1 // Code generated by protoc-gen-go. DO NOT EDIT.
2 // source: google/cloud/ml/v1beta1/job_service.proto
5 Package ml is a generated protocol buffer package.
7 It is generated from these files:
8 google/cloud/ml/v1beta1/job_service.proto
9 google/cloud/ml/v1beta1/model_service.proto
10 google/cloud/ml/v1beta1/operation_metadata.proto
11 google/cloud/ml/v1beta1/prediction_service.proto
12 google/cloud/ml/v1beta1/project_service.proto
14 It has these top-level messages:
41 SetDefaultVersionRequest
49 import proto "github.com/golang/protobuf/proto"
52 import _ "google.golang.org/genproto/googleapis/api/annotations"
53 import _ "google.golang.org/genproto/googleapis/api/serviceconfig"
54 import google_protobuf1 "github.com/golang/protobuf/ptypes/empty"
55 import google_protobuf2 "github.com/golang/protobuf/ptypes/timestamp"
58 context "golang.org/x/net/context"
59 grpc "google.golang.org/grpc"
62 // Reference imports to suppress errors if they are not otherwise used.
67 // This is a compile-time assertion to ensure that this generated file
68 // is compatible with the proto package it is being compiled against.
69 // A compilation error at this line likely means your copy of the
70 // proto package needs to be updated.
71 const _ = proto.ProtoPackageIsVersion2 // please upgrade the proto package
73 // A scale tier is an abstract representation of the resources Cloud ML
74 // will allocate to a training job. When selecting a scale tier for your
75 // training job, you should consider the size of your training dataset and
76 // the complexity of your model. As the tiers increase, virtual machines are
77 // added to handle your job, and the individual machines in the cluster
78 // generally have more memory and greater processing power than they do at
79 // lower tiers. The number of training units charged per hour of processing
80 // increases as tiers get more advanced. Refer to the
81 // [pricing guide](/ml/pricing) for more details. Note that in addition to
82 // incurring costs, your use of training resources is constrained by the
83 // [quota policy](/ml/quota).
84 type TrainingInput_ScaleTier int32
87 // A single worker instance. This tier is suitable for learning how to use
88 // Cloud ML, and for experimenting with new models using small datasets.
89 TrainingInput_BASIC TrainingInput_ScaleTier = 0
90 // Many workers and a few parameter servers.
91 TrainingInput_STANDARD_1 TrainingInput_ScaleTier = 1
92 // A large number of workers with many parameter servers.
93 TrainingInput_PREMIUM_1 TrainingInput_ScaleTier = 3
94 // A single worker instance [with a GPU](ml/docs/how-tos/using-gpus).
95 TrainingInput_BASIC_GPU TrainingInput_ScaleTier = 6
96 // The CUSTOM tier is not a set tier, but rather enables you to use your
97 // own cluster specification. When you use this tier, set values to
98 // configure your processing cluster according to these guidelines:
100 // * You _must_ set `TrainingInput.masterType` to specify the type
101 // of machine to use for your master node. This is the only required
104 // * You _may_ set `TrainingInput.workerCount` to specify the number of
105 // workers to use. If you specify one or more workers, you _must_ also
106 // set `TrainingInput.workerType` to specify the type of machine to use
107 // for your worker nodes.
109 // * You _may_ set `TrainingInput.parameterServerCount` to specify the
110 // number of parameter servers to use. If you specify one or more
111 // parameter servers, you _must_ also set
112 // `TrainingInput.parameterServerType` to specify the type of machine to
113 // use for your parameter servers.
115 // Note that all of your workers must use the same machine type, which can
116 // be different from your parameter server type and master type. Your
117 // parameter servers must likewise use the same machine type, which can be
118 // different from your worker type and master type.
119 TrainingInput_CUSTOM TrainingInput_ScaleTier = 5
122 var TrainingInput_ScaleTier_name = map[int32]string{
129 var TrainingInput_ScaleTier_value = map[string]int32{
137 func (x TrainingInput_ScaleTier) String() string {
138 return proto.EnumName(TrainingInput_ScaleTier_name, int32(x))
140 func (TrainingInput_ScaleTier) EnumDescriptor() ([]byte, []int) { return fileDescriptor0, []int{0, 0} }
142 // The available types of optimization goals.
143 type HyperparameterSpec_GoalType int32
146 // Goal Type will default to maximize.
147 HyperparameterSpec_GOAL_TYPE_UNSPECIFIED HyperparameterSpec_GoalType = 0
148 // Maximize the goal metric.
149 HyperparameterSpec_MAXIMIZE HyperparameterSpec_GoalType = 1
150 // Minimize the goal metric.
151 HyperparameterSpec_MINIMIZE HyperparameterSpec_GoalType = 2
154 var HyperparameterSpec_GoalType_name = map[int32]string{
155 0: "GOAL_TYPE_UNSPECIFIED",
159 var HyperparameterSpec_GoalType_value = map[string]int32{
160 "GOAL_TYPE_UNSPECIFIED": 0,
165 func (x HyperparameterSpec_GoalType) String() string {
166 return proto.EnumName(HyperparameterSpec_GoalType_name, int32(x))
168 func (HyperparameterSpec_GoalType) EnumDescriptor() ([]byte, []int) {
169 return fileDescriptor0, []int{1, 0}
172 // The type of the parameter.
173 type ParameterSpec_ParameterType int32
176 // You must specify a valid type. Using this unspecified type will result in
178 ParameterSpec_PARAMETER_TYPE_UNSPECIFIED ParameterSpec_ParameterType = 0
179 // Type for real-valued parameters.
180 ParameterSpec_DOUBLE ParameterSpec_ParameterType = 1
181 // Type for integral parameters.
182 ParameterSpec_INTEGER ParameterSpec_ParameterType = 2
183 // The parameter is categorical, with a value chosen from the categories
185 ParameterSpec_CATEGORICAL ParameterSpec_ParameterType = 3
186 // The parameter is real valued, with a fixed set of feasible points. If
187 // `type==DISCRETE`, feasible_points must be provided, and
188 // {`min_value`, `max_value`} will be ignored.
189 ParameterSpec_DISCRETE ParameterSpec_ParameterType = 4
192 var ParameterSpec_ParameterType_name = map[int32]string{
193 0: "PARAMETER_TYPE_UNSPECIFIED",
199 var ParameterSpec_ParameterType_value = map[string]int32{
200 "PARAMETER_TYPE_UNSPECIFIED": 0,
207 func (x ParameterSpec_ParameterType) String() string {
208 return proto.EnumName(ParameterSpec_ParameterType_name, int32(x))
210 func (ParameterSpec_ParameterType) EnumDescriptor() ([]byte, []int) {
211 return fileDescriptor0, []int{2, 0}
214 // The type of scaling that should be applied to this parameter.
215 type ParameterSpec_ScaleType int32
218 // By default, no scaling is applied.
219 ParameterSpec_NONE ParameterSpec_ScaleType = 0
220 // Scales the feasible space to (0, 1) linearly.
221 ParameterSpec_UNIT_LINEAR_SCALE ParameterSpec_ScaleType = 1
222 // Scales the feasible space logarithmically to (0, 1). The entire feasible
223 // space must be strictly positive.
