# frozen_string_literal: true
# Copyright 2022 Google LLC
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# https://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# Auto-generated by gapic-generator-ruby. DO NOT EDIT!
module Google
module Cloud
module AIPlatform
module V1
# The TrainingPipeline orchestrates tasks associated with training a Model. It
# always executes the training task, and optionally may also
# export data from Vertex AI's Dataset which becomes the training input,
# {::Google::Cloud::AIPlatform::V1::ModelService::Client#upload_model upload} the Model to Vertex AI, and evaluate the
# Model.
# @!attribute [r] name
# @return [::String]
# Output only. Resource name of the TrainingPipeline.
# @!attribute [rw] display_name
# @return [::String]
# Required. The user-defined name of this TrainingPipeline.
# @!attribute [rw] input_data_config
# @return [::Google::Cloud::AIPlatform::V1::InputDataConfig]
# Specifies Vertex AI owned input data that may be used for training the
# Model. The TrainingPipeline's {::Google::Cloud::AIPlatform::V1::TrainingPipeline#training_task_definition training_task_definition} should make
# clear whether this config is used and if there are any special requirements
# on how it should be filled. If nothing about this config is mentioned in
# the {::Google::Cloud::AIPlatform::V1::TrainingPipeline#training_task_definition training_task_definition}, then it should be assumed that the
# TrainingPipeline does not depend on this configuration.
# @!attribute [rw] training_task_definition
# @return [::String]
# Required. A Google Cloud Storage path to the YAML file that defines the training task
# which is responsible for producing the model artifact, and may also include
# additional auxiliary work.
# The definition files that can be used here are found in
# gs://google-cloud-aiplatform/schema/trainingjob/definition/.
# Note: The URI given on output will be immutable and probably different,
# including the URI scheme, than the one given on input. The output URI will
# point to a location where the user only has a read access.
# @!attribute [rw] training_task_inputs
# @return [::Google::Protobuf::Value]
# Required. The training task's parameter(s), as specified in the
# {::Google::Cloud::AIPlatform::V1::TrainingPipeline#training_task_definition training_task_definition}'s `inputs`.
# @!attribute [r] training_task_metadata
# @return [::Google::Protobuf::Value]
# Output only. The metadata information as specified in the {::Google::Cloud::AIPlatform::V1::TrainingPipeline#training_task_definition training_task_definition}'s
# `metadata`. This metadata is an auxiliary runtime and final information
# about the training task. While the pipeline is running this information is
# populated only at a best effort basis. Only present if the
# pipeline's {::Google::Cloud::AIPlatform::V1::TrainingPipeline#training_task_definition training_task_definition} contains `metadata` object.
# @!attribute [rw] model_to_upload
# @return [::Google::Cloud::AIPlatform::V1::Model]
# Describes the Model that may be uploaded (via {::Google::Cloud::AIPlatform::V1::ModelService::Client#upload_model ModelService.UploadModel})
# by this TrainingPipeline. The TrainingPipeline's
# {::Google::Cloud::AIPlatform::V1::TrainingPipeline#training_task_definition training_task_definition} should make clear whether this Model
# description should be populated, and if there are any special requirements
# regarding how it should be filled. If nothing is mentioned in the
# {::Google::Cloud::AIPlatform::V1::TrainingPipeline#training_task_definition training_task_definition}, then it should be assumed that this field
