| // Copyright 2017 Google Inc. |
| // |
| // 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 v1.22.0 |
| // protoc v3.12.2 |
| // source: google/cloud/ml/v1/job_service.proto |
| |
| package ml |
| |
| import ( |
| context "context" |
| reflect "reflect" |
| sync "sync" |
| |
| proto "github.com/golang/protobuf/proto" |
| empty "github.com/golang/protobuf/ptypes/empty" |
| timestamp "github.com/golang/protobuf/ptypes/timestamp" |
| _ "google.golang.org/genproto/googleapis/api/annotations" |
| _ "google.golang.org/genproto/googleapis/api/serviceconfig" |
| grpc "google.golang.org/grpc" |
| codes "google.golang.org/grpc/codes" |
| status "google.golang.org/grpc/status" |
| protoreflect "google.golang.org/protobuf/reflect/protoreflect" |
| protoimpl "google.golang.org/protobuf/runtime/protoimpl" |
| ) |
| |
| 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) |
| ) |
| |
| // This is a compile-time assertion that a sufficiently up-to-date version |
| // of the legacy proto package is being used. |
| const _ = proto.ProtoPackageIsVersion4 |
| |
| // A scale tier is an abstract representation of the resources Cloud ML |
| // will allocate to a training job. When selecting a scale tier for your |
| // training job, you should consider the size of your training dataset and |
| // the complexity of your model. As the tiers increase, virtual machines are |
| // added to handle your job, and the individual machines in the cluster |
| // generally have more memory and greater processing power than they do at |
| // lower tiers. The number of training units charged per hour of processing |
| // increases as tiers get more advanced. Refer to the |
| // [pricing guide](/ml/pricing) for more details. Note that in addition to |
| // incurring costs, your use of training resources is constrained by the |
| // [quota policy](/ml/quota). |
| type TrainingInput_ScaleTier int32 |
| |
| const ( |
| // A single worker instance. This tier is suitable for learning how to use |
| // Cloud ML, and for experimenting with new models using small datasets. |
| TrainingInput_BASIC TrainingInput_ScaleTier = 0 |
| // Many workers and a few parameter servers. |
| TrainingInput_STANDARD_1 TrainingInput_ScaleTier = 1 |
| // A large number of workers with many parameter servers. |
| TrainingInput_PREMIUM_1 TrainingInput_ScaleTier = 3 |
| // A single worker instance [with a GPU](ml/docs/how-tos/using-gpus). |
| TrainingInput_BASIC_GPU TrainingInput_ScaleTier = 6 |
| // The CUSTOM tier is not a set tier, but rather enables you to use your |
| // own cluster specification. When you use this tier, set values to |
| // configure your processing cluster according to these guidelines: |
| // |
| // * You _must_ set `TrainingInput.masterType` to specify the type |
| // of machine to use for your master node. This is the only required |
| // setting. |
| // |
| // * You _may_ set `TrainingInput.workerCount` to specify the number of |
| // workers to use. If you specify one or more workers, you _must_ also |
| // set `TrainingInput.workerType` to specify the type of machine to use |
| // for your worker nodes. |
| // |
| // * You _may_ set `TrainingInput.parameterServerCount` to specify the |
| // number of parameter servers to use. If you specify one or more |
| // parameter servers, you _must_ also set |
| // `TrainingInput.parameterServerType` to specify the type of machine to |
| // use for your parameter servers. |
| // |
| // Note that all of your workers must use the same machine type, which can |
| // be different from your parameter server type and master type. Your |
| // parameter servers must likewise use the same machine type, which can be |
| // different from your worker type and master type. |
| TrainingInput_CUSTOM TrainingInput_ScaleTier = 5 |
| ) |
| |
| // Enum value maps for TrainingInput_ScaleTier. |
| var ( |
| TrainingInput_ScaleTier_name = map[int32]string{ |
| 0: "BASIC", |
| 1: "STANDARD_1", |
| 3: "PREMIUM_1", |
| 6: "BASIC_GPU", |
| 5: "CUSTOM", |
| } |
| TrainingInput_ScaleTier_value = map[string]int32{ |
| "BASIC": 0, |
| "STANDARD_1": 1, |
| "PREMIUM_1": 3, |
| "BASIC_GPU": 6, |
| "CUSTOM": 5, |
| } |
| ) |
| |
| func (x TrainingInput_ScaleTier) Enum() *TrainingInput_ScaleTier { |
| p := new(TrainingInput_ScaleTier) |
| *p = x |
| return p |
| } |
| |
| func (x TrainingInput_ScaleTier) String() string { |
| return protoimpl.X.EnumStringOf(x.Descriptor(), protoreflect.EnumNumber(x)) |
| } |
| |
| func (TrainingInput_ScaleTier) Descriptor() protoreflect.EnumDescriptor { |
| return file_google_cloud_ml_v1_job_service_proto_enumTypes[0].Descriptor() |
| } |
| |
| func (TrainingInput_ScaleTier) Type() protoreflect.EnumType { |
| return &file_google_cloud_ml_v1_job_service_proto_enumTypes[0] |
| } |
| |
| func (x TrainingInput_ScaleTier) Number() protoreflect.EnumNumber { |
| return protoreflect.EnumNumber(x) |
| } |
| |
| // Deprecated: Use TrainingInput_ScaleTier.Descriptor instead. |
| func (TrainingInput_ScaleTier) EnumDescriptor() ([]byte, []int) { |
| return file_google_cloud_ml_v1_job_service_proto_rawDescGZIP(), []int{0, 0} |
| } |
| |
| // The available types of optimization goals. |
| type HyperparameterSpec_GoalType int32 |
| |
| const ( |
| // Goal Type will default to maximize. |
| HyperparameterSpec_GOAL_TYPE_UNSPECIFIED HyperparameterSpec_GoalType = 0 |
| // Maximize the goal metric. |
| HyperparameterSpec_MAXIMIZE HyperparameterSpec_GoalType = 1 |
| // Minimize the goal metric. |
| HyperparameterSpec_MINIMIZE HyperparameterSpec_GoalType = 2 |
| ) |
| |
| // Enum value maps for HyperparameterSpec_GoalType. |
| var ( |
| HyperparameterSpec_GoalType_name = map[int32]string{ |
| 0: "GOAL_TYPE_UNSPECIFIED", |
| 1: "MAXIMIZE", |
| 2: "MINIMIZE", |
| } |
| HyperparameterSpec_GoalType_value = map[string]int32{ |
| "GOAL_TYPE_UNSPECIFIED": 0, |
| "MAXIMIZE": 1, |
| "MINIMIZE": 2, |
| } |
| ) |
| |
| func (x HyperparameterSpec_GoalType) Enum() *HyperparameterSpec_GoalType { |
| p := new(HyperparameterSpec_GoalType) |
| *p = x |
| return p |
| } |
| |
| func (x HyperparameterSpec_GoalType) String() string { |
| return protoimpl.X.EnumStringOf(x.Descriptor(), protoreflect.EnumNumber(x)) |
| } |
| |
| func (HyperparameterSpec_GoalType) Descriptor() protoreflect.EnumDescriptor { |
| return file_google_cloud_ml_v1_job_service_proto_enumTypes[1].Descriptor() |
| } |
| |
| func (HyperparameterSpec_GoalType) Type() protoreflect.EnumType { |
| return &file_google_cloud_ml_v1_job_service_proto_enumTypes[1] |
| } |
| |
| func (x HyperparameterSpec_GoalType) Number() protoreflect.EnumNumber { |
| return protoreflect.EnumNumber(x) |
| } |
| |
| // Deprecated: Use HyperparameterSpec_GoalType.Descriptor instead. |
| func (HyperparameterSpec_GoalType) EnumDescriptor() ([]byte, []int) { |
| return file_google_cloud_ml_v1_job_service_proto_rawDescGZIP(), []int{1, 0} |
| } |
| |
| // The type of the parameter. |
| type ParameterSpec_ParameterType int32 |
| |
| const ( |
| // You must specify a valid type. Using this unspecified type will result in |
| // an error. |
| ParameterSpec_PARAMETER_TYPE_UNSPECIFIED ParameterSpec_ParameterType = 0 |
| // Type for real-valued parameters. |
| ParameterSpec_DOUBLE ParameterSpec_ParameterType = 1 |
| // Type for integral parameters. |
| ParameterSpec_INTEGER ParameterSpec_ParameterType = 2 |
| // The parameter is categorical, with a value chosen from the categories |
| // field. |
| ParameterSpec_CATEGORICAL ParameterSpec_ParameterType = 3 |
| // The parameter is real valued, with a fixed set of feasible points. If |
| // `type==DISCRETE`, feasible_points must be provided, and |
| // {`min_value`, `max_value`} will be ignored. |
| ParameterSpec_DISCRETE ParameterSpec_ParameterType = 4 |
| ) |
| |
| // Enum value maps for ParameterSpec_ParameterType. |
| var ( |
| ParameterSpec_ParameterType_name = map[int32]string{ |
| 0: "PARAMETER_TYPE_UNSPECIFIED", |
| 1: "DOUBLE", |
| 2: "INTEGER", |
| 3: "CATEGORICAL", |
| 4: "DISCRETE", |
| } |
| ParameterSpec_ParameterType_value = map[string]int32{ |
| "PARAMETER_TYPE_UNSPECIFIED": 0, |
| "DOUBLE": 1, |
| "INTEGER": 2, |
| "CATEGORICAL": 3, |
| "DISCRETE": 4, |
| } |
| ) |
| |
| func (x ParameterSpec_ParameterType) Enum() *ParameterSpec_ParameterType { |
| p := new(ParameterSpec_ParameterType) |
| *p = x |
| return p |
| } |
| |
| func (x ParameterSpec_ParameterType) String() string { |
| return protoimpl.X.EnumStringOf(x.Descriptor(), protoreflect.EnumNumber(x)) |
| } |
| |
| func (ParameterSpec_ParameterType) Descriptor() protoreflect.EnumDescriptor { |
| return file_google_cloud_ml_v1_job_service_proto_enumTypes[2].Descriptor() |
| } |
| |
| func (ParameterSpec_ParameterType) Type() protoreflect.EnumType { |
| return &file_google_cloud_ml_v1_job_service_proto_enumTypes[2] |
| } |
| |
| func (x ParameterSpec_ParameterType) Number() protoreflect.EnumNumber { |
| return protoreflect.EnumNumber(x) |
| } |
| |
| // Deprecated: Use ParameterSpec_ParameterType.Descriptor instead. |
| func (ParameterSpec_ParameterType) EnumDescriptor() ([]byte, []int) { |
| return file_google_cloud_ml_v1_job_service_proto_rawDescGZIP(), []int{2, 0} |
| } |
| |
| // The type of scaling that should be applied to this parameter. |
| type ParameterSpec_ScaleType int32 |
| |
| const ( |
| // By default, no scaling is applied. |
| ParameterSpec_NONE ParameterSpec_ScaleType = 0 |
| // Scales the feasible space to (0, 1) linearly. |
| ParameterSpec_UNIT_LINEAR_SCALE ParameterSpec_ScaleType = 1 |
| // Scales the feasible space logarithmically to (0, 1). The entire feasible |
| // space must be strictly positive. |
| ParameterSpec_UNIT_LOG_SCALE ParameterSpec_ScaleType = 2 |
| // Scales the feasible space "reverse" logarithmically to (0, 1). The result |
| // is that values close to the top of the feasible space are spread out more |
| // than points near the bottom. The entire feasible space must be strictly |
| // positive. |
