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model_deployment_monitoring_job.pb.go
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model_deployment_monitoring_job.pb.go
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// 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
//
// 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_deployment_monitoring_job.proto
package aiplatform
import (
reflect "reflect"
sync "sync"
_ "google.golang.org/genproto/googleapis/api/annotations"
status "google.golang.org/genproto/googleapis/rpc/status"
protoreflect "google.golang.org/protobuf/reflect/protoreflect"
protoimpl "google.golang.org/protobuf/runtime/protoimpl"
durationpb "google.golang.org/protobuf/types/known/durationpb"
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)
)
// The Model Monitoring Objective types.
type ModelDeploymentMonitoringObjectiveType int32
const (
// Default value, should not be set.
ModelDeploymentMonitoringObjectiveType_MODEL_DEPLOYMENT_MONITORING_OBJECTIVE_TYPE_UNSPECIFIED ModelDeploymentMonitoringObjectiveType = 0
// Raw feature values' stats to detect skew between Training-Prediction
// datasets.
ModelDeploymentMonitoringObjectiveType_RAW_FEATURE_SKEW ModelDeploymentMonitoringObjectiveType = 1
// Raw feature values' stats to detect drift between Serving-Prediction
// datasets.
ModelDeploymentMonitoringObjectiveType_RAW_FEATURE_DRIFT ModelDeploymentMonitoringObjectiveType = 2
// Feature attribution scores to detect skew between Training-Prediction
// datasets.
ModelDeploymentMonitoringObjectiveType_FEATURE_ATTRIBUTION_SKEW ModelDeploymentMonitoringObjectiveType = 3
// Feature attribution scores to detect skew between Prediction datasets
// collected within different time windows.
ModelDeploymentMonitoringObjectiveType_FEATURE_ATTRIBUTION_DRIFT ModelDeploymentMonitoringObjectiveType = 4
)
// Enum value maps for ModelDeploymentMonitoringObjectiveType.
var (
ModelDeploymentMonitoringObjectiveType_name = map[int32]string{
0: "MODEL_DEPLOYMENT_MONITORING_OBJECTIVE_TYPE_UNSPECIFIED",
1: "RAW_FEATURE_SKEW",
2: "RAW_FEATURE_DRIFT",
3: "FEATURE_ATTRIBUTION_SKEW",
4: "FEATURE_ATTRIBUTION_DRIFT",
}
ModelDeploymentMonitoringObjectiveType_value = map[string]int32{
"MODEL_DEPLOYMENT_MONITORING_OBJECTIVE_TYPE_UNSPECIFIED": 0,
"RAW_FEATURE_SKEW": 1,
"RAW_FEATURE_DRIFT": 2,
"FEATURE_ATTRIBUTION_SKEW": 3,
"FEATURE_ATTRIBUTION_DRIFT": 4,
}
)
func (x ModelDeploymentMonitoringObjectiveType) Enum() *ModelDeploymentMonitoringObjectiveType {
p := new(ModelDeploymentMonitoringObjectiveType)
*p = x
return p
}
func (x ModelDeploymentMonitoringObjectiveType) String() string {
return protoimpl.X.EnumStringOf(x.Descriptor(), protoreflect.EnumNumber(x))
}
func (ModelDeploymentMonitoringObjectiveType) Descriptor() protoreflect.EnumDescriptor {
return file_google_cloud_aiplatform_v1_model_deployment_monitoring_job_proto_enumTypes[0].Descriptor()
}
func (ModelDeploymentMonitoringObjectiveType) Type() protoreflect.EnumType {
return &file_google_cloud_aiplatform_v1_model_deployment_monitoring_job_proto_enumTypes[0]
}
func (x ModelDeploymentMonitoringObjectiveType) Number() protoreflect.EnumNumber {
return protoreflect.EnumNumber(x)
}
// Deprecated: Use ModelDeploymentMonitoringObjectiveType.Descriptor instead.
func (ModelDeploymentMonitoringObjectiveType) EnumDescriptor() ([]byte, []int) {
return file_google_cloud_aiplatform_v1_model_deployment_monitoring_job_proto_rawDescGZIP(), []int{0}
}
// The state to Specify the monitoring pipeline.
type ModelDeploymentMonitoringJob_MonitoringScheduleState int32
const (
// Unspecified state.
ModelDeploymentMonitoringJob_MONITORING_SCHEDULE_STATE_UNSPECIFIED ModelDeploymentMonitoringJob_MonitoringScheduleState = 0
// The pipeline is picked up and wait to run.
ModelDeploymentMonitoringJob_PENDING ModelDeploymentMonitoringJob_MonitoringScheduleState = 1
// The pipeline is offline and will be scheduled for next run.
ModelDeploymentMonitoringJob_OFFLINE ModelDeploymentMonitoringJob_MonitoringScheduleState = 2
// The pipeline is running.
