# 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
# A trained machine learning Model.
# @!attribute [rw] name
# @return [::String]
# The resource name of the Model.
# @!attribute [r] version_id
# @return [::String]
# Output only. Immutable. The version ID of the model.
# A new version is committed when a new model version is uploaded or
# trained under an existing model id. It is an auto-incrementing decimal
# number in string representation.
# @!attribute [rw] version_aliases
# @return [::Array<::String>]
# User provided version aliases so that a model version can be referenced via
# alias (i.e.
# `projects/{project}/locations/{location}/models/{model_id}@{version_alias}`
# instead of auto-generated version id (i.e.
# `projects/{project}/locations/{location}/models/{model_id}@{version_id})`.
# The format is [a-z][a-zA-Z0-9-]\\{0,126}[a-z0-9] to distinguish from
# version_id. A default version alias will be created for the first version
# of the model, and there must be exactly one default version alias for a
# model.
# @!attribute [r] version_create_time
# @return [::Google::Protobuf::Timestamp]
# Output only. Timestamp when this version was created.
# @!attribute [r] version_update_time
# @return [::Google::Protobuf::Timestamp]
# Output only. Timestamp when this version was most recently updated.
# @!attribute [rw] display_name
# @return [::String]
# Required. The display name of the Model.
# The name can be up to 128 characters long and can consist of any UTF-8
# characters.
# @!attribute [rw] description
# @return [::String]
# The description of the Model.
# @!attribute [rw] version_description
# @return [::String]
# The description of this version.
# @!attribute [rw] predict_schemata
# @return [::Google::Cloud::AIPlatform::V1::PredictSchemata]
# The schemata that describe formats of the Model's predictions and
# explanations as given and returned via
# {::Google::Cloud::AIPlatform::V1::PredictionService::Client#predict PredictionService.Predict}
# and
# {::Google::Cloud::AIPlatform::V1::PredictionService::Client#explain PredictionService.Explain}.
# @!attribute [rw] metadata_schema_uri
# @return [::String]
# Immutable. Points to a YAML file stored on Google Cloud Storage describing
# additional information about the Model, that is specific to it. Unset if
# the Model does not have any additional information. 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).
# AutoML Models always have this field populated by Vertex AI, if no
# additional metadata is needed, this field is set to an empty string.
# 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] metadata
# @return [::Google::Protobuf::Value]
# Immutable. An additional information about the Model; the schema of the
# metadata can be found in
# {::Google::Cloud::AIPlatform::V1::Model#metadata_schema_uri metadata_schema}.
# Unset if the Model does not have any additional information.
# @!attribute [r] supported_export_formats
# @return [::Array<::Google::Cloud::AIPlatform::V1::Model::ExportFormat>]
# Output only. The formats in which this Model may be exported. If empty,
# this Model is not available for export.
# @!attribute [r] training_pipeline
# @return [::String]
# Output only. The resource name of the TrainingPipeline that uploaded this
# Model, if any.
# @!attribute [rw] pipeline_job
# @return [::String]
# Optional. This field is populated if the model is produced by a pipeline
# job.
# @!attribute [rw] container_spec
# @return [::Google::Cloud::AIPlatform::V1::ModelContainerSpec]
# Input only. The specification of the container that is to be used when
# deploying this Model. The specification is ingested upon
# {::Google::Cloud::AIPlatform::V1::ModelService::Client#upload_model ModelService.UploadModel},
# and all binaries it contains are copied and stored internally by Vertex AI.
# Not required for AutoML Models.
# @!attribute [rw] artifact_uri
# @return [::String]
# Immutable. The path to the directory containing the Model artifact and any
# of its supporting files. Not required for AutoML Models.
# @!attribute [r] supported_deployment_resources_types
# @return [::Array<::Google::Cloud::AIPlatform::V1::Model::DeploymentResourcesType>]
# Output only. When this Model is deployed, its prediction resources are
# described by the `prediction_resources` field of the
# {::Google::Cloud::AIPlatform::V1::Endpoint#deployed_models Endpoint.deployed_models}
# object. Because not all Models support all resource configuration types,
# the configuration types this Model supports are listed here. If no
# configuration types are listed, the Model cannot be deployed to an
# {::Google::Cloud::AIPlatform::V1::Endpoint Endpoint} and does not support
# online predictions
# ({::Google::Cloud::AIPlatform::V1::PredictionService::Client#predict PredictionService.Predict}
# or
# {::Google::Cloud::AIPlatform::V1::PredictionService::Client#explain PredictionService.Explain}).
