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model.pb.go
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model.pb.go
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// Copyright 2021 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
//
// http://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.
// Code generated by protoc-gen-go. DO NOT EDIT.
// versions:
// protoc-gen-go v1.26.0
// protoc v3.12.2
// source: google/cloud/aiplatform/v1/model.proto
package aiplatform
import (
reflect "reflect"
sync "sync"
_ "google.golang.org/genproto/googleapis/api/annotations"
protoreflect "google.golang.org/protobuf/reflect/protoreflect"
protoimpl "google.golang.org/protobuf/runtime/protoimpl"
structpb "google.golang.org/protobuf/types/known/structpb"
timestamppb "google.golang.org/protobuf/types/known/timestamppb"
)
const (
// Verify that this generated code is sufficiently up-to-date.
_ = protoimpl.EnforceVersion(20 - protoimpl.MinVersion)
// Verify that runtime/protoimpl is sufficiently up-to-date.
_ = protoimpl.EnforceVersion(protoimpl.MaxVersion - 20)
)
// Identifies a type of Model's prediction resources.
type Model_DeploymentResourcesType int32
const (
// Should not be used.
Model_DEPLOYMENT_RESOURCES_TYPE_UNSPECIFIED Model_DeploymentResourcesType = 0
// Resources that are dedicated to the [DeployedModel][google.cloud.aiplatform.v1.DeployedModel], and that need a
// higher degree of manual configuration.
Model_DEDICATED_RESOURCES Model_DeploymentResourcesType = 1
// Resources that to large degree are decided by Vertex AI, and require
// only a modest additional configuration.
Model_AUTOMATIC_RESOURCES Model_DeploymentResourcesType = 2
)
// Enum value maps for Model_DeploymentResourcesType.
var (
Model_DeploymentResourcesType_name = map[int32]string{
0: "DEPLOYMENT_RESOURCES_TYPE_UNSPECIFIED",
1: "DEDICATED_RESOURCES",
2: "AUTOMATIC_RESOURCES",
}
Model_DeploymentResourcesType_value = map[string]int32{
"DEPLOYMENT_RESOURCES_TYPE_UNSPECIFIED": 0,
"DEDICATED_RESOURCES": 1,
"AUTOMATIC_RESOURCES": 2,
}
)
func (x Model_DeploymentResourcesType) Enum() *Model_DeploymentResourcesType {
p := new(Model_DeploymentResourcesType)
*p = x
return p
}
func (x Model_DeploymentResourcesType) String() string {
return protoimpl.X.EnumStringOf(x.Descriptor(), protoreflect.EnumNumber(x))
}
func (Model_DeploymentResourcesType) Descriptor() protoreflect.EnumDescriptor {
return file_google_cloud_aiplatform_v1_model_proto_enumTypes[0].Descriptor()
}
func (Model_DeploymentResourcesType) Type() protoreflect.EnumType {
return &file_google_cloud_aiplatform_v1_model_proto_enumTypes[0]
}
func (x Model_DeploymentResourcesType) Number() protoreflect.EnumNumber {
return protoreflect.EnumNumber(x)
}
// Deprecated: Use Model_DeploymentResourcesType.Descriptor instead.
func (Model_DeploymentResourcesType) EnumDescriptor() ([]byte, []int) {
return file_google_cloud_aiplatform_v1_model_proto_rawDescGZIP(), []int{0, 0}
}
// The Model content that can be exported.
type Model_ExportFormat_ExportableContent int32
const (
// Should not be used.
Model_ExportFormat_EXPORTABLE_CONTENT_UNSPECIFIED Model_ExportFormat_ExportableContent = 0
// Model artifact and any of its supported files. Will be exported to the
// location specified by the `artifactDestination` field of the
// [ExportModelRequest.output_config][google.cloud.aiplatform.v1.ExportModelRequest.output_config] object.
Model_ExportFormat_ARTIFACT Model_ExportFormat_ExportableContent = 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 [ExportModelRequest.output_config][google.cloud.aiplatform.v1.ExportModelRequest.output_config] object.
