dataset specified by
dataset_id.
Only Annotations that both match this schema and belong to DataItems not
ignored by the split method are used in respectively training, validation
or test role, depending on the role of the DataItem they are on.
When used in conjunction with annotations_filter, the Annotations used
for training are filtered by both annotations_filter and
annotation_schema_uri.
string annotation_schema_uri = 9;
| Type | Description |
| String | The annotationSchemaUri.
|
public ByteString getAnnotationSchemaUriBytes()
Applicable only to custom training with Datasets that have DataItems and
Annotations.
Cloud Storage URI that points to a YAML file describing the annotation
schema. The schema is defined as an OpenAPI 3.0.2 Schema
Object.
The schema files that can be used here are found in
gs://google-cloud-aiplatform/schema/dataset/annotation/ , note that the
chosen schema must be consistent with
metadata of the Dataset specified by
dataset_id.
Only Annotations that both match this schema and belong to DataItems not
ignored by the split method are used in respectively training, validation
or test role, depending on the role of the DataItem they are on.
When used in conjunction with annotations_filter, the Annotations used
for training are filtered by both annotations_filter and
annotation_schema_uri.
string annotation_schema_uri = 9;
| Type | Description |
| ByteString | The bytes for annotationSchemaUri.
|
public String getAnnotationsFilter()
Applicable only to Datasets that have DataItems and Annotations.
A filter on Annotations of the Dataset. Only Annotations that both
match this filter and belong to DataItems not ignored by the split method
are used in respectively training, validation or test role, depending on
the role of the DataItem they are on (for the auto-assigned that role is
decided by Vertex AI). A filter with same syntax as the one used in
ListAnnotations may be used, but note
here it filters across all Annotations of the Dataset, and not just within
a single DataItem.
string annotations_filter = 6;
| Type | Description |
| String | The annotationsFilter.
|
public ByteString getAnnotationsFilterBytes()
Applicable only to Datasets that have DataItems and Annotations.
A filter on Annotations of the Dataset. Only Annotations that both
match this filter and belong to DataItems not ignored by the split method
are used in respectively training, validation or test role, depending on
the role of the DataItem they are on (for the auto-assigned that role is
decided by Vertex AI). A filter with same syntax as the one used in
ListAnnotations may be used, but note
here it filters across all Annotations of the Dataset, and not just within
a single DataItem.
string annotations_filter = 6;
| Type | Description |
| ByteString | The bytes for annotationsFilter.
|
public BigQueryDestination getBigqueryDestination()
Only applicable to custom training with tabular Dataset with BigQuery
source.
The BigQuery project location where the training data is to be written
to. In the given project a new dataset is created with name
dataset_<dataset-id><annotation-type><timestamp-of-training-call>
where timestamp is in YYYY_MM_DDThh_mm_ss_sssZ format. All training
input data is written into that dataset. In the dataset three
tables are created, training, validation and test.
- AIP_DATA_FORMAT = "bigquery".
- AIP_TRAINING_DATA_URI =
"bigquery_destination.dataset_<dataset-id><annotation-type><time>.training"
- AIP_VALIDATION_DATA_URI =
"bigquery_destination.dataset_<dataset-id><annotation-type><time>.validation"
- AIP_TEST_DATA_URI =
"bigquery_destination.dataset_<dataset-id><annotation-type><time>.test"
.google.cloud.aiplatform.v1.BigQueryDestination bigquery_destination = 10;
public BigQueryDestinationOrBuilder getBigqueryDestinationOrBuilder()
Only applicable to custom training with tabular Dataset with BigQuery
source.
The BigQuery project location where the training data is to be written
to. In the given project a new dataset is created with name
dataset_<dataset-id><annotation-type><timestamp-of-training-call>
where timestamp is in YYYY_MM_DDThh_mm_ss_sssZ format. All training
input data is written into that dataset. In the dataset three
tables are created, training, validation and test.
- AIP_DATA_FORMAT = "bigquery".