# Such a Model can serve predictions by using a
# {::Google::Cloud::AIPlatform::V1::BatchPredictionJob BatchPredictionJob}, if it
# has at least one entry each in
# {::Google::Cloud::AIPlatform::V1::Model#supported_input_storage_formats supported_input_storage_formats}
# and
# {::Google::Cloud::AIPlatform::V1::Model#supported_output_storage_formats supported_output_storage_formats}.
# @!attribute [r] supported_input_storage_formats
# @return [::Array<::String>]
# Output only. The formats this Model supports in
# {::Google::Cloud::AIPlatform::V1::BatchPredictionJob#input_config BatchPredictionJob.input_config}.
# If
# {::Google::Cloud::AIPlatform::V1::PredictSchemata#instance_schema_uri PredictSchemata.instance_schema_uri}
# exists, the instances should be given as per that schema.
#
# The possible formats are:
#
# * `jsonl`
# The JSON Lines format, where each instance is a single line. Uses
# {::Google::Cloud::AIPlatform::V1::BatchPredictionJob::InputConfig#gcs_source GcsSource}.
#
# * `csv`
# The CSV format, where each instance is a single comma-separated line.
# The first line in the file is the header, containing comma-separated field
# names. Uses
# {::Google::Cloud::AIPlatform::V1::BatchPredictionJob::InputConfig#gcs_source GcsSource}.
#
# * `tf-record`
# The TFRecord format, where each instance is a single record in tfrecord
# syntax. Uses
# {::Google::Cloud::AIPlatform::V1::BatchPredictionJob::InputConfig#gcs_source GcsSource}.
#
# * `tf-record-gzip`
# Similar to `tf-record`, but the file is gzipped. Uses
# {::Google::Cloud::AIPlatform::V1::BatchPredictionJob::InputConfig#gcs_source GcsSource}.
#
# * `bigquery`
# Each instance is a single row in BigQuery. Uses
# {::Google::Cloud::AIPlatform::V1::BatchPredictionJob::InputConfig#bigquery_source BigQuerySource}.
#
# * `file-list`
# Each line of the file is the location of an instance to process, uses
# `gcs_source` field of the
# {::Google::Cloud::AIPlatform::V1::BatchPredictionJob::InputConfig InputConfig}
# object.
#
#
# If this Model doesn't support any of these formats it means it cannot be
# used with a
# {::Google::Cloud::AIPlatform::V1::BatchPredictionJob BatchPredictionJob}.
# However, if it has
# {::Google::Cloud::AIPlatform::V1::Model#supported_deployment_resources_types supported_deployment_resources_types},
# it could serve online predictions by using
# {::Google::Cloud::AIPlatform::V1::PredictionService::Client#predict PredictionService.Predict}
# or
# {::Google::Cloud::AIPlatform::V1::PredictionService::Client#explain PredictionService.Explain}.
# @!attribute [r] supported_output_storage_formats
# @return [::Array<::String>]
# Output only. The formats this Model supports in
# {::Google::Cloud::AIPlatform::V1::BatchPredictionJob#output_config BatchPredictionJob.output_config}.
# If both
# {::Google::Cloud::AIPlatform::V1::PredictSchemata#instance_schema_uri PredictSchemata.instance_schema_uri}
# and
# {::Google::Cloud::AIPlatform::V1::PredictSchemata#prediction_schema_uri PredictSchemata.prediction_schema_uri}
# exist, the predictions are returned together with their instances. In other
# words, the prediction has the original instance data first, followed by the
# actual prediction content (as per the schema).
#
# The possible formats are:
#
# * `jsonl`
# The JSON Lines format, where each prediction is a single line. Uses
# {::Google::Cloud::AIPlatform::V1::BatchPredictionJob::OutputConfig#gcs_destination GcsDestination}.
#
# * `csv`
# The CSV format, where each prediction is a single comma-separated line.
# The first line in the file is the header, containing comma-separated field
# names. Uses
# {::Google::Cloud::AIPlatform::V1::BatchPredictionJob::OutputConfig#gcs_destination GcsDestination}.
#
# * `bigquery`
# Each prediction is a single row in a BigQuery table, uses
# {::Google::Cloud::AIPlatform::V1::BatchPredictionJob::OutputConfig#bigquery_destination BigQueryDestination}
# .
#
#
# If this Model doesn't support any of these formats it means it cannot be
# used with a
# {::Google::Cloud::AIPlatform::V1::BatchPredictionJob BatchPredictionJob}.
# However, if it has
# {::Google::Cloud::AIPlatform::V1::Model#supported_deployment_resources_types supported_deployment_resources_types},
# it could serve online predictions by using
# {::Google::Cloud::AIPlatform::V1::PredictionService::Client#predict PredictionService.Predict}
# or
# {::Google::Cloud::AIPlatform::V1::PredictionService::Client#explain PredictionService.Explain}.
