ml

package
v0.0.0-...-52d6a69 Latest Latest
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 Go to latest Published: Jun 19, 2019 License: Apache-2.0, Apache-2.0 Imports: 11 Imported by: 0

Documentation

Overview

Package ml is a generated protocol buffer package.

It is generated from these files: 

google/cloud/ml/v1/job_service.proto
google/cloud/ml/v1/model_service.proto
google/cloud/ml/v1/operation_metadata.proto
google/cloud/ml/v1/prediction_service.proto
google/cloud/ml/v1/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

Index

Constants

This section is empty.

Variables

View Source 
var HyperparameterSpec_GoalType_name = map[int32]string{
	0: "GOAL_TYPE_UNSPECIFIED",
	1: "MAXIMIZE",
	2: "MINIMIZE",
}
View Source 
var HyperparameterSpec_GoalType_value = map[string]int32{
	"GOAL_TYPE_UNSPECIFIED": 0,
	"MAXIMIZE":              1,
	"MINIMIZE":              2,
}
View Source 
var Job_State_name = map[int32]string{
	0: "STATE_UNSPECIFIED",
	1: "QUEUED",
	2: "PREPARING",
	3: "RUNNING",
	4: "SUCCEEDED",
	5: "FAILED",
	6: "CANCELLING",
	7: "CANCELLED",
}
View Source 
var Job_State_value = map[string]int32{
	"STATE_UNSPECIFIED": 0,
	"QUEUED":            1,
	"PREPARING":         2,
	"RUNNING":           3,
	"SUCCEEDED":         4,
	"FAILED":            5,
	"CANCELLING":        6,
	"CANCELLED":         7,
}
View Source 
var OperationMetadata_OperationType_name = map[int32]string{
	0: "OPERATION_TYPE_UNSPECIFIED",
	1: "CREATE_VERSION",
	2: "DELETE_VERSION",
	3: "DELETE_MODEL",
}
View Source 
var OperationMetadata_OperationType_value = map[string]int32{
	"OPERATION_TYPE_UNSPECIFIED": 0,
	"CREATE_VERSION":             1,
	"DELETE_VERSION":             2,
	"DELETE_MODEL":               3,
}
View Source 
var ParameterSpec_ParameterType_name = map[int32]string{
	0: "PARAMETER_TYPE_UNSPECIFIED",
	1: "DOUBLE",
	2: "INTEGER",
	3: "CATEGORICAL",
	4: "DISCRETE",
}
View Source 
var ParameterSpec_ParameterType_value = map[string]int32{
	"PARAMETER_TYPE_UNSPECIFIED": 0,
	"DOUBLE":                     1,
	"INTEGER":                    2,
	"CATEGORICAL":                3,
	"DISCRETE":                   4,
}
View Source 
var ParameterSpec_ScaleType_name = map[int32]string{
	0: "NONE",
	1: "UNIT_LINEAR_SCALE",
	2: "UNIT_LOG_SCALE",
	3: "UNIT_REVERSE_LOG_SCALE",
}
View Source 
var ParameterSpec_ScaleType_value = map[string]int32{
	"NONE":                   0,
	"UNIT_LINEAR_SCALE":      1,
	"UNIT_LOG_SCALE":         2,
	"UNIT_REVERSE_LOG_SCALE": 3,
}
View Source 
var PredictionInput_DataFormat_name = map[int32]string{
	0: "DATA_FORMAT_UNSPECIFIED",
	1: "TEXT",
	2: "TF_RECORD",
	3: "TF_RECORD_GZIP",
}
View Source 
var PredictionInput_DataFormat_value = map[string]int32{
	"DATA_FORMAT_UNSPECIFIED": 0,
	"TEXT":                    1,
	"TF_RECORD":               2,
	"TF_RECORD_GZIP":          3,
}
View Source 
var TrainingInput_ScaleTier_name = map[int32]string{
	0: "BASIC",
	1: "STANDARD_1",
	3: "PREMIUM_1",
	6: "BASIC_GPU",
	5: "CUSTOM",
}
View Source 
var TrainingInput_ScaleTier_value = map[string]int32{
	"BASIC":      0,
	"STANDARD_1": 1,
	"PREMIUM_1":  3,
	"BASIC_GPU":  6,
	"CUSTOM":     5,
}

Functions

func RegisterJobServiceServer 

func RegisterJobServiceServer(s *grpc.Server, srv JobServiceServer)

func RegisterModelServiceServer 

func RegisterModelServiceServer(s *grpc.Server, srv ModelServiceServer)

func RegisterOnlinePredictionServiceServer 

func RegisterOnlinePredictionServiceServer(s *grpc.Server, srv OnlinePredictionServiceServer)

func RegisterProjectManagementServiceServer 

func RegisterProjectManagementServiceServer(s *grpc.Server, srv ProjectManagementServiceServer)