- AIP_TRAINING_DATA_URI =
"bigquery_destination.dataset_<dataset-id><annotation-type><time>.training"
- AIP_VALIDATION_DATA_URI =
"bigquery_destination.dataset_<dataset-id><annotation-type><time>.validation"
- AIP_TEST_DATA_URI =
"bigquery_destination.dataset_<dataset-id><annotation-type><time>.test"
.google.cloud.aiplatform.v1.BigQueryDestination bigquery_destination = 10;
public String getDatasetId()
Required. The ID of the Dataset in the same Project and Location which data will be
used to train the Model. The Dataset must use schema compatible with
Model being trained, and what is compatible should be described in the
used TrainingPipeline's [training_task_definition]
[google.cloud.aiplatform.v1.TrainingPipeline.training_task_definition].
For tabular Datasets, all their data is exported to training, to pick
and choose from.
string dataset_id = 1 [(.google.api.field_behavior) = REQUIRED];
| Type | Description |
| String | The datasetId.
|
public ByteString getDatasetIdBytes()
Required. The ID of the Dataset in the same Project and Location which data will be
used to train the Model. The Dataset must use schema compatible with
Model being trained, and what is compatible should be described in the
used TrainingPipeline's [training_task_definition]
[google.cloud.aiplatform.v1.TrainingPipeline.training_task_definition].
For tabular Datasets, all their data is exported to training, to pick
and choose from.
string dataset_id = 1 [(.google.api.field_behavior) = REQUIRED];
public static InputDataConfig getDefaultInstance()
Returns
public InputDataConfig getDefaultInstanceForType()
Returns
public static final Descriptors.Descriptor getDescriptor()
Returns
public InputDataConfig.DestinationCase getDestinationCase()
Returns
public FilterSplit getFilterSplit()
Split based on the provided filters for each set.
.google.cloud.aiplatform.v1.FilterSplit filter_split = 3;
public FilterSplitOrBuilder getFilterSplitOrBuilder()
Split based on the provided filters for each set.
.google.cloud.aiplatform.v1.FilterSplit filter_split = 3;
public FractionSplit getFractionSplit()
Split based on fractions defining the size of each set.
.google.cloud.aiplatform.v1.FractionSplit fraction_split = 2;
public FractionSplitOrBuilder getFractionSplitOrBuilder()
Split based on fractions defining the size of each set.
.google.cloud.aiplatform.v1.FractionSplit fraction_split = 2;
public GcsDestination getGcsDestination()
The Cloud Storage location where the training data is to be
written to. In the given directory a new directory is created with
name:
dataset-<dataset-id>-<annotation-type>-<timestamp-of-training-call>
where timestamp is in YYYY-MM-DDThh:mm:ss.sssZ ISO-8601 format.
All training input data is written into that directory.
The Vertex AI environment variables representing Cloud Storage
data URIs are represented in the Cloud Storage wildcard
format to support sharded data. e.g.: "gs://.../training-*.jsonl"
- AIP_DATA_FORMAT = "jsonl" for non-tabular data, "csv" for tabular data
- AIP_TRAINING_DATA_URI =
"gcs_destination/dataset-<dataset-id>-<annotation-type>-<time>/training-*.${AIP_DATA_FORMAT}"
- AIP_VALIDATION_DATA_URI =
"gcs_destination/dataset-<dataset-id>-<annotation-type>-<time>/validation-*.${AIP_DATA_FORMAT}"
- AIP_TEST_DATA_URI =
"gcs_destination/dataset-<dataset-id>-<annotation-type>-<time>/test-*.${AIP_DATA_FORMAT}"
.google.cloud.aiplatform.v1.GcsDestination gcs_destination = 8;
public GcsDestinationOrBuilder getGcsDestinationOrBuilder()
The Cloud Storage location where the training data is to be
written to. In the given directory a new directory is created with
name:
dataset-<dataset-id>-<annotation-type>-<timestamp-of-training-call>
where timestamp is in YYYY-MM-DDThh:mm:ss.sssZ ISO-8601 format.