224 ParameterSpec_UNIT_LOG_SCALE ParameterSpec_ScaleType = 2
225 // Scales the feasible space "reverse" logarithmically to (0, 1). The result
226 // is that values close to the top of the feasible space are spread out more
227 // than points near the bottom. The entire feasible space must be strictly
229 ParameterSpec_UNIT_REVERSE_LOG_SCALE ParameterSpec_ScaleType = 3
232 var ParameterSpec_ScaleType_name = map[int32]string{
234 1: "UNIT_LINEAR_SCALE",
236 3: "UNIT_REVERSE_LOG_SCALE",
238 var ParameterSpec_ScaleType_value = map[string]int32{
240 "UNIT_LINEAR_SCALE": 1,
242 "UNIT_REVERSE_LOG_SCALE": 3,
245 func (x ParameterSpec_ScaleType) String() string {
246 return proto.EnumName(ParameterSpec_ScaleType_name, int32(x))
248 func (ParameterSpec_ScaleType) EnumDescriptor() ([]byte, []int) { return fileDescriptor0, []int{2, 1} }
250 // The format used to separate data instances in the source files.
251 type PredictionInput_DataFormat int32
254 // Unspecified format.
255 PredictionInput_DATA_FORMAT_UNSPECIFIED PredictionInput_DataFormat = 0
256 // The source file is a text file with instances separated by the
257 // new-line character.
258 PredictionInput_TEXT PredictionInput_DataFormat = 1
259 // The source file is a TFRecord file.
260 PredictionInput_TF_RECORD PredictionInput_DataFormat = 2
261 // The source file is a GZIP-compressed TFRecord file.
262 PredictionInput_TF_RECORD_GZIP PredictionInput_DataFormat = 3
265 var PredictionInput_DataFormat_name = map[int32]string{
266 0: "DATA_FORMAT_UNSPECIFIED",
271 var PredictionInput_DataFormat_value = map[string]int32{
272 "DATA_FORMAT_UNSPECIFIED": 0,
278 func (x PredictionInput_DataFormat) String() string {
279 return proto.EnumName(PredictionInput_DataFormat_name, int32(x))
281 func (PredictionInput_DataFormat) EnumDescriptor() ([]byte, []int) {
282 return fileDescriptor0, []int{5, 0}
285 // Describes the job state.
289 // The job state is unspecified.
290 Job_STATE_UNSPECIFIED Job_State = 0
291 // The job has been just created and processing has not yet begun.
292 Job_QUEUED Job_State = 1
293 // The service is preparing to run the job.
294 Job_PREPARING Job_State = 2
295 // The job is in progress.
296 Job_RUNNING Job_State = 3
297 // The job completed successfully.
298 Job_SUCCEEDED Job_State = 4
300 // `error_message` should contain the details of the failure.
301 Job_FAILED Job_State = 5
302 // The job is being cancelled.
303 // `error_message` should describe the reason for the cancellation.
304 Job_CANCELLING Job_State = 6
305 // The job has been cancelled.
306 // `error_message` should describe the reason for the cancellation.
307 Job_CANCELLED Job_State = 7
310 var Job_State_name = map[int32]string{
311 0: "STATE_UNSPECIFIED",
320 var Job_State_value = map[string]int32{
321 "STATE_UNSPECIFIED": 0,
331 func (x Job_State) String() string {
332 return proto.EnumName(Job_State_name, int32(x))
334 func (Job_State) EnumDescriptor() ([]byte, []int) { return fileDescriptor0, []int{7, 0} }
336 // Represents input parameters for a training job.
337 type TrainingInput struct {
338 // Required. Specifies the machine types, the number of replicas for workers
339 // and parameter servers.
340 ScaleTier TrainingInput_ScaleTier `protobuf:"varint,1,opt,name=scale_tier,json=scaleTier,enum=google.cloud.ml.v1beta1.TrainingInput_ScaleTier" json:"scale_tier,omitempty"`
341 // Optional. Specifies the type of virtual machine to use for your training
342 // job's master worker.
344 // The following types are supported:
349 // A basic machine configuration suitable for training simple models with
350 // small to moderate datasets.
352 // <dt>large_model</dt>
354 // A machine with a lot of memory, specially suited for parameter servers
355 // when your model is large (having many hidden layers or layers with very
356 // large numbers of nodes).
358 // <dt>complex_model_s</dt>
360 // A machine suitable for the master and workers of the cluster when your
361 // model requires more computation than the standard machine can handle
364 // <dt>complex_model_m</dt>
366 // A machine with roughly twice the number of cores and roughly double the
367 // memory of <code suppresswarning="true">complex_model_s</code>.
369 // <dt>complex_model_l</dt>
371 // A machine with roughly twice the number of cores and roughly double the
372 // memory of <code suppresswarning="true">complex_model_m</code>.
374 // <dt>standard_gpu</dt>
376 // A machine equivalent to <code suppresswarning="true">standard</code> that
378 // <a href="ml/docs/how-tos/using-gpus">
379 // GPU that you can use in your trainer</a>.
381 // <dt>complex_model_m_gpu</dt>
383 // A machine equivalent to
384 // <code suppresswarning="true">coplex_model_m</code> that also includes
389 // You must set this value when `scaleTier` is set to `CUSTOM`.
390 MasterType string `protobuf:"bytes,2,opt,name=master_type,json=masterType" json:"master_type,omitempty"`
391 // Optional. Specifies the type of virtual machine to use for your training
392 // job's worker nodes.
394 // The supported values are the same as those described in the entry for
397 // This value must be present when `scaleTier` is set to `CUSTOM` and
398 // `workerCount` is greater than zero.
399 WorkerType string `protobuf:"bytes,3,opt,name=worker_type,json=workerType" json:"worker_type,omitempty"`
400 // Optional. Specifies the type of virtual machine to use for your training
401 // job's parameter server.
403 // The supported values are the same as those described in the entry for
406 // This value must be present when `scaleTier` is set to `CUSTOM` and
407 // `parameter_server_count` is greater than zero.
408 ParameterServerType string `protobuf:"bytes,4,opt,name=parameter_server_type,json=parameterServerType" json:"parameter_server_type,omitempty"`
409 // Optional. The number of worker replicas to use for the training job. Each
410 // replica in the cluster will be of the type specified in `worker_type`.
412 // This value can only be used when `scale_tier` is set to `CUSTOM`. If you
413 // set this value, you must also set `worker_type`.
414 WorkerCount int64 `protobuf:"varint,5,opt,name=worker_count,json=workerCount" json:"worker_count,omitempty"`
415 // Optional. The number of parameter server replicas to use for the training
416 // job. Each replica in the cluster will be of the type specified in
417 // `parameter_server_type`.
419 // This value can only be used when `scale_tier` is set to `CUSTOM`.If you
420 // set this value, you must also set `parameter_server_type`.
421 ParameterServerCount int64 `protobuf:"varint,6,opt,name=parameter_server_count,json=parameterServerCount" json:"parameter_server_count,omitempty"`
422 // Required. The Google Cloud Storage location of the packages with
423 // the training program and any additional dependencies.
424 PackageUris []string `protobuf:"bytes,7,rep,name=package_uris,json=packageUris" json:"package_uris,omitempty"`
425 // Required. The Python module name to run after installing the packages.
426 PythonModule string `protobuf:"bytes,8,opt,name=python_module,json=pythonModule" json:"python_module,omitempty"`
427 // Optional. Command line arguments to pass to the program.
428 Args []string `protobuf:"bytes,10,rep,name=args" json:"args,omitempty"`
429 // Optional. The set of Hyperparameters to tune.
430 Hyperparameters *HyperparameterSpec `protobuf:"bytes,12,opt,name=hyperparameters" json:"hyperparameters,omitempty"`
431 // Required. The Google Compute Engine region to run the training job in.
432 Region string `protobuf:"bytes,14,opt,name=region" json:"region,omitempty"`
433 // Optional. A Google Cloud Storage path in which to store training outputs
434 // and other data needed for training. This path is passed to your TensorFlow
435 // program as the 'job_dir' command-line argument. The benefit of specifying
436 // this field is that Cloud ML validates the path for use in training.