Types

type CancelJobRequest 

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"`
}

Request message for the CancelJob method.

func (*CancelJobRequest) Descriptor 

func (*CancelJobRequest) Descriptor() ([]byte, []int)

func (*CancelJobRequest) GetName 

func (m *CancelJobRequest) GetName() string

func (*CancelJobRequest) ProtoMessage 

func (*CancelJobRequest) ProtoMessage()

func (*CancelJobRequest) Reset 

func (m *CancelJobRequest) Reset()

func (*CancelJobRequest) String 

func (m *CancelJobRequest) String() string

type CreateJobRequest 

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"`
}

Request message for the CreateJob method.

func (*CreateJobRequest) Descriptor 

func (*CreateJobRequest) Descriptor() ([]byte, []int)

func (*CreateJobRequest) GetJob 

func (m *CreateJobRequest) GetJob() *Job

func (*CreateJobRequest) GetParent 

func (m *CreateJobRequest) GetParent() string

func (*CreateJobRequest) ProtoMessage 

func (*CreateJobRequest) ProtoMessage()

func (*CreateJobRequest) Reset 

func (m *CreateJobRequest) Reset()

func (*CreateJobRequest) String 

func (m *CreateJobRequest) String() string

type CreateModelRequest 

type CreateModelRequest 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 model to create.
	Model *Model `protobuf:"bytes,2,opt,name=model" json:"model,omitempty"`
}

Request message for the CreateModel method.

func (*CreateModelRequest) Descriptor 

func (*CreateModelRequest) Descriptor() ([]byte, []int)

func (*CreateModelRequest) GetModel 

func (m *CreateModelRequest) GetModel() *Model

func (*CreateModelRequest) GetParent 

func (m *CreateModelRequest) GetParent() string

func (*CreateModelRequest) ProtoMessage 

func (*CreateModelRequest) ProtoMessage()

func (*CreateModelRequest) Reset 

func (m *CreateModelRequest) Reset()

func (*CreateModelRequest) String 

func (m *CreateModelRequest) String() string

type CreateVersionRequest 

type CreateVersionRequest struct {
	// Required. The name of the model.
	//
	// Authorization: requires `Editor` role on the parent project.
	Parent string `protobuf:"bytes,1,opt,name=parent" json:"parent,omitempty"`
	// Required. The version details.
	Version *Version `protobuf:"bytes,2,opt,name=version" json:"version,omitempty"`
}

Uploads the provided trained model version to Cloud Machine Learning.

func (*CreateVersionRequest) Descriptor 

func (*CreateVersionRequest) Descriptor() ([]byte, []int)

func (*CreateVersionRequest) GetParent 

func (m *CreateVersionRequest) GetParent() string

func (*CreateVersionRequest) GetVersion 

func (m *CreateVersionRequest) GetVersion() *Version

func (*CreateVersionRequest) ProtoMessage 

func (*CreateVersionRequest) ProtoMessage()

func (*CreateVersionRequest) Reset 

func (m *CreateVersionRequest) Reset()

func (*CreateVersionRequest) String 

func (m *CreateVersionRequest) String() string

type DeleteModelRequest 

type DeleteModelRequest struct {
	// Required. The name of the model.
	//
	// Authorization: requires `Editor` role on the parent project.
	Name string `protobuf:"bytes,1,opt,name=name" json:"name,omitempty"`
}

Request message for the DeleteModel method.

func (*DeleteModelRequest) Descriptor 

func (*DeleteModelRequest) Descriptor() ([]byte, []int)

func (*DeleteModelRequest) GetName 

func (m *DeleteModelRequest) GetName() string

func (*DeleteModelRequest) ProtoMessage 

func (*DeleteModelRequest) ProtoMessage()

func (*DeleteModelRequest) Reset 

func (m *DeleteModelRequest) Reset()

func (*DeleteModelRequest) String 

func (m *DeleteModelRequest) String() string

type DeleteVersionRequest 

type DeleteVersionRequest struct {
	// Required. The name of the version. You can get the names of all the
	// versions of a model by calling
	// [projects.models.versions.list](/ml/reference/rest/v1/projects.models.versions/list).
	//
	// Authorization: requires `Editor` role on the parent project.
	Name string `protobuf:"bytes,1,opt,name=name" json:"name,omitempty"`
}

Request message for the DeleteVerionRequest method.