ModelDeploymentMonitoringJob_RUNNING ModelDeploymentMonitoringJob_MonitoringScheduleState = 3
)
// Enum value maps for ModelDeploymentMonitoringJob_MonitoringScheduleState.
var (
ModelDeploymentMonitoringJob_MonitoringScheduleState_name = map[int32]string{
0: "MONITORING_SCHEDULE_STATE_UNSPECIFIED",
1: "PENDING",
2: "OFFLINE",
3: "RUNNING",
}
ModelDeploymentMonitoringJob_MonitoringScheduleState_value = map[string]int32{
"MONITORING_SCHEDULE_STATE_UNSPECIFIED": 0,
"PENDING": 1,
"OFFLINE": 2,
"RUNNING": 3,
}
)
func (x ModelDeploymentMonitoringJob_MonitoringScheduleState) Enum() *ModelDeploymentMonitoringJob_MonitoringScheduleState {
p := new(ModelDeploymentMonitoringJob_MonitoringScheduleState)
*p = x
return p
}
func (x ModelDeploymentMonitoringJob_MonitoringScheduleState) String() string {
return protoimpl.X.EnumStringOf(x.Descriptor(), protoreflect.EnumNumber(x))
}
func (ModelDeploymentMonitoringJob_MonitoringScheduleState) Descriptor() protoreflect.EnumDescriptor {
return file_google_cloud_aiplatform_v1_model_deployment_monitoring_job_proto_enumTypes[1].Descriptor()
}
func (ModelDeploymentMonitoringJob_MonitoringScheduleState) Type() protoreflect.EnumType {
return &file_google_cloud_aiplatform_v1_model_deployment_monitoring_job_proto_enumTypes[1]
}
func (x ModelDeploymentMonitoringJob_MonitoringScheduleState) Number() protoreflect.EnumNumber {
return protoreflect.EnumNumber(x)
}
// Deprecated: Use ModelDeploymentMonitoringJob_MonitoringScheduleState.Descriptor instead.
func (ModelDeploymentMonitoringJob_MonitoringScheduleState) EnumDescriptor() ([]byte, []int) {
return file_google_cloud_aiplatform_v1_model_deployment_monitoring_job_proto_rawDescGZIP(), []int{0, 0}
}
// Indicates where does the log come from.
type ModelDeploymentMonitoringBigQueryTable_LogSource int32
const (
// Unspecified source.
ModelDeploymentMonitoringBigQueryTable_LOG_SOURCE_UNSPECIFIED ModelDeploymentMonitoringBigQueryTable_LogSource = 0
// Logs coming from Training dataset.
ModelDeploymentMonitoringBigQueryTable_TRAINING ModelDeploymentMonitoringBigQueryTable_LogSource = 1
// Logs coming from Serving traffic.
ModelDeploymentMonitoringBigQueryTable_SERVING ModelDeploymentMonitoringBigQueryTable_LogSource = 2
)
// Enum value maps for ModelDeploymentMonitoringBigQueryTable_LogSource.
var (
ModelDeploymentMonitoringBigQueryTable_LogSource_name = map[int32]string{
0: "LOG_SOURCE_UNSPECIFIED",
1: "TRAINING",
2: "SERVING",
}
ModelDeploymentMonitoringBigQueryTable_LogSource_value = map[string]int32{
"LOG_SOURCE_UNSPECIFIED": 0,
"TRAINING": 1,
"SERVING": 2,
}
)
func (x ModelDeploymentMonitoringBigQueryTable_LogSource) Enum() *ModelDeploymentMonitoringBigQueryTable_LogSource {
p := new(ModelDeploymentMonitoringBigQueryTable_LogSource)
*p = x
return p
}
func (x ModelDeploymentMonitoringBigQueryTable_LogSource) String() string {
return protoimpl.X.EnumStringOf(x.Descriptor(), protoreflect.EnumNumber(x))
}
func (ModelDeploymentMonitoringBigQueryTable_LogSource) Descriptor() protoreflect.EnumDescriptor {
return file_google_cloud_aiplatform_v1_model_deployment_monitoring_job_proto_enumTypes[2].Descriptor()
}
func (ModelDeploymentMonitoringBigQueryTable_LogSource) Type() protoreflect.EnumType {
return &file_google_cloud_aiplatform_v1_model_deployment_monitoring_job_proto_enumTypes[2]
}
func (x ModelDeploymentMonitoringBigQueryTable_LogSource) Number() protoreflect.EnumNumber {
return protoreflect.EnumNumber(x)
}
// Deprecated: Use ModelDeploymentMonitoringBigQueryTable_LogSource.Descriptor instead.
func (ModelDeploymentMonitoringBigQueryTable_LogSource) EnumDescriptor() ([]byte, []int) {
return file_google_cloud_aiplatform_v1_model_deployment_monitoring_job_proto_rawDescGZIP(), []int{1, 0}
}
// Indicates what type of traffic does the log belong to.
type ModelDeploymentMonitoringBigQueryTable_LogType int32
const (
// Unspecified type.
ModelDeploymentMonitoringBigQueryTable_LOG_TYPE_UNSPECIFIED ModelDeploymentMonitoringBigQueryTable_LogType = 0
// Predict logs.
ModelDeploymentMonitoringBigQueryTable_PREDICT ModelDeploymentMonitoringBigQueryTable_LogType = 1
// Explain logs.
ModelDeploymentMonitoringBigQueryTable_EXPLAIN ModelDeploymentMonitoringBigQueryTable_LogType = 2
)
// Enum value maps for ModelDeploymentMonitoringBigQueryTable_LogType.
