--- /dev/null
+// Code generated by protoc-gen-go. DO NOT EDIT.
+// source: google/cloud/ml/v1beta1/job_service.proto
+
+/*
+Package ml is a generated protocol buffer package.
+
+It is generated from these files:
+ google/cloud/ml/v1beta1/job_service.proto
+ google/cloud/ml/v1beta1/model_service.proto
+ google/cloud/ml/v1beta1/operation_metadata.proto
+ google/cloud/ml/v1beta1/prediction_service.proto
+ google/cloud/ml/v1beta1/project_service.proto
+
+It has these top-level messages:
+ TrainingInput
+ HyperparameterSpec
+ ParameterSpec
+ HyperparameterOutput
+ TrainingOutput
+ PredictionInput
+ PredictionOutput
+ Job
+ CreateJobRequest
+ ListJobsRequest
+ ListJobsResponse
+ GetJobRequest
+ CancelJobRequest
+ Model
+ Version
+ ManualScaling
+ CreateModelRequest
+ ListModelsRequest
+ ListModelsResponse
+ GetModelRequest
+ DeleteModelRequest
+ CreateVersionRequest
+ ListVersionsRequest
+ ListVersionsResponse
+ GetVersionRequest
+ DeleteVersionRequest
+ SetDefaultVersionRequest
+ OperationMetadata
+ PredictRequest
+ GetConfigRequest
+ GetConfigResponse
+*/
+package ml
+
+import proto "github.com/golang/protobuf/proto"
+import fmt "fmt"
+import math "math"
+import _ "google.golang.org/genproto/googleapis/api/annotations"
+import _ "google.golang.org/genproto/googleapis/api/serviceconfig"
+import google_protobuf1 "github.com/golang/protobuf/ptypes/empty"
+import google_protobuf2 "github.com/golang/protobuf/ptypes/timestamp"
+
+import (
+ context "golang.org/x/net/context"
+ grpc "google.golang.org/grpc"
+)
+
+// Reference imports to suppress errors if they are not otherwise used.
+var _ = proto.Marshal
+var _ = fmt.Errorf
+var _ = math.Inf
+
+// This is a compile-time assertion to ensure that this generated file
+// is compatible with the proto package it is being compiled against.
+// A compilation error at this line likely means your copy of the
+// proto package needs to be updated.
+const _ = proto.ProtoPackageIsVersion2 // please upgrade the proto package
+
+// 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
+)
+
+var TrainingInput_ScaleTier_name = map[int32]string{
+ 0: "BASIC",
+ 1: "STANDARD_1",
+ 3: "PREMIUM_1",
+ 6: "BASIC_GPU",
+ 5: "CUSTOM",
+}
+var TrainingInput_ScaleTier_value = map[string]int32{
+ "BASIC": 0,
+ "STANDARD_1": 1,
+ "PREMIUM_1": 3,
+ "BASIC_GPU": 6,
+ "CUSTOM": 5,
+}
+
+func (x TrainingInput_ScaleTier) String() string {
+ return proto.EnumName(TrainingInput_ScaleTier_name, int32(x))
+}
+func (TrainingInput_ScaleTier) EnumDescriptor() ([]byte, []int) { return fileDescriptor0, []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
+)
+
+var HyperparameterSpec_GoalType_name = map[int32]string{
+ 0: "GOAL_TYPE_UNSPECIFIED",
+ 1: "MAXIMIZE",
+ 2: "MINIMIZE",
+}
+var HyperparameterSpec_GoalType_value = map[string]int32{
+ "GOAL_TYPE_UNSPECIFIED": 0,
+ "MAXIMIZE": 1,
+ "MINIMIZE": 2,
+}
+
+func (x HyperparameterSpec_GoalType) String() string {
+ return proto.EnumName(HyperparameterSpec_GoalType_name, int32(x))
+}
+func (HyperparameterSpec_GoalType) EnumDescriptor() ([]byte, []int) {
+ return fileDescriptor0, []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
+)
+
+var ParameterSpec_ParameterType_name = map[int32]string{
+ 0: "PARAMETER_TYPE_UNSPECIFIED",
+ 1: "DOUBLE",
+ 2: "INTEGER",
+ 3: "CATEGORICAL",
+ 4: "DISCRETE",
+}
+var ParameterSpec_ParameterType_value = map[string]int32{
+ "PARAMETER_TYPE_UNSPECIFIED": 0,
+ "DOUBLE": 1,
+ "INTEGER": 2,
+ "CATEGORICAL": 3,
+ "DISCRETE": 4,
+}
+
+func (x ParameterSpec_ParameterType) String() string {
+ return proto.EnumName(ParameterSpec_ParameterType_name, int32(x))
+}
+func (ParameterSpec_ParameterType) EnumDescriptor() ([]byte, []int) {
+ return fileDescriptor0, []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
+)
+
+var ParameterSpec_ScaleType_name = map[int32]string{
+ 0: "NONE",
+ 1: "UNIT_LINEAR_SCALE",
+ 2: "UNIT_LOG_SCALE",
+ 3: "UNIT_REVERSE_LOG_SCALE",
+}
+var 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) String() string {
+ return proto.EnumName(ParameterSpec_ScaleType_name, int32(x))
+}
+func (ParameterSpec_ScaleType) EnumDescriptor() ([]byte, []int) { return fileDescriptor0, []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
+)
+
+var PredictionInput_DataFormat_name = map[int32]string{
+ 0: "DATA_FORMAT_UNSPECIFIED",
+ 1: "TEXT",
+ 2: "TF_RECORD",
+ 3: "TF_RECORD_GZIP",
+}
+var PredictionInput_DataFormat_value = map[string]int32{
+ "DATA_FORMAT_UNSPECIFIED": 0,
+ "TEXT": 1,
+ "TF_RECORD": 2,
+ "TF_RECORD_GZIP": 3,
+}
+
+func (x PredictionInput_DataFormat) String() string {
+ return proto.EnumName(PredictionInput_DataFormat_name, int32(x))
+}
+func (PredictionInput_DataFormat) EnumDescriptor() ([]byte, []int) {
+ return fileDescriptor0, []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
+)
+
+var Job_State_name = map[int32]string{
+ 0: "STATE_UNSPECIFIED",
+ 1: "QUEUED",
+ 2: "PREPARING",
+ 3: "RUNNING",
+ 4: "SUCCEEDED",
+ 5: "FAILED",
+ 6: "CANCELLING",
+ 7: "CANCELLED",
+}
+var 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) String() string {
+ return proto.EnumName(Job_State_name, int32(x))
+}
+func (Job_State) EnumDescriptor() ([]byte, []int) { return fileDescriptor0, []int{7, 0} }
+
+// Represents input parameters for a training job.