437 JobDir string `protobuf:"bytes,16,opt,name=job_dir,json=jobDir" json:"job_dir,omitempty"`
438 // Optional. The Google Cloud ML runtime version to use for training. If not
439 // set, Google Cloud ML will choose the latest stable version.
440 RuntimeVersion string `protobuf:"bytes,15,opt,name=runtime_version,json=runtimeVersion" json:"runtime_version,omitempty"`
443 func (m *TrainingInput) Reset() { *m = TrainingInput{} }
444 func (m *TrainingInput) String() string { return proto.CompactTextString(m) }
445 func (*TrainingInput) ProtoMessage() {}
446 func (*TrainingInput) Descriptor() ([]byte, []int) { return fileDescriptor0, []int{0} }
448 func (m *TrainingInput) GetScaleTier() TrainingInput_ScaleTier {
452 return TrainingInput_BASIC
455 func (m *TrainingInput) GetMasterType() string {
462 func (m *TrainingInput) GetWorkerType() string {
469 func (m *TrainingInput) GetParameterServerType() string {
471 return m.ParameterServerType
476 func (m *TrainingInput) GetWorkerCount() int64 {
483 func (m *TrainingInput) GetParameterServerCount() int64 {
485 return m.ParameterServerCount
490 func (m *TrainingInput) GetPackageUris() []string {
497 func (m *TrainingInput) GetPythonModule() string {
499 return m.PythonModule
504 func (m *TrainingInput) GetArgs() []string {
511 func (m *TrainingInput) GetHyperparameters() *HyperparameterSpec {
513 return m.Hyperparameters
518 func (m *TrainingInput) GetRegion() string {
525 func (m *TrainingInput) GetJobDir() string {
532 func (m *TrainingInput) GetRuntimeVersion() string {
534 return m.RuntimeVersion
539 // Represents a set of hyperparameters to optimize.
540 type HyperparameterSpec struct {
541 // Required. The type of goal to use for tuning. Available types are
542 // `MAXIMIZE` and `MINIMIZE`.
544 // Defaults to `MAXIMIZE`.
545 Goal HyperparameterSpec_GoalType `protobuf:"varint,1,opt,name=goal,enum=google.cloud.ml.v1beta1.HyperparameterSpec_GoalType" json:"goal,omitempty"`
546 // Required. The set of parameters to tune.
547 Params []*ParameterSpec `protobuf:"bytes,2,rep,name=params" json:"params,omitempty"`
548 // Optional. How many training trials should be attempted to optimize
549 // the specified hyperparameters.
552 MaxTrials int32 `protobuf:"varint,3,opt,name=max_trials,json=maxTrials" json:"max_trials,omitempty"`
553 // Optional. The number of training trials to run concurrently.
554 // You can reduce the time it takes to perform hyperparameter tuning by adding
555 // trials in parallel. However, each trail only benefits from the information
556 // gained in completed trials. That means that a trial does not get access to
557 // the results of trials running at the same time, which could reduce the
558 // quality of the overall optimization.
560 // Each trial will use the same scale tier and machine types.
563 MaxParallelTrials int32 `protobuf:"varint,4,opt,name=max_parallel_trials,json=maxParallelTrials" json:"max_parallel_trials,omitempty"`
564 // Optional. The Tensorflow summary tag name to use for optimizing trials. For
565 // current versions of Tensorflow, this tag name should exactly match what is
566 // shown in Tensorboard, including all scopes. For versions of Tensorflow
567 // prior to 0.12, this should be only the tag passed to tf.Summary.
568 // By default, "training/hptuning/metric" will be used.
569 HyperparameterMetricTag string `protobuf:"bytes,5,opt,name=hyperparameter_metric_tag,json=hyperparameterMetricTag" json:"hyperparameter_metric_tag,omitempty"`
572 func (m *HyperparameterSpec) Reset() { *m = HyperparameterSpec{} }
573 func (m *HyperparameterSpec) String() string { return proto.CompactTextString(m) }
574 func (*HyperparameterSpec) ProtoMessage() {}
575 func (*HyperparameterSpec) Descriptor() ([]byte, []int) { return fileDescriptor0, []int{1} }
577 func (m *HyperparameterSpec) GetGoal() HyperparameterSpec_GoalType {
581 return HyperparameterSpec_GOAL_TYPE_UNSPECIFIED
584 func (m *HyperparameterSpec) GetParams() []*ParameterSpec {
591 func (m *HyperparameterSpec) GetMaxTrials() int32 {
598 func (m *HyperparameterSpec) GetMaxParallelTrials() int32 {
600 return m.MaxParallelTrials
605 func (m *HyperparameterSpec) GetHyperparameterMetricTag() string {
607 return m.HyperparameterMetricTag
612 // Represents a single hyperparameter to optimize.
613 type ParameterSpec struct {
614 // Required. The parameter name must be unique amongst all ParameterConfigs in
615 // a HyperparameterSpec message. E.g., "learning_rate".
616 ParameterName string `protobuf:"bytes,1,opt,name=parameter_name,json=parameterName" json:"parameter_name,omitempty"`
617 // Required. The type of the parameter.
618 Type ParameterSpec_ParameterType `protobuf:"varint,4,opt,name=type,enum=google.cloud.ml.v1beta1.ParameterSpec_ParameterType" json:"type,omitempty"`
619 // Required if type is `DOUBLE` or `INTEGER`. This field
620 // should be unset if type is `CATEGORICAL`. This value should be integers if
622 MinValue float64 `protobuf:"fixed64,2,opt,name=min_value,json=minValue" json:"min_value,omitempty"`
623 // Required if typeis `DOUBLE` or `INTEGER`. This field
624 // should be unset if type is `CATEGORICAL`. This value should be integers if
625 // type is `INTEGER`.
626 MaxValue float64 `protobuf:"fixed64,3,opt,name=max_value,json=maxValue" json:"max_value,omitempty"`
627 // Required if type is `CATEGORICAL`. The list of possible categories.
628 CategoricalValues []string `protobuf:"bytes,5,rep,name=categorical_values,json=categoricalValues" json:"categorical_values,omitempty"`
629 // Required if type is `DISCRETE`.
630 // A list of feasible points.
631 // The list should be in strictly increasing order. For instance, this
632 // parameter might have possible settings of 1.5, 2.5, and 4.0. This list
633 // should not contain more than 1,000 values.
634 DiscreteValues []float64 `protobuf:"fixed64,6,rep,packed,name=discrete_values,json=discreteValues" json:"discrete_values,omitempty"`
635 // Optional. How the parameter should be scaled to the hypercube.
636 // Leave unset for categorical parameters.
637 // Some kind of scaling is strongly recommended for real or integral
638 // parameters (e.g., `UNIT_LINEAR_SCALE`).
