| // Copyright 2023 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 v1.33.0 |
| // protoc v4.25.3 |
| // source: google/cloud/aiplatform/v1/model_deployment_monitoring_job.proto |
| |
| package aiplatformpb |
| |
| 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 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"` |
| // Output only. The schema version of the request/response logging BigQuery |
| // table. Default to v1 if unset. |
| RequestResponseLoggingSchemaVersion string `protobuf:"bytes,4,opt,name=request_response_logging_schema_version,json=requestResponseLoggingSchemaVersion,proto3" json:"request_response_logging_schema_version,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 "" |
| } |
| |
| func (x *ModelDeploymentMonitoringBigQueryTable) GetRequestResponseLoggingSchemaVersion() string { |
| if x != nil { |
| return x.RequestResponseLoggingSchemaVersion |
| } |
| 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 |
| |
| var file_google_cloud_aiplatform_v1_model_deployment_monitoring_job_proto_rawDesc = []byte{ |
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| } |
| |
| var ( |
| file_google_cloud_aiplatform_v1_model_deployment_monitoring_job_proto_rawDescOnce sync.Once |
| file_google_cloud_aiplatform_v1_model_deployment_monitoring_job_proto_rawDescData = file_google_cloud_aiplatform_v1_model_deployment_monitoring_job_proto_rawDesc |
| ) |
| |
| func file_google_cloud_aiplatform_v1_model_deployment_monitoring_job_proto_rawDescGZIP() []byte { |
| file_google_cloud_aiplatform_v1_model_deployment_monitoring_job_proto_rawDescOnce.Do(func() { |
| file_google_cloud_aiplatform_v1_model_deployment_monitoring_job_proto_rawDescData = protoimpl.X.CompressGZIP(file_google_cloud_aiplatform_v1_model_deployment_monitoring_job_proto_rawDescData) |
| }) |
| return file_google_cloud_aiplatform_v1_model_deployment_monitoring_job_proto_rawDescData |
| } |
| |
| var file_google_cloud_aiplatform_v1_model_deployment_monitoring_job_proto_enumTypes = make([]protoimpl.EnumInfo, 4) |
| var file_google_cloud_aiplatform_v1_model_deployment_monitoring_job_proto_msgTypes = make([]protoimpl.MessageInfo, 8) |
| var file_google_cloud_aiplatform_v1_model_deployment_monitoring_job_proto_goTypes = []interface{}{ |
| (ModelDeploymentMonitoringObjectiveType)(0), // 0: google.cloud.aiplatform.v1.ModelDeploymentMonitoringObjectiveType |
| (ModelDeploymentMonitoringJob_MonitoringScheduleState)(0), // 1: google.cloud.aiplatform.v1.ModelDeploymentMonitoringJob.MonitoringScheduleState |
| (ModelDeploymentMonitoringBigQueryTable_LogSource)(0), // 2: google.cloud.aiplatform.v1.ModelDeploymentMonitoringBigQueryTable.LogSource |
| (ModelDeploymentMonitoringBigQueryTable_LogType)(0), // 3: google.cloud.aiplatform.v1.ModelDeploymentMonitoringBigQueryTable.LogType |
| (*ModelDeploymentMonitoringJob)(nil), // 4: google.cloud.aiplatform.v1.ModelDeploymentMonitoringJob |
| (*ModelDeploymentMonitoringBigQueryTable)(nil), // 5: google.cloud.aiplatform.v1.ModelDeploymentMonitoringBigQueryTable |
| (*ModelDeploymentMonitoringObjectiveConfig)(nil), // 6: google.cloud.aiplatform.v1.ModelDeploymentMonitoringObjectiveConfig |
| (*ModelDeploymentMonitoringScheduleConfig)(nil), // 7: google.cloud.aiplatform.v1.ModelDeploymentMonitoringScheduleConfig |
| (*ModelMonitoringStatsAnomalies)(nil), // 8: google.cloud.aiplatform.v1.ModelMonitoringStatsAnomalies |