var (
ModelDeploymentMonitoringBigQueryTable_LogType_name = map[int32]string{
0: "LOG_TYPE_UNSPECIFIED",
1: "PREDICT",
2: "EXPLAIN",
}
ModelDeploymentMonitoringBigQueryTable_LogType_value = map[string]int32{
"LOG_TYPE_UNSPECIFIED": 0,
"PREDICT": 1,
"EXPLAIN": 2,
}
)
func (x ModelDeploymentMonitoringBigQueryTable_LogType) Enum() *ModelDeploymentMonitoringBigQueryTable_LogType {
p := new(ModelDeploymentMonitoringBigQueryTable_LogType)
*p = x
return p
}
func (x ModelDeploymentMonitoringBigQueryTable_LogType) String() string {
return protoimpl.X.EnumStringOf(x.Descriptor(), protoreflect.EnumNumber(x))
}
func (ModelDeploymentMonitoringBigQueryTable_LogType) Descriptor() protoreflect.EnumDescriptor {
return file_google_cloud_aiplatform_v1_model_deployment_monitoring_job_proto_enumTypes[3].Descriptor()
}
func (ModelDeploymentMonitoringBigQueryTable_LogType) Type() protoreflect.EnumType {
return &file_google_cloud_aiplatform_v1_model_deployment_monitoring_job_proto_enumTypes[3]
}
func (x ModelDeploymentMonitoringBigQueryTable_LogType) Number() protoreflect.EnumNumber {
return protoreflect.EnumNumber(x)
}
// Deprecated: Use ModelDeploymentMonitoringBigQueryTable_LogType.Descriptor instead.
func (ModelDeploymentMonitoringBigQueryTable_LogType) EnumDescriptor() ([]byte, []int) {
return file_google_cloud_aiplatform_v1_model_deployment_monitoring_job_proto_rawDescGZIP(), []int{1, 1}
}
// Represents a job that runs periodically to monitor the deployed models in an
// endpoint. It will analyze the logged training & prediction data to detect any
// abnormal behaviors.
type ModelDeploymentMonitoringJob struct {
state protoimpl.MessageState
sizeCache protoimpl.SizeCache
unknownFields protoimpl.UnknownFields
// Output only. Resource name of a ModelDeploymentMonitoringJob.
Name string `protobuf:"bytes,1,opt,name=name,proto3" json:"name,omitempty"`
// Required. The user-defined name of the ModelDeploymentMonitoringJob.
// The name can be up to 128 characters long and can be consist of any UTF-8
// characters.
// Display name of a ModelDeploymentMonitoringJob.
DisplayName string `protobuf:"bytes,2,opt,name=display_name,json=displayName,proto3" json:"display_name,omitempty"`
// Required. Endpoint resource name.
// Format: `projects/{project}/locations/{location}/endpoints/{endpoint}`
Endpoint string `protobuf:"bytes,3,opt,name=endpoint,proto3" json:"endpoint,omitempty"`
// Output only. The detailed state of the monitoring job.
// When the job is still creating, the state will be 'PENDING'.
// Once the job is successfully created, the state will be 'RUNNING'.
// Pause the job, the state will be 'PAUSED'.
// Resume the job, the state will return to 'RUNNING'.
State JobState `protobuf:"varint,4,opt,name=state,proto3,enum=google.cloud.aiplatform.v1.JobState" json:"state,omitempty"`
// Output only. Schedule state when the monitoring job is in Running state.
ScheduleState ModelDeploymentMonitoringJob_MonitoringScheduleState `protobuf:"varint,5,opt,name=schedule_state,json=scheduleState,proto3,enum=google.cloud.aiplatform.v1.ModelDeploymentMonitoringJob_MonitoringScheduleState" json:"schedule_state,omitempty"`
// Output only. Latest triggered monitoring pipeline metadata.
LatestMonitoringPipelineMetadata *ModelDeploymentMonitoringJob_LatestMonitoringPipelineMetadata `protobuf:"bytes,25,opt,name=latest_monitoring_pipeline_metadata,json=latestMonitoringPipelineMetadata,proto3" json:"latest_monitoring_pipeline_metadata,omitempty"`
// Required. The config for monitoring objectives. This is a per DeployedModel config.
// Each DeployedModel needs to be configured separately.
ModelDeploymentMonitoringObjectiveConfigs []*ModelDeploymentMonitoringObjectiveConfig `protobuf:"bytes,6,rep,name=model_deployment_monitoring_objective_configs,json=modelDeploymentMonitoringObjectiveConfigs,proto3" json:"model_deployment_monitoring_objective_configs,omitempty"`
// Required. Schedule config for running the monitoring job.
ModelDeploymentMonitoringScheduleConfig *ModelDeploymentMonitoringScheduleConfig `protobuf:"bytes,7,opt,name=model_deployment_monitoring_schedule_config,json=modelDeploymentMonitoringScheduleConfig,proto3" json:"model_deployment_monitoring_schedule_config,omitempty"`
// Required. Sample Strategy for logging.
LoggingSamplingStrategy *SamplingStrategy `protobuf:"bytes,8,opt,name=logging_sampling_strategy,json=loggingSamplingStrategy,proto3" json:"logging_sampling_strategy,omitempty"`
// Alert config for model monitoring.
ModelMonitoringAlertConfig *ModelMonitoringAlertConfig `protobuf:"bytes,15,opt,name=model_monitoring_alert_config,json=modelMonitoringAlertConfig,proto3" json:"model_monitoring_alert_config,omitempty"`
// YAML schema file uri describing the format of a single instance,
// which are given to format this Endpoint's prediction (and explanation).
// If not set, we will generate predict schema from collected predict
// requests.
PredictInstanceSchemaUri string `protobuf:"bytes,9,opt,name=predict_instance_schema_uri,json=predictInstanceSchemaUri,proto3" json:"predict_instance_schema_uri,omitempty"`
// Sample Predict instance, same format as [PredictRequest.instances][google.cloud.aiplatform.v1.PredictRequest.instances],
// this can be set as a replacement of
// [ModelDeploymentMonitoringJob.predict_instance_schema_uri][google.cloud.aiplatform.v1.ModelDeploymentMonitoringJob.predict_instance_schema_uri]. If not set,
// we will generate predict schema from collected predict requests.