639 ScaleType ParameterSpec_ScaleType `protobuf:"varint,7,opt,name=scale_type,json=scaleType,enum=google.cloud.ml.v1beta1.ParameterSpec_ScaleType" json:"scale_type,omitempty"`
642 func (m *ParameterSpec) Reset() { *m = ParameterSpec{} }
643 func (m *ParameterSpec) String() string { return proto.CompactTextString(m) }
644 func (*ParameterSpec) ProtoMessage() {}
645 func (*ParameterSpec) Descriptor() ([]byte, []int) { return fileDescriptor0, []int{2} }
647 func (m *ParameterSpec) GetParameterName() string {
649 return m.ParameterName
654 func (m *ParameterSpec) GetType() ParameterSpec_ParameterType {
658 return ParameterSpec_PARAMETER_TYPE_UNSPECIFIED
661 func (m *ParameterSpec) GetMinValue() float64 {
668 func (m *ParameterSpec) GetMaxValue() float64 {
675 func (m *ParameterSpec) GetCategoricalValues() []string {
677 return m.CategoricalValues
682 func (m *ParameterSpec) GetDiscreteValues() []float64 {
684 return m.DiscreteValues
689 func (m *ParameterSpec) GetScaleType() ParameterSpec_ScaleType {
693 return ParameterSpec_NONE
696 // Represents the result of a single hyperparameter tuning trial from a
697 // training job. The TrainingOutput object that is returned on successful
698 // completion of a training job with hyperparameter tuning includes a list
699 // of HyperparameterOutput objects, one for each successful trial.
700 type HyperparameterOutput struct {
701 // The trial id for these results.
702 TrialId string `protobuf:"bytes,1,opt,name=trial_id,json=trialId" json:"trial_id,omitempty"`
703 // The hyperparameters given to this trial.
704 Hyperparameters map[string]string `protobuf:"bytes,2,rep,name=hyperparameters" json:"hyperparameters,omitempty" protobuf_key:"bytes,1,opt,name=key" protobuf_val:"bytes,2,opt,name=value"`
705 // The final objective metric seen for this trial.
706 FinalMetric *HyperparameterOutput_HyperparameterMetric `protobuf:"bytes,3,opt,name=final_metric,json=finalMetric" json:"final_metric,omitempty"`
707 // All recorded object metrics for this trial.
708 AllMetrics []*HyperparameterOutput_HyperparameterMetric `protobuf:"bytes,4,rep,name=all_metrics,json=allMetrics" json:"all_metrics,omitempty"`
711 func (m *HyperparameterOutput) Reset() { *m = HyperparameterOutput{} }
712 func (m *HyperparameterOutput) String() string { return proto.CompactTextString(m) }
713 func (*HyperparameterOutput) ProtoMessage() {}
714 func (*HyperparameterOutput) Descriptor() ([]byte, []int) { return fileDescriptor0, []int{3} }
716 func (m *HyperparameterOutput) GetTrialId() string {
723 func (m *HyperparameterOutput) GetHyperparameters() map[string]string {
725 return m.Hyperparameters
730 func (m *HyperparameterOutput) GetFinalMetric() *HyperparameterOutput_HyperparameterMetric {
737 func (m *HyperparameterOutput) GetAllMetrics() []*HyperparameterOutput_HyperparameterMetric {
744 // An observed value of a metric.
745 type HyperparameterOutput_HyperparameterMetric struct {
746 // The global training step for this metric.
747 TrainingStep int64 `protobuf:"varint,1,opt,name=training_step,json=trainingStep" json:"training_step,omitempty"`
748 // The objective value at this training step.
749 ObjectiveValue float64 `protobuf:"fixed64,2,opt,name=objective_value,json=objectiveValue" json:"objective_value,omitempty"`
752 func (m *HyperparameterOutput_HyperparameterMetric) Reset() {
753 *m = HyperparameterOutput_HyperparameterMetric{}
755 func (m *HyperparameterOutput_HyperparameterMetric) String() string { return proto.CompactTextString(m) }
756 func (*HyperparameterOutput_HyperparameterMetric) ProtoMessage() {}
757 func (*HyperparameterOutput_HyperparameterMetric) Descriptor() ([]byte, []int) {
758 return fileDescriptor0, []int{3, 0}
761 func (m *HyperparameterOutput_HyperparameterMetric) GetTrainingStep() int64 {
763 return m.TrainingStep
768 func (m *HyperparameterOutput_HyperparameterMetric) GetObjectiveValue() float64 {
770 return m.ObjectiveValue
775 // Represents results of a training job. Output only.
776 type TrainingOutput struct {
777 // The number of hyperparameter tuning trials that completed successfully.
778 // Only set for hyperparameter tuning jobs.
779 CompletedTrialCount int64 `protobuf:"varint,1,opt,name=completed_trial_count,json=completedTrialCount" json:"completed_trial_count,omitempty"`
780 // Results for individual Hyperparameter trials.
781 // Only set for hyperparameter tuning jobs.
782 Trials []*HyperparameterOutput `protobuf:"bytes,2,rep,name=trials" json:"trials,omitempty"`
783 // The amount of ML units consumed by the job.
784 ConsumedMlUnits float64 `protobuf:"fixed64,3,opt,name=consumed_ml_units,json=consumedMlUnits" json:"consumed_ml_units,omitempty"`
785 // Whether this job is a hyperparameter tuning job.
786 IsHyperparameterTuningJob bool `protobuf:"varint,4,opt,name=is_hyperparameter_tuning_job,json=isHyperparameterTuningJob" json:"is_hyperparameter_tuning_job,omitempty"`
789 func (m *TrainingOutput) Reset() { *m = TrainingOutput{} }
790 func (m *TrainingOutput) String() string { return proto.CompactTextString(m) }
791 func (*TrainingOutput) ProtoMessage() {}
792 func (*TrainingOutput) Descriptor() ([]byte, []int) { return fileDescriptor0, []int{4} }
794 func (m *TrainingOutput) GetCompletedTrialCount() int64 {
796 return m.CompletedTrialCount
801 func (m *TrainingOutput) GetTrials() []*HyperparameterOutput {
808 func (m *TrainingOutput) GetConsumedMlUnits() float64 {
810 return m.ConsumedMlUnits
815 func (m *TrainingOutput) GetIsHyperparameterTuningJob() bool {
817 return m.IsHyperparameterTuningJob
822 // Represents input parameters for a prediction job.
823 type PredictionInput struct {
824 // Required. The model or the version to use for prediction.
826 // Types that are valid to be assigned to ModelVersion:
827 // *PredictionInput_ModelName
828 // *PredictionInput_VersionName
829 // *PredictionInput_Uri
830 ModelVersion isPredictionInput_ModelVersion `protobuf_oneof:"model_version"`
831 // Required. The format of the input data files.
832 DataFormat PredictionInput_DataFormat `protobuf:"varint,3,opt,name=data_format,json=dataFormat,enum=google.cloud.ml.v1beta1.PredictionInput_DataFormat" json:"data_format,omitempty"`
833 // Required. The Google Cloud Storage location of the input data files.
834 // May contain wildcards.
835 InputPaths []string `protobuf:"bytes,4,rep,name=input_paths,json=inputPaths" json:"input_paths,omitempty"`
836 // Required. The output Google Cloud Storage location.
837 OutputPath string `protobuf:"bytes,5,opt,name=output_path,json=outputPath" json:"output_path,omitempty"`
838 // Optional. The maximum number of workers to be used for parallel processing.
839 // Defaults to 10 if not specified.
840 MaxWorkerCount int64 `protobuf:"varint,6,opt,name=max_worker_count,json=maxWorkerCount" json:"max_worker_count,omitempty"`
841 // Required. The Google Compute Engine region to run the prediction job in.
842 Region string `protobuf:"bytes,7,opt,name=region" json:"region,omitempty"`
843 // Optional. The Google Cloud ML runtime version to use for this batch
844 // prediction. If not set, Google Cloud ML will pick the runtime version used
845 // during the CreateVersion request for this model version, or choose the
846 // latest stable version when model version information is not available
847 // such as when the model is specified by uri.