Model_ExportFormat_IMAGE Model_ExportFormat_ExportableContent = 2
)
// Enum value maps for Model_ExportFormat_ExportableContent.
var (
Model_ExportFormat_ExportableContent_name = map[int32]string{
0: "EXPORTABLE_CONTENT_UNSPECIFIED",
1: "ARTIFACT",
2: "IMAGE",
}
Model_ExportFormat_ExportableContent_value = map[string]int32{
"EXPORTABLE_CONTENT_UNSPECIFIED": 0,
"ARTIFACT": 1,
"IMAGE": 2,
}
)
func (x Model_ExportFormat_ExportableContent) Enum() *Model_ExportFormat_ExportableContent {
p := new(Model_ExportFormat_ExportableContent)
*p = x
return p
}
func (x Model_ExportFormat_ExportableContent) String() string {
return protoimpl.X.EnumStringOf(x.Descriptor(), protoreflect.EnumNumber(x))
}
func (Model_ExportFormat_ExportableContent) Descriptor() protoreflect.EnumDescriptor {
return file_google_cloud_aiplatform_v1_model_proto_enumTypes[1].Descriptor()
}
func (Model_ExportFormat_ExportableContent) Type() protoreflect.EnumType {
return &file_google_cloud_aiplatform_v1_model_proto_enumTypes[1]
}
func (x Model_ExportFormat_ExportableContent) Number() protoreflect.EnumNumber {
return protoreflect.EnumNumber(x)
}
// Deprecated: Use Model_ExportFormat_ExportableContent.Descriptor instead.
func (Model_ExportFormat_ExportableContent) EnumDescriptor() ([]byte, []int) {
return file_google_cloud_aiplatform_v1_model_proto_rawDescGZIP(), []int{0, 0, 0}
}
// A trained machine learning Model.
type Model struct {
state protoimpl.MessageState
sizeCache protoimpl.SizeCache
unknownFields protoimpl.UnknownFields
// The resource name of the Model.
Name string `protobuf:"bytes,1,opt,name=name,proto3" json:"name,omitempty"`
// Required. The display name of the Model.
// The name can be up to 128 characters long and can be consist of any UTF-8
// characters.
DisplayName string `protobuf:"bytes,2,opt,name=display_name,json=displayName,proto3" json:"display_name,omitempty"`
// The description of the Model.
Description string `protobuf:"bytes,3,opt,name=description,proto3" json:"description,omitempty"`
// The schemata that describe formats of the Model's predictions and
// explanations as given and returned via
// [PredictionService.Predict][google.cloud.aiplatform.v1.PredictionService.Predict] and [PredictionService.Explain][google.cloud.aiplatform.v1.PredictionService.Explain].
PredictSchemata *PredictSchemata `protobuf:"bytes,4,opt,name=predict_schemata,json=predictSchemata,proto3" json:"predict_schemata,omitempty"`
// 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.
MetadataSchemaUri string `protobuf:"bytes,5,opt,name=metadata_schema_uri,json=metadataSchemaUri,proto3" json:"metadata_schema_uri,omitempty"`
// Immutable. An additional information about the Model; the schema of the metadata can
// be found in [metadata_schema][google.cloud.aiplatform.v1.Model.metadata_schema_uri].
// Unset if the Model does not have any additional information.
Metadata *structpb.Value `protobuf:"bytes,6,opt,name=metadata,proto3" json:"metadata,omitempty"`
// Output only. The formats in which this Model may be exported. If empty, this Model is
// not available for export.
SupportedExportFormats []*Model_ExportFormat `protobuf:"bytes,20,rep,name=supported_export_formats,json=supportedExportFormats,proto3" json:"supported_export_formats,omitempty"`
// Output only. The resource name of the TrainingPipeline that uploaded this Model, if
// any.
TrainingPipeline string `protobuf:"bytes,7,opt,name=training_pipeline,json=trainingPipeline,proto3" json:"training_pipeline,omitempty"`
// Input only. The specification of the container that is to be used when deploying
// this Model. The specification is ingested upon
// [ModelService.UploadModel][google.cloud.aiplatform.v1.ModelService.UploadModel], and all binaries it contains are copied
// and stored internally by Vertex AI.