SamplePredictInstance *structpb.Value `protobuf:"bytes,19,opt,name=sample_predict_instance,json=samplePredictInstance,proto3" json:"sample_predict_instance,omitempty"`
// YAML schema file uri describing the format of a single instance that you
// want Tensorflow Data Validation (TFDV) to analyze.
//
// If this field is empty, all the feature data types are inferred from
// [predict_instance_schema_uri][google.cloud.aiplatform.v1.ModelDeploymentMonitoringJob.predict_instance_schema_uri],
// meaning that TFDV will use the data in the exact format(data type) as
// prediction request/response.
// If there are any data type differences between predict instance and TFDV
// instance, this field can be used to override the schema.
// For models trained with Vertex AI, this field must be set as all the
// fields in predict instance formatted as string.
AnalysisInstanceSchemaUri string `protobuf:"bytes,16,opt,name=analysis_instance_schema_uri,json=analysisInstanceSchemaUri,proto3" json:"analysis_instance_schema_uri,omitempty"`
// Output only. The created bigquery tables for the job under customer project. Customer
// could do their own query & analysis. There could be 4 log tables in
// maximum:
// 1. Training data logging predict request/response
// 2. Serving data logging predict request/response
BigqueryTables []*ModelDeploymentMonitoringBigQueryTable `protobuf:"bytes,10,rep,name=bigquery_tables,json=bigqueryTables,proto3" json:"bigquery_tables,omitempty"`
// The TTL of BigQuery tables in user projects which stores logs.
// A day is the basic unit of the TTL and we take the ceil of TTL/86400(a
// day). e.g. { second: 3600} indicates ttl = 1 day.
LogTtl *durationpb.Duration `protobuf:"bytes,17,opt,name=log_ttl,json=logTtl,proto3" json:"log_ttl,omitempty"`
// The labels with user-defined metadata to organize your
// ModelDeploymentMonitoringJob.
//
// 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,11,rep,name=labels,proto3" json:"labels,omitempty" protobuf_key:"bytes,1,opt,name=key,proto3" protobuf_val:"bytes,2,opt,name=value,proto3"`
// Output only. Timestamp when this ModelDeploymentMonitoringJob was created.
CreateTime *timestamppb.Timestamp `protobuf:"bytes,12,opt,name=create_time,json=createTime,proto3" json:"create_time,omitempty"`
// Output only. Timestamp when this ModelDeploymentMonitoringJob was updated most recently.
UpdateTime *timestamppb.Timestamp `protobuf:"bytes,13,opt,name=update_time,json=updateTime,proto3" json:"update_time,omitempty"`
// Output only. Timestamp when this monitoring pipeline will be scheduled to run for the
// next round.
NextScheduleTime *timestamppb.Timestamp `protobuf:"bytes,14,opt,name=next_schedule_time,json=nextScheduleTime,proto3" json:"next_schedule_time,omitempty"`
// Stats anomalies base folder path.
StatsAnomaliesBaseDirectory *GcsDestination `protobuf:"bytes,20,opt,name=stats_anomalies_base_directory,json=statsAnomaliesBaseDirectory,proto3" json:"stats_anomalies_base_directory,omitempty"`
// Customer-managed encryption key spec for a ModelDeploymentMonitoringJob. If
// set, this ModelDeploymentMonitoringJob and all sub-resources of this
// ModelDeploymentMonitoringJob will be secured by this key.
EncryptionSpec *EncryptionSpec `protobuf:"bytes,21,opt,name=encryption_spec,json=encryptionSpec,proto3" json:"encryption_spec,omitempty"`
// If true, the scheduled monitoring pipeline logs are sent to
// Google Cloud Logging, including pipeline status and anomalies detected.
// Please note the logs incur cost, which are subject to [Cloud Logging
// pricing](https://cloud.google.com/logging#pricing).
EnableMonitoringPipelineLogs bool `protobuf:"varint,22,opt,name=enable_monitoring_pipeline_logs,json=enableMonitoringPipelineLogs,proto3" json:"enable_monitoring_pipeline_logs,omitempty"`
// Output only. Only populated when the job's state is `JOB_STATE_FAILED` or
// `JOB_STATE_CANCELLED`.
Error *status.Status `protobuf:"bytes,23,opt,name=error,proto3" json:"error,omitempty"`
}
func (x *ModelDeploymentMonitoringJob) Reset() {
*x = ModelDeploymentMonitoringJob{}
if protoimpl.UnsafeEnabled {
mi := &file_google_cloud_aiplatform_v1_model_deployment_monitoring_job_proto_msgTypes[0]
ms := protoimpl.X.MessageStateOf(protoimpl.Pointer(x))
ms.StoreMessageInfo(mi)
}
}
func (x *ModelDeploymentMonitoringJob) String() string {
return protoimpl.X.MessageStringOf(x)
}
func (*ModelDeploymentMonitoringJob) ProtoMessage() {}
func (x *ModelDeploymentMonitoringJob) ProtoReflect() protoreflect.Message {
mi := &file_google_cloud_aiplatform_v1_model_deployment_monitoring_job_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 ModelDeploymentMonitoringJob.ProtoReflect.Descriptor instead.