| (*ModelDeploymentMonitoringJob_LatestMonitoringPipelineMetadata)(nil), // 9: google.cloud.aiplatform.v1.ModelDeploymentMonitoringJob.LatestMonitoringPipelineMetadata |
| nil, // 10: google.cloud.aiplatform.v1.ModelDeploymentMonitoringJob.LabelsEntry |
| (*ModelMonitoringStatsAnomalies_FeatureHistoricStatsAnomalies)(nil), // 11: google.cloud.aiplatform.v1.ModelMonitoringStatsAnomalies.FeatureHistoricStatsAnomalies |
| (JobState)(0), // 12: google.cloud.aiplatform.v1.JobState |
| (*SamplingStrategy)(nil), // 13: google.cloud.aiplatform.v1.SamplingStrategy |
| (*ModelMonitoringAlertConfig)(nil), // 14: google.cloud.aiplatform.v1.ModelMonitoringAlertConfig |
| (*structpb.Value)(nil), // 15: google.protobuf.Value |
| (*durationpb.Duration)(nil), // 16: google.protobuf.Duration |
| (*timestamppb.Timestamp)(nil), // 17: google.protobuf.Timestamp |
| (*GcsDestination)(nil), // 18: google.cloud.aiplatform.v1.GcsDestination |
| (*EncryptionSpec)(nil), // 19: google.cloud.aiplatform.v1.EncryptionSpec |
| (*status.Status)(nil), // 20: google.rpc.Status |
| (*ModelMonitoringObjectiveConfig)(nil), // 21: google.cloud.aiplatform.v1.ModelMonitoringObjectiveConfig |
| (*ThresholdConfig)(nil), // 22: google.cloud.aiplatform.v1.ThresholdConfig |
| (*FeatureStatsAnomaly)(nil), // 23: google.cloud.aiplatform.v1.FeatureStatsAnomaly |
| } |
| var file_google_cloud_aiplatform_v1_model_deployment_monitoring_job_proto_depIdxs = []int32{ |
| 12, // 0: google.cloud.aiplatform.v1.ModelDeploymentMonitoringJob.state:type_name -> google.cloud.aiplatform.v1.JobState |
| 1, // 1: google.cloud.aiplatform.v1.ModelDeploymentMonitoringJob.schedule_state:type_name -> google.cloud.aiplatform.v1.ModelDeploymentMonitoringJob.MonitoringScheduleState |
| 9, // 2: google.cloud.aiplatform.v1.ModelDeploymentMonitoringJob.latest_monitoring_pipeline_metadata:type_name -> google.cloud.aiplatform.v1.ModelDeploymentMonitoringJob.LatestMonitoringPipelineMetadata |
| 6, // 3: google.cloud.aiplatform.v1.ModelDeploymentMonitoringJob.model_deployment_monitoring_objective_configs:type_name -> google.cloud.aiplatform.v1.ModelDeploymentMonitoringObjectiveConfig |
| 7, // 4: google.cloud.aiplatform.v1.ModelDeploymentMonitoringJob.model_deployment_monitoring_schedule_config:type_name -> google.cloud.aiplatform.v1.ModelDeploymentMonitoringScheduleConfig |
| 13, // 5: google.cloud.aiplatform.v1.ModelDeploymentMonitoringJob.logging_sampling_strategy:type_name -> google.cloud.aiplatform.v1.SamplingStrategy |
| 14, // 6: google.cloud.aiplatform.v1.ModelDeploymentMonitoringJob.model_monitoring_alert_config:type_name -> google.cloud.aiplatform.v1.ModelMonitoringAlertConfig |
| 15, // 7: google.cloud.aiplatform.v1.ModelDeploymentMonitoringJob.sample_predict_instance:type_name -> google.protobuf.Value |
| 5, // 8: google.cloud.aiplatform.v1.ModelDeploymentMonitoringJob.bigquery_tables:type_name -> google.cloud.aiplatform.v1.ModelDeploymentMonitoringBigQueryTable |
| 16, // 9: google.cloud.aiplatform.v1.ModelDeploymentMonitoringJob.log_ttl:type_name -> google.protobuf.Duration |
| 10, // 10: google.cloud.aiplatform.v1.ModelDeploymentMonitoringJob.labels:type_name -> google.cloud.aiplatform.v1.ModelDeploymentMonitoringJob.LabelsEntry |
| 17, // 11: google.cloud.aiplatform.v1.ModelDeploymentMonitoringJob.create_time:type_name -> google.protobuf.Timestamp |
| 17, // 12: google.cloud.aiplatform.v1.ModelDeploymentMonitoringJob.update_time:type_name -> google.protobuf.Timestamp |
| 17, // 13: google.cloud.aiplatform.v1.ModelDeploymentMonitoringJob.next_schedule_time:type_name -> google.protobuf.Timestamp |