func (*DeleteVersionRequest) Descriptor 

func (*DeleteVersionRequest) Descriptor() ([]byte, []int)

func (*DeleteVersionRequest) GetName 

func (m *DeleteVersionRequest) GetName() string

func (*DeleteVersionRequest) ProtoMessage 

func (*DeleteVersionRequest) ProtoMessage()

func (*DeleteVersionRequest) Reset 

func (m *DeleteVersionRequest) Reset()

func (*DeleteVersionRequest) String 

func (m *DeleteVersionRequest) String() string

type GetConfigRequest 

type GetConfigRequest struct {
	// Required. The project name.
	//
	// Authorization: requires `Viewer` role on the specified project.
	Name string `protobuf:"bytes,1,opt,name=name" json:"name,omitempty"`
}

Requests service account information associated with a project.

func (*GetConfigRequest) Descriptor 

func (*GetConfigRequest) Descriptor() ([]byte, []int)

func (*GetConfigRequest) GetName 

func (m *GetConfigRequest) GetName() string

func (*GetConfigRequest) ProtoMessage 

func (*GetConfigRequest) ProtoMessage()

func (*GetConfigRequest) Reset 

func (m *GetConfigRequest) Reset()

func (*GetConfigRequest) String 

func (m *GetConfigRequest) String() string

type GetConfigResponse 

type GetConfigResponse struct {
	// The service account Cloud ML uses to access resources in the project.
	ServiceAccount string `protobuf:"bytes,1,opt,name=service_account,json=serviceAccount" json:"service_account,omitempty"`
	// The project number for `service_account`.
	ServiceAccountProject int64 `protobuf:"varint,2,opt,name=service_account_project,json=serviceAccountProject" json:"service_account_project,omitempty"`
}

Returns service account information associated with a project.

func (*GetConfigResponse) Descriptor 

func (*GetConfigResponse) Descriptor() ([]byte, []int)

func (*GetConfigResponse) GetServiceAccount 

func (m *GetConfigResponse) GetServiceAccount() string

func (*GetConfigResponse) GetServiceAccountProject 

func (m *GetConfigResponse) GetServiceAccountProject() int64

func (*GetConfigResponse) ProtoMessage 

func (*GetConfigResponse) ProtoMessage()

func (*GetConfigResponse) Reset 

func (m *GetConfigResponse) Reset()

func (*GetConfigResponse) String 

func (m *GetConfigResponse) String() string

type GetJobRequest 

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"`
}

Request message for the GetJob method.

func (*GetJobRequest) Descriptor 

func (*GetJobRequest) Descriptor() ([]byte, []int)

func (*GetJobRequest) GetName 

func (m *GetJobRequest) GetName() string

func (*GetJobRequest) ProtoMessage 

func (*GetJobRequest) ProtoMessage()

func (*GetJobRequest) Reset 

func (m *GetJobRequest) Reset()

func (*GetJobRequest) String 

func (m *GetJobRequest) String() string

type GetModelRequest 

type GetModelRequest struct {
	// Required. The name of the model.
	//
	// Authorization: requires `Viewer` role on the parent project.
	Name string `protobuf:"bytes,1,opt,name=name" json:"name,omitempty"`
}

Request message for the GetModel method.

func (*GetModelRequest) Descriptor 

func (*GetModelRequest) Descriptor() ([]byte, []int)

func (*GetModelRequest) GetName 

func (m *GetModelRequest) GetName() string

func (*GetModelRequest) ProtoMessage 

func (*GetModelRequest) ProtoMessage()

func (*GetModelRequest) Reset 

func (m *GetModelRequest) Reset()

func (*GetModelRequest) String 

func (m *GetModelRequest) String() string

type GetVersionRequest 

type GetVersionRequest struct {
	// Required. The name of the version.
	//
	// Authorization: requires `Viewer` role on the parent project.
	Name string `protobuf:"bytes,1,opt,name=name" json:"name,omitempty"`
}

Request message for the GetVersion method.

func (*GetVersionRequest) Descriptor 

func (*GetVersionRequest) Descriptor() ([]byte, []int)

func (*GetVersionRequest) GetName 

func (m *GetVersionRequest) GetName() string

func (*GetVersionRequest) ProtoMessage 

func (*GetVersionRequest) ProtoMessage()

func (*GetVersionRequest) Reset 

func (m *GetVersionRequest) Reset()

func (*GetVersionRequest) String 

func (m *GetVersionRequest) String() string

type HyperparameterOutput 

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 `` /* 150-byte string literal not displayed */
	// 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"`
}

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.

func (*HyperparameterOutput) Descriptor 

func (*HyperparameterOutput) Descriptor() ([]byte, []int)

func (*HyperparameterOutput) GetAllMetrics 

func (m *HyperparameterOutput) GetAllMetrics() []*HyperparameterOutput_HyperparameterMetric

func (*HyperparameterOutput) GetFinalMetric 

func (m *HyperparameterOutput) GetFinalMetric() *HyperparameterOutput_HyperparameterMetric

func (*HyperparameterOutput) GetHyperparameters 

func (m *HyperparameterOutput) GetHyperparameters() map[string]string

func (*HyperparameterOutput) GetTrialId 

func (m *HyperparameterOutput) GetTrialId() string

func (*HyperparameterOutput) ProtoMessage 

func (*HyperparameterOutput) ProtoMessage()

func (*HyperparameterOutput) Reset 

func (m *HyperparameterOutput) Reset()

func (*HyperparameterOutput) String 

func (m *HyperparameterOutput) String() string

type HyperparameterOutput_HyperparameterMetric 

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"`
}

An observed value of a metric.