func (*ModelDeploymentMonitoringJob) Descriptor() ([]byte, []int) {
return file_google_cloud_aiplatform_v1_model_deployment_monitoring_job_proto_rawDescGZIP(), []int{0}
}
func (x *ModelDeploymentMonitoringJob) GetName() string {
if x != nil {
return x.Name
}
return ""
}
func (x *ModelDeploymentMonitoringJob) GetDisplayName() string {
if x != nil {
return x.DisplayName
}
return ""
}
func (x *ModelDeploymentMonitoringJob) GetEndpoint() string {
if x != nil {
return x.Endpoint
}
return ""
}
func (x *ModelDeploymentMonitoringJob) GetState() JobState {
if x != nil {
return x.State
}
return JobState_JOB_STATE_UNSPECIFIED
}
func (x *ModelDeploymentMonitoringJob) GetScheduleState() ModelDeploymentMonitoringJob_MonitoringScheduleState {
if x != nil {
return x.ScheduleState
}
return ModelDeploymentMonitoringJob_MONITORING_SCHEDULE_STATE_UNSPECIFIED
}
func (x *ModelDeploymentMonitoringJob) GetLatestMonitoringPipelineMetadata() *ModelDeploymentMonitoringJob_LatestMonitoringPipelineMetadata {
if x != nil {
return x.LatestMonitoringPipelineMetadata
}
return nil
}
func (x *ModelDeploymentMonitoringJob) GetModelDeploymentMonitoringObjectiveConfigs() []*ModelDeploymentMonitoringObjectiveConfig {
if x != nil {
return x.ModelDeploymentMonitoringObjectiveConfigs
}
return nil
}
func (x *ModelDeploymentMonitoringJob) GetModelDeploymentMonitoringScheduleConfig() *ModelDeploymentMonitoringScheduleConfig {
if x != nil {
return x.ModelDeploymentMonitoringScheduleConfig
}
return nil
}
func (x *ModelDeploymentMonitoringJob) GetLoggingSamplingStrategy() *SamplingStrategy {
if x != nil {
return x.LoggingSamplingStrategy
}
return nil
}
func (x *ModelDeploymentMonitoringJob) GetModelMonitoringAlertConfig() *ModelMonitoringAlertConfig {
if x != nil {
return x.ModelMonitoringAlertConfig
}
return nil
}
func (x *ModelDeploymentMonitoringJob) GetPredictInstanceSchemaUri() string {
if x != nil {
return x.PredictInstanceSchemaUri
}
return ""
}
func (x *ModelDeploymentMonitoringJob) GetSamplePredictInstance() *structpb.Value {
if x != nil {
return x.SamplePredictInstance
}
return nil
}
func (x *ModelDeploymentMonitoringJob) GetAnalysisInstanceSchemaUri() string {
if x != nil {
return x.AnalysisInstanceSchemaUri
}
return ""
}
func (x *ModelDeploymentMonitoringJob) GetBigqueryTables() []*ModelDeploymentMonitoringBigQueryTable {
if x != nil {
return x.BigqueryTables
}
return nil
}
func (x *ModelDeploymentMonitoringJob) GetLogTtl() *durationpb.Duration {
if x != nil {
return x.LogTtl
}
return nil
}
func (x *ModelDeploymentMonitoringJob) GetLabels() map[string]string {
if x != nil {
return x.Labels
}
return nil
}
func (x *ModelDeploymentMonitoringJob) GetCreateTime() *timestamppb.Timestamp {
if x != nil {
return x.CreateTime
}
return nil
}
func (x *ModelDeploymentMonitoringJob) GetUpdateTime() *timestamppb.Timestamp {
if x != nil {
return x.UpdateTime
}
return nil
}
func (x *ModelDeploymentMonitoringJob) GetNextScheduleTime() *timestamppb.Timestamp {
if x != nil {
return x.NextScheduleTime
}
return nil
}
func (x *ModelDeploymentMonitoringJob) GetStatsAnomaliesBaseDirectory() *GcsDestination {
if x != nil {
return x.StatsAnomaliesBaseDirectory
}
return nil
}
func (x *ModelDeploymentMonitoringJob) GetEncryptionSpec() *EncryptionSpec {
if x != nil {
return x.EncryptionSpec
}
return nil
}
func (x *ModelDeploymentMonitoringJob) GetEnableMonitoringPipelineLogs() bool {
if x != nil {
return x.EnableMonitoringPipelineLogs
}
return false
}
func (x *ModelDeploymentMonitoringJob) GetError() *status.Status {
if x != nil {
return x.Error
}
return nil
}
// ModelDeploymentMonitoringBigQueryTable specifies the BigQuery table name
// as well as some information of the logs stored in this table.
type ModelDeploymentMonitoringBigQueryTable struct {
state protoimpl.MessageState
sizeCache protoimpl.SizeCache
unknownFields protoimpl.UnknownFields
// The source of log.
LogSource ModelDeploymentMonitoringBigQueryTable_LogSource `protobuf:"varint,1,opt,name=log_source,json=logSource,proto3,enum=google.cloud.aiplatform.v1.ModelDeploymentMonitoringBigQueryTable_LogSource" json:"log_source,omitempty"`
// The type of log.
