Class: Google::Cloud::AIPlatform::V1::InputDataConfig

Inherits:

Object

• Object
• Google::Cloud::AIPlatform::V1::InputDataConfig

Extended by:

Protobuf::MessageExts::ClassMethods

Includes:

Protobuf::MessageExts

Defined in:

proto_docs/google/cloud/aiplatform/v1/training_pipeline.rb

Overview

Specifies Vertex AI owned input data to be used for training, and possibly evaluating, the Model.

Instance Attribute Summary

• #annotation_schema_uri ⇒ ::String

  Applicable only to custom training with Datasets that have DataItems and Annotations.

• #annotations_filter ⇒ ::String

  Applicable only to Datasets that have DataItems and Annotations.

• #bigquery_destination ⇒ ::Google::Cloud::AIPlatform::V1::BigQueryDestination

  Only applicable to custom training with tabular Dataset with BigQuery source.

• #dataset_id ⇒ ::String

  Required.

• #filter_split ⇒ ::Google::Cloud::AIPlatform::V1::FilterSplit

  Split based on the provided filters for each set.

• #fraction_split ⇒ ::Google::Cloud::AIPlatform::V1::FractionSplit

  Split based on fractions defining the size of each set.

• #gcs_destination ⇒ ::Google::Cloud::AIPlatform::V1::GcsDestination

  The Cloud Storage location where the training data is to be written to.

• #persist_ml_use_assignment ⇒ ::Boolean

  Whether to persist the ML use assignment to data item system labels.

• #predefined_split ⇒ ::Google::Cloud::AIPlatform::V1::PredefinedSplit

  Supported only for tabular Datasets.

• #saved_query_id ⇒ ::String

  Only applicable to Datasets that have SavedQueries.

• #stratified_split ⇒ ::Google::Cloud::AIPlatform::V1::StratifiedSplit

  Supported only for tabular Datasets.

• #timestamp_split ⇒ ::Google::Cloud::AIPlatform::V1::TimestampSplit

  Supported only for tabular Datasets.

Instance Attribute Details

#annotation_schema_uri ⇒ ::String

Returns 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.

Returns:

• (::String) —

  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.

# File 'proto_docs/google/cloud/aiplatform/v1/training_pipeline.rb', line 290

class InputDataConfig
  include ::Google::Protobuf::MessageExts
  extend ::Google::Protobuf::MessageExts::ClassMethods
end

#annotations_filter ⇒ ::String

Returns 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.

Returns:

• (::String) —

  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.

# File 'proto_docs/google/cloud/aiplatform/v1/training_pipeline.rb', line 290

class InputDataConfig
  include ::Google::Protobuf::MessageExts
  extend ::Google::Protobuf::MessageExts::ClassMethods
end

#bigquery_destination ⇒ ::Google::Cloud::AIPlatform::V1::BigQueryDestination

Returns 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_.training"

• AIP_VALIDATION_DATA_URI = "bigquery_destination.dataset_.validation"

• AIP_TEST_DATA_URI = "bigquery_destination.dataset_.test".

Returns:

• (::Google::Cloud::AIPlatform::V1::BigQueryDestination) —

  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_.training"

  • AIP_VALIDATION_DATA_URI = "bigquery_destination.dataset_.validation"

  • AIP_TEST_DATA_URI = "bigquery_destination.dataset_.test"

# File 'proto_docs/google/cloud/aiplatform/v1/training_pipeline.rb', line 290

class InputDataConfig
  include ::Google::Protobuf::MessageExts
  extend ::Google::Protobuf::MessageExts::ClassMethods
end

#dataset_id ⇒ ::String

Returns 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.

Returns:

• (::String) —

  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.

# File 'proto_docs/google/cloud/aiplatform/v1/training_pipeline.rb', line 290

class InputDataConfig
  include ::Google::Protobuf::MessageExts
  extend ::Google::Protobuf::MessageExts::ClassMethods
end

#filter_split ⇒ ::Google::Cloud::AIPlatform::V1::FilterSplit

Returns Split based on the provided filters for each set.

Returns:

• (::Google::Cloud::AIPlatform::V1::FilterSplit) —

  Split based on the provided filters for each set.

# File 'proto_docs/google/cloud/aiplatform/v1/training_pipeline.rb', line 290

class InputDataConfig
  include ::Google::Protobuf::MessageExts
  extend ::Google::Protobuf::MessageExts::ClassMethods
end

#fraction_split ⇒ ::Google::Cloud::AIPlatform::V1::FractionSplit

Returns Split based on fractions defining the size of each set.