// Not present for AutoML Models.
ContainerSpec *ModelContainerSpec `protobuf:"bytes,9,opt,name=container_spec,json=containerSpec,proto3" json:"container_spec,omitempty"`
// Immutable. The path to the directory containing the Model artifact and any of its
// supporting files.
// Not present for AutoML Models.
ArtifactUri string `protobuf:"bytes,26,opt,name=artifact_uri,json=artifactUri,proto3" json:"artifact_uri,omitempty"`
// Output only. When this Model is deployed, its prediction resources are described by the
// `prediction_resources` field of the [Endpoint.deployed_models][google.cloud.aiplatform.v1.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
// [Endpoint][google.cloud.aiplatform.v1.Endpoint] and does not support
// online predictions ([PredictionService.Predict][google.cloud.aiplatform.v1.PredictionService.Predict] or
// [PredictionService.Explain][google.cloud.aiplatform.v1.PredictionService.Explain]). Such a Model can serve predictions by
// using a [BatchPredictionJob][google.cloud.aiplatform.v1.BatchPredictionJob], if it has at least one entry each in
// [supported_input_storage_formats][google.cloud.aiplatform.v1.Model.supported_input_storage_formats] and
// [supported_output_storage_formats][google.cloud.aiplatform.v1.Model.supported_output_storage_formats].
SupportedDeploymentResourcesTypes []Model_DeploymentResourcesType `protobuf:"varint,10,rep,packed,name=supported_deployment_resources_types,json=supportedDeploymentResourcesTypes,proto3,enum=google.cloud.aiplatform.v1.Model_DeploymentResourcesType" json:"supported_deployment_resources_types,omitempty"`
// Output only. The formats this Model supports in
// [BatchPredictionJob.input_config][google.cloud.aiplatform.v1.BatchPredictionJob.input_config]. If
// [PredictSchemata.instance_schema_uri][google.cloud.aiplatform.v1.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
// [GcsSource][google.cloud.aiplatform.v1.BatchPredictionJob.InputConfig.gcs_source].
//
// * `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 [GcsSource][google.cloud.aiplatform.v1.BatchPredictionJob.InputConfig.gcs_source].
//
// * `tf-record`
// The TFRecord format, where each instance is a single record in tfrecord
// syntax. Uses [GcsSource][google.cloud.aiplatform.v1.BatchPredictionJob.InputConfig.gcs_source].
//
// * `tf-record-gzip`
// Similar to `tf-record`, but the file is gzipped. Uses
// [GcsSource][google.cloud.aiplatform.v1.BatchPredictionJob.InputConfig.gcs_source].
//
// * `bigquery`
// Each instance is a single row in BigQuery. Uses
// [BigQuerySource][google.cloud.aiplatform.v1.BatchPredictionJob.InputConfig.bigquery_source].
//
// * `file-list`
// Each line of the file is the location of an instance to process, uses
// `gcs_source` field of the
// [InputConfig][google.cloud.aiplatform.v1.BatchPredictionJob.InputConfig] object.
//
//
// If this Model doesn't support any of these formats it means it cannot be
// used with a [BatchPredictionJob][google.cloud.aiplatform.v1.BatchPredictionJob]. However, if it has
// [supported_deployment_resources_types][google.cloud.aiplatform.v1.Model.supported_deployment_resources_types], it could serve online
// predictions by using [PredictionService.Predict][google.cloud.aiplatform.v1.PredictionService.Predict] or
// [PredictionService.Explain][google.cloud.aiplatform.v1.PredictionService.Explain].
SupportedInputStorageFormats []string `protobuf:"bytes,11,rep,name=supported_input_storage_formats,json=supportedInputStorageFormats,proto3" json:"supported_input_storage_formats,omitempty"`
// Output only. The formats this Model supports in
// [BatchPredictionJob.output_config][google.cloud.aiplatform.v1.BatchPredictionJob.output_config]. If both
// [PredictSchemata.instance_schema_uri][google.cloud.aiplatform.v1.PredictSchemata.instance_schema_uri] and
// [PredictSchemata.prediction_schema_uri][google.cloud.aiplatform.v1.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
// [GcsDestination][google.cloud.aiplatform.v1.BatchPredictionJob.OutputConfig.gcs_destination].