LogType ModelDeploymentMonitoringBigQueryTable_LogType `protobuf:"varint,2,opt,name=log_type,json=logType,proto3,enum=google.cloud.aiplatform.v1.ModelDeploymentMonitoringBigQueryTable_LogType" json:"log_type,omitempty"`
// The created BigQuery table to store logs. Customer could do their own query
// & analysis. Format:
// `bq://<project_id>.model_deployment_monitoring_<endpoint_id>.<tolower(log_source)>_<tolower(log_type)>`
BigqueryTablePath string `protobuf:"bytes,3,opt,name=bigquery_table_path,json=bigqueryTablePath,proto3" json:"bigquery_table_path,omitempty"`
}
func (x *ModelDeploymentMonitoringBigQueryTable) Reset() {
*x = ModelDeploymentMonitoringBigQueryTable{}
if protoimpl.UnsafeEnabled {
mi := &file_google_cloud_aiplatform_v1_model_deployment_monitoring_job_proto_msgTypes[1]
ms := protoimpl.X.MessageStateOf(protoimpl.Pointer(x))
ms.StoreMessageInfo(mi)
}
}
func (x *ModelDeploymentMonitoringBigQueryTable) String() string {
return protoimpl.X.MessageStringOf(x)
}
func (*ModelDeploymentMonitoringBigQueryTable) ProtoMessage() {}
func (x *ModelDeploymentMonitoringBigQueryTable) ProtoReflect() protoreflect.Message {
mi := &file_google_cloud_aiplatform_v1_model_deployment_monitoring_job_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 ModelDeploymentMonitoringBigQueryTable.ProtoReflect.Descriptor instead.
func (*ModelDeploymentMonitoringBigQueryTable) Descriptor() ([]byte, []int) {
return file_google_cloud_aiplatform_v1_model_deployment_monitoring_job_proto_rawDescGZIP(), []int{1}
}
func (x *ModelDeploymentMonitoringBigQueryTable) GetLogSource() ModelDeploymentMonitoringBigQueryTable_LogSource {
if x != nil {
return x.LogSource
}
return ModelDeploymentMonitoringBigQueryTable_LOG_SOURCE_UNSPECIFIED
}
func (x *ModelDeploymentMonitoringBigQueryTable) GetLogType() ModelDeploymentMonitoringBigQueryTable_LogType {
if x != nil {
return x.LogType
}
return ModelDeploymentMonitoringBigQueryTable_LOG_TYPE_UNSPECIFIED
}
func (x *ModelDeploymentMonitoringBigQueryTable) GetBigqueryTablePath() string {
if x != nil {
return x.BigqueryTablePath
}
return ""
}
// ModelDeploymentMonitoringObjectiveConfig contains the pair of
// deployed_model_id to ModelMonitoringObjectiveConfig.
type ModelDeploymentMonitoringObjectiveConfig struct {
state protoimpl.MessageState
sizeCache protoimpl.SizeCache
unknownFields protoimpl.UnknownFields
// The DeployedModel ID of the objective config.
DeployedModelId string `protobuf:"bytes,1,opt,name=deployed_model_id,json=deployedModelId,proto3" json:"deployed_model_id,omitempty"`
// The objective config of for the modelmonitoring job of this deployed model.
ObjectiveConfig *ModelMonitoringObjectiveConfig `protobuf:"bytes,2,opt,name=objective_config,json=objectiveConfig,proto3" json:"objective_config,omitempty"`
}
func (x *ModelDeploymentMonitoringObjectiveConfig) Reset() {
*x = ModelDeploymentMonitoringObjectiveConfig{}
if protoimpl.UnsafeEnabled {
mi := &file_google_cloud_aiplatform_v1_model_deployment_monitoring_job_proto_msgTypes[2]
ms := protoimpl.X.MessageStateOf(protoimpl.Pointer(x))
ms.StoreMessageInfo(mi)
}
}
func (x *ModelDeploymentMonitoringObjectiveConfig) String() string {
return protoimpl.X.MessageStringOf(x)
}
func (*ModelDeploymentMonitoringObjectiveConfig) ProtoMessage() {}
func (x *ModelDeploymentMonitoringObjectiveConfig) ProtoReflect() protoreflect.Message {
mi := &file_google_cloud_aiplatform_v1_model_deployment_monitoring_job_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 ModelDeploymentMonitoringObjectiveConfig.ProtoReflect.Descriptor instead.
func (*ModelDeploymentMonitoringObjectiveConfig) Descriptor() ([]byte, []int) {
return file_google_cloud_aiplatform_v1_model_deployment_monitoring_job_proto_rawDescGZIP(), []int{2}
}
func (x *ModelDeploymentMonitoringObjectiveConfig) GetDeployedModelId() string {
if x != nil {
return x.DeployedModelId
}
return ""
}
func (x *ModelDeploymentMonitoringObjectiveConfig) GetObjectiveConfig() *ModelMonitoringObjectiveConfig {
if x != nil {
return x.ObjectiveConfig
}
return nil
}
// The config for scheduling monitoring job.
type ModelDeploymentMonitoringScheduleConfig struct {
state protoimpl.MessageState
sizeCache protoimpl.SizeCache
unknownFields protoimpl.UnknownFields
// Required. The model monitoring job scheduling interval. It will be rounded up to next
// full hour. This defines how often the monitoring jobs are triggered.
MonitorInterval *durationpb.Duration `protobuf:"bytes,1,opt,name=monitor_interval,json=monitorInterval,proto3" json:"monitor_interval,omitempty"`
// The time window of the prediction data being included in each prediction
// dataset. This window specifies how long the data should be collected from
// historical model results for each run. If not set,
// [ModelDeploymentMonitoringScheduleConfig.monitor_interval][google.cloud.aiplatform.v1.ModelDeploymentMonitoringScheduleConfig.monitor_interval] will be used.
