public sealed class InputDataConfig : IMessage<InputDataConfig>, IEquatable<InputDataConfig>, IDeepCloneable<InputDataConfig>, IBufferMessage, IMessageReference documentation and code samples for the Cloud AI Platform v1 API class InputDataConfig.
Specifies Vertex AI owned input data to be used for training, and possibly evaluating, the Model.
Implements
IMessageInputDataConfig, IEquatableInputDataConfig, IDeepCloneableInputDataConfig, IBufferMessage, IMessageNamespace
Google.Cloud.AIPlatform.V1Assembly
Google.Cloud.AIPlatform.V1.dll
Constructors
InputDataConfig()
public InputDataConfig()InputDataConfig(InputDataConfig)
public InputDataConfig(InputDataConfig other)| Parameter | |
|---|---|
| Name | Description |
other | InputDataConfig |
Properties
AnnotationSchemaUri
public string AnnotationSchemaUri { get; set; }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][google.cloud.aiplatform.v1.Dataset.metadata_schema_uri] of the Dataset specified by [dataset_id][google.cloud.aiplatform.v1.InputDataConfig.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][google.cloud.aiplatform.v1.InputDataConfig.annotations_filter], the Annotations used for training are filtered by both [annotations_filter][google.cloud.aiplatform.v1.InputDataConfig.annotations_filter] and [annotation_schema_uri][google.cloud.aiplatform.v1.InputDataConfig.annotation_schema_uri].
| Property Value | |
|---|---|
| Type | Description |
string | |
AnnotationsFilter
public string AnnotationsFilter { get; set; }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][google.cloud.aiplatform.v1.DatasetService.ListAnnotations] may be used, but note here it filters across all Annotations of the Dataset, and not just within a single DataItem.
| Property Value | |
|---|---|
| Type | Description |
string | |
BigqueryDestination
public BigQueryDestination BigqueryDestination { get; set; }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"
| Property Value | |
|---|---|
| Type | Description |
BigQueryDestination | |
DatasetId
public string DatasetId { get; set; }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.
| Property Value | |
|---|---|
| Type | Description |
string | |
DestinationCase
public InputDataConfig.DestinationOneofCase DestinationCase { get; }| Property Value | |
|---|---|
| Type | Description |
InputDataConfigDestinationOneofCase | |
FilterSplit
public FilterSplit FilterSplit { get; set; }Split based on the provided filters for each set.
| Property Value | |
|---|---|
| Type | Description |
FilterSplit | |
FractionSplit
public FractionSplit FractionSplit { get; set; }Split based on fractions defining the size of each set.
| Property Value | |
|---|---|
| Type | Description |
FractionSplit | |
GcsDestination
public GcsDestination GcsDestination { get; set; }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}"
| Property Value | |
|---|---|
| Type | Description |
GcsDestination | |
PersistMlUseAssignment
public bool PersistMlUseAssignment { get; set; }Whether to persist the ML use assignment to data item system labels.
| Property Value | |
|---|---|
| Type | Description |
bool | |
PredefinedSplit
public PredefinedSplit PredefinedSplit { get; set; }Supported only for tabular Datasets.
Split based on a predefined key.
| Property Value | |
|---|---|
| Type | Description |
PredefinedSplit | |
SavedQueryId
public string SavedQueryId { get; set; }Only applicable to Datasets that have SavedQueries.
The ID of a SavedQuery (annotation set) under the Dataset specified by [dataset_id][google.cloud.aiplatform.v1.InputDataConfig.dataset_id] used for filtering Annotations for training.
Only Annotations that are associated with this SavedQuery are used in respectively training. When used in conjunction with [annotations_filter][google.cloud.aiplatform.v1.InputDataConfig.annotations_filter], the Annotations used for training are filtered by both [saved_query_id][google.cloud.aiplatform.v1.InputDataConfig.saved_query_id] and [annotations_filter][google.cloud.aiplatform.v1.InputDataConfig.annotations_filter].
Only one of [saved_query_id][google.cloud.aiplatform.v1.InputDataConfig.saved_query_id] and [annotation_schema_uri][google.cloud.aiplatform.v1.InputDataConfig.annotation_schema_uri] should be specified as both of them represent the same thing: problem type.
| Property Value | |
|---|---|
| Type | Description |
string | |
SplitCase
public InputDataConfig.SplitOneofCase SplitCase { get; }| Property Value | |
|---|---|
| Type | Description |
InputDataConfigSplitOneofCase | |
StratifiedSplit
public StratifiedSplit StratifiedSplit { get; set; }Supported only for tabular Datasets.
Split based on the distribution of the specified column.
| Property Value | |
|---|---|
| Type | Description |
StratifiedSplit | |
TimestampSplit
public TimestampSplit TimestampSplit { get; set; }Supported only for tabular Datasets.
Split based on the timestamp of the input data pieces.
| Property Value | |
|---|---|
| Type | Description |
TimestampSplit | |