Returns:

• (::Google::Cloud::AIPlatform::V1::FractionSplit) —

  Split based on fractions defining the size of each set.

# File 'proto_docs/google/cloud/aiplatform/v1/training_pipeline.rb', line 290

class InputDataConfig
  include ::Google::Protobuf::MessageExts
  extend ::Google::Protobuf::MessageExts::ClassMethods
end

#gcs_destination ⇒ ::Google::Cloud::AIPlatform::V1::GcsDestination

Returns 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---/training-*.${AIP_DATA_FORMAT}"

• AIP_VALIDATION_DATA_URI = "gcs_destination/dataset---/validation-*.${AIP_DATA_FORMAT}"

• AIP_TEST_DATA_URI = "gcs_destination/dataset---/test-*.${AIP_DATA_FORMAT}".

Returns:

• (::Google::Cloud::AIPlatform::V1::GcsDestination) —

  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---/training-*.${AIP_DATA_FORMAT}"

  • AIP_VALIDATION_DATA_URI = "gcs_destination/dataset---/validation-*.${AIP_DATA_FORMAT}"

  • AIP_TEST_DATA_URI = "gcs_destination/dataset---/test-*.${AIP_DATA_FORMAT}"

# File 'proto_docs/google/cloud/aiplatform/v1/training_pipeline.rb', line 290

class InputDataConfig
  include ::Google::Protobuf::MessageExts
  extend ::Google::Protobuf::MessageExts::ClassMethods
end

#persist_ml_use_assignment ⇒ ::Boolean

Returns Whether to persist the ML use assignment to data item system labels.

Returns:

• (::Boolean) —

  Whether to persist the ML use assignment to data item system labels.

# File 'proto_docs/google/cloud/aiplatform/v1/training_pipeline.rb', line 290

class InputDataConfig
  include ::Google::Protobuf::MessageExts
  extend ::Google::Protobuf::MessageExts::ClassMethods
end

#predefined_split ⇒ ::Google::Cloud::AIPlatform::V1::PredefinedSplit

Returns Supported only for tabular Datasets.

Split based on a predefined key.

Returns:

• (::Google::Cloud::AIPlatform::V1::PredefinedSplit) —

  Supported only for tabular Datasets.

  Split based on a predefined key.

# File 'proto_docs/google/cloud/aiplatform/v1/training_pipeline.rb', line 290

class InputDataConfig
  include ::Google::Protobuf::MessageExts
  extend ::Google::Protobuf::MessageExts::ClassMethods
end

#saved_query_id ⇒ ::String

Returns Only applicable to Datasets that have SavedQueries.

The ID of a SavedQuery (annotation set) under the Dataset specified by 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, the Annotations used for training are filtered by both saved_query_id and annotations_filter.

Only one of saved_query_id and annotation_schema_uri should be specified as both of them represent the same thing: problem type.

Returns:

• (::String) —

  Only applicable to Datasets that have SavedQueries.

  The ID of a SavedQuery (annotation set) under the Dataset specified by 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, the Annotations used for training are filtered by both saved_query_id and annotations_filter.

  Only one of saved_query_id and annotation_schema_uri should be specified as both of them represent the same thing: problem type.

# File 'proto_docs/google/cloud/aiplatform/v1/training_pipeline.rb', line 290

class InputDataConfig
  include ::Google::Protobuf::MessageExts
  extend ::Google::Protobuf::MessageExts::ClassMethods
end

#stratified_split ⇒ ::Google::Cloud::AIPlatform::V1::StratifiedSplit

Returns Supported only for tabular Datasets.

Split based on the distribution of the specified column.

Returns:

• (::Google::Cloud::AIPlatform::V1::StratifiedSplit) —

  Supported only for tabular Datasets.

  Split based on the distribution of the specified column.

# File 'proto_docs/google/cloud/aiplatform/v1/training_pipeline.rb', line 290

class InputDataConfig
  include ::Google::Protobuf::MessageExts
  extend ::Google::Protobuf::MessageExts::ClassMethods
end

#timestamp_split ⇒ ::Google::Cloud::AIPlatform::V1::TimestampSplit

Returns Supported only for tabular Datasets.

Split based on the timestamp of the input data pieces.

Returns:

• (::Google::Cloud::AIPlatform::V1::TimestampSplit) —

  Supported only for tabular Datasets.

  Split based on the timestamp of the input data pieces.

# File 'proto_docs/google/cloud/aiplatform/v1/training_pipeline.rb', line 290

class InputDataConfig
  include ::Google::Protobuf::MessageExts
  extend ::Google::Protobuf::MessageExts::ClassMethods
end