# @!attribute [r] create_time
# @return [::Google::Protobuf::Timestamp]
# Output only. Timestamp when this Model was uploaded into Vertex AI.
# @!attribute [r] update_time
# @return [::Google::Protobuf::Timestamp]
# Output only. Timestamp when this Model was most recently updated.
# @!attribute [r] deployed_models
# @return [::Array<::Google::Cloud::AIPlatform::V1::DeployedModelRef>]
# Output only. The pointers to DeployedModels created from this Model. Note
# that Model could have been deployed to Endpoints in different Locations.
# @!attribute [rw] explanation_spec
# @return [::Google::Cloud::AIPlatform::V1::ExplanationSpec]
# The default explanation specification for this Model.
#
# The Model can be used for
# [requesting
# explanation][google.cloud.aiplatform.v1.PredictionService.Explain] after
# being {::Google::Cloud::AIPlatform::V1::EndpointService::Client#deploy_model deployed} if
# it is populated. The Model can be used for [batch
# explanation][google.cloud.aiplatform.v1.BatchPredictionJob.generate_explanation]
# if it is populated.
#
# All fields of the explanation_spec can be overridden by
# {::Google::Cloud::AIPlatform::V1::DeployedModel#explanation_spec explanation_spec}
# of
# {::Google::Cloud::AIPlatform::V1::DeployModelRequest#deployed_model DeployModelRequest.deployed_model},
# or
# {::Google::Cloud::AIPlatform::V1::BatchPredictionJob#explanation_spec explanation_spec}
# of {::Google::Cloud::AIPlatform::V1::BatchPredictionJob BatchPredictionJob}.
#
# If the default explanation specification is not set for this Model, this
# Model can still be used for
# [requesting
# explanation][google.cloud.aiplatform.v1.PredictionService.Explain] by
# setting
# {::Google::Cloud::AIPlatform::V1::DeployedModel#explanation_spec explanation_spec}
# of
# {::Google::Cloud::AIPlatform::V1::DeployModelRequest#deployed_model DeployModelRequest.deployed_model}
# and for [batch
# explanation][google.cloud.aiplatform.v1.BatchPredictionJob.generate_explanation]
# by setting
# {::Google::Cloud::AIPlatform::V1::BatchPredictionJob#explanation_spec explanation_spec}
# of {::Google::Cloud::AIPlatform::V1::BatchPredictionJob BatchPredictionJob}.
# @!attribute [rw] etag
# @return [::String]
# Used to perform consistent read-modify-write updates. If not set, a blind
# "overwrite" update happens.
# @!attribute [rw] labels
# @return [::Google::Protobuf::Map{::String => ::String}]
# The labels with user-defined metadata to organize your Models.
#
# 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] data_stats
# @return [::Google::Cloud::AIPlatform::V1::Model::DataStats]
# Stats of data used for training or evaluating the Model.
#
# Only populated when the Model is trained by a TrainingPipeline with
# [data_input_config][TrainingPipeline.data_input_config].
# @!attribute [rw] encryption_spec
# @return [::Google::Cloud::AIPlatform::V1::EncryptionSpec]
# Customer-managed encryption key spec for a Model. If set, this
# Model and all sub-resources of this Model will be secured by this key.
# @!attribute [r] model_source_info
# @return [::Google::Cloud::AIPlatform::V1::ModelSourceInfo]
# Output only. Source of a model. It can either be automl training pipeline,
# custom training pipeline, BigQuery ML, or saved and tuned from Genie or
# Model Garden.
# @!attribute [r] original_model_info
# @return [::Google::Cloud::AIPlatform::V1::Model::OriginalModelInfo]
# Output only. If this Model is a copy of another Model, this contains info
# about the original.
# @!attribute [r] metadata_artifact
# @return [::String]
# Output only. The resource name of the Artifact that was created in
# MetadataStore when creating the Model. The Artifact resource name pattern
# is
# `projects/{project}/locations/{location}/metadataStores/{metadata_store}/artifacts/{artifact}`.
# @!attribute [rw] base_model_source
# @return [::Google::Cloud::AIPlatform::V1::Model::BaseModelSource]
# Optional. User input field to specify the base model source. Currently it
# only supports specifing the Model Garden models and Genie models.
# @!attribute [r] satisfies_pzs
# @return [::Boolean]
# Output only. Reserved for future use.
# @!attribute [r] satisfies_pzi
# @return [::Boolean]
# Output only. Reserved for future use.
class Model
include ::Google::Protobuf::MessageExts
extend ::Google::Protobuf::MessageExts::ClassMethods