func (*HyperparameterOutput_HyperparameterMetric) Descriptor 

func (*HyperparameterOutput_HyperparameterMetric) Descriptor() ([]byte, []int)

func (*HyperparameterOutput_HyperparameterMetric) GetObjectiveValue 

func (m *HyperparameterOutput_HyperparameterMetric) GetObjectiveValue() float64

func (*HyperparameterOutput_HyperparameterMetric) GetTrainingStep 

func (m *HyperparameterOutput_HyperparameterMetric) GetTrainingStep() int64

func (*HyperparameterOutput_HyperparameterMetric) ProtoMessage 

func (*HyperparameterOutput_HyperparameterMetric) ProtoMessage()

func (*HyperparameterOutput_HyperparameterMetric) Reset 

func (m *HyperparameterOutput_HyperparameterMetric) Reset()

func (*HyperparameterOutput_HyperparameterMetric) String 

func (m *HyperparameterOutput_HyperparameterMetric) String() string

type HyperparameterSpec 

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.v1.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"`
}

Represents a set of hyperparameters to optimize.

func (*HyperparameterSpec) Descriptor 

func (*HyperparameterSpec) Descriptor() ([]byte, []int)

func (*HyperparameterSpec) GetGoal 

func (m *HyperparameterSpec) GetGoal() HyperparameterSpec_GoalType

func (*HyperparameterSpec) GetHyperparameterMetricTag 

func (m *HyperparameterSpec) GetHyperparameterMetricTag() string

func (*HyperparameterSpec) GetMaxParallelTrials 

func (m *HyperparameterSpec) GetMaxParallelTrials() int32

func (*HyperparameterSpec) GetMaxTrials 

func (m *HyperparameterSpec) GetMaxTrials() int32

func (*HyperparameterSpec) GetParams 

func (m *HyperparameterSpec) GetParams() []*ParameterSpec

func (*HyperparameterSpec) ProtoMessage 

func (*HyperparameterSpec) ProtoMessage()

func (*HyperparameterSpec) Reset 

func (m *HyperparameterSpec) Reset()

func (*HyperparameterSpec) String 

func (m *HyperparameterSpec) String() string

type HyperparameterSpec_GoalType 

type HyperparameterSpec_GoalType int32

The available types of optimization goals. 

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
)

func (HyperparameterSpec_GoalType) EnumDescriptor 

func (HyperparameterSpec_GoalType) EnumDescriptor() ([]byte, []int)

func (HyperparameterSpec_GoalType) String 

func (x HyperparameterSpec_GoalType) String() string

type 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.v1.Job_State" json:"state,omitempty"`
	// Output only. The details of a failure or a cancellation.
	ErrorMessage string `protobuf:"bytes,8,opt,name=error_message,json=errorMessage" 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"`
}

Represents a training or prediction job.

func (*Job) Descriptor 

func (*Job) Descriptor() ([]byte, []int)

func (*Job) GetCreateTime 

func (m *Job) GetCreateTime() *google_protobuf2.Timestamp

func (*Job) GetEndTime 

func (m *Job) GetEndTime() *google_protobuf2.Timestamp

func (*Job) GetErrorMessage 

func (m *Job) GetErrorMessage() string

func (*Job) GetInput 

func (m *Job) GetInput() isJob_Input

func (*Job) GetJobId 

func (m *Job) GetJobId() string

func (*Job) GetOutput 

func (m *Job) GetOutput() isJob_Output

func (*Job) GetPredictionInput 

func (m *Job) GetPredictionInput() *PredictionInput

func (*Job) GetPredictionOutput 

func (m *Job) GetPredictionOutput() *PredictionOutput

func (*Job) GetStartTime 

func (m *Job) GetStartTime() *google_protobuf2.Timestamp

func (*Job) GetState 

func (m *Job) GetState() Job_State

func (*Job) GetTrainingInput 

func (m *Job) GetTrainingInput() *TrainingInput

func (*Job) GetTrainingOutput 

func (m *Job) GetTrainingOutput() *TrainingOutput

func (*Job) ProtoMessage 

func (*Job) ProtoMessage()

func (*Job) Reset 

func (m *Job) Reset()

func (*Job) String 

func (m *Job) String() string

func (*Job) XXX_OneofFuncs 

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{})