//
// * `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
// [GcsDestination][google.cloud.aiplatform.v1.BatchPredictionJob.OutputConfig.gcs_destination].
//
// * `bigquery`
// Each prediction is a single row in a BigQuery table, uses
// [BigQueryDestination][google.cloud.aiplatform.v1.BatchPredictionJob.OutputConfig.bigquery_destination]
// .
//
//
// If this Model doesn't support any of these formats it means it cannot be
// used with a [BatchPredictionJob][google.cloud.aiplatform.v1.BatchPredictionJob]. However, if it has
// [supported_deployment_resources_types][google.cloud.aiplatform.v1.Model.supported_deployment_resources_types], it could serve online
// predictions by using [PredictionService.Predict][google.cloud.aiplatform.v1.PredictionService.Predict] or
// [PredictionService.Explain][google.cloud.aiplatform.v1.PredictionService.Explain].
SupportedOutputStorageFormats []string `protobuf:"bytes,12,rep,name=supported_output_storage_formats,json=supportedOutputStorageFormats,proto3" json:"supported_output_storage_formats,omitempty"`
// Output only. Timestamp when this Model was uploaded into Vertex AI.
CreateTime *timestamppb.Timestamp `protobuf:"bytes,13,opt,name=create_time,json=createTime,proto3" json:"create_time,omitempty"`
// Output only. Timestamp when this Model was most recently updated.
UpdateTime *timestamppb.Timestamp `protobuf:"bytes,14,opt,name=update_time,json=updateTime,proto3" json:"update_time,omitempty"`
// Output only. The pointers to DeployedModels created from this Model. Note that
// Model could have been deployed to Endpoints in different Locations.
DeployedModels []*DeployedModelRef `protobuf:"bytes,15,rep,name=deployed_models,json=deployedModels,proto3" json:"deployed_models,omitempty"`
// The default explanation specification for this Model.
//
// The Model can be used for [requesting
// explanation][PredictionService.Explain] after being
// [deployed][google.cloud.aiplatform.v1.EndpointService.DeployModel] if it is populated.
// The Model can be used for [batch
// explanation][BatchPredictionJob.generate_explanation] if it is populated.
//
// All fields of the explanation_spec can be overridden by
// [explanation_spec][google.cloud.aiplatform.v1.DeployedModel.explanation_spec] of
// [DeployModelRequest.deployed_model][google.cloud.aiplatform.v1.DeployModelRequest.deployed_model], or
// [explanation_spec][google.cloud.aiplatform.v1.BatchPredictionJob.explanation_spec] of
// [BatchPredictionJob][google.cloud.aiplatform.v1.BatchPredictionJob].
//
// If the default explanation specification is not set for this Model, this
// Model can still be used for [requesting
// explanation][PredictionService.Explain] by setting
// [explanation_spec][google.cloud.aiplatform.v1.DeployedModel.explanation_spec] of
// [DeployModelRequest.deployed_model][google.cloud.aiplatform.v1.DeployModelRequest.deployed_model] and for [batch
// explanation][BatchPredictionJob.generate_explanation] by setting
// [explanation_spec][google.cloud.aiplatform.v1.BatchPredictionJob.explanation_spec] of
// [BatchPredictionJob][google.cloud.aiplatform.v1.BatchPredictionJob].
ExplanationSpec *ExplanationSpec `protobuf:"bytes,23,opt,name=explanation_spec,json=explanationSpec,proto3" json:"explanation_spec,omitempty"`
// Used to perform consistent read-modify-write updates. If not set, a blind
// "overwrite" update happens.
Etag string `protobuf:"bytes,16,opt,name=etag,proto3" json:"etag,omitempty"`
// 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.