+type TrainingInput struct {
+ // 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,enum=google.cloud.ml.v1beta1.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" 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" 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" 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" 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" 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" 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" json:"python_module,omitempty"`
+ // Optional. Command line arguments to pass to the program.
+ Args []string `protobuf:"bytes,10,rep,name=args" json:"args,omitempty"`
+ // Optional. The set of Hyperparameters to tune.
+ Hyperparameters *HyperparameterSpec `protobuf:"bytes,12,opt,name=hyperparameters" json:"hyperparameters,omitempty"`
+ // Required. The Google Compute Engine region to run the training job in.
+ Region string `protobuf:"bytes,14,opt,name=region" 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" 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" json:"runtime_version,omitempty"`
+}
+
+func (m *TrainingInput) Reset() { *m = TrainingInput{} }
+func (m *TrainingInput) String() string { return proto.CompactTextString(m) }
+func (*TrainingInput) ProtoMessage() {}
+func (*TrainingInput) Descriptor() ([]byte, []int) { return fileDescriptor0, []int{0} }
+
+func (m *TrainingInput) GetScaleTier() TrainingInput_ScaleTier {
+ if m != nil {
+ return m.ScaleTier
+ }
+ return TrainingInput_BASIC
+}
+
+func (m *TrainingInput) GetMasterType() string {
+ if m != nil {
+ return m.MasterType
+ }
+ return ""
+}
+
+func (m *TrainingInput) GetWorkerType() string {
+ if m != nil {
+ return m.WorkerType
+ }
+ return ""
+}
+
+func (m *TrainingInput) GetParameterServerType() string {
+ if m != nil {
+ return m.ParameterServerType
+ }
+ return ""
+}
+
+func (m *TrainingInput) GetWorkerCount() int64 {
+ if m != nil {
+ return m.WorkerCount
+ }
+ return 0
+}
+
+func (m *TrainingInput) GetParameterServerCount() int64 {
+ if m != nil {
+ return m.ParameterServerCount
+ }
+ return 0
+}
+
+func (m *TrainingInput) GetPackageUris() []string {
+ if m != nil {
+ return m.PackageUris
+ }
+ return nil
+}
+
+func (m *TrainingInput) GetPythonModule() string {
+ if m != nil {
+ return m.PythonModule
+ }
+ return ""
+}
+
+func (m *TrainingInput) GetArgs() []string {
+ if m != nil {
+ return m.Args
+ }
+ return nil
+}
+
+func (m *TrainingInput) GetHyperparameters() *HyperparameterSpec {
+ if m != nil {
+ return m.Hyperparameters
+ }
+ return nil
+}
+
+func (m *TrainingInput) GetRegion() string {
+ if m != nil {
+ return m.Region
+ }
+ return ""
+}
+
+func (m *TrainingInput) GetJobDir() string {
+ if m != nil {
+ return m.JobDir
+ }
+ return ""
+}
+
+func (m *TrainingInput) GetRuntimeVersion() string {
+ if m != nil {
+ return m.RuntimeVersion
+ }
+ return ""
+}
+
+// Represents a set of hyperparameters to optimize.
+type HyperparameterSpec struct {
+ // 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,enum=google.cloud.ml.v1beta1.HyperparameterSpec_GoalType" json:"goal,omitempty"`
+ // Required. The set of parameters to tune.
+ Params []*ParameterSpec `protobuf:"bytes,2,rep,name=params" 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" 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" 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" json:"hyperparameter_metric_tag,omitempty"`
+}
+
+func (m *HyperparameterSpec) Reset() { *m = HyperparameterSpec{} }
+func (m *HyperparameterSpec) String() string { return proto.CompactTextString(m) }
+func (*HyperparameterSpec) ProtoMessage() {}
+func (*HyperparameterSpec) Descriptor() ([]byte, []int) { return fileDescriptor0, []int{1} }
+
+func (m *HyperparameterSpec) GetGoal() HyperparameterSpec_GoalType {
+ if m != nil {
+ return m.Goal
+ }
+ return HyperparameterSpec_GOAL_TYPE_UNSPECIFIED
+}
+
+func (m *HyperparameterSpec) GetParams() []*ParameterSpec {
+ if m != nil {
+ return m.Params
+ }
+ return nil
+}
+
+func (m *HyperparameterSpec) GetMaxTrials() int32 {
+ if m != nil {
+ return m.MaxTrials
+ }
+ return 0
+}
+
+func (m *HyperparameterSpec) GetMaxParallelTrials() int32 {
+ if m != nil {
+ return m.MaxParallelTrials
+ }
+ return 0
+}
+
+func (m *HyperparameterSpec) GetHyperparameterMetricTag() string {
+ if m != nil {
+ return m.HyperparameterMetricTag
+ }
+ return ""
+}
+
+// Represents a single hyperparameter to optimize.