XXX_OneofFuncs is for the internal use of the proto package.

type JobServiceClient 

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)
}

func NewJobServiceClient 

func NewJobServiceClient(cc *grpc.ClientConn) JobServiceClient

type JobServiceServer 

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)
}

type Job_PredictionInput 

type Job_PredictionInput struct {
	PredictionInput *PredictionInput `protobuf:"bytes,3,opt,name=prediction_input,json=predictionInput,oneof"`
}

type Job_PredictionOutput 

type Job_PredictionOutput struct {
	PredictionOutput *PredictionOutput `protobuf:"bytes,10,opt,name=prediction_output,json=predictionOutput,oneof"`
}

type Job_State 

type Job_State int32

Describes the job state. 

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
)

func (Job_State) EnumDescriptor 

func (Job_State) EnumDescriptor() ([]byte, []int)

func (Job_State) String 

func (x Job_State) String() string

type Job_TrainingInput 

type Job_TrainingInput struct {
	TrainingInput *TrainingInput `protobuf:"bytes,2,opt,name=training_input,json=trainingInput,oneof"`
}

type Job_TrainingOutput 

type Job_TrainingOutput struct {
	TrainingOutput *TrainingOutput `protobuf:"bytes,9,opt,name=training_output,json=trainingOutput,oneof"`
}

type ListJobsRequest 

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"`
}

Request message for the ListJobs method.

func (*ListJobsRequest) Descriptor 

func (*ListJobsRequest) Descriptor() ([]byte, []int)

func (*ListJobsRequest) GetFilter 

func (m *ListJobsRequest) GetFilter() string

func (*ListJobsRequest) GetPageSize 

func (m *ListJobsRequest) GetPageSize() int32

func (*ListJobsRequest) GetPageToken 

func (m *ListJobsRequest) GetPageToken() string

func (*ListJobsRequest) GetParent 

func (m *ListJobsRequest) GetParent() string

func (*ListJobsRequest) ProtoMessage 

func (*ListJobsRequest) ProtoMessage()

func (*ListJobsRequest) Reset 

func (m *ListJobsRequest) Reset()

func (*ListJobsRequest) String 

func (m *ListJobsRequest) String() string

type ListJobsResponse 

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"`
}

Response message for the ListJobs method.

func (*ListJobsResponse) Descriptor 

func (*ListJobsResponse) Descriptor() ([]byte, []int)

func (*ListJobsResponse) GetJobs 

func (m *ListJobsResponse) GetJobs() []*Job

func (*ListJobsResponse) GetNextPageToken 

func (m *ListJobsResponse) GetNextPageToken() string

func (*ListJobsResponse) ProtoMessage 

func (*ListJobsResponse) ProtoMessage()

func (*ListJobsResponse) Reset 

func (m *ListJobsResponse) Reset()

func (*ListJobsResponse) String 

func (m *ListJobsResponse) String() string

type ListModelsRequest 

type ListModelsRequest struct {
	// Required. The name of the project whose models are to be listed.
	//
	// Authorization: requires `Viewer` role on the specified project.
	Parent string `protobuf:"bytes,1,opt,name=parent" json:"parent,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 models 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"`
}

Request message for the ListModels method.

func (*ListModelsRequest) Descriptor 

func (*ListModelsRequest) Descriptor() ([]byte, []int)

func (*ListModelsRequest) GetPageSize 

func (m *ListModelsRequest) GetPageSize() int32

func (*ListModelsRequest) GetPageToken 

func (m *ListModelsRequest) GetPageToken() string

func (*ListModelsRequest) GetParent 

func (m *ListModelsRequest) GetParent() string

func (*ListModelsRequest) ProtoMessage 

func (*ListModelsRequest) ProtoMessage()

func (*ListModelsRequest) Reset 

func (m *ListModelsRequest) Reset()

func (*ListModelsRequest) String 

func (m *ListModelsRequest) String() string

type ListModelsResponse 

type ListModelsResponse struct {
	// The list of models.
	Models []*Model `protobuf:"bytes,1,rep,name=models" json:"models,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"`
}

Response message for the ListModels method.

func (*ListModelsResponse) Descriptor 

func (*ListModelsResponse) Descriptor() ([]byte, []int)

func (*ListModelsResponse) GetModels 

func (m *ListModelsResponse) GetModels() []*Model

func (*ListModelsResponse) GetNextPageToken 

func (m *ListModelsResponse) GetNextPageToken() string

func (*ListModelsResponse) ProtoMessage 

func (*ListModelsResponse) ProtoMessage()

func (*ListModelsResponse) Reset 

func (m *ListModelsResponse) Reset()

func (*ListModelsResponse) String 

func (m *ListModelsResponse) String() string

type ListVersionsRequest 

type ListVersionsRequest struct {
	// Required. The name of the model for which to list the version.
	//
	// Authorization: requires `Viewer` role on the parent project.
	Parent string `protobuf:"bytes,1,opt,name=parent" json:"parent,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 versions 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"`
}