All training input data is written into that directory.
The Vertex AI environment variables representing Cloud Storage
data URIs are represented in the Cloud Storage wildcard
format to support sharded data. e.g.: "gs://.../training-*.jsonl"
- AIP_DATA_FORMAT = "jsonl" for non-tabular data, "csv" for tabular data
- AIP_TRAINING_DATA_URI =
"gcs_destination/dataset-<dataset-id>-<annotation-type>-<time>/training-*.${AIP_DATA_FORMAT}"
- AIP_VALIDATION_DATA_URI =
"gcs_destination/dataset-<dataset-id>-<annotation-type>-<time>/validation-*.${AIP_DATA_FORMAT}"
- AIP_TEST_DATA_URI =
"gcs_destination/dataset-<dataset-id>-<annotation-type>-<time>/test-*.${AIP_DATA_FORMAT}"
.google.cloud.aiplatform.v1.GcsDestination gcs_destination = 8;
public Parser<InputDataConfig> getParserForType()
Returns
Overrides
public PredefinedSplit getPredefinedSplit()
Supported only for tabular Datasets.
Split based on a predefined key.
.google.cloud.aiplatform.v1.PredefinedSplit predefined_split = 4;
public PredefinedSplitOrBuilder getPredefinedSplitOrBuilder()
Supported only for tabular Datasets.
Split based on a predefined key.
.google.cloud.aiplatform.v1.PredefinedSplit predefined_split = 4;
public int getSerializedSize()
Returns
Overrides
public InputDataConfig.SplitCase getSplitCase()
Returns
public TimestampSplit getTimestampSplit()
Supported only for tabular Datasets.
Split based on the timestamp of the input data pieces.
.google.cloud.aiplatform.v1.TimestampSplit timestamp_split = 5;
public TimestampSplitOrBuilder getTimestampSplitOrBuilder()
Supported only for tabular Datasets.
Split based on the timestamp of the input data pieces.
.google.cloud.aiplatform.v1.TimestampSplit timestamp_split = 5;
public final UnknownFieldSet getUnknownFields()
Returns
Overrides
public boolean hasBigqueryDestination()
Only applicable to custom training with tabular Dataset with BigQuery
source.
The BigQuery project location where the training data is to be written
to. In the given project a new dataset is created with name
dataset_<dataset-id><annotation-type><timestamp-of-training-call>
where timestamp is in YYYY_MM_DDThh_mm_ss_sssZ format. All training
input data is written into that dataset. In the dataset three
tables are created, training, validation and test.
- AIP_DATA_FORMAT = "bigquery".
- AIP_TRAINING_DATA_URI =
"bigquery_destination.dataset_<dataset-id><annotation-type><time>.training"
- AIP_VALIDATION_DATA_URI =
"bigquery_destination.dataset_<dataset-id><annotation-type><time>.validation"
- AIP_TEST_DATA_URI =
"bigquery_destination.dataset_<dataset-id><annotation-type><time>.test"
.google.cloud.aiplatform.v1.BigQueryDestination bigquery_destination = 10;
| Type | Description |
| boolean | Whether the bigqueryDestination field is set.
|
public boolean hasFilterSplit()
Split based on the provided filters for each set.
.google.cloud.aiplatform.v1.FilterSplit filter_split = 3;
| Type | Description |
| boolean | Whether the filterSplit field is set.
|
public boolean hasFractionSplit()
Split based on fractions defining the size of each set.
.google.cloud.aiplatform.v1.FractionSplit fraction_split = 2;
| Type | Description |
| boolean | Whether the fractionSplit field is set.
|
public boolean hasGcsDestination()
The Cloud Storage location where the training data is to be
written to. In the given directory a new directory is created with
name:
dataset-<dataset-id>-<annotation-type>-<timestamp-of-training-call>
where timestamp is in YYYY-MM-DDThh:mm:ss.sssZ ISO-8601 format.