+type ParameterSpec struct {
+ // 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" json:"parameter_name,omitempty"`
+ // Required. The type of the parameter.
+ Type ParameterSpec_ParameterType `protobuf:"varint,4,opt,name=type,enum=google.cloud.ml.v1beta1.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" 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" 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" 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" 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,enum=google.cloud.ml.v1beta1.ParameterSpec_ScaleType" json:"scale_type,omitempty"`
+}
+
+func (m *ParameterSpec) Reset() { *m = ParameterSpec{} }
+func (m *ParameterSpec) String() string { return proto.CompactTextString(m) }
+func (*ParameterSpec) ProtoMessage() {}
+func (*ParameterSpec) Descriptor() ([]byte, []int) { return fileDescriptor0, []int{2} }
+
+func (m *ParameterSpec) GetParameterName() string {
+ if m != nil {
+ return m.ParameterName
+ }
+ return ""
+}
+
+func (m *ParameterSpec) GetType() ParameterSpec_ParameterType {
+ if m != nil {
+ return m.Type
+ }
+ return ParameterSpec_PARAMETER_TYPE_UNSPECIFIED
+}
+
+func (m *ParameterSpec) GetMinValue() float64 {
+ if m != nil {
+ return m.MinValue
+ }
+ return 0
+}
+
+func (m *ParameterSpec) GetMaxValue() float64 {
+ if m != nil {
+ return m.MaxValue
+ }
+ return 0
+}
+
+func (m *ParameterSpec) GetCategoricalValues() []string {
+ if m != nil {
+ return m.CategoricalValues
+ }
+ return nil
+}
+
+func (m *ParameterSpec) GetDiscreteValues() []float64 {
+ if m != nil {
+ return m.DiscreteValues
+ }
+ return nil
+}
+
+func (m *ParameterSpec) GetScaleType() ParameterSpec_ScaleType {
+ if m != nil {
+ return m.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 {
+ // The trial id for these results.
+ TrialId string `protobuf:"bytes,1,opt,name=trial_id,json=trialId" json:"trial_id,omitempty"`
+ // The hyperparameters given to this trial.
+ 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"`
+ // The final objective metric seen for this trial.
+ FinalMetric *HyperparameterOutput_HyperparameterMetric `protobuf:"bytes,3,opt,name=final_metric,json=finalMetric" json:"final_metric,omitempty"`
+ // All recorded object metrics for this trial.
+ AllMetrics []*HyperparameterOutput_HyperparameterMetric `protobuf:"bytes,4,rep,name=all_metrics,json=allMetrics" json:"all_metrics,omitempty"`
+}
+
+func (m *HyperparameterOutput) Reset() { *m = HyperparameterOutput{} }
+func (m *HyperparameterOutput) String() string { return proto.CompactTextString(m) }
+func (*HyperparameterOutput) ProtoMessage() {}
+func (*HyperparameterOutput) Descriptor() ([]byte, []int) { return fileDescriptor0, []int{3} }
+
+func (m *HyperparameterOutput) GetTrialId() string {
+ if m != nil {
+ return m.TrialId
+ }
+ return ""
+}
+
+func (m *HyperparameterOutput) GetHyperparameters() map[string]string {
+ if m != nil {
+ return m.Hyperparameters
+ }
+ return nil
+}
+
+func (m *HyperparameterOutput) GetFinalMetric() *HyperparameterOutput_HyperparameterMetric {
+ if m != nil {
+ return m.FinalMetric
+ }
+ return nil
+}
+
+func (m *HyperparameterOutput) GetAllMetrics() []*HyperparameterOutput_HyperparameterMetric {
+ if m != nil {
+ return m.AllMetrics
+ }
+ return nil
+}
+
+// An observed value of a metric.
+type HyperparameterOutput_HyperparameterMetric struct {
+ // The global training step for this metric.
+ TrainingStep int64 `protobuf:"varint,1,opt,name=training_step,json=trainingStep" json:"training_step,omitempty"`
+ // The objective value at this training step.
+ ObjectiveValue float64 `protobuf:"fixed64,2,opt,name=objective_value,json=objectiveValue" json:"objective_value,omitempty"`
+}
+
+func (m *HyperparameterOutput_HyperparameterMetric) Reset() {
+ *m = HyperparameterOutput_HyperparameterMetric{}
+}
+func (m *HyperparameterOutput_HyperparameterMetric) String() string { return proto.CompactTextString(m) }
+func (*HyperparameterOutput_HyperparameterMetric) ProtoMessage() {}
+func (*HyperparameterOutput_HyperparameterMetric) Descriptor() ([]byte, []int) {
+ return fileDescriptor0, []int{3, 0}
+}
+
+func (m *HyperparameterOutput_HyperparameterMetric) GetTrainingStep() int64 {
+ if m != nil {
+ return m.TrainingStep
+ }
+ return 0
+}
+
+func (m *HyperparameterOutput_HyperparameterMetric) GetObjectiveValue() float64 {
+ if m != nil {
+ return m.ObjectiveValue
+ }
+ return 0
+}
+
+// Represents results of a training job. Output only.
+type TrainingOutput struct {
+ // 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" json:"completed_trial_count,omitempty"`
+ // Results for individual Hyperparameter trials.
+ // Only set for hyperparameter tuning jobs.
+ Trials []*HyperparameterOutput `protobuf:"bytes,2,rep,name=trials" json:"trials,omitempty"`
+ // The amount of ML units consumed by the job.