// e.g. If currently the cutoff time is 2022-01-08 14:30:00 and the
// monitor_window is set to be 3600, then data from 2022-01-08 13:30:00
// to 2022-01-08 14:30:00 will be retrieved and aggregated to calculate the
// monitoring statistics.
MonitorWindow *durationpb.Duration `protobuf:"bytes,2,opt,name=monitor_window,json=monitorWindow,proto3" json:"monitor_window,omitempty"`
}
func (x *ModelDeploymentMonitoringScheduleConfig) Reset() {
*x = ModelDeploymentMonitoringScheduleConfig{}
if protoimpl.UnsafeEnabled {
mi := &file_google_cloud_aiplatform_v1_model_deployment_monitoring_job_proto_msgTypes[3]
ms := protoimpl.X.MessageStateOf(protoimpl.Pointer(x))
ms.StoreMessageInfo(mi)
}
}
func (x *ModelDeploymentMonitoringScheduleConfig) String() string {
return protoimpl.X.MessageStringOf(x)
}
func (*ModelDeploymentMonitoringScheduleConfig) ProtoMessage() {}
func (x *ModelDeploymentMonitoringScheduleConfig) ProtoReflect() protoreflect.Message {
mi := &file_google_cloud_aiplatform_v1_model_deployment_monitoring_job_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 ModelDeploymentMonitoringScheduleConfig.ProtoReflect.Descriptor instead.
func (*ModelDeploymentMonitoringScheduleConfig) Descriptor() ([]byte, []int) {
return file_google_cloud_aiplatform_v1_model_deployment_monitoring_job_proto_rawDescGZIP(), []int{3}
}
func (x *ModelDeploymentMonitoringScheduleConfig) GetMonitorInterval() *durationpb.Duration {
if x != nil {
return x.MonitorInterval
}
return nil
}
func (x *ModelDeploymentMonitoringScheduleConfig) GetMonitorWindow() *durationpb.Duration {
if x != nil {
return x.MonitorWindow
}
return nil
}
// Statistics and anomalies generated by Model Monitoring.
type ModelMonitoringStatsAnomalies struct {
state protoimpl.MessageState
sizeCache protoimpl.SizeCache
unknownFields protoimpl.UnknownFields
// Model Monitoring Objective those stats and anomalies belonging to.
Objective ModelDeploymentMonitoringObjectiveType `protobuf:"varint,1,opt,name=objective,proto3,enum=google.cloud.aiplatform.v1.ModelDeploymentMonitoringObjectiveType" json:"objective,omitempty"`
// Deployed Model ID.
DeployedModelId string `protobuf:"bytes,2,opt,name=deployed_model_id,json=deployedModelId,proto3" json:"deployed_model_id,omitempty"`
// Number of anomalies within all stats.
AnomalyCount int32 `protobuf:"varint,3,opt,name=anomaly_count,json=anomalyCount,proto3" json:"anomaly_count,omitempty"`
// A list of historical Stats and Anomalies generated for all Features.
FeatureStats []*ModelMonitoringStatsAnomalies_FeatureHistoricStatsAnomalies `protobuf:"bytes,4,rep,name=feature_stats,json=featureStats,proto3" json:"feature_stats,omitempty"`
}
func (x *ModelMonitoringStatsAnomalies) Reset() {
*x = ModelMonitoringStatsAnomalies{}
if protoimpl.UnsafeEnabled {
mi := &file_google_cloud_aiplatform_v1_model_deployment_monitoring_job_proto_msgTypes[4]
ms := protoimpl.X.MessageStateOf(protoimpl.Pointer(x))
ms.StoreMessageInfo(mi)
}
}
func (x *ModelMonitoringStatsAnomalies) String() string {
return protoimpl.X.MessageStringOf(x)
}
func (*ModelMonitoringStatsAnomalies) ProtoMessage() {}
func (x *ModelMonitoringStatsAnomalies) ProtoReflect() protoreflect.Message {
mi := &file_google_cloud_aiplatform_v1_model_deployment_monitoring_job_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 ModelMonitoringStatsAnomalies.ProtoReflect.Descriptor instead.
func (*ModelMonitoringStatsAnomalies) Descriptor() ([]byte, []int) {
return file_google_cloud_aiplatform_v1_model_deployment_monitoring_job_proto_rawDescGZIP(), []int{4}
}
func (x *ModelMonitoringStatsAnomalies) GetObjective() ModelDeploymentMonitoringObjectiveType {
if x != nil {
return x.Objective
}
return ModelDeploymentMonitoringObjectiveType_MODEL_DEPLOYMENT_MONITORING_OBJECTIVE_TYPE_UNSPECIFIED
}
func (x *ModelMonitoringStatsAnomalies) GetDeployedModelId() string {
if x != nil {
return x.DeployedModelId
}
return ""
}
func (x *ModelMonitoringStatsAnomalies) GetAnomalyCount() int32 {
if x != nil {
return x.AnomalyCount
}
return 0
}
func (x *ModelMonitoringStatsAnomalies) GetFeatureStats() []*ModelMonitoringStatsAnomalies_FeatureHistoricStatsAnomalies {
if x != nil {
return x.FeatureStats
}
return nil
}
// All metadata of most recent monitoring pipelines.
type ModelDeploymentMonitoringJob_LatestMonitoringPipelineMetadata struct {
state protoimpl.MessageState
sizeCache protoimpl.SizeCache
unknownFields protoimpl.UnknownFields
// The time that most recent monitoring pipelines that is related to this
// run.
RunTime *timestamppb.Timestamp `protobuf:"bytes,1,opt,name=run_time,json=runTime,proto3" json:"run_time,omitempty"`
// The status of the most recent monitoring pipeline.