| 18, // 14: google.cloud.aiplatform.v1.ModelDeploymentMonitoringJob.stats_anomalies_base_directory:type_name -> google.cloud.aiplatform.v1.GcsDestination |
| 19, // 15: google.cloud.aiplatform.v1.ModelDeploymentMonitoringJob.encryption_spec:type_name -> google.cloud.aiplatform.v1.EncryptionSpec |
| 20, // 16: google.cloud.aiplatform.v1.ModelDeploymentMonitoringJob.error:type_name -> google.rpc.Status |
| 2, // 17: google.cloud.aiplatform.v1.ModelDeploymentMonitoringBigQueryTable.log_source:type_name -> google.cloud.aiplatform.v1.ModelDeploymentMonitoringBigQueryTable.LogSource |
| 3, // 18: google.cloud.aiplatform.v1.ModelDeploymentMonitoringBigQueryTable.log_type:type_name -> google.cloud.aiplatform.v1.ModelDeploymentMonitoringBigQueryTable.LogType |
| 21, // 19: google.cloud.aiplatform.v1.ModelDeploymentMonitoringObjectiveConfig.objective_config:type_name -> google.cloud.aiplatform.v1.ModelMonitoringObjectiveConfig |
| 16, // 20: google.cloud.aiplatform.v1.ModelDeploymentMonitoringScheduleConfig.monitor_interval:type_name -> google.protobuf.Duration |
| 16, // 21: google.cloud.aiplatform.v1.ModelDeploymentMonitoringScheduleConfig.monitor_window:type_name -> google.protobuf.Duration |
| 0, // 22: google.cloud.aiplatform.v1.ModelMonitoringStatsAnomalies.objective:type_name -> google.cloud.aiplatform.v1.ModelDeploymentMonitoringObjectiveType |
| 11, // 23: google.cloud.aiplatform.v1.ModelMonitoringStatsAnomalies.feature_stats:type_name -> google.cloud.aiplatform.v1.ModelMonitoringStatsAnomalies.FeatureHistoricStatsAnomalies |
| 17, // 24: google.cloud.aiplatform.v1.ModelDeploymentMonitoringJob.LatestMonitoringPipelineMetadata.run_time:type_name -> google.protobuf.Timestamp |
| 20, // 25: google.cloud.aiplatform.v1.ModelDeploymentMonitoringJob.LatestMonitoringPipelineMetadata.status:type_name -> google.rpc.Status |
| 22, // 26: google.cloud.aiplatform.v1.ModelMonitoringStatsAnomalies.FeatureHistoricStatsAnomalies.threshold:type_name -> google.cloud.aiplatform.v1.ThresholdConfig |
| 23, // 27: google.cloud.aiplatform.v1.ModelMonitoringStatsAnomalies.FeatureHistoricStatsAnomalies.training_stats:type_name -> google.cloud.aiplatform.v1.FeatureStatsAnomaly |
| 23, // 28: google.cloud.aiplatform.v1.ModelMonitoringStatsAnomalies.FeatureHistoricStatsAnomalies.prediction_stats:type_name -> google.cloud.aiplatform.v1.FeatureStatsAnomaly |
| 29, // [29:29] is the sub-list for method output_type |
| 29, // [29:29] is the sub-list for method input_type |
| 29, // [29:29] is the sub-list for extension type_name |
| 29, // [29:29] is the sub-list for extension extendee |
| 0, // [0:29] is the sub-list for field type_name |
| } |
| |
| func init() { file_google_cloud_aiplatform_v1_model_deployment_monitoring_job_proto_init() } |
| func file_google_cloud_aiplatform_v1_model_deployment_monitoring_job_proto_init() { |
| if File_google_cloud_aiplatform_v1_model_deployment_monitoring_job_proto != nil { |
| return |
| } |
| file_google_cloud_aiplatform_v1_encryption_spec_proto_init() |
| file_google_cloud_aiplatform_v1_feature_monitoring_stats_proto_init() |
| file_google_cloud_aiplatform_v1_io_proto_init() |
| file_google_cloud_aiplatform_v1_job_state_proto_init() |
| file_google_cloud_aiplatform_v1_model_monitoring_proto_init() |
| if !protoimpl.UnsafeEnabled { |
| file_google_cloud_aiplatform_v1_model_deployment_monitoring_job_proto_msgTypes[0].Exporter = func(v interface{}, i int) interface{} { |