Labels map[string]string `protobuf:"bytes,17,rep,name=labels,proto3" json:"labels,omitempty" protobuf_key:"bytes,1,opt,name=key,proto3" protobuf_val:"bytes,2,opt,name=value,proto3"`
// 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.
EncryptionSpec *EncryptionSpec `protobuf:"bytes,24,opt,name=encryption_spec,json=encryptionSpec,proto3" json:"encryption_spec,omitempty"`
}
func (x *Model) Reset() {
*x = Model{}
if protoimpl.UnsafeEnabled {
mi := &file_google_cloud_aiplatform_v1_model_proto_msgTypes[0]
ms := protoimpl.X.MessageStateOf(protoimpl.Pointer(x))
ms.StoreMessageInfo(mi)
}
}
func (x *Model) String() string {
return protoimpl.X.MessageStringOf(x)
}
func (*Model) ProtoMessage() {}
func (x *Model) ProtoReflect() protoreflect.Message {
mi := &file_google_cloud_aiplatform_v1_model_proto_msgTypes[0]
if protoimpl.UnsafeEnabled && x != nil {
ms := protoimpl.X.MessageStateOf(protoimpl.Pointer(x))
if ms.LoadMessageInfo() == nil {
ms.StoreMessageInfo(mi)
}
return ms
}
return mi.MessageOf(x)
}
// Deprecated: Use Model.ProtoReflect.Descriptor instead.
func (*Model) Descriptor() ([]byte, []int) {
return file_google_cloud_aiplatform_v1_model_proto_rawDescGZIP(), []int{0}
}
func (x *Model) GetName() string {
if x != nil {
return x.Name
}
return ""
}
func (x *Model) GetDisplayName() string {
if x != nil {
return x.DisplayName
}
return ""
}
func (x *Model) GetDescription() string {
if x != nil {
return x.Description
}
return ""
}
func (x *Model) GetPredictSchemata() *PredictSchemata {
if x != nil {
return x.PredictSchemata
}
return nil
}
func (x *Model) GetMetadataSchemaUri() string {
if x != nil {
return x.MetadataSchemaUri
}
return ""
}
func (x *Model) GetMetadata() *structpb.Value {
if x != nil {
return x.Metadata
}
return nil
}
func (x *Model) GetSupportedExportFormats() []*Model_ExportFormat {
if x != nil {
return x.SupportedExportFormats
}
return nil
}
func (x *Model) GetTrainingPipeline() string {
if x != nil {
return x.TrainingPipeline
}
return ""
}
func (x *Model) GetContainerSpec() *ModelContainerSpec {
if x != nil {
return x.ContainerSpec
}
return nil
}
func (x *Model) GetArtifactUri() string {
if x != nil {
return x.ArtifactUri
}
return ""
}
func (x *Model) GetSupportedDeploymentResourcesTypes() []Model_DeploymentResourcesType {
if x != nil {
return x.SupportedDeploymentResourcesTypes
}
return nil
}
func (x *Model) GetSupportedInputStorageFormats() []string {
if x != nil {
return x.SupportedInputStorageFormats
}
return nil
}
func (x *Model) GetSupportedOutputStorageFormats() []string {
if x != nil {
return x.SupportedOutputStorageFormats
}
return nil
}
func (x *Model) GetCreateTime() *timestamppb.Timestamp {
if x != nil {
return x.CreateTime
}
return nil
}
func (x *Model) GetUpdateTime() *timestamppb.Timestamp {
if x != nil {
return x.UpdateTime
}
return nil
}
func (x *Model) GetDeployedModels() []*DeployedModelRef {
if x != nil {
return x.DeployedModels
}
return nil
}
func (x *Model) GetExplanationSpec() *ExplanationSpec {
if x != nil {
return x.ExplanationSpec
}
return nil
}
func (x *Model) GetEtag() string {
if x != nil {
return x.Etag
}
return ""
}
func (x *Model) GetLabels() map[string]string {
if x != nil {
return x.Labels
}
return nil
}
func (x *Model) GetEncryptionSpec() *EncryptionSpec {
if x != nil {
return x.EncryptionSpec
}
return nil
}
// Contains the schemata used in Model's predictions and explanations via
// [PredictionService.Predict][google.cloud.aiplatform.v1.PredictionService.Predict], [PredictionService.Explain][google.cloud.aiplatform.v1.PredictionService.Explain] and
// [BatchPredictionJob][google.cloud.aiplatform.v1.BatchPredictionJob].