| ParameterSpec_UNIT_REVERSE_LOG_SCALE ParameterSpec_ScaleType = 3 |
| ) |
| |
| // Enum value maps for ParameterSpec_ScaleType. |
| var ( |
| ParameterSpec_ScaleType_name = map[int32]string{ |
| 0: "NONE", |
| 1: "UNIT_LINEAR_SCALE", |
| 2: "UNIT_LOG_SCALE", |
| 3: "UNIT_REVERSE_LOG_SCALE", |
| } |
| ParameterSpec_ScaleType_value = map[string]int32{ |
| "NONE": 0, |
| "UNIT_LINEAR_SCALE": 1, |
| "UNIT_LOG_SCALE": 2, |
| "UNIT_REVERSE_LOG_SCALE": 3, |
| } |
| ) |
| |
| func (x ParameterSpec_ScaleType) Enum() *ParameterSpec_ScaleType { |
| p := new(ParameterSpec_ScaleType) |
| *p = x |
| return p |
| } |
| |
| func (x ParameterSpec_ScaleType) String() string { |
| return protoimpl.X.EnumStringOf(x.Descriptor(), protoreflect.EnumNumber(x)) |
| } |
| |
| func (ParameterSpec_ScaleType) Descriptor() protoreflect.EnumDescriptor { |
| return file_google_cloud_ml_v1_job_service_proto_enumTypes[3].Descriptor() |
| } |
| |
| func (ParameterSpec_ScaleType) Type() protoreflect.EnumType { |
| return &file_google_cloud_ml_v1_job_service_proto_enumTypes[3] |
| } |
| |
| func (x ParameterSpec_ScaleType) Number() protoreflect.EnumNumber { |
| return protoreflect.EnumNumber(x) |
| } |
| |
| // Deprecated: Use ParameterSpec_ScaleType.Descriptor instead. |
| func (ParameterSpec_ScaleType) EnumDescriptor() ([]byte, []int) { |
| return file_google_cloud_ml_v1_job_service_proto_rawDescGZIP(), []int{2, 1} |
| } |
| |
| // The format used to separate data instances in the source files. |
| type PredictionInput_DataFormat int32 |
| |
| const ( |
| // Unspecified format. |
| PredictionInput_DATA_FORMAT_UNSPECIFIED PredictionInput_DataFormat = 0 |
| // The source file is a text file with instances separated by the |
| // new-line character. |
| PredictionInput_TEXT PredictionInput_DataFormat = 1 |
| // The source file is a TFRecord file. |
| PredictionInput_TF_RECORD PredictionInput_DataFormat = 2 |
| // The source file is a GZIP-compressed TFRecord file. |
| PredictionInput_TF_RECORD_GZIP PredictionInput_DataFormat = 3 |
| ) |
| |
| // Enum value maps for PredictionInput_DataFormat. |
| var ( |
| PredictionInput_DataFormat_name = map[int32]string{ |
| 0: "DATA_FORMAT_UNSPECIFIED", |
| 1: "TEXT", |
| 2: "TF_RECORD", |
| 3: "TF_RECORD_GZIP", |
| } |
| PredictionInput_DataFormat_value = map[string]int32{ |
| "DATA_FORMAT_UNSPECIFIED": 0, |
| "TEXT": 1, |
| "TF_RECORD": 2, |
| "TF_RECORD_GZIP": 3, |
| } |
| ) |
| |
| func (x PredictionInput_DataFormat) Enum() *PredictionInput_DataFormat { |
| p := new(PredictionInput_DataFormat) |
| *p = x |
| return p |
| } |
| |
| func (x PredictionInput_DataFormat) String() string { |
| return protoimpl.X.EnumStringOf(x.Descriptor(), protoreflect.EnumNumber(x)) |
| } |
| |
| func (PredictionInput_DataFormat) Descriptor() protoreflect.EnumDescriptor { |
| return file_google_cloud_ml_v1_job_service_proto_enumTypes[4].Descriptor() |
| } |
| |
| func (PredictionInput_DataFormat) Type() protoreflect.EnumType { |
| return &file_google_cloud_ml_v1_job_service_proto_enumTypes[4] |
| } |
| |
| func (x PredictionInput_DataFormat) Number() protoreflect.EnumNumber { |
| return protoreflect.EnumNumber(x) |
| } |
| |
| // Deprecated: Use PredictionInput_DataFormat.Descriptor instead. |
| func (PredictionInput_DataFormat) EnumDescriptor() ([]byte, []int) { |
| return file_google_cloud_ml_v1_job_service_proto_rawDescGZIP(), []int{5, 0} |
| } |
| |
| // Describes the job state. |
| type Job_State int32 |
| |
| const ( |
| // The job state is unspecified. |
| Job_STATE_UNSPECIFIED Job_State = 0 |
| // The job has been just created and processing has not yet begun. |
| Job_QUEUED Job_State = 1 |
| // The service is preparing to run the job. |
| Job_PREPARING Job_State = 2 |
| // The job is in progress. |
| Job_RUNNING Job_State = 3 |
| // The job completed successfully. |
| Job_SUCCEEDED Job_State = 4 |
| // The job failed. |
| // `error_message` should contain the details of the failure. |
| Job_FAILED Job_State = 5 |
| // The job is being cancelled. |
| // `error_message` should describe the reason for the cancellation. |
| Job_CANCELLING Job_State = 6 |
| // The job has been cancelled. |
| // `error_message` should describe the reason for the cancellation. |
| Job_CANCELLED Job_State = 7 |
| ) |
| |
| // Enum value maps for Job_State. |
| var ( |
| Job_State_name = map[int32]string{ |
| 0: "STATE_UNSPECIFIED", |
| 1: "QUEUED", |
| 2: "PREPARING", |
| 3: "RUNNING", |
| 4: "SUCCEEDED", |
| 5: "FAILED", |
| 6: "CANCELLING", |
| 7: "CANCELLED", |
| } |
| Job_State_value = map[string]int32{ |
| "STATE_UNSPECIFIED": 0, |
| "QUEUED": 1, |
| "PREPARING": 2, |
| "RUNNING": 3, |
| "SUCCEEDED": 4, |
| "FAILED": 5, |
| "CANCELLING": 6, |
| "CANCELLED": 7, |
| } |
| ) |
| |
| func (x Job_State) Enum() *Job_State { |
| p := new(Job_State) |
| *p = x |
| return p |
| } |
| |
| func (x Job_State) String() string { |
| return protoimpl.X.EnumStringOf(x.Descriptor(), protoreflect.EnumNumber(x)) |
| } |
| |
| func (Job_State) Descriptor() protoreflect.EnumDescriptor { |
| return file_google_cloud_ml_v1_job_service_proto_enumTypes[5].Descriptor() |
| } |
| |
| func (Job_State) Type() protoreflect.EnumType { |
| return &file_google_cloud_ml_v1_job_service_proto_enumTypes[5] |
| } |
| |
| func (x Job_State) Number() protoreflect.EnumNumber { |
| return protoreflect.EnumNumber(x) |
| } |
| |
| // Deprecated: Use Job_State.Descriptor instead. |
| func (Job_State) EnumDescriptor() ([]byte, []int) { |
| return file_google_cloud_ml_v1_job_service_proto_rawDescGZIP(), []int{7, 0} |
| } |
| |
| // Represents input parameters for a training job. |
| type TrainingInput struct { |
| state protoimpl.MessageState |
| sizeCache protoimpl.SizeCache |
| unknownFields protoimpl.UnknownFields |
| |
| // Required. Specifies the machine types, the number of replicas for workers |
| // and parameter servers. |
| ScaleTier TrainingInput_ScaleTier `protobuf:"varint,1,opt,name=scale_tier,json=scaleTier,proto3,enum=google.cloud.ml.v1.TrainingInput_ScaleTier" json:"scale_tier,omitempty"` |
| // Optional. Specifies the type of virtual machine to use for your training |
| // job's master worker. |
| // |
| // The following types are supported: |
| // |
| // <dl> |
| // <dt>standard</dt> |
| // <dd> |
| // A basic machine configuration suitable for training simple models with |
| // small to moderate datasets. |
| // </dd> |
| // <dt>large_model</dt> |
| // <dd> |
| // A machine with a lot of memory, specially suited for parameter servers |
| // when your model is large (having many hidden layers or layers with very |
| // large numbers of nodes). |
| // </dd> |
| // <dt>complex_model_s</dt> |
| // <dd> |
| // A machine suitable for the master and workers of the cluster when your |
| // model requires more computation than the standard machine can handle |
| // satisfactorily. |
| // </dd> |
| // <dt>complex_model_m</dt> |
| // <dd> |
| // A machine with roughly twice the number of cores and roughly double the |
| // memory of <code suppresswarning="true">complex_model_s</code>. |
| // </dd> |
| // <dt>complex_model_l</dt> |
| // <dd> |
| // A machine with roughly twice the number of cores and roughly double the |
| // memory of <code suppresswarning="true">complex_model_m</code>. |
| // </dd> |
| // <dt>standard_gpu</dt> |
| // <dd> |
| // A machine equivalent to <code suppresswarning="true">standard</code> that |
| // also includes a |
| // <a href="ml/docs/how-tos/using-gpus"> |
| // GPU that you can use in your trainer</a>. |
| // </dd> |
| // <dt>complex_model_m_gpu</dt> |
| // <dd> |
| // A machine equivalent to |
| // <code suppresswarning="true">coplex_model_m</code> that also includes |
| // four GPUs. |
| // </dd> |
| // </dl> |
| // |
| // You must set this value when `scaleTier` is set to `CUSTOM`. |
| MasterType string `protobuf:"bytes,2,opt,name=master_type,json=masterType,proto3" json:"master_type,omitempty"` |
| // Optional. Specifies the type of virtual machine to use for your training |
| // job's worker nodes. |
| // |
| // The supported values are the same as those described in the entry for |
| // `masterType`. |
| // |
| // This value must be present when `scaleTier` is set to `CUSTOM` and |
| // `workerCount` is greater than zero. |
| WorkerType string `protobuf:"bytes,3,opt,name=worker_type,json=workerType,proto3" json:"worker_type,omitempty"` |
| // Optional. Specifies the type of virtual machine to use for your training |
| // job's parameter server. |
| // |
| // The supported values are the same as those described in the entry for |
| // `master_type`. |
| // |
| // This value must be present when `scaleTier` is set to `CUSTOM` and |
| // `parameter_server_count` is greater than zero. |
| ParameterServerType string `protobuf:"bytes,4,opt,name=parameter_server_type,json=parameterServerType,proto3" json:"parameter_server_type,omitempty"` |
| // Optional. The number of worker replicas to use for the training job. Each |
| // replica in the cluster will be of the type specified in `worker_type`. |
| // |
| // This value can only be used when `scale_tier` is set to `CUSTOM`. If you |
| // set this value, you must also set `worker_type`. |
| WorkerCount int64 `protobuf:"varint,5,opt,name=worker_count,json=workerCount,proto3" json:"worker_count,omitempty"` |
| // Optional. The number of parameter server replicas to use for the training |
| // job. Each replica in the cluster will be of the type specified in |
| // `parameter_server_type`. |
| // |
| // This value can only be used when `scale_tier` is set to `CUSTOM`.If you |
| // set this value, you must also set `parameter_server_type`. |
| ParameterServerCount int64 `protobuf:"varint,6,opt,name=parameter_server_count,json=parameterServerCount,proto3" json:"parameter_server_count,omitempty"` |
| // Required. The Google Cloud Storage location of the packages with |
| // the training program and any additional dependencies. |