Request message for the ListVersions method.

func (*ListVersionsRequest) Descriptor 

func (*ListVersionsRequest) Descriptor() ([]byte, []int)

func (*ListVersionsRequest) GetPageSize 

func (m *ListVersionsRequest) GetPageSize() int32

func (*ListVersionsRequest) GetPageToken 

func (m *ListVersionsRequest) GetPageToken() string

func (*ListVersionsRequest) GetParent 

func (m *ListVersionsRequest) GetParent() string

func (*ListVersionsRequest) ProtoMessage 

func (*ListVersionsRequest) ProtoMessage()

func (*ListVersionsRequest) Reset 

func (m *ListVersionsRequest) Reset()

func (*ListVersionsRequest) String 

func (m *ListVersionsRequest) String() string

type ListVersionsResponse 

type ListVersionsResponse struct {
	// The list of versions.
	Versions []*Version `protobuf:"bytes,1,rep,name=versions" json:"versions,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"`
}

Response message for the ListVersions method.

func (*ListVersionsResponse) Descriptor 

func (*ListVersionsResponse) Descriptor() ([]byte, []int)

func (*ListVersionsResponse) GetNextPageToken 

func (m *ListVersionsResponse) GetNextPageToken() string

func (*ListVersionsResponse) GetVersions 

func (m *ListVersionsResponse) GetVersions() []*Version

func (*ListVersionsResponse) ProtoMessage 

func (*ListVersionsResponse) ProtoMessage()

func (*ListVersionsResponse) Reset 

func (m *ListVersionsResponse) Reset()

func (*ListVersionsResponse) String 

func (m *ListVersionsResponse) String() string

type ManualScaling 

type ManualScaling struct {
	// The number of nodes to allocate for this model. These nodes are always up,
	// starting from the time the model is deployed, so the cost of operating
	// this model will be proportional to nodes * number of hours since
	// deployment.
	Nodes int32 `protobuf:"varint,1,opt,name=nodes" json:"nodes,omitempty"`
}

Options for manually scaling a model.

func (*ManualScaling) Descriptor 

func (*ManualScaling) Descriptor() ([]byte, []int)

func (*ManualScaling) GetNodes 

func (m *ManualScaling) GetNodes() int32

func (*ManualScaling) ProtoMessage 

func (*ManualScaling) ProtoMessage()

func (*ManualScaling) Reset 

func (m *ManualScaling) Reset()

func (*ManualScaling) String 

func (m *ManualScaling) String() string

type Model 

type Model struct {
	// Required. The name specified for the model when it was created.
	//
	// The model name must be unique within the project it is created in.
	Name string `protobuf:"bytes,1,opt,name=name" json:"name,omitempty"`
	// Optional. The description specified for the model when it was created.
	Description string `protobuf:"bytes,2,opt,name=description" json:"description,omitempty"`
	// Output only. The default version of the model. This version will be used to
	// handle prediction requests that do not specify a version.
	//
	// You can change the default version by calling
	// [projects.methods.versions.setDefault](/ml/reference/rest/v1/projects.models.versions/setDefault).
	DefaultVersion *Version `protobuf:"bytes,3,opt,name=default_version,json=defaultVersion" json:"default_version,omitempty"`
	// Optional. The list of regions where the model is going to be deployed.
	// Currently only one region per model is supported.
	// Defaults to 'us-central1' if nothing is set.
	Regions []string `protobuf:"bytes,4,rep,name=regions" json:"regions,omitempty"`
	// Optional. If true, enables StackDriver Logging for online prediction.
	// Default is false.
	OnlinePredictionLogging bool `` /* 126-byte string literal not displayed */
}

Represents a machine learning solution.

A model can have multiple versions, each of which is a deployed, trained model ready to receive prediction requests. The model itself is just a container.

func (*Model) Descriptor 

func (*Model) Descriptor() ([]byte, []int)

func (*Model) GetDefaultVersion 

func (m *Model) GetDefaultVersion() *Version

func (*Model) GetDescription 

func (m *Model) GetDescription() string

func (*Model) GetName 

func (m *Model) GetName() string

func (*Model) GetOnlinePredictionLogging 

func (m *Model) GetOnlinePredictionLogging() bool

func (*Model) GetRegions 

func (m *Model) GetRegions() []string

func (*Model) ProtoMessage 

func (*Model) ProtoMessage()

func (*Model) Reset 

func (m *Model) Reset()