+ ConsumedMlUnits float64 `protobuf:"fixed64,3,opt,name=consumed_ml_units,json=consumedMlUnits" 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" json:"is_hyperparameter_tuning_job,omitempty"`
+}
+
+func (m *TrainingOutput) Reset() { *m = TrainingOutput{} }
+func (m *TrainingOutput) String() string { return proto.CompactTextString(m) }
+func (*TrainingOutput) ProtoMessage() {}
+func (*TrainingOutput) Descriptor() ([]byte, []int) { return fileDescriptor0, []int{4} }
+
+func (m *TrainingOutput) GetCompletedTrialCount() int64 {
+ if m != nil {
+ return m.CompletedTrialCount
+ }
+ return 0
+}
+
+func (m *TrainingOutput) GetTrials() []*HyperparameterOutput {
+ if m != nil {
+ return m.Trials
+ }
+ return nil
+}
+
+func (m *TrainingOutput) GetConsumedMlUnits() float64 {
+ if m != nil {
+ return m.ConsumedMlUnits
+ }
+ return 0
+}
+
+func (m *TrainingOutput) GetIsHyperparameterTuningJob() bool {
+ if m != nil {
+ return m.IsHyperparameterTuningJob
+ }
+ return false
+}
+
+// Represents input parameters for a prediction job.
+type PredictionInput struct {
+ // Required. The model or the version to use for prediction.
+ //
+ // Types that are valid to be assigned 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,enum=google.cloud.ml.v1beta1.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" json:"input_paths,omitempty"`
+ // Required. The output Google Cloud Storage location.
+ OutputPath string `protobuf:"bytes,5,opt,name=output_path,json=outputPath" 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" 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" 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" json:"runtime_version,omitempty"`
+}
+
+func (m *PredictionInput) Reset() { *m = PredictionInput{} }
+func (m *PredictionInput) String() string { return proto.CompactTextString(m) }
+func (*PredictionInput) ProtoMessage() {}
+func (*PredictionInput) Descriptor() ([]byte, []int) { return fileDescriptor0, []int{5} }
+
+type isPredictionInput_ModelVersion interface {
+ isPredictionInput_ModelVersion()
+}
+
+type PredictionInput_ModelName struct {
+ ModelName string `protobuf:"bytes,1,opt,name=model_name,json=modelName,oneof"`
+}
+type PredictionInput_VersionName struct {
+ VersionName string `protobuf:"bytes,2,opt,name=version_name,json=versionName,oneof"`
+}
+type PredictionInput_Uri struct {
+ Uri string `protobuf:"bytes,9,opt,name=uri,oneof"`
+}
+
+func (*PredictionInput_ModelName) isPredictionInput_ModelVersion() {}
+func (*PredictionInput_VersionName) isPredictionInput_ModelVersion() {}
+func (*PredictionInput_Uri) isPredictionInput_ModelVersion() {}
+
+func (m *PredictionInput) GetModelVersion() isPredictionInput_ModelVersion {
+ if m != nil {
+ return m.ModelVersion
+ }
+ return nil
+}
+
+func (m *PredictionInput) GetModelName() string {
+ if x, ok := m.GetModelVersion().(*PredictionInput_ModelName); ok {
+ return x.ModelName
+ }
+ return ""
+}
+
+func (m *PredictionInput) GetVersionName() string {
+ if x, ok := m.GetModelVersion().(*PredictionInput_VersionName); ok {
+ return x.VersionName
+ }
+ return ""
+}
+
+func (m *PredictionInput) GetUri() string {
+ if x, ok := m.GetModelVersion().(*PredictionInput_Uri); ok {
+ return x.Uri
+ }
+ return ""
+}
+
+func (m *PredictionInput) GetDataFormat() PredictionInput_DataFormat {
+ if m != nil {
+ return m.DataFormat
+ }
+ return PredictionInput_DATA_FORMAT_UNSPECIFIED
+}
+
+func (m *PredictionInput) GetInputPaths() []string {
+ if m != nil {
+ return m.InputPaths
+ }
+ return nil
+}
+
+func (m *PredictionInput) GetOutputPath() string {
+ if m != nil {
+ return m.OutputPath
+ }
+ return ""
+}
+
+func (m *PredictionInput) GetMaxWorkerCount() int64 {
+ if m != nil {
+ return m.MaxWorkerCount
+ }
+ return 0
+}
+
+func (m *PredictionInput) GetRegion() string {
+ if m != nil {
+ return m.Region
+ }
+ return ""
+}
+
+func (m *PredictionInput) GetRuntimeVersion() string {
+ if m != nil {
+ return m.RuntimeVersion
+ }
+ return ""
+}
+
+// XXX_OneofFuncs is for the internal use of the proto package.