| PackageUris []string `protobuf:"bytes,7,rep,name=package_uris,json=packageUris,proto3" json:"package_uris,omitempty"` |
| // Required. The Python module name to run after installing the packages. |
| PythonModule string `protobuf:"bytes,8,opt,name=python_module,json=pythonModule,proto3" json:"python_module,omitempty"` |
| // Optional. Command line arguments to pass to the program. |
| Args []string `protobuf:"bytes,10,rep,name=args,proto3" json:"args,omitempty"` |
| // Optional. The set of Hyperparameters to tune. |
| Hyperparameters *HyperparameterSpec `protobuf:"bytes,12,opt,name=hyperparameters,proto3" json:"hyperparameters,omitempty"` |
| // Required. The Google Compute Engine region to run the training job in. |
| Region string `protobuf:"bytes,14,opt,name=region,proto3" json:"region,omitempty"` |
| // Optional. A Google Cloud Storage path in which to store training outputs |
| // and other data needed for training. This path is passed to your TensorFlow |
| // program as the 'job_dir' command-line argument. The benefit of specifying |
| // this field is that Cloud ML validates the path for use in training. |
| JobDir string `protobuf:"bytes,16,opt,name=job_dir,json=jobDir,proto3" json:"job_dir,omitempty"` |
| // Optional. The Google Cloud ML runtime version to use for training. If not |
| // set, Google Cloud ML will choose the latest stable version. |
| RuntimeVersion string `protobuf:"bytes,15,opt,name=runtime_version,json=runtimeVersion,proto3" json:"runtime_version,omitempty"` |
| } |
| |
| func (x *TrainingInput) Reset() { |
| *x = TrainingInput{} |
| if protoimpl.UnsafeEnabled { |
| mi := &file_google_cloud_ml_v1_job_service_proto_msgTypes[0] |
| ms := protoimpl.X.MessageStateOf(protoimpl.Pointer(x)) |
| ms.StoreMessageInfo(mi) |
| } |
| } |
| |
| func (x *TrainingInput) String() string { |
| return protoimpl.X.MessageStringOf(x) |
| } |
| |
| func (*TrainingInput) ProtoMessage() {} |
| |
| func (x *TrainingInput) ProtoReflect() protoreflect.Message { |
| mi := &file_google_cloud_ml_v1_job_service_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 TrainingInput.ProtoReflect.Descriptor instead. |
| func (*TrainingInput) Descriptor() ([]byte, []int) { |
| return file_google_cloud_ml_v1_job_service_proto_rawDescGZIP(), []int{0} |
| } |
| |
| func (x *TrainingInput) GetScaleTier() TrainingInput_ScaleTier { |
| if x != nil { |
| return x.ScaleTier |
| } |
| return TrainingInput_BASIC |
| } |
| |
| func (x *TrainingInput) GetMasterType() string { |
| if x != nil { |
| return x.MasterType |
| } |
| return "" |
| } |
| |
| func (x *TrainingInput) GetWorkerType() string { |
| if x != nil { |
| return x.WorkerType |
| } |
| return "" |
| } |
| |
| func (x *TrainingInput) GetParameterServerType() string { |
| if x != nil { |
| return x.ParameterServerType |
| } |
| return "" |
| } |
| |
| func (x *TrainingInput) GetWorkerCount() int64 { |
| if x != nil { |
| return x.WorkerCount |
| } |
| return 0 |
| } |
| |
| func (x *TrainingInput) GetParameterServerCount() int64 { |
| if x != nil { |
| return x.ParameterServerCount |
| } |
| return 0 |
| } |
| |
| func (x *TrainingInput) GetPackageUris() []string { |
| if x != nil { |
| return x.PackageUris |
| } |
| return nil |
| } |
| |
| func (x *TrainingInput) GetPythonModule() string { |
| if x != nil { |
| return x.PythonModule |
| } |
| return "" |
| } |
| |
| func (x *TrainingInput) GetArgs() []string { |
| if x != nil { |
| return x.Args |
| } |
| return nil |
| } |
| |
| func (x *TrainingInput) GetHyperparameters() *HyperparameterSpec { |
| if x != nil { |
| return x.Hyperparameters |
| } |
| return nil |
| } |
| |
| func (x *TrainingInput) GetRegion() string { |
| if x != nil { |
| return x.Region |
| } |
| return "" |
| } |
| |
| func (x *TrainingInput) GetJobDir() string { |
| if x != nil { |
| return x.JobDir |
| } |
| return "" |
| } |
| |
| func (x *TrainingInput) GetRuntimeVersion() string { |
| if x != nil { |
| return x.RuntimeVersion |
| } |
| return "" |
| } |
| |
| // Represents a set of hyperparameters to optimize. |
| type HyperparameterSpec struct { |
| state protoimpl.MessageState |
| sizeCache protoimpl.SizeCache |
| unknownFields protoimpl.UnknownFields |
| |
| // Required. The type of goal to use for tuning. Available types are |
| // `MAXIMIZE` and `MINIMIZE`. |
| // |
| // Defaults to `MAXIMIZE`. |
| Goal HyperparameterSpec_GoalType `protobuf:"varint,1,opt,name=goal,proto3,enum=google.cloud.ml.v1.HyperparameterSpec_GoalType" json:"goal,omitempty"` |
| // Required. The set of parameters to tune. |
| Params []*ParameterSpec `protobuf:"bytes,2,rep,name=params,proto3" json:"params,omitempty"` |
| // Optional. How many training trials should be attempted to optimize |
| // the specified hyperparameters. |
| // |
| // Defaults to one. |
| MaxTrials int32 `protobuf:"varint,3,opt,name=max_trials,json=maxTrials,proto3" json:"max_trials,omitempty"` |
| // Optional. The number of training trials to run concurrently. |
| // You can reduce the time it takes to perform hyperparameter tuning by adding |
| // trials in parallel. However, each trail only benefits from the information |
| // gained in completed trials. That means that a trial does not get access to |
| // the results of trials running at the same time, which could reduce the |
| // quality of the overall optimization. |
| // |
| // Each trial will use the same scale tier and machine types. |
| // |
| // Defaults to one. |
| MaxParallelTrials int32 `protobuf:"varint,4,opt,name=max_parallel_trials,json=maxParallelTrials,proto3" json:"max_parallel_trials,omitempty"` |
| // Optional. The Tensorflow summary tag name to use for optimizing trials. For |
| // current versions of Tensorflow, this tag name should exactly match what is |
| // shown in Tensorboard, including all scopes. For versions of Tensorflow |
| // prior to 0.12, this should be only the tag passed to tf.Summary. |
| // By default, "training/hptuning/metric" will be used. |
| HyperparameterMetricTag string `protobuf:"bytes,5,opt,name=hyperparameter_metric_tag,json=hyperparameterMetricTag,proto3" json:"hyperparameter_metric_tag,omitempty"` |
| } |
| |
| func (x *HyperparameterSpec) Reset() { |
| *x = HyperparameterSpec{} |
| if protoimpl.UnsafeEnabled { |
| mi := &file_google_cloud_ml_v1_job_service_proto_msgTypes[1] |
| ms := protoimpl.X.MessageStateOf(protoimpl.Pointer(x)) |
| ms.StoreMessageInfo(mi) |
| } |
| } |
| |
| func (x *HyperparameterSpec) String() string { |
| return protoimpl.X.MessageStringOf(x) |
| } |
| |
| func (*HyperparameterSpec) ProtoMessage() {} |
| |
| func (x *HyperparameterSpec) ProtoReflect() protoreflect.Message { |
| mi := &file_google_cloud_ml_v1_job_service_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 HyperparameterSpec.ProtoReflect.Descriptor instead. |
| func (*HyperparameterSpec) Descriptor() ([]byte, []int) { |
| return file_google_cloud_ml_v1_job_service_proto_rawDescGZIP(), []int{1} |
| } |
| |
| func (x *HyperparameterSpec) GetGoal() HyperparameterSpec_GoalType { |
| if x != nil { |
| return x.Goal |
| } |
| return HyperparameterSpec_GOAL_TYPE_UNSPECIFIED |
| } |
| |
| func (x *HyperparameterSpec) GetParams() []*ParameterSpec { |
| if x != nil { |
| return x.Params |
| } |
| return nil |
| } |
| |
| func (x *HyperparameterSpec) GetMaxTrials() int32 { |
| if x != nil { |
| return x.MaxTrials |
| } |
| return 0 |
| } |
| |
| func (x *HyperparameterSpec) GetMaxParallelTrials() int32 { |
| if x != nil { |
| return x.MaxParallelTrials |
| } |
| return 0 |
| } |
| |
| func (x *HyperparameterSpec) GetHyperparameterMetricTag() string { |
| if x != nil { |
| return x.HyperparameterMetricTag |
| } |
| return "" |
| } |
| |
| // Represents a single hyperparameter to optimize. |
| type ParameterSpec struct { |
| state protoimpl.MessageState |
| sizeCache protoimpl.SizeCache |
| unknownFields protoimpl.UnknownFields |
| |
| // Required. The parameter name must be unique amongst all ParameterConfigs in |
| // a HyperparameterSpec message. E.g., "learning_rate". |
| ParameterName string `protobuf:"bytes,1,opt,name=parameter_name,json=parameterName,proto3" json:"parameter_name,omitempty"` |
| // Required. The type of the parameter. |
| Type ParameterSpec_ParameterType `protobuf:"varint,4,opt,name=type,proto3,enum=google.cloud.ml.v1.ParameterSpec_ParameterType" json:"type,omitempty"` |
| // Required if type is `DOUBLE` or `INTEGER`. This field |
| // should be unset if type is `CATEGORICAL`. This value should be integers if |
| // type is INTEGER. |
| MinValue float64 `protobuf:"fixed64,2,opt,name=min_value,json=minValue,proto3" json:"min_value,omitempty"` |
| // Required if typeis `DOUBLE` or `INTEGER`. This field |
| // should be unset if type is `CATEGORICAL`. This value should be integers if |
| // type is `INTEGER`. |
| MaxValue float64 `protobuf:"fixed64,3,opt,name=max_value,json=maxValue,proto3" json:"max_value,omitempty"` |
| // Required if type is `CATEGORICAL`. The list of possible categories. |
| CategoricalValues []string `protobuf:"bytes,5,rep,name=categorical_values,json=categoricalValues,proto3" json:"categorical_values,omitempty"` |
| // Required if type is `DISCRETE`. |
| // A list of feasible points. |
| // The list should be in strictly increasing order. For instance, this |
| // parameter might have possible settings of 1.5, 2.5, and 4.0. This list |
| // should not contain more than 1,000 values. |
| DiscreteValues []float64 `protobuf:"fixed64,6,rep,packed,name=discrete_values,json=discreteValues,proto3" json:"discrete_values,omitempty"` |
| // Optional. How the parameter should be scaled to the hypercube. |
| // Leave unset for categorical parameters. |
| // Some kind of scaling is strongly recommended for real or integral |
| // parameters (e.g., `UNIT_LINEAR_SCALE`). |
| ScaleType ParameterSpec_ScaleType `protobuf:"varint,7,opt,name=scale_type,json=scaleType,proto3,enum=google.cloud.ml.v1.ParameterSpec_ScaleType" json:"scale_type,omitempty"` |
| } |
| |
| func (x *ParameterSpec) Reset() { |