# Represents export format supported by the Model.
# All formats export to Google Cloud Storage.
# @!attribute [r] id
# @return [::String]
# Output only. The ID of the export format.
# The possible format IDs are:
#
# * `tflite`
# Used for Android mobile devices.
#
# * `edgetpu-tflite`
# Used for [Edge TPU](https://cloud.google.com/edge-tpu/) devices.
#
# * `tf-saved-model`
# A tensorflow model in SavedModel format.
#
# * `tf-js`
# A [TensorFlow.js](https://www.tensorflow.org/js) model that can be used
# in the browser and in Node.js using JavaScript.
#
# * `core-ml`
# Used for iOS mobile devices.
#
# * `custom-trained`
# A Model that was uploaded or trained by custom code.
# @!attribute [r] exportable_contents
# @return [::Array<::Google::Cloud::AIPlatform::V1::Model::ExportFormat::ExportableContent>]
# Output only. The content of this Model that may be exported.
class ExportFormat
include ::Google::Protobuf::MessageExts
extend ::Google::Protobuf::MessageExts::ClassMethods
# The Model content that can be exported.
module ExportableContent
# Should not be used.
EXPORTABLE_CONTENT_UNSPECIFIED = 0
# Model artifact and any of its supported files. Will be exported to the
# location specified by the `artifactDestination` field of the
# {::Google::Cloud::AIPlatform::V1::ExportModelRequest#output_config ExportModelRequest.output_config}
# object.
ARTIFACT = 1
# The container image that is to be used when deploying this Model. Will
# be exported to the location specified by the `imageDestination` field
# of the
# {::Google::Cloud::AIPlatform::V1::ExportModelRequest#output_config ExportModelRequest.output_config}
# object.
IMAGE = 2
end
end
# Stats of data used for train or evaluate the Model.
# @!attribute [rw] training_data_items_count
# @return [::Integer]
# Number of DataItems that were used for training this Model.
# @!attribute [rw] validation_data_items_count
# @return [::Integer]
# Number of DataItems that were used for validating this Model during
# training.
# @!attribute [rw] test_data_items_count
# @return [::Integer]
# Number of DataItems that were used for evaluating this Model. If the
# Model is evaluated multiple times, this will be the number of test
# DataItems used by the first evaluation. If the Model is not evaluated,
# the number is 0.
# @!attribute [rw] training_annotations_count
# @return [::Integer]
# Number of Annotations that are used for training this Model.
# @!attribute [rw] validation_annotations_count
# @return [::Integer]
# Number of Annotations that are used for validating this Model during
# training.
# @!attribute [rw] test_annotations_count
# @return [::Integer]
# Number of Annotations that are used for evaluating this Model. If the
# Model is evaluated multiple times, this will be the number of test
# Annotations used by the first evaluation. If the Model is not evaluated,
# the number is 0.
class DataStats
include ::Google::Protobuf::MessageExts
extend ::Google::Protobuf::MessageExts::ClassMethods
end
# Contains information about the original Model if this Model is a copy.
# @!attribute [r] model
# @return [::String]
# Output only. The resource name of the Model this Model is a copy of,
# including the revision. Format:
# `projects/{project}/locations/{location}/models/{model_id}@{version_id}`
class OriginalModelInfo
include ::Google::Protobuf::MessageExts
extend ::Google::Protobuf::MessageExts::ClassMethods
end
# User input field to specify the base model source. Currently it only
# supports specifing the Model Garden models and Genie models.
# @!attribute [rw] model_garden_source
# @return [::Google::Cloud::AIPlatform::V1::ModelGardenSource]
# Source information of Model Garden models.
# @!attribute [rw] genie_source
# @return [::Google::Cloud::AIPlatform::V1::GenieSource]
# Information about the base model of Genie models.
class BaseModelSource
include ::Google::Protobuf::MessageExts
extend ::Google::Protobuf::MessageExts::ClassMethods
end
# @!attribute [rw] key
# @return [::String]
# @!attribute [rw] value
# @return [::String]
class LabelsEntry
include ::Google::Protobuf::MessageExts
extend ::Google::Protobuf::MessageExts::ClassMethods
end
# Identifies a type of Model's prediction resources.
module DeploymentResourcesType
# Should not be used.
DEPLOYMENT_RESOURCES_TYPE_UNSPECIFIED = 0
# Resources that are dedicated to the
# {::Google::Cloud::AIPlatform::V1::DeployedModel DeployedModel}, and that need
# a higher degree of manual configuration.
DEDICATED_RESOURCES = 1
# Resources that to large degree are decided by Vertex AI, and require
# only a modest additional configuration.
AUTOMATIC_RESOURCES = 2
# Resources that can be shared by multiple
# {::Google::Cloud::AIPlatform::V1::DeployedModel DeployedModels}. A
# pre-configured
# {::Google::Cloud::AIPlatform::V1::DeploymentResourcePool DeploymentResourcePool}
# is required.
SHARED_RESOURCES = 3
end
end
# Contains information about the Large Model.
# @!attribute [rw] name
# @return [::String]
# Required. The unique name of the large Foundation or pre-built model. Like
# "chat-bison", "text-bison". Or model name with version ID, like
# "chat-bison@001", "text-bison@005", etc.
class LargeModelReference