type PredictSchemata struct {
state protoimpl.MessageState
sizeCache protoimpl.SizeCache
unknownFields protoimpl.UnknownFields
// Immutable. Points to a YAML file stored on Google Cloud Storage describing the format
// of a single instance, which are used in [PredictRequest.instances][google.cloud.aiplatform.v1.PredictRequest.instances],
// [ExplainRequest.instances][google.cloud.aiplatform.v1.ExplainRequest.instances] and
// [BatchPredictionJob.input_config][google.cloud.aiplatform.v1.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.
InstanceSchemaUri string `protobuf:"bytes,1,opt,name=instance_schema_uri,json=instanceSchemaUri,proto3" json:"instance_schema_uri,omitempty"`
// Immutable. Points to a YAML file stored on Google Cloud Storage describing the
// parameters of prediction and explanation via
// [PredictRequest.parameters][google.cloud.aiplatform.v1.PredictRequest.parameters], [ExplainRequest.parameters][google.cloud.aiplatform.v1.ExplainRequest.parameters] and
// [BatchPredictionJob.model_parameters][google.cloud.aiplatform.v1.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.
ParametersSchemaUri string `protobuf:"bytes,2,opt,name=parameters_schema_uri,json=parametersSchemaUri,proto3" json:"parameters_schema_uri,omitempty"`
// 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
// [PredictResponse.predictions][google.cloud.aiplatform.v1.PredictResponse.predictions], [ExplainResponse.explanations][google.cloud.aiplatform.v1.ExplainResponse.explanations], and
// [BatchPredictionJob.output_config][google.cloud.aiplatform.v1.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.
PredictionSchemaUri string `protobuf:"bytes,3,opt,name=prediction_schema_uri,json=predictionSchemaUri,proto3" json:"prediction_schema_uri,omitempty"`
}
func (x *PredictSchemata) Reset() {
*x = PredictSchemata{}
if protoimpl.UnsafeEnabled {
mi := &file_google_cloud_aiplatform_v1_model_proto_msgTypes[1]
ms := protoimpl.X.MessageStateOf(protoimpl.Pointer(x))
ms.StoreMessageInfo(mi)
}
}
func (x *PredictSchemata) String() string {
return protoimpl.X.MessageStringOf(x)
}
func (*PredictSchemata) ProtoMessage() {}
func (x *PredictSchemata) ProtoReflect() protoreflect.Message {
mi := &file_google_cloud_aiplatform_v1_model_proto_msgTypes[1]
if protoimpl.UnsafeEnabled && x != nil {
ms := protoimpl.X.MessageStateOf(protoimpl.Pointer(x))
if ms.LoadMessageInfo() == nil {
ms.StoreMessageInfo(mi)
}
return ms
}
return mi.MessageOf(x)
}
// Deprecated: Use PredictSchemata.ProtoReflect.Descriptor instead.
func (*PredictSchemata) Descriptor() ([]byte, []int) {
return file_google_cloud_aiplatform_v1_model_proto_rawDescGZIP(), []int{1}
}
func (x *PredictSchemata) GetInstanceSchemaUri() string {
if x != nil {
return x.InstanceSchemaUri
}
return ""
}
func (x *PredictSchemata) GetParametersSchemaUri() string {
if x != nil {
return x.ParametersSchemaUri
}
return ""
}
func (x *PredictSchemata) GetPredictionSchemaUri() string {
if x != nil {
return x.PredictionSchemaUri
}
return ""
}
// Specification of a container for serving predictions. Some fields in this
// message correspond to fields in the [Kubernetes Container v1 core
// specification](https://v1-18.docs.kubernetes.io/docs/reference/generated/kubernetes-api/v1.18/#container-v1-core).
type ModelContainerSpec struct {
state protoimpl.MessageState
sizeCache protoimpl.SizeCache
unknownFields protoimpl.UnknownFields
// 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 [ModelService.UploadModel][google.cloud.aiplatform.v1.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.