# should not be filled and the training task either uploads the Model without
# a need of this information, or that training task does not support
# uploading a Model as part of the pipeline.
# When the Pipeline's state becomes `PIPELINE_STATE_SUCCEEDED` and
# the trained Model had been uploaded into Vertex AI, then the
# model_to_upload's resource {::Google::Cloud::AIPlatform::V1::Model#name name} is populated. The Model
# is always uploaded into the Project and Location in which this pipeline
# is.
# @!attribute [rw] model_id
# @return [::String]
# Optional. The ID to use for the uploaded Model, which will become the final
# component of the model resource name.
#
# This value may be up to 63 characters, and valid characters are
# `[a-z0-9_-]`. The first character cannot be a number or hyphen.
# @!attribute [rw] parent_model
# @return [::String]
# Optional. When specify this field, the `model_to_upload` will not be uploaded as a
# new model, instead, it will become a new version of this `parent_model`.
# @!attribute [r] state
# @return [::Google::Cloud::AIPlatform::V1::PipelineState]
# Output only. The detailed state of the pipeline.
# @!attribute [r] error
# @return [::Google::Rpc::Status]
# Output only. Only populated when the pipeline's state is `PIPELINE_STATE_FAILED` or
# `PIPELINE_STATE_CANCELLED`.
# @!attribute [r] create_time
# @return [::Google::Protobuf::Timestamp]
# Output only. Time when the TrainingPipeline was created.
# @!attribute [r] start_time
# @return [::Google::Protobuf::Timestamp]
# Output only. Time when the TrainingPipeline for the first time entered the
# `PIPELINE_STATE_RUNNING` state.
# @!attribute [r] end_time
# @return [::Google::Protobuf::Timestamp]
# Output only. Time when the TrainingPipeline entered any of the following states:
# `PIPELINE_STATE_SUCCEEDED`, `PIPELINE_STATE_FAILED`,
# `PIPELINE_STATE_CANCELLED`.
# @!attribute [r] update_time
# @return [::Google::Protobuf::Timestamp]
# Output only. Time when the TrainingPipeline was most recently updated.
# @!attribute [rw] labels
# @return [::Google::Protobuf::Map{::String => ::String}]
# The labels with user-defined metadata to organize TrainingPipelines.
#
# Label keys and values can be no longer than 64 characters
# (Unicode codepoints), can only contain lowercase letters, numeric
# characters, underscores and dashes. International characters are allowed.
#
# See https://goo.gl/xmQnxf for more information and examples of labels.
# @!attribute [rw] encryption_spec
# @return [::Google::Cloud::AIPlatform::V1::EncryptionSpec]
# Customer-managed encryption key spec for a TrainingPipeline. If set, this
# TrainingPipeline will be secured by this key.
#
# Note: Model trained by this TrainingPipeline is also secured by this key if
# {::Google::Cloud::AIPlatform::V1::TrainingPipeline#encryption_spec model_to_upload} is not set separately.
class TrainingPipeline
include ::Google::Protobuf::MessageExts
extend ::Google::Protobuf::MessageExts::ClassMethods
# @!attribute [rw] key
# @return [::String]
# @!attribute [rw] value
# @return [::String]
class LabelsEntry
include ::Google::Protobuf::MessageExts
extend ::Google::Protobuf::MessageExts::ClassMethods
end
end
# Specifies Vertex AI owned input data to be used for training, and
# possibly evaluating, the Model.
# @!attribute [rw] fraction_split
# @return [::Google::Cloud::AIPlatform::V1::FractionSplit]
# Split based on fractions defining the size of each set.
# @!attribute [rw] filter_split
# @return [::Google::Cloud::AIPlatform::V1::FilterSplit]
# Split based on the provided filters for each set.
# @!attribute [rw] predefined_split
# @return [::Google::Cloud::AIPlatform::V1::PredefinedSplit]
# Supported only for tabular Datasets.
#
# Split based on a predefined key.
# @!attribute [rw] timestamp_split
# @return [::Google::Cloud::AIPlatform::V1::TimestampSplit]
# Supported only for tabular Datasets.
#
# Split based on the timestamp of the input data pieces.
# @!attribute [rw] stratified_split
# @return [::Google::Cloud::AIPlatform::V1::StratifiedSplit]
# Supported only for tabular Datasets.
#
# Split based on the distribution of the specified column.
# @!attribute [rw] gcs_destination
# @return [::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-<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}"
# @!attribute [rw] bigquery_destination
# @return [::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_<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"
# @!attribute [rw] dataset_id
# @return [::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.
# @!attribute [rw] annotations_filter
# @return [::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
# {::Google::Cloud::AIPlatform::V1::DatasetService::Client#list_annotations ListAnnotations} may be used, but note
# here it filters across all Annotations of the Dataset, and not just within
# a single DataItem.
# @!attribute [rw] annotation_schema_uri
# @return [::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](https://github.com/OAI/OpenAPI-Specification/blob/main/versions/3.0.2.md#schemaObject).