+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{}) {
+ return _PredictionInput_OneofMarshaler, _PredictionInput_OneofUnmarshaler, _PredictionInput_OneofSizer, []interface{}{
+ (*PredictionInput_ModelName)(nil),
+ (*PredictionInput_VersionName)(nil),
+ (*PredictionInput_Uri)(nil),
+ }
+}
+
+func _PredictionInput_OneofMarshaler(msg proto.Message, b *proto.Buffer) error {
+ m := msg.(*PredictionInput)
+ // model_version
+ switch x := m.ModelVersion.(type) {
+ case *PredictionInput_ModelName:
+ b.EncodeVarint(1<<3 | proto.WireBytes)
+ b.EncodeStringBytes(x.ModelName)
+ case *PredictionInput_VersionName:
+ b.EncodeVarint(2<<3 | proto.WireBytes)
+ b.EncodeStringBytes(x.VersionName)
+ case *PredictionInput_Uri:
+ b.EncodeVarint(9<<3 | proto.WireBytes)
+ b.EncodeStringBytes(x.Uri)
+ case nil:
+ default:
+ return fmt.Errorf("PredictionInput.ModelVersion has unexpected type %T", x)
+ }
+ return nil
+}
+
+func _PredictionInput_OneofUnmarshaler(msg proto.Message, tag, wire int, b *proto.Buffer) (bool, error) {
+ m := msg.(*PredictionInput)
+ switch tag {
+ case 1: // model_version.model_name
+ if wire != proto.WireBytes {
+ return true, proto.ErrInternalBadWireType
+ }
+ x, err := b.DecodeStringBytes()
+ m.ModelVersion = &PredictionInput_ModelName{x}
+ return true, err
+ case 2: // model_version.version_name
+ if wire != proto.WireBytes {
+ return true, proto.ErrInternalBadWireType
+ }
+ x, err := b.DecodeStringBytes()
+ m.ModelVersion = &PredictionInput_VersionName{x}
+ return true, err
+ case 9: // model_version.uri
+ if wire != proto.WireBytes {
+ return true, proto.ErrInternalBadWireType
+ }
+ x, err := b.DecodeStringBytes()
+ m.ModelVersion = &PredictionInput_Uri{x}
+ return true, err
+ default:
+ return false, nil
+ }
+}
+
+func _PredictionInput_OneofSizer(msg proto.Message) (n int) {
+ m := msg.(*PredictionInput)
+ // model_version
+ switch x := m.ModelVersion.(type) {
+ case *PredictionInput_ModelName:
+ n += proto.SizeVarint(1<<3 | proto.WireBytes)
+ n += proto.SizeVarint(uint64(len(x.ModelName)))
+ n += len(x.ModelName)
+ case *PredictionInput_VersionName:
+ n += proto.SizeVarint(2<<3 | proto.WireBytes)
+ n += proto.SizeVarint(uint64(len(x.VersionName)))
+ n += len(x.VersionName)
+ case *PredictionInput_Uri:
+ n += proto.SizeVarint(9<<3 | proto.WireBytes)
+ n += proto.SizeVarint(uint64(len(x.Uri)))
+ n += len(x.Uri)
+ case nil:
+ default:
+ panic(fmt.Sprintf("proto: unexpected type %T in oneof", x))
+ }
+ return n
+}
+
+// Represents results of a prediction job.
+type PredictionOutput struct {
+ // The output Google Cloud Storage location provided at the job creation time.
+ OutputPath string `protobuf:"bytes,1,opt,name=output_path,json=outputPath" json:"output_path,omitempty"`
+ // The number of generated predictions.
+ PredictionCount int64 `protobuf:"varint,2,opt,name=prediction_count,json=predictionCount" json:"prediction_count,omitempty"`
+ // The number of data instances which resulted in errors.
+ ErrorCount int64 `protobuf:"varint,3,opt,name=error_count,json=errorCount" json:"error_count,omitempty"`
+ // Node hours used by the batch prediction job.
+ NodeHours float64 `protobuf:"fixed64,4,opt,name=node_hours,json=nodeHours" json:"node_hours,omitempty"`
+}
+
+func (m *PredictionOutput) Reset() { *m = PredictionOutput{} }
+func (m *PredictionOutput) String() string { return proto.CompactTextString(m) }
+func (*PredictionOutput) ProtoMessage() {}
+func (*PredictionOutput) Descriptor() ([]byte, []int) { return fileDescriptor0, []int{6} }
+
+func (m *PredictionOutput) GetOutputPath() string {
+ if m != nil {
+ return m.OutputPath
+ }
+ return ""
+}
+
+func (m *PredictionOutput) GetPredictionCount() int64 {
+ if m != nil {
+ return m.PredictionCount
+ }
+ return 0
+}
+
+func (m *PredictionOutput) GetErrorCount() int64 {
+ if m != nil {
+ return m.ErrorCount
+ }
+ return 0
+}
+
+func (m *PredictionOutput) GetNodeHours() float64 {
+ if m != nil {
+ return m.NodeHours
+ }
+ return 0
+}
+
+// Represents a training or prediction job.
+type Job struct {
+ // Required. The user-specified id of the job.
+ JobId string `protobuf:"bytes,1,opt,name=job_id,json=jobId" json:"job_id,omitempty"`
+ // Required. Parameters to create a job.
+ //
+ // Types that are valid to be assigned to Input:
+ // *Job_TrainingInput
+ // *Job_PredictionInput
+ Input isJob_Input `protobuf_oneof:"input"`
+ // Output only. When the job was created.
+ CreateTime *google_protobuf2.Timestamp `protobuf:"bytes,4,opt,name=create_time,json=createTime" json:"create_time,omitempty"`
+ // Output only. When the job processing was started.
+ StartTime *google_protobuf2.Timestamp `protobuf:"bytes,5,opt,name=start_time,json=startTime" json:"start_time,omitempty"`
+ // Output only. When the job processing was completed.
+ EndTime *google_protobuf2.Timestamp `protobuf:"bytes,6,opt,name=end_time,json=endTime" json:"end_time,omitempty"`
+ // Output only. The detailed state of a job.
+ State Job_State `protobuf:"varint,7,opt,name=state,enum=google.cloud.ml.v1beta1.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" json:"error_message,omitempty"`
+ // Output only. The current result of the job.