include ::Google::Protobuf::MessageExts
extend ::Google::Protobuf::MessageExts::ClassMethods
end
# Contains information about the source of the models generated from Model
# Garden.
# @!attribute [rw] public_model_name
# @return [::String]
# Required. The model garden source model resource name.
class ModelGardenSource
include ::Google::Protobuf::MessageExts
extend ::Google::Protobuf::MessageExts::ClassMethods
end
# Contains information about the source of the models generated from Generative
# AI Studio.
# @!attribute [rw] base_model_uri
# @return [::String]
# Required. The public base model URI.
class GenieSource
include ::Google::Protobuf::MessageExts
extend ::Google::Protobuf::MessageExts::ClassMethods
end
# Contains the schemata used in Model's predictions and explanations via
# {::Google::Cloud::AIPlatform::V1::PredictionService::Client#predict PredictionService.Predict},
# {::Google::Cloud::AIPlatform::V1::PredictionService::Client#explain PredictionService.Explain}
# and {::Google::Cloud::AIPlatform::V1::BatchPredictionJob BatchPredictionJob}.
# @!attribute [rw] instance_schema_uri
# @return [::String]
# Immutable. Points to a YAML file stored on Google Cloud Storage describing
# the format of a single instance, which are used in
# {::Google::Cloud::AIPlatform::V1::PredictRequest#instances PredictRequest.instances},
# {::Google::Cloud::AIPlatform::V1::ExplainRequest#instances ExplainRequest.instances}
# and
# {::Google::Cloud::AIPlatform::V1::BatchPredictionJob#input_config BatchPredictionJob.input_config}.
# 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).
# AutoML Models always have this field populated by Vertex AI.
# 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] parameters_schema_uri
# @return [::String]
# Immutable. Points to a YAML file stored on Google Cloud Storage describing
# the parameters of prediction and explanation via
# {::Google::Cloud::AIPlatform::V1::PredictRequest#parameters PredictRequest.parameters},
# {::Google::Cloud::AIPlatform::V1::ExplainRequest#parameters ExplainRequest.parameters}
# and
# {::Google::Cloud::AIPlatform::V1::BatchPredictionJob#model_parameters BatchPredictionJob.model_parameters}.
# 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).
# AutoML Models always have this field populated by Vertex AI, if no
# parameters are supported, then it is set to an empty string.
# 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] prediction_schema_uri
# @return [::String]
# Immutable. Points to a YAML file stored on Google Cloud Storage describing
# the format of a single prediction produced by this Model, which are
# returned via
# {::Google::Cloud::AIPlatform::V1::PredictResponse#predictions PredictResponse.predictions},
# {::Google::Cloud::AIPlatform::V1::ExplainResponse#explanations ExplainResponse.explanations},
# and
# {::Google::Cloud::AIPlatform::V1::BatchPredictionJob#output_config BatchPredictionJob.output_config}.
# 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).
# AutoML Models always have this field populated by Vertex AI.
# 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.
class PredictSchemata
include ::Google::Protobuf::MessageExts
extend ::Google::Protobuf::MessageExts::ClassMethods
end
# Specification of a container for serving predictions. Some fields in this
# message correspond to fields in the [Kubernetes Container v1 core
# specification](https://kubernetes.io/docs/reference/generated/kubernetes-api/v1.23/#container-v1-core).
# @!attribute [rw] image_uri
# @return [::String]
# Required. Immutable. URI of the Docker image to be used as the custom
# container for serving predictions. This URI must identify an image in
# Artifact Registry or Container Registry. Learn more about the [container
# publishing
# requirements](https://cloud.google.com/vertex-ai/docs/predictions/custom-container-requirements#publishing),
# including permissions requirements for the Vertex AI Service Agent.
#
# The container image is ingested upon
# {::Google::Cloud::AIPlatform::V1::ModelService::Client#upload_model ModelService.UploadModel},
# stored internally, and this original path is afterwards not used.
#
# To learn about the requirements for the Docker image itself, see
# [Custom container
# requirements](https://cloud.google.com/vertex-ai/docs/predictions/custom-container-requirements#).
#
# You can use the URI to one of Vertex AI's [pre-built container images for
# prediction](https://cloud.google.com/vertex-ai/docs/predictions/pre-built-containers)
# in this field.
# @!attribute [rw] command
# @return [::Array<::String>]
# Immutable. Specifies the command that runs when the container starts. This
# overrides the container's
# [ENTRYPOINT](https://docs.docker.com/engine/reference/builder/#entrypoint).