848 RuntimeVersion string `protobuf:"bytes,8,opt,name=runtime_version,json=runtimeVersion" json:"runtime_version,omitempty"`
851 func (m *PredictionInput) Reset() { *m = PredictionInput{} }
852 func (m *PredictionInput) String() string { return proto.CompactTextString(m) }
853 func (*PredictionInput) ProtoMessage() {}
854 func (*PredictionInput) Descriptor() ([]byte, []int) { return fileDescriptor0, []int{5} }
856 type isPredictionInput_ModelVersion interface {
857 isPredictionInput_ModelVersion()
860 type PredictionInput_ModelName struct {
861 ModelName string `protobuf:"bytes,1,opt,name=model_name,json=modelName,oneof"`
863 type PredictionInput_VersionName struct {
864 VersionName string `protobuf:"bytes,2,opt,name=version_name,json=versionName,oneof"`
866 type PredictionInput_Uri struct {
867 Uri string `protobuf:"bytes,9,opt,name=uri,oneof"`
870 func (*PredictionInput_ModelName) isPredictionInput_ModelVersion() {}
871 func (*PredictionInput_VersionName) isPredictionInput_ModelVersion() {}
872 func (*PredictionInput_Uri) isPredictionInput_ModelVersion() {}
874 func (m *PredictionInput) GetModelVersion() isPredictionInput_ModelVersion {
876 return m.ModelVersion
881 func (m *PredictionInput) GetModelName() string {
882 if x, ok := m.GetModelVersion().(*PredictionInput_ModelName); ok {
888 func (m *PredictionInput) GetVersionName() string {
889 if x, ok := m.GetModelVersion().(*PredictionInput_VersionName); ok {
895 func (m *PredictionInput) GetUri() string {
896 if x, ok := m.GetModelVersion().(*PredictionInput_Uri); ok {
902 func (m *PredictionInput) GetDataFormat() PredictionInput_DataFormat {
906 return PredictionInput_DATA_FORMAT_UNSPECIFIED
909 func (m *PredictionInput) GetInputPaths() []string {
916 func (m *PredictionInput) GetOutputPath() string {
923 func (m *PredictionInput) GetMaxWorkerCount() int64 {
925 return m.MaxWorkerCount
930 func (m *PredictionInput) GetRegion() string {
937 func (m *PredictionInput) GetRuntimeVersion() string {
939 return m.RuntimeVersion
944 // XXX_OneofFuncs is for the internal use of the proto package.
945 func (*PredictionInput) XXX_OneofFuncs() (func(msg proto.Message, b *proto.Buffer) error, func(msg proto.Message, tag, wire int, b *proto.Buffer) (bool, error), func(msg proto.Message) (n int), []interface{}) {
946 return _PredictionInput_OneofMarshaler, _PredictionInput_OneofUnmarshaler, _PredictionInput_OneofSizer, []interface{}{
947 (*PredictionInput_ModelName)(nil),
948 (*PredictionInput_VersionName)(nil),
949 (*PredictionInput_Uri)(nil),
953 func _PredictionInput_OneofMarshaler(msg proto.Message, b *proto.Buffer) error {
954 m := msg.(*PredictionInput)
956 switch x := m.ModelVersion.(type) {
957 case *PredictionInput_ModelName:
958 b.EncodeVarint(1<<3 | proto.WireBytes)
959 b.EncodeStringBytes(x.ModelName)
960 case *PredictionInput_VersionName:
961 b.EncodeVarint(2<<3 | proto.WireBytes)
962 b.EncodeStringBytes(x.VersionName)
963 case *PredictionInput_Uri:
964 b.EncodeVarint(9<<3 | proto.WireBytes)
965 b.EncodeStringBytes(x.Uri)
968 return fmt.Errorf("PredictionInput.ModelVersion has unexpected type %T", x)
973 func _PredictionInput_OneofUnmarshaler(msg proto.Message, tag, wire int, b *proto.Buffer) (bool, error) {
974 m := msg.(*PredictionInput)
976 case 1: // model_version.model_name
977 if wire != proto.WireBytes {
978 return true, proto.ErrInternalBadWireType
980 x, err := b.DecodeStringBytes()
981 m.ModelVersion = &PredictionInput_ModelName{x}
983 case 2: // model_version.version_name
984 if wire != proto.WireBytes {
985 return true, proto.ErrInternalBadWireType
987 x, err := b.DecodeStringBytes()
988 m.ModelVersion = &PredictionInput_VersionName{x}
990 case 9: // model_version.uri
991 if wire != proto.WireBytes {
992 return true, proto.ErrInternalBadWireType
994 x, err := b.DecodeStringBytes()
995 m.ModelVersion = &PredictionInput_Uri{x}
1002 func _PredictionInput_OneofSizer(msg proto.Message) (n int) {
1003 m := msg.(*PredictionInput)
1005 switch x := m.ModelVersion.(type) {
1006 case *PredictionInput_ModelName:
1007 n += proto.SizeVarint(1<<3 | proto.WireBytes)
1008 n += proto.SizeVarint(uint64(len(x.ModelName)))
1009 n += len(x.ModelName)
1010 case *PredictionInput_VersionName:
1011 n += proto.SizeVarint(2<<3 | proto.WireBytes)
1012 n += proto.SizeVarint(uint64(len(x.VersionName)))
1013 n += len(x.VersionName)
1014 case *PredictionInput_Uri:
1015 n += proto.SizeVarint(9<<3 | proto.WireBytes)
1016 n += proto.SizeVarint(uint64(len(x.Uri)))
1020 panic(fmt.Sprintf("proto: unexpected type %T in oneof", x))
1025 // Represents results of a prediction job.
1026 type PredictionOutput struct {
1027 // The output Google Cloud Storage location provided at the job creation time.
1028 OutputPath string `protobuf:"bytes,1,opt,name=output_path,json=outputPath" json:"output_path,omitempty"`
1029 // The number of generated predictions.
1030 PredictionCount int64 `protobuf:"varint,2,opt,name=prediction_count,json=predictionCount" json:"prediction_count,omitempty"`
1031 // The number of data instances which resulted in errors.
1032 ErrorCount int64 `protobuf:"varint,3,opt,name=error_count,json=errorCount" json:"error_count,omitempty"`
1033 // Node hours used by the batch prediction job.
1034 NodeHours float64 `protobuf:"fixed64,4,opt,name=node_hours,json=nodeHours" json:"node_hours,omitempty"`
1037 func (m *PredictionOutput) Reset() { *m = PredictionOutput{} }
1038 func (m *PredictionOutput) String() string { return proto.CompactTextString(m) }
1039 func (*PredictionOutput) ProtoMessage() {}
1040 func (*PredictionOutput) Descriptor() ([]byte, []int) { return fileDescriptor0, []int{6} }
1042 func (m *PredictionOutput) GetOutputPath() string {
1049 func (m *PredictionOutput) GetPredictionCount() int64 {
1051 return m.PredictionCount
1056 func (m *PredictionOutput) GetErrorCount() int64 {
1063 func (m *PredictionOutput) GetNodeHours() float64 {
1070 // Represents a training or prediction job.
1072 // Required. The user-specified id of the job.
1073 JobId string `protobuf:"bytes,1,opt,name=job_id,json=jobId" json:"job_id,omitempty"`
1074 // Required. Parameters to create a job.
1076 // Types that are valid to be assigned to Input:
1077 // *Job_TrainingInput
1078 // *Job_PredictionInput
1079 Input isJob_Input `protobuf_oneof:"input"`
1080 // Output only. When the job was created.
1081 CreateTime *google_protobuf2.Timestamp `protobuf:"bytes,4,opt,name=create_time,json=createTime" json:"create_time,omitempty"`
1082 // Output only. When the job processing was started.
1083 StartTime *google_protobuf2.Timestamp `protobuf:"bytes,5,opt,name=start_time,json=startTime" json:"start_time,omitempty"`
1084 // Output only. When the job processing was completed.