| *x = ParameterSpec{} |
| if protoimpl.UnsafeEnabled { |
| mi := &file_google_cloud_ml_v1_job_service_proto_msgTypes[2] |
| ms := protoimpl.X.MessageStateOf(protoimpl.Pointer(x)) |
| ms.StoreMessageInfo(mi) |
| } |
| } |
| |
| func (x *ParameterSpec) String() string { |
| return protoimpl.X.MessageStringOf(x) |
| } |
| |
| func (*ParameterSpec) ProtoMessage() {} |
| |
| func (x *ParameterSpec) ProtoReflect() protoreflect.Message { |
| mi := &file_google_cloud_ml_v1_job_service_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 ParameterSpec.ProtoReflect.Descriptor instead. |
| func (*ParameterSpec) Descriptor() ([]byte, []int) { |
| return file_google_cloud_ml_v1_job_service_proto_rawDescGZIP(), []int{2} |
| } |
| |
| func (x *ParameterSpec) GetParameterName() string { |
| if x != nil { |
| return x.ParameterName |
| } |
| return "" |
| } |
| |
| func (x *ParameterSpec) GetType() ParameterSpec_ParameterType { |
| if x != nil { |
| return x.Type |
| } |
| return ParameterSpec_PARAMETER_TYPE_UNSPECIFIED |
| } |
| |
| func (x *ParameterSpec) GetMinValue() float64 { |
| if x != nil { |
| return x.MinValue |
| } |
| return 0 |
| } |
| |
| func (x *ParameterSpec) GetMaxValue() float64 { |
| if x != nil { |
| return x.MaxValue |
| } |
| return 0 |
| } |
| |
| func (x *ParameterSpec) GetCategoricalValues() []string { |
| if x != nil { |
| return x.CategoricalValues |
| } |
| return nil |
| } |
| |
| func (x *ParameterSpec) GetDiscreteValues() []float64 { |
| if x != nil { |
| return x.DiscreteValues |
| } |
| return nil |
| } |
| |
| func (x *ParameterSpec) GetScaleType() ParameterSpec_ScaleType { |
| if x != nil { |
| return x.ScaleType |
| } |
| return ParameterSpec_NONE |
| } |
| |
| // Represents the result of a single hyperparameter tuning trial from a |
| // training job. The TrainingOutput object that is returned on successful |
| // completion of a training job with hyperparameter tuning includes a list |
| // of HyperparameterOutput objects, one for each successful trial. |
| type HyperparameterOutput struct { |
| state protoimpl.MessageState |
| sizeCache protoimpl.SizeCache |
| unknownFields protoimpl.UnknownFields |
| |
| // The trial id for these results. |
| TrialId string `protobuf:"bytes,1,opt,name=trial_id,json=trialId,proto3" json:"trial_id,omitempty"` |
| // The hyperparameters given to this trial. |
| Hyperparameters map[string]string `protobuf:"bytes,2,rep,name=hyperparameters,proto3" json:"hyperparameters,omitempty" protobuf_key:"bytes,1,opt,name=key,proto3" protobuf_val:"bytes,2,opt,name=value,proto3"` |
| // The final objective metric seen for this trial. |
| FinalMetric *HyperparameterOutput_HyperparameterMetric `protobuf:"bytes,3,opt,name=final_metric,json=finalMetric,proto3" json:"final_metric,omitempty"` |
| // All recorded object metrics for this trial. |
| AllMetrics []*HyperparameterOutput_HyperparameterMetric `protobuf:"bytes,4,rep,name=all_metrics,json=allMetrics,proto3" json:"all_metrics,omitempty"` |
| } |
| |
| func (x *HyperparameterOutput) Reset() { |
| *x = HyperparameterOutput{} |
| if protoimpl.UnsafeEnabled { |
| mi := &file_google_cloud_ml_v1_job_service_proto_msgTypes[3] |
| ms := protoimpl.X.MessageStateOf(protoimpl.Pointer(x)) |
| ms.StoreMessageInfo(mi) |
| } |
| } |
| |
| func (x *HyperparameterOutput) String() string { |
| return protoimpl.X.MessageStringOf(x) |
| } |
| |
| func (*HyperparameterOutput) ProtoMessage() {} |
| |
| func (x *HyperparameterOutput) ProtoReflect() protoreflect.Message { |
| mi := &file_google_cloud_ml_v1_job_service_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 HyperparameterOutput.ProtoReflect.Descriptor instead. |
| func (*HyperparameterOutput) Descriptor() ([]byte, []int) { |
| return file_google_cloud_ml_v1_job_service_proto_rawDescGZIP(), []int{3} |
| } |
| |
| func (x *HyperparameterOutput) GetTrialId() string { |
| if x != nil { |
| return x.TrialId |
| } |
| return "" |
| } |
| |
| func (x *HyperparameterOutput) GetHyperparameters() map[string]string { |
| if x != nil { |
| return x.Hyperparameters |
| } |
| return nil |
| } |
| |
| func (x *HyperparameterOutput) GetFinalMetric() *HyperparameterOutput_HyperparameterMetric { |
| if x != nil { |
| return x.FinalMetric |
| } |
| return nil |
| } |
| |
| func (x *HyperparameterOutput) GetAllMetrics() []*HyperparameterOutput_HyperparameterMetric { |
| if x != nil { |
| return x.AllMetrics |
| } |
| return nil |
| } |
| |
| // Represents results of a training job. Output only. |
| type TrainingOutput struct { |
| state protoimpl.MessageState |
| sizeCache protoimpl.SizeCache |
| unknownFields protoimpl.UnknownFields |
| |
| // The number of hyperparameter tuning trials that completed successfully. |
| // Only set for hyperparameter tuning jobs. |
| CompletedTrialCount int64 `protobuf:"varint,1,opt,name=completed_trial_count,json=completedTrialCount,proto3" json:"completed_trial_count,omitempty"` |
| // Results for individual Hyperparameter trials. |
| // Only set for hyperparameter tuning jobs. |
| Trials []*HyperparameterOutput `protobuf:"bytes,2,rep,name=trials,proto3" json:"trials,omitempty"` |
| // The amount of ML units consumed by the job. |
| ConsumedMlUnits float64 `protobuf:"fixed64,3,opt,name=consumed_ml_units,json=consumedMlUnits,proto3" json:"consumed_ml_units,omitempty"` |
| // Whether this job is a hyperparameter tuning job. |
| IsHyperparameterTuningJob bool `protobuf:"varint,4,opt,name=is_hyperparameter_tuning_job,json=isHyperparameterTuningJob,proto3" json:"is_hyperparameter_tuning_job,omitempty"` |
| } |
| |
| func (x *TrainingOutput) Reset() { |
| *x = TrainingOutput{} |
| if protoimpl.UnsafeEnabled { |
| mi := &file_google_cloud_ml_v1_job_service_proto_msgTypes[4] |
| ms := protoimpl.X.MessageStateOf(protoimpl.Pointer(x)) |
| ms.StoreMessageInfo(mi) |
| } |
| } |
| |
| func (x *TrainingOutput) String() string { |
| return protoimpl.X.MessageStringOf(x) |
| } |
| |
| func (*TrainingOutput) ProtoMessage() {} |
| |
| func (x *TrainingOutput) ProtoReflect() protoreflect.Message { |
| mi := &file_google_cloud_ml_v1_job_service_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 TrainingOutput.ProtoReflect.Descriptor instead. |
| func (*TrainingOutput) Descriptor() ([]byte, []int) { |
| return file_google_cloud_ml_v1_job_service_proto_rawDescGZIP(), []int{4} |
| } |
| |
| func (x *TrainingOutput) GetCompletedTrialCount() int64 { |
| if x != nil { |
| return x.CompletedTrialCount |
| } |
| return 0 |
| } |
| |
| func (x *TrainingOutput) GetTrials() []*HyperparameterOutput { |
| if x != nil { |
| return x.Trials |
| } |
| return nil |
| } |
| |
| func (x *TrainingOutput) GetConsumedMlUnits() float64 { |
| if x != nil { |
| return x.ConsumedMlUnits |
| } |
| return 0 |
| } |
| |
| func (x *TrainingOutput) GetIsHyperparameterTuningJob() bool { |
| if x != nil { |
| return x.IsHyperparameterTuningJob |
| } |
| return false |
| } |
| |
| // Represents input parameters for a prediction job. |
| type PredictionInput struct { |
| state protoimpl.MessageState |
| sizeCache protoimpl.SizeCache |
| unknownFields protoimpl.UnknownFields |
| |
| // Required. The model or the version to use for prediction. |
| // |
| // Types that are assignable to ModelVersion: |
| // *PredictionInput_ModelName |
| // *PredictionInput_VersionName |
| // *PredictionInput_Uri |
| ModelVersion isPredictionInput_ModelVersion `protobuf_oneof:"model_version"` |
| // Required. The format of the input data files. |
| DataFormat PredictionInput_DataFormat `protobuf:"varint,3,opt,name=data_format,json=dataFormat,proto3,enum=google.cloud.ml.v1.PredictionInput_DataFormat" json:"data_format,omitempty"` |
| // Required. The Google Cloud Storage location of the input data files. |
| // May contain wildcards. |
| InputPaths []string `protobuf:"bytes,4,rep,name=input_paths,json=inputPaths,proto3" json:"input_paths,omitempty"` |
| // Required. The output Google Cloud Storage location. |
| OutputPath string `protobuf:"bytes,5,opt,name=output_path,json=outputPath,proto3" json:"output_path,omitempty"` |
| // Optional. The maximum number of workers to be used for parallel processing. |
| // Defaults to 10 if not specified. |
| MaxWorkerCount int64 `protobuf:"varint,6,opt,name=max_worker_count,json=maxWorkerCount,proto3" json:"max_worker_count,omitempty"` |
| // Required. The Google Compute Engine region to run the prediction job in. |
| Region string `protobuf:"bytes,7,opt,name=region,proto3" json:"region,omitempty"` |
| // Optional. The Google Cloud ML runtime version to use for this batch |
| // prediction. If not set, Google Cloud ML will pick the runtime version used |
| // during the CreateVersion request for this model version, or choose the |
| // latest stable version when model version information is not available |
| // such as when the model is specified by uri. |
| RuntimeVersion string `protobuf:"bytes,8,opt,name=runtime_version,json=runtimeVersion,proto3" json:"runtime_version,omitempty"` |
| } |
| |
| func (x *PredictionInput) Reset() { |
| *x = PredictionInput{} |
| if protoimpl.UnsafeEnabled { |
| mi := &file_google_cloud_ml_v1_job_service_proto_msgTypes[5] |
| ms := protoimpl.X.MessageStateOf(protoimpl.Pointer(x)) |
| ms.StoreMessageInfo(mi) |
| } |
| } |
| |
| func (x *PredictionInput) String() string { |
| return protoimpl.X.MessageStringOf(x) |
| } |
| |
| func (*PredictionInput) ProtoMessage() {} |
| |
| func (x *PredictionInput) ProtoReflect() protoreflect.Message { |
| mi := &file_google_cloud_ml_v1_job_service_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 PredictionInput.ProtoReflect.Descriptor instead. |
| func (*PredictionInput) Descriptor() ([]byte, []int) { |
| return file_google_cloud_ml_v1_job_service_proto_rawDescGZIP(), []int{5} |
| } |
| |
| func (m *PredictionInput) GetModelVersion() isPredictionInput_ModelVersion { |
| if m != nil { |
| return m.ModelVersion |
| } |
| return nil |
| } |
| |
| func (x *PredictionInput) GetModelName() string { |