# Specify this field as an array of executable and arguments, similar to a
# Docker `ENTRYPOINT`'s "exec" form, not its "shell" form.
#
# If you do not specify this field, then the container's `ENTRYPOINT` runs,
# in conjunction with the
# {::Google::Cloud::AIPlatform::V1::ModelContainerSpec#args args} field or the
# container's [`CMD`](https://docs.docker.com/engine/reference/builder/#cmd),
# if either exists. If this field is not specified and the container does not
# have an `ENTRYPOINT`, then refer to the Docker documentation about [how
# `CMD` and `ENTRYPOINT`
# interact](https://docs.docker.com/engine/reference/builder/#understand-how-cmd-and-entrypoint-interact).
#
# If you specify this field, then you can also specify the `args` field to
# provide additional arguments for this command. However, if you specify this
# field, then the container's `CMD` is ignored. See the
# [Kubernetes documentation about how the
# `command` and `args` fields interact with a container's `ENTRYPOINT` and
# `CMD`](https://kubernetes.io/docs/tasks/inject-data-application/define-command-argument-container/#notes).
#
# In this field, you can reference [environment variables set by Vertex
# AI](https://cloud.google.com/vertex-ai/docs/predictions/custom-container-requirements#aip-variables)
# and environment variables set in the
# {::Google::Cloud::AIPlatform::V1::ModelContainerSpec#env env} field. You cannot
# reference environment variables set in the Docker image. In order for
# environment variables to be expanded, reference them by using the following
# syntax: <code>$(<var>VARIABLE_NAME</var>)</code> Note that this differs
# from Bash variable expansion, which does not use parentheses. If a variable
# cannot be resolved, the reference in the input string is used unchanged. To
# avoid variable expansion, you can escape this syntax with `$$`; for
# example: <code>$$(<var>VARIABLE_NAME</var>)</code> This field corresponds
# to the `command` field of the Kubernetes Containers [v1 core
# API](https://kubernetes.io/docs/reference/generated/kubernetes-api/v1.23/#container-v1-core).
# @!attribute [rw] args
# @return [::Array<::String>]
# Immutable. Specifies arguments for the command that runs when the container
# starts. This overrides the container's
# [`CMD`](https://docs.docker.com/engine/reference/builder/#cmd). Specify
# this field as an array of executable and arguments, similar to a Docker
# `CMD`'s "default parameters" form.
#
# If you don't specify this field but do specify the
# {::Google::Cloud::AIPlatform::V1::ModelContainerSpec#command command} field,
# then the command from the `command` field runs without any additional
# arguments. See the [Kubernetes documentation about how the `command` and
# `args` fields interact with a container's `ENTRYPOINT` and
# `CMD`](https://kubernetes.io/docs/tasks/inject-data-application/define-command-argument-container/#notes).
#
# If you don't specify this field and don't specify the `command` field,
# then the container's
# [`ENTRYPOINT`](https://docs.docker.com/engine/reference/builder/#cmd) and
# `CMD` determine what runs based on their default behavior. See the Docker
# documentation about [how `CMD` and `ENTRYPOINT`
# interact](https://docs.docker.com/engine/reference/builder/#understand-how-cmd-and-entrypoint-interact).
#
# In this field, you can reference [environment variables
# set by Vertex
# AI](https://cloud.google.com/vertex-ai/docs/predictions/custom-container-requirements#aip-variables)
# and environment variables set in the
# {::Google::Cloud::AIPlatform::V1::ModelContainerSpec#env env} field. You cannot
# reference environment variables set in the Docker image. In order for
# environment variables to be expanded, reference them by using the following
# syntax: <code>$(<var>VARIABLE_NAME</var>)</code> Note that this differs
# from Bash variable expansion, which does not use parentheses. If a variable
# cannot be resolved, the reference in the input string is used unchanged. To
# avoid variable expansion, you can escape this syntax with `$$`; for
# example: <code>$$(<var>VARIABLE_NAME</var>)</code> This field corresponds
# to the `args` field of the Kubernetes Containers [v1 core
# API](https://kubernetes.io/docs/reference/generated/kubernetes-api/v1.23/#container-v1-core).
# @!attribute [rw] env
# @return [::Array<::Google::Cloud::AIPlatform::V1::EnvVar>]
# Immutable. List of environment variables to set in the container. After the
# container starts running, code running in the container can read these
# environment variables.