1085 EndTime *google_protobuf2.Timestamp `protobuf:"bytes,6,opt,name=end_time,json=endTime" json:"end_time,omitempty"`
1086 // Output only. The detailed state of a job.
1087 State Job_State `protobuf:"varint,7,opt,name=state,enum=google.cloud.ml.v1beta1.Job_State" json:"state,omitempty"`
1088 // Output only. The details of a failure or a cancellation.
1089 ErrorMessage string `protobuf:"bytes,8,opt,name=error_message,json=errorMessage" json:"error_message,omitempty"`
1090 // Output only. The current result of the job.
1092 // Types that are valid to be assigned to Output:
1093 // *Job_TrainingOutput
1094 // *Job_PredictionOutput
1095 Output isJob_Output `protobuf_oneof:"output"`
1098 func (m *Job) Reset() { *m = Job{} }
1099 func (m *Job) String() string { return proto.CompactTextString(m) }
1100 func (*Job) ProtoMessage() {}
1101 func (*Job) Descriptor() ([]byte, []int) { return fileDescriptor0, []int{7} }
1103 type isJob_Input interface {
1106 type isJob_Output interface {
1110 type Job_TrainingInput struct {
1111 TrainingInput *TrainingInput `protobuf:"bytes,2,opt,name=training_input,json=trainingInput,oneof"`
1113 type Job_PredictionInput struct {
1114 PredictionInput *PredictionInput `protobuf:"bytes,3,opt,name=prediction_input,json=predictionInput,oneof"`
1116 type Job_TrainingOutput struct {
1117 TrainingOutput *TrainingOutput `protobuf:"bytes,9,opt,name=training_output,json=trainingOutput,oneof"`
1119 type Job_PredictionOutput struct {
1120 PredictionOutput *PredictionOutput `protobuf:"bytes,10,opt,name=prediction_output,json=predictionOutput,oneof"`
1123 func (*Job_TrainingInput) isJob_Input() {}
1124 func (*Job_PredictionInput) isJob_Input() {}
1125 func (*Job_TrainingOutput) isJob_Output() {}
1126 func (*Job_PredictionOutput) isJob_Output() {}
1128 func (m *Job) GetInput() isJob_Input {
1134 func (m *Job) GetOutput() isJob_Output {
1141 func (m *Job) GetJobId() string {
1148 func (m *Job) GetTrainingInput() *TrainingInput {
1149 if x, ok := m.GetInput().(*Job_TrainingInput); ok {
1150 return x.TrainingInput
1155 func (m *Job) GetPredictionInput() *PredictionInput {
1156 if x, ok := m.GetInput().(*Job_PredictionInput); ok {
1157 return x.PredictionInput
1162 func (m *Job) GetCreateTime() *google_protobuf2.Timestamp {
1169 func (m *Job) GetStartTime() *google_protobuf2.Timestamp {
1176 func (m *Job) GetEndTime() *google_protobuf2.Timestamp {
1183 func (m *Job) GetState() Job_State {
1187 return Job_STATE_UNSPECIFIED
1190 func (m *Job) GetErrorMessage() string {
1192 return m.ErrorMessage
1197 func (m *Job) GetTrainingOutput() *TrainingOutput {
1198 if x, ok := m.GetOutput().(*Job_TrainingOutput); ok {
1199 return x.TrainingOutput
1204 func (m *Job) GetPredictionOutput() *PredictionOutput {
1205 if x, ok := m.GetOutput().(*Job_PredictionOutput); ok {
1206 return x.PredictionOutput
1211 // XXX_OneofFuncs is for the internal use of the proto package.
1212 func (*Job) XXX_OneofFuncs() (func(msg proto.Message, b *proto.Buffer) error, func(msg proto.Message, tag, wire int, b *proto.Buffer) (bool, error), func(msg proto.Message) (n int), []interface{}) {
1213 return _Job_OneofMarshaler, _Job_OneofUnmarshaler, _Job_OneofSizer, []interface{}{
1214 (*Job_TrainingInput)(nil),
1215 (*Job_PredictionInput)(nil),
1216 (*Job_TrainingOutput)(nil),
1217 (*Job_PredictionOutput)(nil),
1221 func _Job_OneofMarshaler(msg proto.Message, b *proto.Buffer) error {
1224 switch x := m.Input.(type) {
1225 case *Job_TrainingInput:
1226 b.EncodeVarint(2<<3 | proto.WireBytes)
1227 if err := b.EncodeMessage(x.TrainingInput); err != nil {
1230 case *Job_PredictionInput:
1231 b.EncodeVarint(3<<3 | proto.WireBytes)
1232 if err := b.EncodeMessage(x.PredictionInput); err != nil {
1237 return fmt.Errorf("Job.Input has unexpected type %T", x)
1240 switch x := m.Output.(type) {
1241 case *Job_TrainingOutput:
1242 b.EncodeVarint(9<<3 | proto.WireBytes)
1243 if err := b.EncodeMessage(x.TrainingOutput); err != nil {
1246 case *Job_PredictionOutput:
1247 b.EncodeVarint(10<<3 | proto.WireBytes)
1248 if err := b.EncodeMessage(x.PredictionOutput); err != nil {
1253 return fmt.Errorf("Job.Output has unexpected type %T", x)
1258 func _Job_OneofUnmarshaler(msg proto.Message, tag, wire int, b *proto.Buffer) (bool, error) {
1261 case 2: // input.training_input
1262 if wire != proto.WireBytes {
1263 return true, proto.ErrInternalBadWireType
1265 msg := new(TrainingInput)
1266 err := b.DecodeMessage(msg)
1267 m.Input = &Job_TrainingInput{msg}
1269 case 3: // input.prediction_input
1270 if wire != proto.WireBytes {
1271 return true, proto.ErrInternalBadWireType
1273 msg := new(PredictionInput)
1274 err := b.DecodeMessage(msg)
1275 m.Input = &Job_PredictionInput{msg}
1277 case 9: // output.training_output
1278 if wire != proto.WireBytes {
1279 return true, proto.ErrInternalBadWireType
1281 msg := new(TrainingOutput)
1282 err := b.DecodeMessage(msg)
1283 m.Output = &Job_TrainingOutput{msg}
1285 case 10: // output.prediction_output
1286 if wire != proto.WireBytes {
1287 return true, proto.ErrInternalBadWireType
1289 msg := new(PredictionOutput)
1290 err := b.DecodeMessage(msg)
1291 m.Output = &Job_PredictionOutput{msg}
1298 func _Job_OneofSizer(msg proto.Message) (n int) {
1301 switch x := m.Input.(type) {
1302 case *Job_TrainingInput:
1303 s := proto.Size(x.TrainingInput)
1304 n += proto.SizeVarint(2<<3 | proto.WireBytes)
1305 n += proto.SizeVarint(uint64(s))
1307 case *Job_PredictionInput:
1308 s := proto.Size(x.PredictionInput)
1309 n += proto.SizeVarint(3<<3 | proto.WireBytes)
1310 n += proto.SizeVarint(uint64(s))
1314 panic(fmt.Sprintf("proto: unexpected type %T in oneof", x))
1317 switch x := m.Output.(type) {
1318 case *Job_TrainingOutput:
1319 s := proto.Size(x.TrainingOutput)
1320 n += proto.SizeVarint(9<<3 | proto.WireBytes)
1321 n += proto.SizeVarint(uint64(s))
1323 case *Job_PredictionOutput:
1324 s := proto.Size(x.PredictionOutput)
1325 n += proto.SizeVarint(10<<3 | proto.WireBytes)
1326 n += proto.SizeVarint(uint64(s))
1330 panic(fmt.Sprintf("proto: unexpected type %T in oneof", x))
1335 // Request message for the CreateJob method.