+ //
+ // Types that are valid to be assigned to Output:
+ // *Job_TrainingOutput
+ // *Job_PredictionOutput
+ Output isJob_Output `protobuf_oneof:"output"`
+}
+
+func (m *Job) Reset() { *m = Job{} }
+func (m *Job) String() string { return proto.CompactTextString(m) }
+func (*Job) ProtoMessage() {}
+func (*Job) Descriptor() ([]byte, []int) { return fileDescriptor0, []int{7} }
+
+type isJob_Input interface {
+ isJob_Input()
+}
+type isJob_Output interface {
+ isJob_Output()
+}
+
+type Job_TrainingInput struct {
+ TrainingInput *TrainingInput `protobuf:"bytes,2,opt,name=training_input,json=trainingInput,oneof"`
+}
+type Job_PredictionInput struct {
+ PredictionInput *PredictionInput `protobuf:"bytes,3,opt,name=prediction_input,json=predictionInput,oneof"`
+}
+type Job_TrainingOutput struct {
+ TrainingOutput *TrainingOutput `protobuf:"bytes,9,opt,name=training_output,json=trainingOutput,oneof"`
+}
+type Job_PredictionOutput struct {
+ PredictionOutput *PredictionOutput `protobuf:"bytes,10,opt,name=prediction_output,json=predictionOutput,oneof"`
+}
+
+func (*Job_TrainingInput) isJob_Input() {}
+func (*Job_PredictionInput) isJob_Input() {}
+func (*Job_TrainingOutput) isJob_Output() {}
+func (*Job_PredictionOutput) isJob_Output() {}
+
+func (m *Job) GetInput() isJob_Input {
+ if m != nil {
+ return m.Input
+ }
+ return nil
+}
+func (m *Job) GetOutput() isJob_Output {
+ if m != nil {
+ return m.Output
+ }
+ return nil
+}
+
+func (m *Job) GetJobId() string {
+ if m != nil {
+ return m.JobId
+ }
+ return ""
+}
+
+func (m *Job) GetTrainingInput() *TrainingInput {
+ if x, ok := m.GetInput().(*Job_TrainingInput); ok {
+ return x.TrainingInput
+ }
+ return nil
+}
+
+func (m *Job) GetPredictionInput() *PredictionInput {
+ if x, ok := m.GetInput().(*Job_PredictionInput); ok {
+ return x.PredictionInput
+ }
+ return nil
+}
+
+func (m *Job) GetCreateTime() *google_protobuf2.Timestamp {
+ if m != nil {
+ return m.CreateTime
+ }
+ return nil
+}
+
+func (m *Job) GetStartTime() *google_protobuf2.Timestamp {
+ if m != nil {
+ return m.StartTime
+ }
+ return nil
+}
+
+func (m *Job) GetEndTime() *google_protobuf2.Timestamp {
+ if m != nil {
+ return m.EndTime
+ }
+ return nil
+}
+
+func (m *Job) GetState() Job_State {
+ if m != nil {
+ return m.State
+ }
+ return Job_STATE_UNSPECIFIED
+}
+
+func (m *Job) GetErrorMessage() string {
+ if m != nil {
+ return m.ErrorMessage
+ }
+ return ""
+}
+
+func (m *Job) GetTrainingOutput() *TrainingOutput {
+ if x, ok := m.GetOutput().(*Job_TrainingOutput); ok {
+ return x.TrainingOutput
+ }
+ return nil
+}
+
+func (m *Job) GetPredictionOutput() *PredictionOutput {
+ if x, ok := m.GetOutput().(*Job_PredictionOutput); ok {
+ return x.PredictionOutput
+ }
+ return nil
+}
+
+// XXX_OneofFuncs is for the internal use of the proto package.
+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{}) {
+ return _Job_OneofMarshaler, _Job_OneofUnmarshaler, _Job_OneofSizer, []interface{}{
+ (*Job_TrainingInput)(nil),
+ (*Job_PredictionInput)(nil),
+ (*Job_TrainingOutput)(nil),
+ (*Job_PredictionOutput)(nil),
+ }
+}
+
+func _Job_OneofMarshaler(msg proto.Message, b *proto.Buffer) error {
+ m := msg.(*Job)
+ // input
+ switch x := m.Input.(type) {
+ case *Job_TrainingInput:
+ b.EncodeVarint(2<<3 | proto.WireBytes)
+ if err := b.EncodeMessage(x.TrainingInput); err != nil {
+ return err
+ }
+ case *Job_PredictionInput:
+ b.EncodeVarint(3<<3 | proto.WireBytes)
+ if err := b.EncodeMessage(x.PredictionInput); err != nil {
+ return err
+ }
+ case nil:
+ default:
+ return fmt.Errorf("Job.Input has unexpected type %T", x)
+ }
+ // output
+ switch x := m.Output.(type) {
+ case *Job_TrainingOutput:
+ b.EncodeVarint(9<<3 | proto.WireBytes)
+ if err := b.EncodeMessage(x.TrainingOutput); err != nil {
+ return err
+ }
+ case *Job_PredictionOutput:
+ b.EncodeVarint(10<<3 | proto.WireBytes)
+ if err := b.EncodeMessage(x.PredictionOutput); err != nil {
+ return err
+ }
+ case nil:
+ default:
+ return fmt.Errorf("Job.Output has unexpected type %T", x)
+ }
+ return nil
+}
+
+func _Job_OneofUnmarshaler(msg proto.Message, tag, wire int, b *proto.Buffer) (bool, error) {
+ m := msg.(*Job)
+ switch tag {
+ case 2: // input.training_input
+ if wire != proto.WireBytes {
+ return true, proto.ErrInternalBadWireType
+ }
+ msg := new(TrainingInput)
+ err := b.DecodeMessage(msg)
+ m.Input = &Job_TrainingInput{msg}
+ return true, err
+ case 3: // input.prediction_input
+ if wire != proto.WireBytes {
+ return true, proto.ErrInternalBadWireType
+ }
+ msg := new(PredictionInput)
+ err := b.DecodeMessage(msg)
+ m.Input = &Job_PredictionInput{msg}
+ return true, err
+ case 9: // output.training_output
+ if wire != proto.WireBytes {