#
# Additionally, the
# {::Google::Cloud::AIPlatform::V1::ModelContainerSpec#command command} and
# {::Google::Cloud::AIPlatform::V1::ModelContainerSpec#args args} fields can
# reference these variables. Later entries in this list can also reference
# earlier entries. For example, the following example sets the variable
# `VAR_2` to have the value `foo bar`:
#
# ```json
# [
# {
# "name": "VAR_1",
# "value": "foo"
# },
# {
# "name": "VAR_2",
# "value": "$(VAR_1) bar"
# }
# ]
# ```
#
# If you switch the order of the variables in the example, then the expansion
# does not occur.
#
# This field corresponds to the `env` field of the Kubernetes Containers
# [v1 core
# API](https://kubernetes.io/docs/reference/generated/kubernetes-api/v1.23/#container-v1-core).
# @!attribute [rw] ports
# @return [::Array<::Google::Cloud::AIPlatform::V1::Port>]
# Immutable. List of ports to expose from the container. Vertex AI sends any
# prediction requests that it receives to the first port on this list. Vertex
# AI also sends
# [liveness and health
# checks](https://cloud.google.com/vertex-ai/docs/predictions/custom-container-requirements#liveness)
# to this port.
#
# If you do not specify this field, it defaults to following value:
#
# ```json
# [
# {
# "containerPort": 8080
# }
# ]
# ```
#
# Vertex AI does not use ports other than the first one listed. This field
# corresponds to the `ports` field of the Kubernetes Containers
# [v1 core
# API](https://kubernetes.io/docs/reference/generated/kubernetes-api/v1.23/#container-v1-core).
# @!attribute [rw] predict_route
# @return [::String]
# Immutable. HTTP path on the container to send prediction requests to.
# Vertex AI forwards requests sent using
# {::Google::Cloud::AIPlatform::V1::PredictionService::Client#predict projects.locations.endpoints.predict}
# to this path on the container's IP address and port. Vertex AI then returns
# the container's response in the API response.
#
# For example, if you set this field to `/foo`, then when Vertex AI
# receives a prediction request, it forwards the request body in a POST
# request to the `/foo` path on the port of your container specified by the
# first value of this `ModelContainerSpec`'s
# {::Google::Cloud::AIPlatform::V1::ModelContainerSpec#ports ports} field.
#
# If you don't specify this field, it defaults to the following value when
# you [deploy this Model to an
# Endpoint][google.cloud.aiplatform.v1.EndpointService.DeployModel]:
# <code>/v1/endpoints/<var>ENDPOINT</var>/deployedModels/<var>DEPLOYED_MODEL</var>:predict</code>
# The placeholders in this value are replaced as follows:
#
# * <var>ENDPOINT</var>: The last segment (following `endpoints/`)of the
# Endpoint.name][] field of the Endpoint where this Model has been
# deployed. (Vertex AI makes this value available to your container code
# as the [`AIP_ENDPOINT_ID` environment
# variable](https://cloud.google.com/vertex-ai/docs/predictions/custom-container-requirements#aip-variables).)
#
# * <var>DEPLOYED_MODEL</var>:
# {::Google::Cloud::AIPlatform::V1::DeployedModel#id DeployedModel.id} of the
# `DeployedModel`.
# (Vertex AI makes this value available to your container code
# as the [`AIP_DEPLOYED_MODEL_ID` environment
# variable](https://cloud.google.com/vertex-ai/docs/predictions/custom-container-requirements#aip-variables).)
# @!attribute [rw] health_route
# @return [::String]
# Immutable. HTTP path on the container to send health checks to. Vertex AI
# intermittently sends GET requests to this path on the container's IP
# address and port to check that the container is healthy. Read more about
# [health
# checks](https://cloud.google.com/vertex-ai/docs/predictions/custom-container-requirements#health).
#
# For example, if you set this field to `/bar`, then Vertex AI
# intermittently sends a GET request to the `/bar` path on the port of your
# container specified by the first value of this `ModelContainerSpec`'s
# {::Google::Cloud::AIPlatform::V1::ModelContainerSpec#ports ports} field.
#
# If you don't specify this field, it defaults to the following value when
# you [deploy this Model to an
# Endpoint][google.cloud.aiplatform.v1.EndpointService.DeployModel]:
# <code>/v1/endpoints/<var>ENDPOINT</var>/deployedModels/<var>DEPLOYED_MODEL</var>:predict</code>
# The placeholders in this value are replaced as follows:
#
# * <var>ENDPOINT</var>: The last segment (following `endpoints/`)of the
# Endpoint.name][] field of the Endpoint where this Model has been
# deployed. (Vertex AI makes this value available to your container code
# as the [`AIP_ENDPOINT_ID` environment
# variable](https://cloud.google.com/vertex-ai/docs/predictions/custom-container-requirements#aip-variables).)
#
# * <var>DEPLOYED_MODEL</var>:
# {::Google::Cloud::AIPlatform::V1::DeployedModel#id DeployedModel.id} of the
# `DeployedModel`.