| if x, ok := x.GetModelVersion().(*PredictionInput_ModelName); ok { |
| return x.ModelName |
| } |
| return "" |
| } |
| |
| func (x *PredictionInput) GetVersionName() string { |
| if x, ok := x.GetModelVersion().(*PredictionInput_VersionName); ok { |
| return x.VersionName |
| } |
| return "" |
| } |
| |
| func (x *PredictionInput) GetUri() string { |
| if x, ok := x.GetModelVersion().(*PredictionInput_Uri); ok { |
| return x.Uri |
| } |
| return "" |
| } |
| |
| func (x *PredictionInput) GetDataFormat() PredictionInput_DataFormat { |
| if x != nil { |
| return x.DataFormat |
| } |
| return PredictionInput_DATA_FORMAT_UNSPECIFIED |
| } |
| |
| func (x *PredictionInput) GetInputPaths() []string { |
| if x != nil { |
| return x.InputPaths |
| } |
| return nil |
| } |
| |
| func (x *PredictionInput) GetOutputPath() string { |
| if x != nil { |
| return x.OutputPath |
| } |
| return "" |
| } |
| |
| func (x *PredictionInput) GetMaxWorkerCount() int64 { |
| if x != nil { |
| return x.MaxWorkerCount |
| } |
| return 0 |
| } |
| |
| func (x *PredictionInput) GetRegion() string { |
| if x != nil { |
| return x.Region |
| } |
| return "" |
| } |
| |
| func (x *PredictionInput) GetRuntimeVersion() string { |
| if x != nil { |
| return x.RuntimeVersion |
| } |
| return "" |
| } |
| |
| type isPredictionInput_ModelVersion interface { |
| isPredictionInput_ModelVersion() |
| } |
| |
| type PredictionInput_ModelName struct { |
| // Use this field if you want to use the default version for the specified |
| // model. The string must use the following format: |
| // |
| // `"projects/<var>[YOUR_PROJECT]</var>/models/<var>[YOUR_MODEL]</var>"` |
| ModelName string `protobuf:"bytes,1,opt,name=model_name,json=modelName,proto3,oneof"` |
| } |
| |
| type PredictionInput_VersionName struct { |
| // Use this field if you want to specify a version of the model to use. The |
| // string is formatted the same way as `model_version`, with the addition |
| // of the version information: |
| // |
| // `"projects/<var>[YOUR_PROJECT]</var>/models/<var>YOUR_MODEL/versions/<var>[YOUR_VERSION]</var>"` |
| VersionName string `protobuf:"bytes,2,opt,name=version_name,json=versionName,proto3,oneof"` |
| } |
| |
| type PredictionInput_Uri struct { |
| // Use this field if you want to specify a Google Cloud Storage path for |
| // the model to use. |
| Uri string `protobuf:"bytes,9,opt,name=uri,proto3,oneof"` |
| } |
| |
| func (*PredictionInput_ModelName) isPredictionInput_ModelVersion() {} |
| |
| func (*PredictionInput_VersionName) isPredictionInput_ModelVersion() {} |
| |
| func (*PredictionInput_Uri) isPredictionInput_ModelVersion() {} |
| |
| // Represents results of a prediction job. |
| type PredictionOutput struct { |
| state protoimpl.MessageState |
| sizeCache protoimpl.SizeCache |
| unknownFields protoimpl.UnknownFields |
| |
| // The output Google Cloud Storage location provided at the job creation time. |
| OutputPath string `protobuf:"bytes,1,opt,name=output_path,json=outputPath,proto3" json:"output_path,omitempty"` |
| // The number of generated predictions. |
| PredictionCount int64 `protobuf:"varint,2,opt,name=prediction_count,json=predictionCount,proto3" json:"prediction_count,omitempty"` |
| // The number of data instances which resulted in errors. |
| ErrorCount int64 `protobuf:"varint,3,opt,name=error_count,json=errorCount,proto3" json:"error_count,omitempty"` |
| // Node hours used by the batch prediction job. |
| NodeHours float64 `protobuf:"fixed64,4,opt,name=node_hours,json=nodeHours,proto3" json:"node_hours,omitempty"` |
| } |
| |
| func (x *PredictionOutput) Reset() { |
| *x = PredictionOutput{} |
| if protoimpl.UnsafeEnabled { |
| mi := &file_google_cloud_ml_v1_job_service_proto_msgTypes[6] |
| ms := protoimpl.X.MessageStateOf(protoimpl.Pointer(x)) |
| ms.StoreMessageInfo(mi) |
| } |
| } |
| |
| func (x *PredictionOutput) String() string { |
| return protoimpl.X.MessageStringOf(x) |
| } |
| |
| func (*PredictionOutput) ProtoMessage() {} |
| |
| func (x *PredictionOutput) ProtoReflect() protoreflect.Message { |
| mi := &file_google_cloud_ml_v1_job_service_proto_msgTypes[6] |
| 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 PredictionOutput.ProtoReflect.Descriptor instead. |
| func (*PredictionOutput) Descriptor() ([]byte, []int) { |
| return file_google_cloud_ml_v1_job_service_proto_rawDescGZIP(), []int{6} |
| } |
| |
| func (x *PredictionOutput) GetOutputPath() string { |
| if x != nil { |
| return x.OutputPath |
| } |
| return "" |
| } |
| |
| func (x *PredictionOutput) GetPredictionCount() int64 { |
| if x != nil { |
| return x.PredictionCount |
| } |
| return 0 |
| } |
| |
| func (x *PredictionOutput) GetErrorCount() int64 { |
| if x != nil { |
| return x.ErrorCount |
| } |
| return 0 |
| } |
| |
| func (x *PredictionOutput) GetNodeHours() float64 { |
| if x != nil { |
| return x.NodeHours |
| } |
| return 0 |
| } |
| |
| // Represents a training or prediction job. |
| type Job struct { |
| state protoimpl.MessageState |
| sizeCache protoimpl.SizeCache |
| unknownFields protoimpl.UnknownFields |
| |
| // Required. The user-specified id of the job. |
| JobId string `protobuf:"bytes,1,opt,name=job_id,json=jobId,proto3" json:"job_id,omitempty"` |
| // Required. Parameters to create a job. |
| // |
| // Types that are assignable to Input: |
| // *Job_TrainingInput |
| // *Job_PredictionInput |
| Input isJob_Input `protobuf_oneof:"input"` |
| // Output only. When the job was created. |
| CreateTime *timestamp.Timestamp `protobuf:"bytes,4,opt,name=create_time,json=createTime,proto3" json:"create_time,omitempty"` |
| // Output only. When the job processing was started. |
| StartTime *timestamp.Timestamp `protobuf:"bytes,5,opt,name=start_time,json=startTime,proto3" json:"start_time,omitempty"` |
| // Output only. When the job processing was completed. |
| EndTime *timestamp.Timestamp `protobuf:"bytes,6,opt,name=end_time,json=endTime,proto3" json:"end_time,omitempty"` |
| // Output only. The detailed state of a job. |
| State Job_State `protobuf:"varint,7,opt,name=state,proto3,enum=google.cloud.ml.v1.Job_State" json:"state,omitempty"` |
| // Output only. The details of a failure or a cancellation. |
| ErrorMessage string `protobuf:"bytes,8,opt,name=error_message,json=errorMessage,proto3" json:"error_message,omitempty"` |
| // Output only. The current result of the job. |
| // |
| // Types that are assignable to Output: |
| // *Job_TrainingOutput |
| // *Job_PredictionOutput |
| Output isJob_Output `protobuf_oneof:"output"` |
| } |
| |
| func (x *Job) Reset() { |
| *x = Job{} |
| if protoimpl.UnsafeEnabled { |
| mi := &file_google_cloud_ml_v1_job_service_proto_msgTypes[7] |
| ms := protoimpl.X.MessageStateOf(protoimpl.Pointer(x)) |
| ms.StoreMessageInfo(mi) |
| } |
| } |
| |
| func (x *Job) String() string { |
| return protoimpl.X.MessageStringOf(x) |
| } |
| |
| func (*Job) ProtoMessage() {} |
| |
| func (x *Job) ProtoReflect() protoreflect.Message { |
| mi := &file_google_cloud_ml_v1_job_service_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 Job.ProtoReflect.Descriptor instead. |
| func (*Job) Descriptor() ([]byte, []int) { |
| return file_google_cloud_ml_v1_job_service_proto_rawDescGZIP(), []int{7} |
| } |
| |
| func (x *Job) GetJobId() string { |
| if x != nil { |
| return x.JobId |
| } |
| return "" |
| } |
| |
| func (m *Job) GetInput() isJob_Input { |
| if m != nil { |
| return m.Input |
| } |
| return nil |
| } |
| |
| func (x *Job) GetTrainingInput() *TrainingInput { |
| if x, ok := x.GetInput().(*Job_TrainingInput); ok { |
| return x.TrainingInput |
| } |
| return nil |
| } |
| |
| func (x *Job) GetPredictionInput() *PredictionInput { |
| if x, ok := x.GetInput().(*Job_PredictionInput); ok { |
| return x.PredictionInput |
| } |
| return nil |
| } |
| |
| func (x *Job) GetCreateTime() *timestamp.Timestamp { |
| if x != nil { |
| return x.CreateTime |
| } |
| return nil |
| } |
| |
| func (x *Job) GetStartTime() *timestamp.Timestamp { |
| if x != nil { |
| return x.StartTime |
| } |
| return nil |
| } |
| |
| func (x *Job) GetEndTime() *timestamp.Timestamp { |
| if x != nil { |
| return x.EndTime |
| } |
| return nil |
| } |
| |
| func (x *Job) GetState() Job_State { |
| if x != nil { |
| return x.State |
| } |
| return Job_STATE_UNSPECIFIED |
| } |
| |
| func (x *Job) GetErrorMessage() string { |
| if x != nil { |
| return x.ErrorMessage |
| } |
| return "" |
| } |
| |
| func (m *Job) GetOutput() isJob_Output { |
| if m != nil { |
| return m.Output |
| } |
| return nil |
| } |
| |
| func (x *Job) GetTrainingOutput() *TrainingOutput { |
| if x, ok := x.GetOutput().(*Job_TrainingOutput); ok { |
| return x.TrainingOutput |
| } |
| return nil |
| } |
| |
| func (x *Job) GetPredictionOutput() *PredictionOutput { |
| if x, ok := x.GetOutput().(*Job_PredictionOutput); ok { |
| return x.PredictionOutput |
| } |
| return nil |
| } |
| |
| type isJob_Input interface { |
| isJob_Input() |
| } |
| |
| type Job_TrainingInput struct { |
| // Input parameters to create a training job. |
| TrainingInput *TrainingInput `protobuf:"bytes,2,opt,name=training_input,json=trainingInput,proto3,oneof"` |
| } |
| |
| type Job_PredictionInput struct { |
| // Input parameters to create a prediction job. |
| PredictionInput *PredictionInput `protobuf:"bytes,3,opt,name=prediction_input,json=predictionInput,proto3,oneof"` |
| } |
| |
| func (*Job_TrainingInput) isJob_Input() {} |
| |
| func (*Job_PredictionInput) isJob_Input() {} |
| |
| type isJob_Output interface { |
| isJob_Output() |
| } |
| |
| type Job_TrainingOutput struct { |
| // The current training job result. |
| TrainingOutput *TrainingOutput `protobuf:"bytes,9,opt,name=training_output,json=trainingOutput,proto3,oneof"` |
| } |
| |
| type Job_PredictionOutput struct { |
| // The current prediction job result. |
| PredictionOutput *PredictionOutput `protobuf:"bytes,10,opt,name=prediction_output,json=predictionOutput,proto3,oneof"` |