# 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
# {::Google::Cloud::AIPlatform::V1::Dataset#metadata_schema_uri metadata} of the Dataset specified by
# {::Google::Cloud::AIPlatform::V1::InputDataConfig#dataset_id 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 {::Google::Cloud::AIPlatform::V1::InputDataConfig#annotations_filter annotations_filter}, the Annotations used
# for training are filtered by both {::Google::Cloud::AIPlatform::V1::InputDataConfig#annotations_filter annotations_filter} and
# {::Google::Cloud::AIPlatform::V1::InputDataConfig#annotation_schema_uri annotation_schema_uri}.
# @!attribute [rw] saved_query_id
# @return [::String]
# Only applicable to Datasets that have SavedQueries.
#
# The ID of a SavedQuery (annotation set) under the Dataset specified by
# {::Google::Cloud::AIPlatform::V1::InputDataConfig#dataset_id 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
# {::Google::Cloud::AIPlatform::V1::InputDataConfig#annotations_filter annotations_filter}, the Annotations used for training are filtered by
# both {::Google::Cloud::AIPlatform::V1::InputDataConfig#saved_query_id saved_query_id} and {::Google::Cloud::AIPlatform::V1::InputDataConfig#annotations_filter annotations_filter}.
#
# Only one of {::Google::Cloud::AIPlatform::V1::InputDataConfig#saved_query_id saved_query_id} and {::Google::Cloud::AIPlatform::V1::InputDataConfig#annotation_schema_uri annotation_schema_uri} should be
# specified as both of them represent the same thing: problem type.
class InputDataConfig
include ::Google::Protobuf::MessageExts
extend ::Google::Protobuf::MessageExts::ClassMethods
end
# Assigns the input data to training, validation, and test sets as per the
# given fractions. Any of `training_fraction`, `validation_fraction` and
# `test_fraction` may optionally be provided, they must sum to up to 1. If the
# provided ones sum to less than 1, the remainder is assigned to sets as
# decided by Vertex AI. If none of the fractions are set, by default roughly
# 80% of data is used for training, 10% for validation, and 10% for test.
# @!attribute [rw] training_fraction
# @return [::Float]
# The fraction of the input data that is to be used to train the Model.
# @!attribute [rw] validation_fraction
# @return [::Float]
# The fraction of the input data that is to be used to validate the Model.
# @!attribute [rw] test_fraction
# @return [::Float]
# The fraction of the input data that is to be used to evaluate the Model.
class FractionSplit
include ::Google::Protobuf::MessageExts
extend ::Google::Protobuf::MessageExts::ClassMethods
end
# Assigns input data to training, validation, and test sets based on the given
# filters, data pieces not matched by any filter are ignored. Currently only
# supported for Datasets containing DataItems.
# If any of the filters in this message are to match nothing, then they can be
# set as '-' (the minus sign).
#
# Supported only for unstructured Datasets.
# @!attribute [rw] training_filter
# @return [::String]
# Required. A filter on DataItems of the Dataset. DataItems that match
# this filter are used to train the Model. A filter with same syntax
# as the one used in {::Google::Cloud::AIPlatform::V1::DatasetService::Client#list_data_items DatasetService.ListDataItems} may be used. If a