# (Vertex AI makes this value available to your container code as the
# [`AIP_DEPLOYED_MODEL_ID` environment
# variable](https://cloud.google.com/vertex-ai/docs/predictions/custom-container-requirements#aip-variables).)
# @!attribute [rw] grpc_ports
# @return [::Array<::Google::Cloud::AIPlatform::V1::Port>]
# Immutable. List of ports to expose from the container. Vertex AI sends gRPC
# prediction requests that it receives to the first port on this list. Vertex
# AI also sends liveness and health checks to this port.
#
# If you do not specify this field, gRPC requests to the container will be
# disabled.
#
# Vertex AI does not use ports other than the first one listed. This field
# corresponds to the `ports` field of the Kubernetes Containers v1 core API.
# @!attribute [rw] deployment_timeout
# @return [::Google::Protobuf::Duration]
# Immutable. Deployment timeout.
# Limit for deployment timeout is 2 hours.
# @!attribute [rw] shared_memory_size_mb
# @return [::Integer]
# Immutable. The amount of the VM memory to reserve as the shared memory for
# the model in megabytes.
# @!attribute [rw] startup_probe
# @return [::Google::Cloud::AIPlatform::V1::Probe]
# Immutable. Specification for Kubernetes startup probe.
# @!attribute [rw] health_probe
# @return [::Google::Cloud::AIPlatform::V1::Probe]
# Immutable. Specification for Kubernetes readiness probe.
class ModelContainerSpec
include ::Google::Protobuf::MessageExts
extend ::Google::Protobuf::MessageExts::ClassMethods
end
# Represents a network port in a container.
# @!attribute [rw] container_port
# @return [::Integer]
# The number of the port to expose on the pod's IP address.
# Must be a valid port number, between 1 and 65535 inclusive.
class Port
include ::Google::Protobuf::MessageExts
extend ::Google::Protobuf::MessageExts::ClassMethods
end
# Detail description of the source information of the model.
# @!attribute [rw] source_type
# @return [::Google::Cloud::AIPlatform::V1::ModelSourceInfo::ModelSourceType]
# Type of the model source.
# @!attribute [rw] copy
# @return [::Boolean]
# If this Model is copy of another Model. If true then
# {::Google::Cloud::AIPlatform::V1::ModelSourceInfo#source_type source_type}
# pertains to the original.
class ModelSourceInfo
include ::Google::Protobuf::MessageExts
extend ::Google::Protobuf::MessageExts::ClassMethods
# Source of the model.
# Different from `objective` field, this `ModelSourceType` enum
# indicates the source from which the model was accessed or obtained,
# whereas the `objective` indicates the overall aim or function of this
# model.
module ModelSourceType
# Should not be used.
MODEL_SOURCE_TYPE_UNSPECIFIED = 0
# The Model is uploaded by automl training pipeline.
AUTOML = 1
# The Model is uploaded by user or custom training pipeline.
CUSTOM = 2
# The Model is registered and sync'ed from BigQuery ML.
BQML = 3
# The Model is saved or tuned from Model Garden.
MODEL_GARDEN = 4
# The Model is saved or tuned from Genie.
GENIE = 5
# The Model is uploaded by text embedding finetuning pipeline.
CUSTOM_TEXT_EMBEDDING = 6
# The Model is saved or tuned from Marketplace.
MARKETPLACE = 7
end
end
# Probe describes a health check to be performed against a container to
# determine whether it is alive or ready to receive traffic.
# @!attribute [rw] exec
# @return [::Google::Cloud::AIPlatform::V1::Probe::ExecAction]
# ExecAction probes the health of a container by executing a command.
# @!attribute [rw] period_seconds
# @return [::Integer]
# How often (in seconds) to perform the probe. Default to 10 seconds.
# Minimum value is 1. Must be less than timeout_seconds.
#
# Maps to Kubernetes probe argument 'periodSeconds'.
# @!attribute [rw] timeout_seconds
# @return [::Integer]
# Number of seconds after which the probe times out. Defaults to 1 second.
# Minimum value is 1. Must be greater or equal to period_seconds.
#
# Maps to Kubernetes probe argument 'timeoutSeconds'.
class Probe
include ::Google::Protobuf::MessageExts
extend ::Google::Protobuf::MessageExts::ClassMethods
# ExecAction specifies a command to execute.
# @!attribute [rw] command
# @return [::Array<::String>]
# Command is the command line to execute inside the container, the working
# directory for the command is root ('/') in the container's filesystem.
# The command is simply exec'd, it is not run inside a shell, so
# traditional shell instructions ('|', etc) won't work. To use a shell, you
# need to explicitly call out to that shell. Exit status of 0 is treated as
# live/healthy and non-zero is unhealthy.
class ExecAction
include ::Google::Protobuf::MessageExts
extend ::Google::Protobuf::MessageExts::ClassMethods
end
end
end
end
end
end