All training input data is written into that directory.
The Vertex AI environment variables representing Cloud Storage
data URIs are represented in the Cloud Storage wildcard
format to support sharded data. e.g.: "gs://.../training-*.jsonl"
- AIP_DATA_FORMAT = "jsonl" for non-tabular data, "csv" for tabular data
- AIP_TRAINING_DATA_URI =
"gcs_destination/dataset-<dataset-id>-<annotation-type>-<time>/training-*.${AIP_DATA_FORMAT}"
- AIP_VALIDATION_DATA_URI =
"gcs_destination/dataset-<dataset-id>-<annotation-type>-<time>/validation-*.${AIP_DATA_FORMAT}"
- AIP_TEST_DATA_URI =
"gcs_destination/dataset-<dataset-id>-<annotation-type>-<time>/test-*.${AIP_DATA_FORMAT}"
.google.cloud.aiplatform.v1.GcsDestination gcs_destination = 8;
| Type | Description |
| boolean | Whether the gcsDestination field is set.
|
public boolean hasPredefinedSplit()
Supported only for tabular Datasets.
Split based on a predefined key.
.google.cloud.aiplatform.v1.PredefinedSplit predefined_split = 4;
| Type | Description |
| boolean | Whether the predefinedSplit field is set.
|
public boolean hasTimestampSplit()
Supported only for tabular Datasets.
Split based on the timestamp of the input data pieces.
.google.cloud.aiplatform.v1.TimestampSplit timestamp_split = 5;
| Type | Description |
| boolean | Whether the timestampSplit field is set.
|
Returns
Overrides
protected GeneratedMessageV3.FieldAccessorTable internalGetFieldAccessorTable()
Returns
Overrides
public final boolean isInitialized()
Returns
Overrides
public static InputDataConfig.Builder newBuilder()
Returns
public static InputDataConfig.Builder newBuilder(InputDataConfig prototype)
Parameter
Returns
public InputDataConfig.Builder newBuilderForType()
Returns
protected InputDataConfig.Builder newBuilderForType(GeneratedMessageV3.BuilderParent parent)
Parameter
Returns
Overrides
protected Object newInstance(GeneratedMessageV3.UnusedPrivateParameter unused)
Parameter
Returns
Overrides
public static InputDataConfig parseDelimitedFrom(InputStream input)
Parameter
Returns
Exceptions
public static InputDataConfig parseDelimitedFrom(InputStream input, ExtensionRegistryLite extensionRegistry)
Parameters
Returns
Exceptions
public static InputDataConfig parseFrom(byte[] data)
Parameter
| Name | Description |
| data | byte[]
|
Returns
Exceptions
public static InputDataConfig parseFrom(byte[] data, ExtensionRegistryLite extensionRegistry)
Parameters
Returns
Exceptions
public static InputDataConfig parseFrom(ByteString data)
Parameter
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Exceptions
public static InputDataConfig parseFrom(ByteString data, ExtensionRegistryLite extensionRegistry)
Parameters
Returns
Exceptions
public static InputDataConfig parseFrom(CodedInputStream input)
Parameter
Returns
Exceptions
public static InputDataConfig parseFrom(CodedInputStream input, ExtensionRegistryLite extensionRegistry)
Parameters
Returns
Exceptions
public static InputDataConfig parseFrom(InputStream input)
Parameter
Returns
Exceptions
public static InputDataConfig parseFrom(InputStream input, ExtensionRegistryLite extensionRegistry)
Parameters
Returns
Exceptions
public static InputDataConfig parseFrom(ByteBuffer data)
Parameter
Returns
Exceptions
public static InputDataConfig parseFrom(ByteBuffer data, ExtensionRegistryLite extensionRegistry)
Parameters
Returns
Exceptions
public static Parser<InputDataConfig> parser()
Returns
public InputDataConfig.Builder toBuilder()
Returns
public void writeTo(CodedOutputStream output)
Parameter
Overrides
Exceptions