+ return true, proto.ErrInternalBadWireType
+ }
+ msg := new(TrainingOutput)
+ err := b.DecodeMessage(msg)
+ m.Output = &Job_TrainingOutput{msg}
+ return true, err
+ case 10: // output.prediction_output
+ if wire != proto.WireBytes {
+ return true, proto.ErrInternalBadWireType
+ }
+ msg := new(PredictionOutput)
+ err := b.DecodeMessage(msg)
+ m.Output = &Job_PredictionOutput{msg}
+ return true, err
+ default:
+ return false, nil
+ }
+}
+
+func _Job_OneofSizer(msg proto.Message) (n int) {
+ m := msg.(*Job)
+ // input
+ switch x := m.Input.(type) {
+ case *Job_TrainingInput:
+ s := proto.Size(x.TrainingInput)
+ n += proto.SizeVarint(2<<3 | proto.WireBytes)
+ n += proto.SizeVarint(uint64(s))
+ n += s
+ case *Job_PredictionInput:
+ s := proto.Size(x.PredictionInput)
+ n += proto.SizeVarint(3<<3 | proto.WireBytes)
+ n += proto.SizeVarint(uint64(s))
+ n += s
+ case nil:
+ default:
+ panic(fmt.Sprintf("proto: unexpected type %T in oneof", x))
+ }
+ // output
+ switch x := m.Output.(type) {
+ case *Job_TrainingOutput:
+ s := proto.Size(x.TrainingOutput)
+ n += proto.SizeVarint(9<<3 | proto.WireBytes)
+ n += proto.SizeVarint(uint64(s))
+ n += s
+ case *Job_PredictionOutput:
+ s := proto.Size(x.PredictionOutput)
+ n += proto.SizeVarint(10<<3 | proto.WireBytes)
+ n += proto.SizeVarint(uint64(s))
+ n += s
+ case nil:
+ default:
+ panic(fmt.Sprintf("proto: unexpected type %T in oneof", x))
+ }
+ return n
+}
+
+// Request message for the CreateJob method.
+type CreateJobRequest struct {
+ // Required. The project name.
+ //
+ // Authorization: requires `Editor` role on the specified project.
+ Parent string `protobuf:"bytes,1,opt,name=parent" json:"parent,omitempty"`
+ // Required. The job to create.
+ Job *Job `protobuf:"bytes,2,opt,name=job" json:"job,omitempty"`
+}
+
+func (m *CreateJobRequest) Reset() { *m = CreateJobRequest{} }
+func (m *CreateJobRequest) String() string { return proto.CompactTextString(m) }
+func (*CreateJobRequest) ProtoMessage() {}
+func (*CreateJobRequest) Descriptor() ([]byte, []int) { return fileDescriptor0, []int{8} }
+
+func (m *CreateJobRequest) GetParent() string {
+ if m != nil {
+ return m.Parent
+ }
+ return ""
+}
+
+func (m *CreateJobRequest) GetJob() *Job {
+ if m != nil {
+ return m.Job
+ }
+ return nil
+}
+
+// Request message for the ListJobs method.
+type ListJobsRequest struct {
+ // 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" json:"parent,omitempty"`
+ // Optional. Specifies the subset of jobs to retrieve.
+ Filter string `protobuf:"bytes,2,opt,name=filter" 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" 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" json:"page_size,omitempty"`
+}
+
+func (m *ListJobsRequest) Reset() { *m = ListJobsRequest{} }
+func (m *ListJobsRequest) String() string { return proto.CompactTextString(m) }
+func (*ListJobsRequest) ProtoMessage() {}
+func (*ListJobsRequest) Descriptor() ([]byte, []int) { return fileDescriptor0, []int{9} }
+
+func (m *ListJobsRequest) GetParent() string {
+ if m != nil {
+ return m.Parent
+ }
+ return ""
+}
+
+func (m *ListJobsRequest) GetFilter() string {
+ if m != nil {
+ return m.Filter
+ }
+ return ""
+}
+
+func (m *ListJobsRequest) GetPageToken() string {
+ if m != nil {
+ return m.PageToken
+ }
+ return ""
+}
+
+func (m *ListJobsRequest) GetPageSize() int32 {
+ if m != nil {
+ return m.PageSize
+ }
+ return 0
+}
+
+// Response message for the ListJobs method.
+type ListJobsResponse struct {
+ // The list of jobs.
+ Jobs []*Job `protobuf:"bytes,1,rep,name=jobs" 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" json:"next_page_token,omitempty"`
+}
+
+func (m *ListJobsResponse) Reset() { *m = ListJobsResponse{} }
+func (m *ListJobsResponse) String() string { return proto.CompactTextString(m) }
+func (*ListJobsResponse) ProtoMessage() {}
+func (*ListJobsResponse) Descriptor() ([]byte, []int) { return fileDescriptor0, []int{10} }
+
+func (m *ListJobsResponse) GetJobs() []*Job {
+ if m != nil {
+ return m.Jobs
+ }
+ return nil
+}
+
+func (m *ListJobsResponse) GetNextPageToken() string {
+ if m != nil {
+ return m.NextPageToken
+ }
+ return ""
+}
+
+// Request message for the GetJob method.
+type GetJobRequest struct {
+ // 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" json:"name,omitempty"`
+}
+
+func (m *GetJobRequest) Reset() { *m = GetJobRequest{} }
+func (m *GetJobRequest) String() string { return proto.CompactTextString(m) }
+func (*GetJobRequest) ProtoMessage() {}
+func (*GetJobRequest) Descriptor() ([]byte, []int) { return fileDescriptor0, []int{11} }
+
+func (m *GetJobRequest) GetName() string {
+ if m != nil {
+ return m.Name
+ }
+ return ""
+}
+
+// Request message for the CancelJob method.