func (*Model) String 

func (m *Model) String() string

type ModelServiceClient 

type ModelServiceClient interface {
	// Creates a model which will later contain one or more versions.
	//
	// You must add at least one version before you can request predictions from
	// the model. Add versions by calling
	// [projects.models.versions.create](/ml/reference/rest/v1/projects.models.versions/create).
	CreateModel(ctx context.Context, in *CreateModelRequest, opts ...grpc.CallOption) (*Model, error)
	// Lists the models in a project.
	//
	// Each project can contain multiple models, and each model can have multiple
	// versions.
	ListModels(ctx context.Context, in *ListModelsRequest, opts ...grpc.CallOption) (*ListModelsResponse, error)
	// Gets information about a model, including its name, the description (if
	// set), and the default version (if at least one version of the model has
	// been deployed).
	GetModel(ctx context.Context, in *GetModelRequest, opts ...grpc.CallOption) (*Model, error)
	// Deletes a model.
	//
	// You can only delete a model if there are no versions in it. You can delete
	// versions by calling
	// [projects.models.versions.delete](/ml/reference/rest/v1/projects.models.versions/delete).
	DeleteModel(ctx context.Context, in *DeleteModelRequest, opts ...grpc.CallOption) (*google_longrunning.Operation, error)
	// Creates a new version of a model from a trained TensorFlow model.
	//
	// If the version created in the cloud by this call is the first deployed
	// version of the specified model, it will be made the default version of the
	// model. When you add a version to a model that already has one or more
	// versions, the default version does not automatically change. If you want a
	// new version to be the default, you must call
	// [projects.models.versions.setDefault](/ml/reference/rest/v1/projects.models.versions/setDefault).
	CreateVersion(ctx context.Context, in *CreateVersionRequest, opts ...grpc.CallOption) (*google_longrunning.Operation, error)
	// Gets basic information about all the versions of a model.
	//
	// If you expect that a model has a lot of versions, or if you need to handle
	// only a limited number of results at a time, you can request that the list
	// be retrieved in batches (called pages):
	ListVersions(ctx context.Context, in *ListVersionsRequest, opts ...grpc.CallOption) (*ListVersionsResponse, error)
	// Gets information about a model version.
	//
	// Models can have multiple versions. You can call
	// [projects.models.versions.list](/ml/reference/rest/v1/projects.models.versions/list)
	// to get the same information that this method returns for all of the
	// versions of a model.
	GetVersion(ctx context.Context, in *GetVersionRequest, opts ...grpc.CallOption) (*Version, error)
	// Deletes a model version.
	//
	// Each model can have multiple versions deployed and in use at any given
	// time. Use this method to remove a single version.
	//
	// Note: You cannot delete the version that is set as the default version
	// of the model unless it is the only remaining version.
	DeleteVersion(ctx context.Context, in *DeleteVersionRequest, opts ...grpc.CallOption) (*google_longrunning.Operation, error)
	// Designates a version to be the default for the model.
	//
	// The default version is used for prediction requests made against the model
	// that don't specify a version.
	//
	// The first version to be created for a model is automatically set as the
	// default. You must make any subsequent changes to the default version
	// setting manually using this method.
	SetDefaultVersion(ctx context.Context, in *SetDefaultVersionRequest, opts ...grpc.CallOption) (*Version, error)
}

func NewModelServiceClient 

func NewModelServiceClient(cc *grpc.ClientConn) ModelServiceClient

type ModelServiceServer 

type ModelServiceServer interface {
	// Creates a model which will later contain one or more versions.
	//
	// You must add at least one version before you can request predictions from
	// the model. Add versions by calling
	// [projects.models.versions.create](/ml/reference/rest/v1/projects.models.versions/create).
	CreateModel(context.Context, *CreateModelRequest) (*Model, error)
	// Lists the models in a project.
	//
	// Each project can contain multiple models, and each model can have multiple
	// versions.
	ListModels(context.Context, *ListModelsRequest) (*ListModelsResponse, error)
	// Gets information about a model, including its name, the description (if
	// set), and the default version (if at least one version of the model has
	// been deployed).
	GetModel(context.Context, *GetModelRequest) (*Model, error)
	// Deletes a model.
	//
	// You can only delete a model if there are no versions in it. You can delete
	// versions by calling
	// [projects.models.versions.delete](/ml/reference/rest/v1/projects.models.versions/delete).
	DeleteModel(context.Context, *DeleteModelRequest) (*google_longrunning.Operation, error)
	// Creates a new version of a model from a trained TensorFlow model.
	//
	// If the version created in the cloud by this call is the first deployed
	// version of the specified model, it will be made the default version of the
	// model. When you add a version to a model that already has one or more
	// versions, the default version does not automatically change. If you want a
	// new version to be the default, you must call
	// [projects.models.versions.setDefault](/ml/reference/rest/v1/projects.models.versions/setDefault).
	CreateVersion(context.Context, *CreateVersionRequest) (*google_longrunning.Operation, error)
	// Gets basic information about all the versions of a model.
	//
	// If you expect that a model has a lot of versions, or if you need to handle
	// only a limited number of results at a time, you can request that the list
	// be retrieved in batches (called pages):
	ListVersions(context.Context, *ListVersionsRequest) (*ListVersionsResponse, error)
	// Gets information about a model version.
	//
	// Models can have multiple versions. You can call
	// [projects.models.versions.list](/ml/reference/rest/v1/projects.models.versions/list)
	// to get the same information that this method returns for all of the
	// versions of a model.
	GetVersion(context.Context, *GetVersionRequest) (*Version, error)
	// Deletes a model version.
	//
	// Each model can have multiple versions deployed and in use at any given
	// time. Use this method to remove a single version.
	//
	// Note: You cannot delete the version that is set as the default version
	// of the model unless it is the only remaining version.
	DeleteVersion(context.Context, *DeleteVersionRequest) (*google_longrunning.Operation, error)
	// Designates a version to be the default for the model.
	//
	// The default version is used for prediction requests made against the model
	// that don't specify a version.
	//
	// The first version to be created for a model is automatically set as the
	// default. You must make any subsequent changes to the default version
	// setting manually using this method.
	SetDefaultVersion(context.Context, *SetDefaultVersionRequest) (*Version, error)
}