| switch v := v.(*ModelDeploymentMonitoringJob); i { |
| case 0: |
| return &v.state |
| case 1: |
| return &v.sizeCache |
| case 2: |
| return &v.unknownFields |
| default: |
| return nil |
| } |
| } |
| file_google_cloud_aiplatform_v1_model_deployment_monitoring_job_proto_msgTypes[1].Exporter = func(v interface{}, i int) interface{} { |
| switch v := v.(*ModelDeploymentMonitoringBigQueryTable); i { |
| case 0: |
| return &v.state |
| case 1: |
| return &v.sizeCache |
| case 2: |
| return &v.unknownFields |
| default: |
| return nil |
| } |
| } |
| file_google_cloud_aiplatform_v1_model_deployment_monitoring_job_proto_msgTypes[2].Exporter = func(v interface{}, i int) interface{} { |
| switch v := v.(*ModelDeploymentMonitoringObjectiveConfig); i { |
| case 0: |
| return &v.state |
| case 1: |
| return &v.sizeCache |
| case 2: |
| return &v.unknownFields |
| default: |
| return nil |
| } |
| } |
| file_google_cloud_aiplatform_v1_model_deployment_monitoring_job_proto_msgTypes[3].Exporter = func(v interface{}, i int) interface{} { |
| switch v := v.(*ModelDeploymentMonitoringScheduleConfig); i { |
| case 0: |
| return &v.state |
| case 1: |
| return &v.sizeCache |
| case 2: |
| return &v.unknownFields |
| default: |
| return nil |
| } |
| } |
| file_google_cloud_aiplatform_v1_model_deployment_monitoring_job_proto_msgTypes[4].Exporter = func(v interface{}, i int) interface{} { |
| switch v := v.(*ModelMonitoringStatsAnomalies); i { |
| case 0: |
| return &v.state |
| case 1: |
| return &v.sizeCache |
| case 2: |
| return &v.unknownFields |
| default: |
| return nil |
| } |
| } |
| file_google_cloud_aiplatform_v1_model_deployment_monitoring_job_proto_msgTypes[5].Exporter = func(v interface{}, i int) interface{} { |
| switch v := v.(*ModelDeploymentMonitoringJob_LatestMonitoringPipelineMetadata); i { |
| case 0: |
| return &v.state |
| case 1: |
| return &v.sizeCache |
| case 2: |
| return &v.unknownFields |
| default: |
| return nil |
| } |
| } |
| file_google_cloud_aiplatform_v1_model_deployment_monitoring_job_proto_msgTypes[7].Exporter = func(v interface{}, i int) interface{} { |
| switch v := v.(*ModelMonitoringStatsAnomalies_FeatureHistoricStatsAnomalies); i { |
| case 0: |
| return &v.state |
| case 1: |
| return &v.sizeCache |
| case 2: |
| return &v.unknownFields |
| default: |
| return nil |
| } |
| } |
| } |
| type x struct{} |
| out := protoimpl.TypeBuilder{ |
| File: protoimpl.DescBuilder{ |
| GoPackagePath: reflect.TypeOf(x{}).PkgPath(), |
| RawDescriptor: file_google_cloud_aiplatform_v1_model_deployment_monitoring_job_proto_rawDesc, |
| NumEnums: 4, |
| NumMessages: 8, |
| NumExtensions: 0, |
| NumServices: 0, |
| }, |
| GoTypes: file_google_cloud_aiplatform_v1_model_deployment_monitoring_job_proto_goTypes, |
| DependencyIndexes: file_google_cloud_aiplatform_v1_model_deployment_monitoring_job_proto_depIdxs, |
| EnumInfos: file_google_cloud_aiplatform_v1_model_deployment_monitoring_job_proto_enumTypes, |
| MessageInfos: file_google_cloud_aiplatform_v1_model_deployment_monitoring_job_proto_msgTypes, |
| }.Build() |
| File_google_cloud_aiplatform_v1_model_deployment_monitoring_job_proto = out.File |
| file_google_cloud_aiplatform_v1_model_deployment_monitoring_job_proto_rawDesc = nil |
| file_google_cloud_aiplatform_v1_model_deployment_monitoring_job_proto_goTypes = nil |
| file_google_cloud_aiplatform_v1_model_deployment_monitoring_job_proto_depIdxs = nil |
| } |