ImageUri string `protobuf:"bytes,1,opt,name=image_uri,json=imageUri,proto3" json:"image_uri,omitempty"`
// 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 [args][google.cloud.aiplatform.v1.ModelContainerSpec.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 [env][google.cloud.aiplatform.v1.ModelContainerSpec.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://v1-18.docs.kubernetes.io/docs/reference/generated/kubernetes-api/v1.18/#container-v1-core).
Command []string `protobuf:"bytes,2,rep,name=command,proto3" json:"command,omitempty"`
// 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
// [command][google.cloud.aiplatform.v1.ModelContainerSpec.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 [env][google.cloud.aiplatform.v1.ModelContainerSpec.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://v1-18.docs.kubernetes.io/docs/reference/generated/kubernetes-api/v1.18/#container-v1-core).
Args []string `protobuf:"bytes,3,rep,name=args,proto3" json:"args,omitempty"`
// 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 [command][google.cloud.aiplatform.v1.ModelContainerSpec.command] and
// [args][google.cloud.aiplatform.v1.ModelContainerSpec.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://v1-18.docs.kubernetes.io/docs/reference/generated/kubernetes-api/v1.18/#container-v1-core).
Env []*EnvVar `protobuf:"bytes,4,rep,name=env,proto3" json:"env,omitempty"`
// 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://v1-18.docs.kubernetes.io/docs/reference/generated/kubernetes-api/v1.18/#container-v1-core).
Ports []*Port `protobuf:"bytes,5,rep,name=ports,proto3" json:"ports,omitempty"`
// Immutable. HTTP path on the container to send prediction requests to. Vertex AI
// forwards requests sent using
// [projects.locations.endpoints.predict][google.cloud.aiplatform.v1.PredictionService.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
// [ports][google.cloud.aiplatform.v1.ModelContainerSpec.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>: [DeployedModel.id][google.cloud.aiplatform.v1.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).)
PredictRoute string `protobuf:"bytes,6,opt,name=predict_route,json=predictRoute,proto3" json:"predict_route,omitempty"`
// 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
// [ports][google.cloud.aiplatform.v1.ModelContainerSpec.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>: [DeployedModel.id][google.cloud.aiplatform.v1.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).)
HealthRoute string `protobuf:"bytes,7,opt,name=health_route,json=healthRoute,proto3" json:"health_route,omitempty"`
}
func (x *ModelContainerSpec) Reset() {
*x = ModelContainerSpec{}
if protoimpl.UnsafeEnabled {
mi := &file_google_cloud_aiplatform_v1_model_proto_msgTypes[2]
ms := protoimpl.X.MessageStateOf(protoimpl.Pointer(x))
ms.StoreMessageInfo(mi)
}
}
func (x *ModelContainerSpec) String() string {
return protoimpl.X.MessageStringOf(x)
}
func (*ModelContainerSpec) ProtoMessage() {}
func (x *ModelContainerSpec) ProtoReflect() protoreflect.Message {
mi := &file_google_cloud_aiplatform_v1_model_proto_msgTypes[2]
if protoimpl.UnsafeEnabled && x != nil {
ms := protoimpl.X.MessageStateOf(protoimpl.Pointer(x))
if ms.LoadMessageInfo() == nil {
ms.StoreMessageInfo(mi)
}
return ms
}
return mi.MessageOf(x)
}
// Deprecated: Use ModelContainerSpec.ProtoReflect.Descriptor instead.