Status *status.Status `protobuf:"bytes,2,opt,name=status,proto3" json:"status,omitempty"`
}
func (x *ModelDeploymentMonitoringJob_LatestMonitoringPipelineMetadata) Reset() {
*x = ModelDeploymentMonitoringJob_LatestMonitoringPipelineMetadata{}
if protoimpl.UnsafeEnabled {
mi := &file_google_cloud_aiplatform_v1_model_deployment_monitoring_job_proto_msgTypes[5]
ms := protoimpl.X.MessageStateOf(protoimpl.Pointer(x))
ms.StoreMessageInfo(mi)
}
}
func (x *ModelDeploymentMonitoringJob_LatestMonitoringPipelineMetadata) String() string {
return protoimpl.X.MessageStringOf(x)
}
func (*ModelDeploymentMonitoringJob_LatestMonitoringPipelineMetadata) ProtoMessage() {}
func (x *ModelDeploymentMonitoringJob_LatestMonitoringPipelineMetadata) ProtoReflect() protoreflect.Message {
mi := &file_google_cloud_aiplatform_v1_model_deployment_monitoring_job_proto_msgTypes[5]
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 ModelDeploymentMonitoringJob_LatestMonitoringPipelineMetadata.ProtoReflect.Descriptor instead.
func (*ModelDeploymentMonitoringJob_LatestMonitoringPipelineMetadata) Descriptor() ([]byte, []int) {
return file_google_cloud_aiplatform_v1_model_deployment_monitoring_job_proto_rawDescGZIP(), []int{0, 0}
}
func (x *ModelDeploymentMonitoringJob_LatestMonitoringPipelineMetadata) GetRunTime() *timestamppb.Timestamp {
if x != nil {
return x.RunTime
}
return nil
}
func (x *ModelDeploymentMonitoringJob_LatestMonitoringPipelineMetadata) GetStatus() *status.Status {
if x != nil {
return x.Status
}
return nil
}
// Historical Stats (and Anomalies) for a specific Feature.
type ModelMonitoringStatsAnomalies_FeatureHistoricStatsAnomalies struct {
state protoimpl.MessageState
sizeCache protoimpl.SizeCache
unknownFields protoimpl.UnknownFields
// Display Name of the Feature.
FeatureDisplayName string `protobuf:"bytes,1,opt,name=feature_display_name,json=featureDisplayName,proto3" json:"feature_display_name,omitempty"`
// Threshold for anomaly detection.
Threshold *ThresholdConfig `protobuf:"bytes,3,opt,name=threshold,proto3" json:"threshold,omitempty"`
// Stats calculated for the Training Dataset.
TrainingStats *FeatureStatsAnomaly `protobuf:"bytes,4,opt,name=training_stats,json=trainingStats,proto3" json:"training_stats,omitempty"`
// A list of historical stats generated by different time window's
// Prediction Dataset.
PredictionStats []*FeatureStatsAnomaly `protobuf:"bytes,5,rep,name=prediction_stats,json=predictionStats,proto3" json:"prediction_stats,omitempty"`
}
func (x *ModelMonitoringStatsAnomalies_FeatureHistoricStatsAnomalies) Reset() {
*x = ModelMonitoringStatsAnomalies_FeatureHistoricStatsAnomalies{}
if protoimpl.UnsafeEnabled {
mi := &file_google_cloud_aiplatform_v1_model_deployment_monitoring_job_proto_msgTypes[7]
ms := protoimpl.X.MessageStateOf(protoimpl.Pointer(x))
ms.StoreMessageInfo(mi)
}
}
func (x *ModelMonitoringStatsAnomalies_FeatureHistoricStatsAnomalies) String() string {
return protoimpl.X.MessageStringOf(x)
}
func (*ModelMonitoringStatsAnomalies_FeatureHistoricStatsAnomalies) ProtoMessage() {}
func (x *ModelMonitoringStatsAnomalies_FeatureHistoricStatsAnomalies) ProtoReflect() protoreflect.Message {
mi := &file_google_cloud_aiplatform_v1_model_deployment_monitoring_job_proto_msgTypes[7]
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 ModelMonitoringStatsAnomalies_FeatureHistoricStatsAnomalies.ProtoReflect.Descriptor instead.
func (*ModelMonitoringStatsAnomalies_FeatureHistoricStatsAnomalies) Descriptor() ([]byte, []int) {
return file_google_cloud_aiplatform_v1_model_deployment_monitoring_job_proto_rawDescGZIP(), []int{4, 0}
}
func (x *ModelMonitoringStatsAnomalies_FeatureHistoricStatsAnomalies) GetFeatureDisplayName() string {
if x != nil {
return x.FeatureDisplayName
}
return ""
}
func (x *ModelMonitoringStatsAnomalies_FeatureHistoricStatsAnomalies) GetThreshold() *ThresholdConfig {
if x != nil {
return x.Threshold
}
return nil
}
func (x *ModelMonitoringStatsAnomalies_FeatureHistoricStatsAnomalies) GetTrainingStats() *FeatureStatsAnomaly {
if x != nil {
return x.TrainingStats
}
return nil
}
func (x *ModelMonitoringStatsAnomalies_FeatureHistoricStatsAnomalies) GetPredictionStats() []*FeatureStatsAnomaly {
if x != nil {
return x.PredictionStats
}
return nil
}
var File_google_cloud_aiplatform_v1_model_deployment_monitoring_job_proto protoreflect.FileDescriptor
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