| } |
| |
| func (*Job_TrainingOutput) isJob_Output() {} |
| |
| func (*Job_PredictionOutput) isJob_Output() {} |
| |
| // Request message for the CreateJob method. |
| type CreateJobRequest struct { |
| state protoimpl.MessageState |
| sizeCache protoimpl.SizeCache |
| unknownFields protoimpl.UnknownFields |
| |
| // Required. The project name. |
| // |
| // Authorization: requires `Editor` role on the specified project. |
| Parent string `protobuf:"bytes,1,opt,name=parent,proto3" json:"parent,omitempty"` |
| // Required. The job to create. |
| Job *Job `protobuf:"bytes,2,opt,name=job,proto3" json:"job,omitempty"` |
| } |
| |
| func (x *CreateJobRequest) Reset() { |
| *x = CreateJobRequest{} |
| if protoimpl.UnsafeEnabled { |
| mi := &file_google_cloud_ml_v1_job_service_proto_msgTypes[8] |
| ms := protoimpl.X.MessageStateOf(protoimpl.Pointer(x)) |
| ms.StoreMessageInfo(mi) |
| } |
| } |
| |
| func (x *CreateJobRequest) String() string { |
| return protoimpl.X.MessageStringOf(x) |
| } |
| |
| func (*CreateJobRequest) ProtoMessage() {} |
| |
| func (x *CreateJobRequest) ProtoReflect() protoreflect.Message { |
| mi := &file_google_cloud_ml_v1_job_service_proto_msgTypes[8] |
| 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 CreateJobRequest.ProtoReflect.Descriptor instead. |
| func (*CreateJobRequest) Descriptor() ([]byte, []int) { |
| return file_google_cloud_ml_v1_job_service_proto_rawDescGZIP(), []int{8} |
| } |
| |
| func (x *CreateJobRequest) GetParent() string { |
| if x != nil { |
| return x.Parent |
| } |
| return "" |
| } |
| |
| func (x *CreateJobRequest) GetJob() *Job { |
| if x != nil { |
| return x.Job |
| } |
| return nil |
| } |
| |
| // Request message for the ListJobs method. |
| type ListJobsRequest struct { |
| state protoimpl.MessageState |
| sizeCache protoimpl.SizeCache |
| unknownFields protoimpl.UnknownFields |
| |
| // Required. The name of the project for which to list jobs. |
| // |
| // Authorization: requires `Viewer` role on the specified project. |
| Parent string `protobuf:"bytes,1,opt,name=parent,proto3" json:"parent,omitempty"` |
| // Optional. Specifies the subset of jobs to retrieve. |
| Filter string `protobuf:"bytes,2,opt,name=filter,proto3" json:"filter,omitempty"` |
| // Optional. A page token to request the next page of results. |
| // |
| // You get the token from the `next_page_token` field of the response from |
| // the previous call. |
| PageToken string `protobuf:"bytes,4,opt,name=page_token,json=pageToken,proto3" json:"page_token,omitempty"` |
| // Optional. The number of jobs to retrieve per "page" of results. If there |
| // are more remaining results than this number, the response message will |
| // contain a valid value in the `next_page_token` field. |
| // |
| // The default value is 20, and the maximum page size is 100. |
| PageSize int32 `protobuf:"varint,5,opt,name=page_size,json=pageSize,proto3" json:"page_size,omitempty"` |
| } |
| |
| func (x *ListJobsRequest) Reset() { |
| *x = ListJobsRequest{} |
| if protoimpl.UnsafeEnabled { |
| mi := &file_google_cloud_ml_v1_job_service_proto_msgTypes[9] |
| ms := protoimpl.X.MessageStateOf(protoimpl.Pointer(x)) |
| ms.StoreMessageInfo(mi) |
| } |
| } |
| |
| func (x *ListJobsRequest) String() string { |
| return protoimpl.X.MessageStringOf(x) |
| } |
| |
| func (*ListJobsRequest) ProtoMessage() {} |
| |
| func (x *ListJobsRequest) ProtoReflect() protoreflect.Message { |
| mi := &file_google_cloud_ml_v1_job_service_proto_msgTypes[9] |
| 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 ListJobsRequest.ProtoReflect.Descriptor instead. |
| func (*ListJobsRequest) Descriptor() ([]byte, []int) { |
| return file_google_cloud_ml_v1_job_service_proto_rawDescGZIP(), []int{9} |
| } |
| |
| func (x *ListJobsRequest) GetParent() string { |
| if x != nil { |
| return x.Parent |
| } |
| return "" |
| } |
| |
| func (x *ListJobsRequest) GetFilter() string { |
| if x != nil { |
| return x.Filter |
| } |
| return "" |
| } |
| |
| func (x *ListJobsRequest) GetPageToken() string { |
| if x != nil { |
| return x.PageToken |
| } |
| return "" |
| } |
| |
| func (x *ListJobsRequest) GetPageSize() int32 { |
| if x != nil { |
| return x.PageSize |
| } |
| return 0 |
| } |
| |
| // Response message for the ListJobs method. |
| type ListJobsResponse struct { |
| state protoimpl.MessageState |
| sizeCache protoimpl.SizeCache |
| unknownFields protoimpl.UnknownFields |
| |
| // The list of jobs. |
| Jobs []*Job `protobuf:"bytes,1,rep,name=jobs,proto3" json:"jobs,omitempty"` |
| // Optional. Pass this token as the `page_token` field of the request for a |
| // subsequent call. |
| NextPageToken string `protobuf:"bytes,2,opt,name=next_page_token,json=nextPageToken,proto3" json:"next_page_token,omitempty"` |
| } |
| |
| func (x *ListJobsResponse) Reset() { |
| *x = ListJobsResponse{} |
| if protoimpl.UnsafeEnabled { |
| mi := &file_google_cloud_ml_v1_job_service_proto_msgTypes[10] |
| ms := protoimpl.X.MessageStateOf(protoimpl.Pointer(x)) |
| ms.StoreMessageInfo(mi) |
| } |
| } |
| |
| func (x *ListJobsResponse) String() string { |
| return protoimpl.X.MessageStringOf(x) |
| } |
| |
| func (*ListJobsResponse) ProtoMessage() {} |
| |
| func (x *ListJobsResponse) ProtoReflect() protoreflect.Message { |
| mi := &file_google_cloud_ml_v1_job_service_proto_msgTypes[10] |
| 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 ListJobsResponse.ProtoReflect.Descriptor instead. |
| func (*ListJobsResponse) Descriptor() ([]byte, []int) { |
| return file_google_cloud_ml_v1_job_service_proto_rawDescGZIP(), []int{10} |
| } |
| |
| func (x *ListJobsResponse) GetJobs() []*Job { |
| if x != nil { |
| return x.Jobs |
| } |
| return nil |
| } |
| |
| func (x *ListJobsResponse) GetNextPageToken() string { |
| if x != nil { |
| return x.NextPageToken |
| } |
| return "" |
| } |
| |
| // Request message for the GetJob method. |
| type GetJobRequest struct { |
| state protoimpl.MessageState |
| sizeCache protoimpl.SizeCache |
| unknownFields protoimpl.UnknownFields |
| |
| // Required. The name of the job to get the description of. |
| // |
| // Authorization: requires `Viewer` role on the parent project. |
| Name string `protobuf:"bytes,1,opt,name=name,proto3" json:"name,omitempty"` |
| } |
| |
| func (x *GetJobRequest) Reset() { |
| *x = GetJobRequest{} |
| if protoimpl.UnsafeEnabled { |
| mi := &file_google_cloud_ml_v1_job_service_proto_msgTypes[11] |
| ms := protoimpl.X.MessageStateOf(protoimpl.Pointer(x)) |
| ms.StoreMessageInfo(mi) |
| } |
| } |
| |
| func (x *GetJobRequest) String() string { |
| return protoimpl.X.MessageStringOf(x) |
| } |
| |
| func (*GetJobRequest) ProtoMessage() {} |
| |
| func (x *GetJobRequest) ProtoReflect() protoreflect.Message { |
| mi := &file_google_cloud_ml_v1_job_service_proto_msgTypes[11] |
| 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 GetJobRequest.ProtoReflect.Descriptor instead. |
| func (*GetJobRequest) Descriptor() ([]byte, []int) { |
| return file_google_cloud_ml_v1_job_service_proto_rawDescGZIP(), []int{11} |
| } |
| |
| func (x *GetJobRequest) GetName() string { |
| if x != nil { |
| return x.Name |
| } |
| return "" |
| } |
| |
| // Request message for the CancelJob method. |
| type CancelJobRequest struct { |
| state protoimpl.MessageState |
| sizeCache protoimpl.SizeCache |
| unknownFields protoimpl.UnknownFields |
| |
| // Required. The name of the job to cancel. |
| // |
| // Authorization: requires `Editor` role on the parent project. |
| Name string `protobuf:"bytes,1,opt,name=name,proto3" json:"name,omitempty"` |
| } |
| |
| func (x *CancelJobRequest) Reset() { |
| *x = CancelJobRequest{} |
| if protoimpl.UnsafeEnabled { |
| mi := &file_google_cloud_ml_v1_job_service_proto_msgTypes[12] |
| ms := protoimpl.X.MessageStateOf(protoimpl.Pointer(x)) |
| ms.StoreMessageInfo(mi) |
| } |
| } |
| |
| func (x *CancelJobRequest) String() string { |
| return protoimpl.X.MessageStringOf(x) |
| } |
| |
| func (*CancelJobRequest) ProtoMessage() {} |
| |
| func (x *CancelJobRequest) ProtoReflect() protoreflect.Message { |
| mi := &file_google_cloud_ml_v1_job_service_proto_msgTypes[12] |
| 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 CancelJobRequest.ProtoReflect.Descriptor instead. |
| func (*CancelJobRequest) Descriptor() ([]byte, []int) { |
| return file_google_cloud_ml_v1_job_service_proto_rawDescGZIP(), []int{12} |
| } |
| |
| func (x *CancelJobRequest) GetName() string { |
| if x != nil { |
| return x.Name |
| } |
| return "" |
| } |
| |
| // An observed value of a metric. |
| type HyperparameterOutput_HyperparameterMetric struct { |
| state protoimpl.MessageState |
| sizeCache protoimpl.SizeCache |
| unknownFields protoimpl.UnknownFields |
| |
| // The global training step for this metric. |
| TrainingStep int64 `protobuf:"varint,1,opt,name=training_step,json=trainingStep,proto3" json:"training_step,omitempty"` |
| // The objective value at this training step. |
| ObjectiveValue float64 `protobuf:"fixed64,2,opt,name=objective_value,json=objectiveValue,proto3" json:"objective_value,omitempty"` |
| } |
| |
| func (x *HyperparameterOutput_HyperparameterMetric) Reset() { |
| *x = HyperparameterOutput_HyperparameterMetric{} |
| if protoimpl.UnsafeEnabled { |
| mi := &file_google_cloud_ml_v1_job_service_proto_msgTypes[13] |
| ms := protoimpl.X.MessageStateOf(protoimpl.Pointer(x)) |
| ms.StoreMessageInfo(mi) |
| } |
| } |
| |
| func (x *HyperparameterOutput_HyperparameterMetric) String() string { |
| return protoimpl.X.MessageStringOf(x) |
| } |
| |
| func (*HyperparameterOutput_HyperparameterMetric) ProtoMessage() {} |
| |
| func (x *HyperparameterOutput_HyperparameterMetric) ProtoReflect() protoreflect.Message { |
| mi := &file_google_cloud_ml_v1_job_service_proto_msgTypes[13] |
| 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 HyperparameterOutput_HyperparameterMetric.ProtoReflect.Descriptor instead. |
| func (*HyperparameterOutput_HyperparameterMetric) Descriptor() ([]byte, []int) { |