func (*ModelContainerSpec) Descriptor() ([]byte, []int) {
return file_google_cloud_aiplatform_v1_model_proto_rawDescGZIP(), []int{2}
}
func (x *ModelContainerSpec) GetImageUri() string {
if x != nil {
return x.ImageUri
}
return ""
}
func (x *ModelContainerSpec) GetCommand() []string {
if x != nil {
return x.Command
}
return nil
}
func (x *ModelContainerSpec) GetArgs() []string {
if x != nil {
return x.Args
}
return nil
}
func (x *ModelContainerSpec) GetEnv() []*EnvVar {
if x != nil {
return x.Env
}
return nil
}
func (x *ModelContainerSpec) GetPorts() []*Port {
if x != nil {
return x.Ports
}
return nil
}
func (x *ModelContainerSpec) GetPredictRoute() string {
if x != nil {
return x.PredictRoute
}
return ""
}
func (x *ModelContainerSpec) GetHealthRoute() string {
if x != nil {
return x.HealthRoute
}
return ""
}
// Represents a network port in a container.
type Port struct {
state protoimpl.MessageState
sizeCache protoimpl.SizeCache
unknownFields protoimpl.UnknownFields
// The number of the port to expose on the pod's IP address.
// Must be a valid port number, between 1 and 65535 inclusive.
ContainerPort int32 `protobuf:"varint,3,opt,name=container_port,json=containerPort,proto3" json:"container_port,omitempty"`
}
func (x *Port) Reset() {
*x = Port{}
if protoimpl.UnsafeEnabled {
mi := &file_google_cloud_aiplatform_v1_model_proto_msgTypes[3]
ms := protoimpl.X.MessageStateOf(protoimpl.Pointer(x))
ms.StoreMessageInfo(mi)
}
}
func (x *Port) String() string {
return protoimpl.X.MessageStringOf(x)
}
func (*Port) ProtoMessage() {}
func (x *Port) ProtoReflect() protoreflect.Message {
mi := &file_google_cloud_aiplatform_v1_model_proto_msgTypes[3]
if protoimpl.UnsafeEnabled && x != nil {
ms := protoimpl.X.MessageStateOf(protoimpl.Pointer(x))
if ms.LoadMessageInfo() == nil {
ms.StoreMessageInfo(mi)
}
return ms
}
return mi.MessageOf(x)
}
// Deprecated: Use Port.ProtoReflect.Descriptor instead.
func (*Port) Descriptor() ([]byte, []int) {
return file_google_cloud_aiplatform_v1_model_proto_rawDescGZIP(), []int{3}
}
func (x *Port) GetContainerPort() int32 {
if x != nil {
return x.ContainerPort
}
return 0
}
// Represents export format supported by the Model.
// All formats export to Google Cloud Storage.
type Model_ExportFormat struct {
state protoimpl.MessageState
sizeCache protoimpl.SizeCache
unknownFields protoimpl.UnknownFields
// 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.
Id string `protobuf:"bytes,1,opt,name=id,proto3" json:"id,omitempty"`
// Output only. The content of this Model that may be exported.
ExportableContents []Model_ExportFormat_ExportableContent `protobuf:"varint,2,rep,packed,name=exportable_contents,json=exportableContents,proto3,enum=google.cloud.aiplatform.v1.Model_ExportFormat_ExportableContent" json:"exportable_contents,omitempty"`
}
func (x *Model_ExportFormat) Reset() {
*x = Model_ExportFormat{}
if protoimpl.UnsafeEnabled {
mi := &file_google_cloud_aiplatform_v1_model_proto_msgTypes[4]
ms := protoimpl.X.MessageStateOf(protoimpl.Pointer(x))
ms.StoreMessageInfo(mi)
}
}
func (x *Model_ExportFormat) String() string {
return protoimpl.X.MessageStringOf(x)
}
func (*Model_ExportFormat) ProtoMessage() {}
func (x *Model_ExportFormat) ProtoReflect() protoreflect.Message {
mi := &file_google_cloud_aiplatform_v1_model_proto_msgTypes[4]
if protoimpl.UnsafeEnabled && x != nil {
ms := protoimpl.X.MessageStateOf(protoimpl.Pointer(x))
if ms.LoadMessageInfo() == nil {
ms.StoreMessageInfo(mi)
}
return ms
}
return mi.MessageOf(x)
}
// Deprecated: Use Model_ExportFormat.ProtoReflect.Descriptor instead.
func (*Model_ExportFormat) Descriptor() ([]byte, []int) {