1336 type CreateJobRequest struct {
1337 // Required. The project name.
1339 // Authorization: requires `Editor` role on the specified project.
1340 Parent string `protobuf:"bytes,1,opt,name=parent" json:"parent,omitempty"`
1341 // Required. The job to create.
1342 Job *Job `protobuf:"bytes,2,opt,name=job" json:"job,omitempty"`
1345 func (m *CreateJobRequest) Reset() { *m = CreateJobRequest{} }
1346 func (m *CreateJobRequest) String() string { return proto.CompactTextString(m) }
1347 func (*CreateJobRequest) ProtoMessage() {}
1348 func (*CreateJobRequest) Descriptor() ([]byte, []int) { return fileDescriptor0, []int{8} }
1350 func (m *CreateJobRequest) GetParent() string {
1357 func (m *CreateJobRequest) GetJob() *Job {
1364 // Request message for the ListJobs method.
1365 type ListJobsRequest struct {
1366 // Required. The name of the project for which to list jobs.
1368 // Authorization: requires `Viewer` role on the specified project.
1369 Parent string `protobuf:"bytes,1,opt,name=parent" json:"parent,omitempty"`
1370 // Optional. Specifies the subset of jobs to retrieve.
1371 Filter string `protobuf:"bytes,2,opt,name=filter" json:"filter,omitempty"`
1372 // Optional. A page token to request the next page of results.
1374 // You get the token from the `next_page_token` field of the response from
1375 // the previous call.
1376 PageToken string `protobuf:"bytes,4,opt,name=page_token,json=pageToken" json:"page_token,omitempty"`
1377 // Optional. The number of jobs to retrieve per "page" of results. If there
1378 // are more remaining results than this number, the response message will
1379 // contain a valid value in the `next_page_token` field.
1381 // The default value is 20, and the maximum page size is 100.
1382 PageSize int32 `protobuf:"varint,5,opt,name=page_size,json=pageSize" json:"page_size,omitempty"`
1385 func (m *ListJobsRequest) Reset() { *m = ListJobsRequest{} }
1386 func (m *ListJobsRequest) String() string { return proto.CompactTextString(m) }
1387 func (*ListJobsRequest) ProtoMessage() {}
1388 func (*ListJobsRequest) Descriptor() ([]byte, []int) { return fileDescriptor0, []int{9} }
1390 func (m *ListJobsRequest) GetParent() string {
1397 func (m *ListJobsRequest) GetFilter() string {
1404 func (m *ListJobsRequest) GetPageToken() string {
1411 func (m *ListJobsRequest) GetPageSize() int32 {
1418 // Response message for the ListJobs method.
1419 type ListJobsResponse struct {
1420 // The list of jobs.
1421 Jobs []*Job `protobuf:"bytes,1,rep,name=jobs" json:"jobs,omitempty"`
1422 // Optional. Pass this token as the `page_token` field of the request for a
1424 NextPageToken string `protobuf:"bytes,2,opt,name=next_page_token,json=nextPageToken" json:"next_page_token,omitempty"`
1427 func (m *ListJobsResponse) Reset() { *m = ListJobsResponse{} }
1428 func (m *ListJobsResponse) String() string { return proto.CompactTextString(m) }
1429 func (*ListJobsResponse) ProtoMessage() {}
1430 func (*ListJobsResponse) Descriptor() ([]byte, []int) { return fileDescriptor0, []int{10} }
1432 func (m *ListJobsResponse) GetJobs() []*Job {
1439 func (m *ListJobsResponse) GetNextPageToken() string {
1441 return m.NextPageToken
1446 // Request message for the GetJob method.
1447 type GetJobRequest struct {
1448 // Required. The name of the job to get the description of.
1450 // Authorization: requires `Viewer` role on the parent project.
1451 Name string `protobuf:"bytes,1,opt,name=name" json:"name,omitempty"`
1454 func (m *GetJobRequest) Reset() { *m = GetJobRequest{} }
1455 func (m *GetJobRequest) String() string { return proto.CompactTextString(m) }
1456 func (*GetJobRequest) ProtoMessage() {}
1457 func (*GetJobRequest) Descriptor() ([]byte, []int) { return fileDescriptor0, []int{11} }
1459 func (m *GetJobRequest) GetName() string {
1466 // Request message for the CancelJob method.
1467 type CancelJobRequest struct {
1468 // Required. The name of the job to cancel.
1470 // Authorization: requires `Editor` role on the parent project.
1471 Name string `protobuf:"bytes,1,opt,name=name" json:"name,omitempty"`
1474 func (m *CancelJobRequest) Reset() { *m = CancelJobRequest{} }
1475 func (m *CancelJobRequest) String() string { return proto.CompactTextString(m) }
1476 func (*CancelJobRequest) ProtoMessage() {}
1477 func (*CancelJobRequest) Descriptor() ([]byte, []int) { return fileDescriptor0, []int{12} }
1479 func (m *CancelJobRequest) GetName() string {
1487 proto.RegisterType((*TrainingInput)(nil), "google.cloud.ml.v1beta1.TrainingInput")
1488 proto.RegisterType((*HyperparameterSpec)(nil), "google.cloud.ml.v1beta1.HyperparameterSpec")
1489 proto.RegisterType((*ParameterSpec)(nil), "google.cloud.ml.v1beta1.ParameterSpec")
1490 proto.RegisterType((*HyperparameterOutput)(nil), "google.cloud.ml.v1beta1.HyperparameterOutput")
1491 proto.RegisterType((*HyperparameterOutput_HyperparameterMetric)(nil), "google.cloud.ml.v1beta1.HyperparameterOutput.HyperparameterMetric")
1492 proto.RegisterType((*TrainingOutput)(nil), "google.cloud.ml.v1beta1.TrainingOutput")
1493 proto.RegisterType((*PredictionInput)(nil), "google.cloud.ml.v1beta1.PredictionInput")
1494 proto.RegisterType((*PredictionOutput)(nil), "google.cloud.ml.v1beta1.PredictionOutput")
1495 proto.RegisterType((*Job)(nil), "google.cloud.ml.v1beta1.Job")
1496 proto.RegisterType((*CreateJobRequest)(nil), "google.cloud.ml.v1beta1.CreateJobRequest")
1497 proto.RegisterType((*ListJobsRequest)(nil), "google.cloud.ml.v1beta1.ListJobsRequest")
1498 proto.RegisterType((*ListJobsResponse)(nil), "google.cloud.ml.v1beta1.ListJobsResponse")
1499 proto.RegisterType((*GetJobRequest)(nil), "google.cloud.ml.v1beta1.GetJobRequest")
1500 proto.RegisterType((*CancelJobRequest)(nil), "google.cloud.ml.v1beta1.CancelJobRequest")
1501 proto.RegisterEnum("google.cloud.ml.v1beta1.TrainingInput_ScaleTier", TrainingInput_ScaleTier_name, TrainingInput_ScaleTier_value)
1502 proto.RegisterEnum("google.cloud.ml.v1beta1.HyperparameterSpec_GoalType", HyperparameterSpec_GoalType_name, HyperparameterSpec_GoalType_value)
1503 proto.RegisterEnum("google.cloud.ml.v1beta1.ParameterSpec_ParameterType", ParameterSpec_ParameterType_name, ParameterSpec_ParameterType_value)
1504 proto.RegisterEnum("google.cloud.ml.v1beta1.ParameterSpec_ScaleType", ParameterSpec_ScaleType_name, ParameterSpec_ScaleType_value)
1505 proto.RegisterEnum("google.cloud.ml.v1beta1.PredictionInput_DataFormat", PredictionInput_DataFormat_name, PredictionInput_DataFormat_value)
1506 proto.RegisterEnum("google.cloud.ml.v1beta1.Job_State", Job_State_name, Job_State_value)
1509 // Reference imports to suppress errors if they are not otherwise used.