# single DataItem is matched by more than one of the FilterSplit filters,
# then it is assigned to the first set that applies to it in the
# training, validation, test order.
# @!attribute [rw] validation_filter
# @return [::String]
# Required. A filter on DataItems of the Dataset. DataItems that match
# this filter are used to validate the Model. A filter with same syntax
# as the one used in {::Google::Cloud::AIPlatform::V1::DatasetService::Client#list_data_items DatasetService.ListDataItems} may be used. If a
# single DataItem is matched by more than one of the FilterSplit filters,
# then it is assigned to the first set that applies to it in the
# training, validation, test order.
# @!attribute [rw] test_filter
# @return [::String]
# Required. A filter on DataItems of the Dataset. DataItems that match
# this filter are used to test the Model. A filter with same syntax
# as the one used in {::Google::Cloud::AIPlatform::V1::DatasetService::Client#list_data_items DatasetService.ListDataItems} may be used. If a
# single DataItem is matched by more than one of the FilterSplit filters,
# then it is assigned to the first set that applies to it in the
# training, validation, test order.
class FilterSplit
include ::Google::Protobuf::MessageExts
extend ::Google::Protobuf::MessageExts::ClassMethods
end
# Assigns input data to training, validation, and test sets based on the
# value of a provided key.
#
# Supported only for tabular Datasets.
# @!attribute [rw] key
# @return [::String]
# Required. The key is a name of one of the Dataset's data columns.
# The value of the key (either the label's value or value in the column)
# must be one of \\{`training`, `validation`, `test`}, and it defines to which
# set the given piece of data is assigned. If for a piece of data the key
# is not present or has an invalid value, that piece is ignored by the
# pipeline.
class PredefinedSplit
include ::Google::Protobuf::MessageExts
extend ::Google::Protobuf::MessageExts::ClassMethods
end
# Assigns input data to training, validation, and test sets based on a
# provided timestamps. The youngest data pieces are assigned to training set,
# next to validation set, and the oldest to the test set.
#
# Supported only for tabular Datasets.
# @!attribute [rw] training_fraction
# @return [::Float]
# The fraction of the input data that is to be used to train the Model.
# @!attribute [rw] validation_fraction
# @return [::Float]
# The fraction of the input data that is to be used to validate the Model.
# @!attribute [rw] test_fraction
# @return [::Float]
# The fraction of the input data that is to be used to evaluate the Model.
# @!attribute [rw] key
# @return [::String]
# Required. The key is a name of one of the Dataset's data columns.
# The values of the key (the values in the column) must be in RFC 3339
# `date-time` format, where `time-offset` = `"Z"`
# (e.g. 1985-04-12T23:20:50.52Z). If for a piece of data the key is not
# present or has an invalid value, that piece is ignored by the pipeline.
class TimestampSplit
include ::Google::Protobuf::MessageExts
extend ::Google::Protobuf::MessageExts::ClassMethods
end
# Assigns input data to the training, validation, and test sets so that the
# distribution of values found in the categorical column (as specified by the
# `key` field) is mirrored within each split. The fraction values determine
# the relative sizes of the splits.
#
# For example, if the specified column has three values, with 50% of the rows
# having value "A", 25% value "B", and 25% value "C", and the split fractions
# are specified as 80/10/10, then the training set will constitute 80% of the
# training data, with about 50% of the training set rows having the value "A"
# for the specified column, about 25% having the value "B", and about 25%
# having the value "C".
#
# Only the top 500 occurring values are used; any values not in the top
# 500 values are randomly assigned to a split. If less than three rows contain
# a specific value, those rows are randomly assigned.
#
# Supported only for tabular Datasets.
# @!attribute [rw] training_fraction
# @return [::Float]
# The fraction of the input data that is to be used to train the Model.
# @!attribute [rw] validation_fraction
# @return [::Float]
# The fraction of the input data that is to be used to validate the Model.
# @!attribute [rw] test_fraction
# @return [::Float]
# The fraction of the input data that is to be used to evaluate the Model.
# @!attribute [rw] key
# @return [::String]
# Required. The key is a name of one of the Dataset's data columns.
# The key provided must be for a categorical column.
class StratifiedSplit
include ::Google::Protobuf::MessageExts
extend ::Google::Protobuf::MessageExts::ClassMethods
end
end
end
end
end