| return file_google_cloud_ml_v1_job_service_proto_rawDescGZIP(), []int{3, 0} |
| } |
| |
| func (x *HyperparameterOutput_HyperparameterMetric) GetTrainingStep() int64 { |
| if x != nil { |
| return x.TrainingStep |
| } |
| return 0 |
| } |
| |
| func (x *HyperparameterOutput_HyperparameterMetric) GetObjectiveValue() float64 { |
| if x != nil { |
| return x.ObjectiveValue |
| } |
| return 0 |
| } |
| |
| var File_google_cloud_ml_v1_job_service_proto protoreflect.FileDescriptor |
| |
| var file_google_cloud_ml_v1_job_service_proto_rawDesc = []byte{ |
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| } |
| |
| var ( |
| file_google_cloud_ml_v1_job_service_proto_rawDescOnce sync.Once |
| file_google_cloud_ml_v1_job_service_proto_rawDescData = file_google_cloud_ml_v1_job_service_proto_rawDesc |
| ) |
| |
| func file_google_cloud_ml_v1_job_service_proto_rawDescGZIP() []byte { |
| file_google_cloud_ml_v1_job_service_proto_rawDescOnce.Do(func() { |
| file_google_cloud_ml_v1_job_service_proto_rawDescData = protoimpl.X.CompressGZIP(file_google_cloud_ml_v1_job_service_proto_rawDescData) |
| }) |
| return file_google_cloud_ml_v1_job_service_proto_rawDescData |
| } |
| |
| var file_google_cloud_ml_v1_job_service_proto_enumTypes = make([]protoimpl.EnumInfo, 6) |
| var file_google_cloud_ml_v1_job_service_proto_msgTypes = make([]protoimpl.MessageInfo, 15) |
| var file_google_cloud_ml_v1_job_service_proto_goTypes = []interface{}{ |
| (TrainingInput_ScaleTier)(0), // 0: google.cloud.ml.v1.TrainingInput.ScaleTier |
| (HyperparameterSpec_GoalType)(0), // 1: google.cloud.ml.v1.HyperparameterSpec.GoalType |
| (ParameterSpec_ParameterType)(0), // 2: google.cloud.ml.v1.ParameterSpec.ParameterType |
| (ParameterSpec_ScaleType)(0), // 3: google.cloud.ml.v1.ParameterSpec.ScaleType |
| (PredictionInput_DataFormat)(0), // 4: google.cloud.ml.v1.PredictionInput.DataFormat |
| (Job_State)(0), // 5: google.cloud.ml.v1.Job.State |
| (*TrainingInput)(nil), // 6: google.cloud.ml.v1.TrainingInput |
| (*HyperparameterSpec)(nil), // 7: google.cloud.ml.v1.HyperparameterSpec |
| (*ParameterSpec)(nil), // 8: google.cloud.ml.v1.ParameterSpec |
| (*HyperparameterOutput)(nil), // 9: google.cloud.ml.v1.HyperparameterOutput |
| (*TrainingOutput)(nil), // 10: google.cloud.ml.v1.TrainingOutput |
| (*PredictionInput)(nil), // 11: google.cloud.ml.v1.PredictionInput |
| (*PredictionOutput)(nil), // 12: google.cloud.ml.v1.PredictionOutput |
| (*Job)(nil), // 13: google.cloud.ml.v1.Job |
| (*CreateJobRequest)(nil), // 14: google.cloud.ml.v1.CreateJobRequest |
| (*ListJobsRequest)(nil), // 15: google.cloud.ml.v1.ListJobsRequest |
| (*ListJobsResponse)(nil), // 16: google.cloud.ml.v1.ListJobsResponse |
| (*GetJobRequest)(nil), // 17: google.cloud.ml.v1.GetJobRequest |
| (*CancelJobRequest)(nil), // 18: google.cloud.ml.v1.CancelJobRequest |
| (*HyperparameterOutput_HyperparameterMetric)(nil), // 19: google.cloud.ml.v1.HyperparameterOutput.HyperparameterMetric |
| nil, // 20: google.cloud.ml.v1.HyperparameterOutput.HyperparametersEntry |
| (*timestamp.Timestamp)(nil), // 21: google.protobuf.Timestamp |
| (*empty.Empty)(nil), // 22: google.protobuf.Empty |
| } |
| var file_google_cloud_ml_v1_job_service_proto_depIdxs = []int32{ |
| 0, // 0: google.cloud.ml.v1.TrainingInput.scale_tier:type_name -> google.cloud.ml.v1.TrainingInput.ScaleTier |
| 7, // 1: google.cloud.ml.v1.TrainingInput.hyperparameters:type_name -> google.cloud.ml.v1.HyperparameterSpec |
| 1, // 2: google.cloud.ml.v1.HyperparameterSpec.goal:type_name -> google.cloud.ml.v1.HyperparameterSpec.GoalType |
| 8, // 3: google.cloud.ml.v1.HyperparameterSpec.params:type_name -> google.cloud.ml.v1.ParameterSpec |
| 2, // 4: google.cloud.ml.v1.ParameterSpec.type:type_name -> google.cloud.ml.v1.ParameterSpec.ParameterType |
| 3, // 5: google.cloud.ml.v1.ParameterSpec.scale_type:type_name -> google.cloud.ml.v1.ParameterSpec.ScaleType |
| 20, // 6: google.cloud.ml.v1.HyperparameterOutput.hyperparameters:type_name -> google.cloud.ml.v1.HyperparameterOutput.HyperparametersEntry |
| 19, // 7: google.cloud.ml.v1.HyperparameterOutput.final_metric:type_name -> google.cloud.ml.v1.HyperparameterOutput.HyperparameterMetric |
| 19, // 8: google.cloud.ml.v1.HyperparameterOutput.all_metrics:type_name -> google.cloud.ml.v1.HyperparameterOutput.HyperparameterMetric |
| 9, // 9: google.cloud.ml.v1.TrainingOutput.trials:type_name -> google.cloud.ml.v1.HyperparameterOutput |
| 4, // 10: google.cloud.ml.v1.PredictionInput.data_format:type_name -> google.cloud.ml.v1.PredictionInput.DataFormat |
| 6, // 11: google.cloud.ml.v1.Job.training_input:type_name -> google.cloud.ml.v1.TrainingInput |
| 11, // 12: google.cloud.ml.v1.Job.prediction_input:type_name -> google.cloud.ml.v1.PredictionInput |
| 21, // 13: google.cloud.ml.v1.Job.create_time:type_name -> google.protobuf.Timestamp |
| 21, // 14: google.cloud.ml.v1.Job.start_time:type_name -> google.protobuf.Timestamp |
| 21, // 15: google.cloud.ml.v1.Job.end_time:type_name -> google.protobuf.Timestamp |
| 5, // 16: google.cloud.ml.v1.Job.state:type_name -> google.cloud.ml.v1.Job.State |
| 10, // 17: google.cloud.ml.v1.Job.training_output:type_name -> google.cloud.ml.v1.TrainingOutput |
| 12, // 18: google.cloud.ml.v1.Job.prediction_output:type_name -> google.cloud.ml.v1.PredictionOutput |
| 13, // 19: google.cloud.ml.v1.CreateJobRequest.job:type_name -> google.cloud.ml.v1.Job |
| 13, // 20: google.cloud.ml.v1.ListJobsResponse.jobs:type_name -> google.cloud.ml.v1.Job |
| 14, // 21: google.cloud.ml.v1.JobService.CreateJob:input_type -> google.cloud.ml.v1.CreateJobRequest |
| 15, // 22: google.cloud.ml.v1.JobService.ListJobs:input_type -> google.cloud.ml.v1.ListJobsRequest |
| 17, // 23: google.cloud.ml.v1.JobService.GetJob:input_type -> google.cloud.ml.v1.GetJobRequest |
| 18, // 24: google.cloud.ml.v1.JobService.CancelJob:input_type -> google.cloud.ml.v1.CancelJobRequest |
| 13, // 25: google.cloud.ml.v1.JobService.CreateJob:output_type -> google.cloud.ml.v1.Job |
| 16, // 26: google.cloud.ml.v1.JobService.ListJobs:output_type -> google.cloud.ml.v1.ListJobsResponse |
| 13, // 27: google.cloud.ml.v1.JobService.GetJob:output_type -> google.cloud.ml.v1.Job |
| 22, // 28: google.cloud.ml.v1.JobService.CancelJob:output_type -> google.protobuf.Empty |
| 25, // [25:29] is the sub-list for method output_type |
| 21, // [21:25] is the sub-list for method input_type |
| 21, // [21:21] is the sub-list for extension type_name |
| 21, // [21:21] is the sub-list for extension extendee |
| 0, // [0:21] is the sub-list for field type_name |
| } |
| |
| func init() { file_google_cloud_ml_v1_job_service_proto_init() } |
| func file_google_cloud_ml_v1_job_service_proto_init() { |
| if File_google_cloud_ml_v1_job_service_proto != nil { |
| return |
| } |
| if !protoimpl.UnsafeEnabled { |
| file_google_cloud_ml_v1_job_service_proto_msgTypes[0].Exporter = func(v interface{}, i int) interface{} { |
| switch v := v.(*TrainingInput); i { |
| case 0: |
| return &v.state |
| case 1: |
| return &v.sizeCache |
| case 2: |
| return &v.unknownFields |
| default: |
| return nil |
| } |
| } |
| file_google_cloud_ml_v1_job_service_proto_msgTypes[1].Exporter = func(v interface{}, i int) interface{} { |
| switch v := v.(*HyperparameterSpec); i { |
| case 0: |
| return &v.state |
| case 1: |
| return &v.sizeCache |
| case 2: |
| return &v.unknownFields |
| default: |
| return nil |
| } |
| } |
| file_google_cloud_ml_v1_job_service_proto_msgTypes[2].Exporter = func(v interface{}, i int) interface{} { |
| switch v := v.(*ParameterSpec); i { |
| case 0: |
| return &v.state |
| case 1: |
| return &v.sizeCache |
| case 2: |
| return &v.unknownFields |
| default: |
| return nil |
| } |
| } |
| file_google_cloud_ml_v1_job_service_proto_msgTypes[3].Exporter = func(v interface{}, i int) interface{} { |
| switch v := v.(*HyperparameterOutput); i { |
| case 0: |
| return &v.state |
| case 1: |
| return &v.sizeCache |
| case 2: |
| return &v.unknownFields |
| default: |
| return nil |
| } |
| } |
| file_google_cloud_ml_v1_job_service_proto_msgTypes[4].Exporter = func(v interface{}, i int) interface{} { |
| switch v := v.(*TrainingOutput); i { |
| case 0: |
| return &v.state |
| case 1: |
| return &v.sizeCache |
| case 2: |
| return &v.unknownFields |
| default: |
| return nil |
| } |
| } |
| file_google_cloud_ml_v1_job_service_proto_msgTypes[5].Exporter = func(v interface{}, i int) interface{} { |
| switch v := v.(*PredictionInput); i { |
| case 0: |
| return &v.state |
| case 1: |
| return &v.sizeCache |
| case 2: |
| return &v.unknownFields |
| default: |
| return nil |
| } |
| } |
| file_google_cloud_ml_v1_job_service_proto_msgTypes[6].Exporter = func(v interface{}, i int) interface{} { |
| switch v := v.(*PredictionOutput); i { |
| case 0: |
| return &v.state |
| case 1: |
| return &v.sizeCache |
| case 2: |
| return &v.unknownFields |
| default: |
| return nil |
| } |
| } |
| file_google_cloud_ml_v1_job_service_proto_msgTypes[7].Exporter = func(v interface{}, i int) interface{} { |
| switch v := v.(*Job); i { |
| case 0: |
| return &v.state |
| case 1: |
| return &v.sizeCache |
| case 2: |
| return &v.unknownFields |
| default: |
| return nil |
| } |
| } |
| file_google_cloud_ml_v1_job_service_proto_msgTypes[8].Exporter = func(v interface{}, i int) interface{} { |
| switch v := v.(*CreateJobRequest); i { |
| case 0: |
| return &v.state |
| case 1: |
| return &v.sizeCache |
| case 2: |
| return &v.unknownFields |
| default: |
| return nil |
| } |
| } |
| file_google_cloud_ml_v1_job_service_proto_msgTypes[9].Exporter = func(v interface{}, i int) interface{} { |
| switch v := v.(*ListJobsRequest); i { |
| case 0: |
| return &v.state |
| case 1: |
| return &v.sizeCache |
| case 2: |
| return &v.unknownFields |
| default: |
| return nil |
| } |
| } |
| file_google_cloud_ml_v1_job_service_proto_msgTypes[10].Exporter = func(v interface{}, i int) interface{} { |