1510 var _ context.Context
1511 var _ grpc.ClientConn
1513 // This is a compile-time assertion to ensure that this generated file
1514 // is compatible with the grpc package it is being compiled against.
1515 const _ = grpc.SupportPackageIsVersion4
1517 // Client API for JobService service
1519 type JobServiceClient interface {
1520 // Creates a training or a batch prediction job.
1521 CreateJob(ctx context.Context, in *CreateJobRequest, opts ...grpc.CallOption) (*Job, error)
1522 // Lists the jobs in the project.
1523 ListJobs(ctx context.Context, in *ListJobsRequest, opts ...grpc.CallOption) (*ListJobsResponse, error)
1525 GetJob(ctx context.Context, in *GetJobRequest, opts ...grpc.CallOption) (*Job, error)
1526 // Cancels a running job.
1527 CancelJob(ctx context.Context, in *CancelJobRequest, opts ...grpc.CallOption) (*google_protobuf1.Empty, error)
1530 type jobServiceClient struct {
1534 func NewJobServiceClient(cc *grpc.ClientConn) JobServiceClient {
1535 return &jobServiceClient{cc}
1538 func (c *jobServiceClient) CreateJob(ctx context.Context, in *CreateJobRequest, opts ...grpc.CallOption) (*Job, error) {
1540 err := grpc.Invoke(ctx, "/google.cloud.ml.v1beta1.JobService/CreateJob", in, out, c.cc, opts...)
1547 func (c *jobServiceClient) ListJobs(ctx context.Context, in *ListJobsRequest, opts ...grpc.CallOption) (*ListJobsResponse, error) {
1548 out := new(ListJobsResponse)
1549 err := grpc.Invoke(ctx, "/google.cloud.ml.v1beta1.JobService/ListJobs", in, out, c.cc, opts...)
1556 func (c *jobServiceClient) GetJob(ctx context.Context, in *GetJobRequest, opts ...grpc.CallOption) (*Job, error) {
1558 err := grpc.Invoke(ctx, "/google.cloud.ml.v1beta1.JobService/GetJob", in, out, c.cc, opts...)
1565 func (c *jobServiceClient) CancelJob(ctx context.Context, in *CancelJobRequest, opts ...grpc.CallOption) (*google_protobuf1.Empty, error) {
1566 out := new(google_protobuf1.Empty)
1567 err := grpc.Invoke(ctx, "/google.cloud.ml.v1beta1.JobService/CancelJob", in, out, c.cc, opts...)
1574 // Server API for JobService service
1576 type JobServiceServer interface {
1577 // Creates a training or a batch prediction job.
1578 CreateJob(context.Context, *CreateJobRequest) (*Job, error)
1579 // Lists the jobs in the project.
1580 ListJobs(context.Context, *ListJobsRequest) (*ListJobsResponse, error)
1582 GetJob(context.Context, *GetJobRequest) (*Job, error)
1583 // Cancels a running job.
1584 CancelJob(context.Context, *CancelJobRequest) (*google_protobuf1.Empty, error)
1587 func RegisterJobServiceServer(s *grpc.Server, srv JobServiceServer) {
1588 s.RegisterService(&_JobService_serviceDesc, srv)
1591 func _JobService_CreateJob_Handler(srv interface{}, ctx context.Context, dec func(interface{}) error, interceptor grpc.UnaryServerInterceptor) (interface{}, error) {
1592 in := new(CreateJobRequest)
1593 if err := dec(in); err != nil {
1596 if interceptor == nil {
1597 return srv.(JobServiceServer).CreateJob(ctx, in)
1599 info := &grpc.UnaryServerInfo{
1601 FullMethod: "/google.cloud.ml.v1beta1.JobService/CreateJob",
1603 handler := func(ctx context.Context, req interface{}) (interface{}, error) {
1604 return srv.(JobServiceServer).CreateJob(ctx, req.(*CreateJobRequest))
1606 return interceptor(ctx, in, info, handler)
1609 func _JobService_ListJobs_Handler(srv interface{}, ctx context.Context, dec func(interface{}) error, interceptor grpc.UnaryServerInterceptor) (interface{}, error) {
1610 in := new(ListJobsRequest)
1611 if err := dec(in); err != nil {
1614 if interceptor == nil {
1615 return srv.(JobServiceServer).ListJobs(ctx, in)
1617 info := &grpc.UnaryServerInfo{
1619 FullMethod: "/google.cloud.ml.v1beta1.JobService/ListJobs",
1621 handler := func(ctx context.Context, req interface{}) (interface{}, error) {
1622 return srv.(JobServiceServer).ListJobs(ctx, req.(*ListJobsRequest))
1624 return interceptor(ctx, in, info, handler)
1627 func _JobService_GetJob_Handler(srv interface{}, ctx context.Context, dec func(interface{}) error, interceptor grpc.UnaryServerInterceptor) (interface{}, error) {
1628 in := new(GetJobRequest)
1629 if err := dec(in); err != nil {
1632 if interceptor == nil {
1633 return srv.(JobServiceServer).GetJob(ctx, in)
1635 info := &grpc.UnaryServerInfo{
1637 FullMethod: "/google.cloud.ml.v1beta1.JobService/GetJob",
1639 handler := func(ctx context.Context, req interface{}) (interface{}, error) {
1640 return srv.(JobServiceServer).GetJob(ctx, req.(*GetJobRequest))
1642 return interceptor(ctx, in, info, handler)
1645 func _JobService_CancelJob_Handler(srv interface{}, ctx context.Context, dec func(interface{}) error, interceptor grpc.UnaryServerInterceptor) (interface{}, error) {
1646 in := new(CancelJobRequest)
1647 if err := dec(in); err != nil {
1650 if interceptor == nil {
1651 return srv.(JobServiceServer).CancelJob(ctx, in)
1653 info := &grpc.UnaryServerInfo{
1655 FullMethod: "/google.cloud.ml.v1beta1.JobService/CancelJob",
1657 handler := func(ctx context.Context, req interface{}) (interface{}, error) {
1658 return srv.(JobServiceServer).CancelJob(ctx, req.(*CancelJobRequest))
1660 return interceptor(ctx, in, info, handler)
1663 var _JobService_serviceDesc = grpc.ServiceDesc{
1664 ServiceName: "google.cloud.ml.v1beta1.JobService",
1665 HandlerType: (*JobServiceServer)(nil),
1666 Methods: []grpc.MethodDesc{
1668 MethodName: "CreateJob",
1669 Handler: _JobService_CreateJob_Handler,
1672 MethodName: "ListJobs",
1673 Handler: _JobService_ListJobs_Handler,
1676 MethodName: "GetJob",
1677 Handler: _JobService_GetJob_Handler,
1680 MethodName: "CancelJob",
1681 Handler: _JobService_CancelJob_Handler,
1684 Streams: []grpc.StreamDesc{},
1685 Metadata: "google/cloud/ml/v1beta1/job_service.proto",
1688 func init() { proto.RegisterFile("google/cloud/ml/v1beta1/job_service.proto", fileDescriptor0) }
1690 var fileDescriptor0 = []byte{
1691 // 2082 bytes of a gzipped FileDescriptorProto
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