+type CancelJobRequest struct {
+ // Required. The name of the job to cancel.
+ //
+ // Authorization: requires `Editor` role on the parent project.
+ Name string `protobuf:"bytes,1,opt,name=name" json:"name,omitempty"`
+}
+
+func (m *CancelJobRequest) Reset() { *m = CancelJobRequest{} }
+func (m *CancelJobRequest) String() string { return proto.CompactTextString(m) }
+func (*CancelJobRequest) ProtoMessage() {}
+func (*CancelJobRequest) Descriptor() ([]byte, []int) { return fileDescriptor0, []int{12} }
+
+func (m *CancelJobRequest) GetName() string {
+ if m != nil {
+ return m.Name
+ }
+ return ""
+}
+
+func init() {
+ proto.RegisterType((*TrainingInput)(nil), "google.cloud.ml.v1beta1.TrainingInput")
+ proto.RegisterType((*HyperparameterSpec)(nil), "google.cloud.ml.v1beta1.HyperparameterSpec")
+ proto.RegisterType((*ParameterSpec)(nil), "google.cloud.ml.v1beta1.ParameterSpec")
+ proto.RegisterType((*HyperparameterOutput)(nil), "google.cloud.ml.v1beta1.HyperparameterOutput")
+ proto.RegisterType((*HyperparameterOutput_HyperparameterMetric)(nil), "google.cloud.ml.v1beta1.HyperparameterOutput.HyperparameterMetric")
+ proto.RegisterType((*TrainingOutput)(nil), "google.cloud.ml.v1beta1.TrainingOutput")
+ proto.RegisterType((*PredictionInput)(nil), "google.cloud.ml.v1beta1.PredictionInput")
+ proto.RegisterType((*PredictionOutput)(nil), "google.cloud.ml.v1beta1.PredictionOutput")
+ proto.RegisterType((*Job)(nil), "google.cloud.ml.v1beta1.Job")
+ proto.RegisterType((*CreateJobRequest)(nil), "google.cloud.ml.v1beta1.CreateJobRequest")
+ proto.RegisterType((*ListJobsRequest)(nil), "google.cloud.ml.v1beta1.ListJobsRequest")
+ proto.RegisterType((*ListJobsResponse)(nil), "google.cloud.ml.v1beta1.ListJobsResponse")
+ proto.RegisterType((*GetJobRequest)(nil), "google.cloud.ml.v1beta1.GetJobRequest")
+ proto.RegisterType((*CancelJobRequest)(nil), "google.cloud.ml.v1beta1.CancelJobRequest")
+ proto.RegisterEnum("google.cloud.ml.v1beta1.TrainingInput_ScaleTier", TrainingInput_ScaleTier_name, TrainingInput_ScaleTier_value)
+ proto.RegisterEnum("google.cloud.ml.v1beta1.HyperparameterSpec_GoalType", HyperparameterSpec_GoalType_name, HyperparameterSpec_GoalType_value)
+ proto.RegisterEnum("google.cloud.ml.v1beta1.ParameterSpec_ParameterType", ParameterSpec_ParameterType_name, ParameterSpec_ParameterType_value)
+ proto.RegisterEnum("google.cloud.ml.v1beta1.ParameterSpec_ScaleType", ParameterSpec_ScaleType_name, ParameterSpec_ScaleType_value)
+ proto.RegisterEnum("google.cloud.ml.v1beta1.PredictionInput_DataFormat", PredictionInput_DataFormat_name, PredictionInput_DataFormat_value)
+ proto.RegisterEnum("google.cloud.ml.v1beta1.Job_State", Job_State_name, Job_State_value)
+}
+
+// Reference imports to suppress errors if they are not otherwise used.
+var _ context.Context
+var _ grpc.ClientConn
+
+// 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.SupportPackageIsVersion4
+
+// Client API for JobService service
+
+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) (*google_protobuf1.Empty, error)
+}
+
+type jobServiceClient struct {
+ cc *grpc.ClientConn
+}
+
+func NewJobServiceClient(cc *grpc.ClientConn) JobServiceClient {
+ return &jobServiceClient{cc}
+}
+
+func (c *jobServiceClient) CreateJob(ctx context.Context, in *CreateJobRequest, opts ...grpc.CallOption) (*Job, error) {
+ out := new(Job)
+ err := grpc.Invoke(ctx, "/google.cloud.ml.v1beta1.JobService/CreateJob", in, out, c.cc, 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 := grpc.Invoke(ctx, "/google.cloud.ml.v1beta1.JobService/ListJobs", in, out, c.cc, 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 := grpc.Invoke(ctx, "/google.cloud.ml.v1beta1.JobService/GetJob", in, out, c.cc, opts...)
+ if err != nil {
+ return nil, err
+ }
+ return out, nil
+}
+
+func (c *jobServiceClient) CancelJob(ctx context.Context, in *CancelJobRequest, opts ...grpc.CallOption) (*google_protobuf1.Empty, error) {
+ out := new(google_protobuf1.Empty)
+ err := grpc.Invoke(ctx, "/google.cloud.ml.v1beta1.JobService/CancelJob", in, out, c.cc, opts...)
+ if err != nil {
+ return nil, err
+ }
+ return out, nil
+}
+
+// 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) (*google_protobuf1.Empty, error)
+}
+
+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.v1beta1.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.v1beta1.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.v1beta1.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.v1beta1.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.v1beta1.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/v1beta1/job_service.proto",
+}
+
+func init() { proto.RegisterFile("google/cloud/ml/v1beta1/job_service.proto", fileDescriptor0) }
+
+var fileDescriptor0 = []byte{
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+}
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