| switch v := v.(*ListJobsResponse); i { |
| case 0: |
| return &v.state |
| case 1: |
| return &v.sizeCache |
| case 2: |
| return &v.unknownFields |
| default: |
| return nil |
| } |
| } |
| file_google_cloud_ml_v1_job_service_proto_msgTypes[11].Exporter = func(v interface{}, i int) interface{} { |
| switch v := v.(*GetJobRequest); i { |
| case 0: |
| return &v.state |
| case 1: |
| return &v.sizeCache |
| case 2: |
| return &v.unknownFields |
| default: |
| return nil |
| } |
| } |
| file_google_cloud_ml_v1_job_service_proto_msgTypes[12].Exporter = func(v interface{}, i int) interface{} { |
| switch v := v.(*CancelJobRequest); i { |
| case 0: |
| return &v.state |
| case 1: |
| return &v.sizeCache |
| case 2: |
| return &v.unknownFields |
| default: |
| return nil |
| } |
| } |
| file_google_cloud_ml_v1_job_service_proto_msgTypes[13].Exporter = func(v interface{}, i int) interface{} { |
| switch v := v.(*HyperparameterOutput_HyperparameterMetric); i { |
| case 0: |
| return &v.state |
| case 1: |
| return &v.sizeCache |
| case 2: |
| return &v.unknownFields |
| default: |
| return nil |
| } |
| } |
| } |
| file_google_cloud_ml_v1_job_service_proto_msgTypes[5].OneofWrappers = []interface{}{ |
| (*PredictionInput_ModelName)(nil), |
| (*PredictionInput_VersionName)(nil), |
| (*PredictionInput_Uri)(nil), |
| } |
| file_google_cloud_ml_v1_job_service_proto_msgTypes[7].OneofWrappers = []interface{}{ |
| (*Job_TrainingInput)(nil), |
| (*Job_PredictionInput)(nil), |
| (*Job_TrainingOutput)(nil), |
| (*Job_PredictionOutput)(nil), |
| } |
| type x struct{} |
| out := protoimpl.TypeBuilder{ |
| File: protoimpl.DescBuilder{ |
| GoPackagePath: reflect.TypeOf(x{}).PkgPath(), |
| RawDescriptor: file_google_cloud_ml_v1_job_service_proto_rawDesc, |
| NumEnums: 6, |
| NumMessages: 15, |
| NumExtensions: 0, |
| NumServices: 1, |
| }, |
| GoTypes: file_google_cloud_ml_v1_job_service_proto_goTypes, |
| DependencyIndexes: file_google_cloud_ml_v1_job_service_proto_depIdxs, |
| EnumInfos: file_google_cloud_ml_v1_job_service_proto_enumTypes, |
| MessageInfos: file_google_cloud_ml_v1_job_service_proto_msgTypes, |
| }.Build() |
| File_google_cloud_ml_v1_job_service_proto = out.File |
| file_google_cloud_ml_v1_job_service_proto_rawDesc = nil |
| file_google_cloud_ml_v1_job_service_proto_goTypes = nil |
| file_google_cloud_ml_v1_job_service_proto_depIdxs = nil |
| } |
| |
| // Reference imports to suppress errors if they are not otherwise used. |
| var _ context.Context |
| var _ grpc.ClientConnInterface |
| |
| // This is a compile-time assertion to ensure that this generated file |
| // is compatible with the grpc package it is being compiled against. |
| const _ = grpc.SupportPackageIsVersion6 |
| |
| // JobServiceClient is the client API for JobService service. |
| // |
| // For semantics around ctx use and closing/ending streaming RPCs, please refer to https://godoc.org/google.golang.org/grpc#ClientConn.NewStream. |
| type JobServiceClient interface { |
| // Creates a training or a batch prediction job. |
| CreateJob(ctx context.Context, in *CreateJobRequest, opts ...grpc.CallOption) (*Job, error) |
| // Lists the jobs in the project. |
| ListJobs(ctx context.Context, in *ListJobsRequest, opts ...grpc.CallOption) (*ListJobsResponse, error) |
| // Describes a job. |
| GetJob(ctx context.Context, in *GetJobRequest, opts ...grpc.CallOption) (*Job, error) |
| // Cancels a running job. |
| CancelJob(ctx context.Context, in *CancelJobRequest, opts ...grpc.CallOption) (*empty.Empty, error) |
| } |
| |
| type jobServiceClient struct { |
| cc grpc.ClientConnInterface |
| } |
| |
| func NewJobServiceClient(cc grpc.ClientConnInterface) JobServiceClient { |
| return &jobServiceClient{cc} |
| } |
| |
| func (c *jobServiceClient) CreateJob(ctx context.Context, in *CreateJobRequest, opts ...grpc.CallOption) (*Job, error) { |
| out := new(Job) |
| err := c.cc.Invoke(ctx, "/google.cloud.ml.v1.JobService/CreateJob", in, out, opts...) |
| if err != nil { |
| return nil, err |
| } |
| return out, nil |
| } |
| |
| func (c *jobServiceClient) ListJobs(ctx context.Context, in *ListJobsRequest, opts ...grpc.CallOption) (*ListJobsResponse, error) { |
| out := new(ListJobsResponse) |
| err := c.cc.Invoke(ctx, "/google.cloud.ml.v1.JobService/ListJobs", in, out, opts...) |
| if err != nil { |
| return nil, err |
| } |
| return out, nil |
| } |
| |
| func (c *jobServiceClient) GetJob(ctx context.Context, in *GetJobRequest, opts ...grpc.CallOption) (*Job, error) { |
| out := new(Job) |
| err := c.cc.Invoke(ctx, "/google.cloud.ml.v1.JobService/GetJob", in, out, opts...) |
| if err != nil { |
| return nil, err |
| } |
| return out, nil |
| } |
| |
| func (c *jobServiceClient) CancelJob(ctx context.Context, in *CancelJobRequest, opts ...grpc.CallOption) (*empty.Empty, error) { |
| out := new(empty.Empty) |
| err := c.cc.Invoke(ctx, "/google.cloud.ml.v1.JobService/CancelJob", in, out, opts...) |
| if err != nil { |
| return nil, err |
| } |
| return out, nil |
| } |
| |
| // JobServiceServer is the server API for JobService service. |
| type JobServiceServer interface { |
| // Creates a training or a batch prediction job. |
| CreateJob(context.Context, *CreateJobRequest) (*Job, error) |
| // Lists the jobs in the project. |
| ListJobs(context.Context, *ListJobsRequest) (*ListJobsResponse, error) |
| // Describes a job. |
| GetJob(context.Context, *GetJobRequest) (*Job, error) |
| // Cancels a running job. |
| CancelJob(context.Context, *CancelJobRequest) (*empty.Empty, error) |
| } |
| |
| // UnimplementedJobServiceServer can be embedded to have forward compatible implementations. |
| type UnimplementedJobServiceServer struct { |
| } |
| |
| func (*UnimplementedJobServiceServer) CreateJob(context.Context, *CreateJobRequest) (*Job, error) { |
| return nil, status.Errorf(codes.Unimplemented, "method CreateJob not implemented") |
| } |
| func (*UnimplementedJobServiceServer) ListJobs(context.Context, *ListJobsRequest) (*ListJobsResponse, error) { |
| return nil, status.Errorf(codes.Unimplemented, "method ListJobs not implemented") |
| } |
| func (*UnimplementedJobServiceServer) GetJob(context.Context, *GetJobRequest) (*Job, error) { |
| return nil, status.Errorf(codes.Unimplemented, "method GetJob not implemented") |
| } |
| func (*UnimplementedJobServiceServer) CancelJob(context.Context, *CancelJobRequest) (*empty.Empty, error) { |
| return nil, status.Errorf(codes.Unimplemented, "method CancelJob not implemented") |
| } |
| |
| func RegisterJobServiceServer(s *grpc.Server, srv JobServiceServer) { |
| s.RegisterService(&_JobService_serviceDesc, srv) |
| } |
| |
| func _JobService_CreateJob_Handler(srv interface{}, ctx context.Context, dec func(interface{}) error, interceptor grpc.UnaryServerInterceptor) (interface{}, error) { |
| in := new(CreateJobRequest) |
| if err := dec(in); err != nil { |
| return nil, err |
| } |
| if interceptor == nil { |
| return srv.(JobServiceServer).CreateJob(ctx, in) |
| } |
| info := &grpc.UnaryServerInfo{ |
| Server: srv, |
| FullMethod: "/google.cloud.ml.v1.JobService/CreateJob", |
| } |
| handler := func(ctx context.Context, req interface{}) (interface{}, error) { |
| return srv.(JobServiceServer).CreateJob(ctx, req.(*CreateJobRequest)) |
| } |
| return interceptor(ctx, in, info, handler) |
| } |
| |
| func _JobService_ListJobs_Handler(srv interface{}, ctx context.Context, dec func(interface{}) error, interceptor grpc.UnaryServerInterceptor) (interface{}, error) { |
| in := new(ListJobsRequest) |
| if err := dec(in); err != nil { |
| return nil, err |
| } |
| if interceptor == nil { |
| return srv.(JobServiceServer).ListJobs(ctx, in) |
| } |
| info := &grpc.UnaryServerInfo{ |
| Server: srv, |
| FullMethod: "/google.cloud.ml.v1.JobService/ListJobs", |
| } |
| handler := func(ctx context.Context, req interface{}) (interface{}, error) { |
| return srv.(JobServiceServer).ListJobs(ctx, req.(*ListJobsRequest)) |
| } |
| return interceptor(ctx, in, info, handler) |
| } |
| |
| func _JobService_GetJob_Handler(srv interface{}, ctx context.Context, dec func(interface{}) error, interceptor grpc.UnaryServerInterceptor) (interface{}, error) { |
| in := new(GetJobRequest) |
| if err := dec(in); err != nil { |
| return nil, err |
| } |
| if interceptor == nil { |
| return srv.(JobServiceServer).GetJob(ctx, in) |
| } |
| info := &grpc.UnaryServerInfo{ |
| Server: srv, |
| FullMethod: "/google.cloud.ml.v1.JobService/GetJob", |
| } |
| handler := func(ctx context.Context, req interface{}) (interface{}, error) { |
| return srv.(JobServiceServer).GetJob(ctx, req.(*GetJobRequest)) |
| } |
| return interceptor(ctx, in, info, handler) |
| } |
| |
| func _JobService_CancelJob_Handler(srv interface{}, ctx context.Context, dec func(interface{}) error, interceptor grpc.UnaryServerInterceptor) (interface{}, error) { |
| in := new(CancelJobRequest) |
| if err := dec(in); err != nil { |
| return nil, err |
| } |
| if interceptor == nil { |
| return srv.(JobServiceServer).CancelJob(ctx, in) |
| } |
| info := &grpc.UnaryServerInfo{ |
| Server: srv, |
| FullMethod: "/google.cloud.ml.v1.JobService/CancelJob", |
| } |
| handler := func(ctx context.Context, req interface{}) (interface{}, error) { |
| return srv.(JobServiceServer).CancelJob(ctx, req.(*CancelJobRequest)) |
| } |
| return interceptor(ctx, in, info, handler) |
| } |
| |
| var _JobService_serviceDesc = grpc.ServiceDesc{ |
| ServiceName: "google.cloud.ml.v1.JobService", |
| HandlerType: (*JobServiceServer)(nil), |
| Methods: []grpc.MethodDesc{ |
| { |
| MethodName: "CreateJob", |
| Handler: _JobService_CreateJob_Handler, |
| }, |
| { |
| MethodName: "ListJobs", |
| Handler: _JobService_ListJobs_Handler, |
| }, |
| { |
| MethodName: "GetJob", |
| Handler: _JobService_GetJob_Handler, |
| }, |
| { |
| MethodName: "CancelJob", |
| Handler: _JobService_CancelJob_Handler, |
| }, |
| }, |
| Streams: []grpc.StreamDesc{}, |
| Metadata: "google/cloud/ml/v1/job_service.proto", |
| } |