type OnlinePredictionServiceClient 

type OnlinePredictionServiceClient interface {
	// Performs prediction on the data in the request.
	//
	// **** REMOVE FROM GENERATED DOCUMENTATION
	Predict(ctx context.Context, in *PredictRequest, opts ...grpc.CallOption) (*google_api3.HttpBody, error)
}

func NewOnlinePredictionServiceClient 

func NewOnlinePredictionServiceClient(cc *grpc.ClientConn) OnlinePredictionServiceClient

type OnlinePredictionServiceServer 

type OnlinePredictionServiceServer interface {
	// Performs prediction on the data in the request.
	//
	// **** REMOVE FROM GENERATED DOCUMENTATION
	Predict(context.Context, *PredictRequest) (*google_api3.HttpBody, error)
}

type OperationMetadata 

type OperationMetadata struct {
	// The time the operation was submitted.
	CreateTime *google_protobuf2.Timestamp `protobuf:"bytes,1,opt,name=create_time,json=createTime" json:"create_time,omitempty"`
	// The time operation processing started.
	StartTime *google_protobuf2.Timestamp `protobuf:"bytes,2,opt,name=start_time,json=startTime" json:"start_time,omitempty"`
	// The time operation processing completed.
	EndTime *google_protobuf2.Timestamp `protobuf:"bytes,3,opt,name=end_time,json=endTime" json:"end_time,omitempty"`
	// Indicates whether a request to cancel this operation has been made.
	IsCancellationRequested bool `` /* 126-byte string literal not displayed */
	// The operation type.
	OperationType OperationMetadata_OperationType `` /* 150-byte string literal not displayed */
	// Contains the name of the model associated with the operation.
	ModelName string `protobuf:"bytes,6,opt,name=model_name,json=modelName" json:"model_name,omitempty"`
	// Contains the version associated with the operation.
	Version *Version `protobuf:"bytes,7,opt,name=version" json:"version,omitempty"`
}

Represents the metadata of the long-running operation.

func (*OperationMetadata) Descriptor 

func (*OperationMetadata) Descriptor() ([]byte, []int)

func (*OperationMetadata) GetCreateTime 

func (m *OperationMetadata) GetCreateTime() *google_protobuf2.Timestamp

func (*OperationMetadata) GetEndTime 

func (m *OperationMetadata) GetEndTime() *google_protobuf2.Timestamp

func (*OperationMetadata) GetIsCancellationRequested 

func (m *OperationMetadata) GetIsCancellationRequested() bool

func (*OperationMetadata) GetModelName 

func (m *OperationMetadata) GetModelName() string

func (*OperationMetadata) GetOperationType 

func (m *OperationMetadata) GetOperationType() OperationMetadata_OperationType

func (*OperationMetadata) GetStartTime 

func (m *OperationMetadata) GetStartTime() *google_protobuf2.Timestamp

func (*OperationMetadata) GetVersion 

func (m *OperationMetadata) GetVersion() *Version

func (*OperationMetadata) ProtoMessage 

func (*OperationMetadata) ProtoMessage()

func (*OperationMetadata) Reset 

func (m *OperationMetadata) Reset()

func (*OperationMetadata) String 

func (m *OperationMetadata) String() string

type OperationMetadata_OperationType 

type OperationMetadata_OperationType int32

The operation type. 

const (
	// Unspecified operation type.
	OperationMetadata_OPERATION_TYPE_UNSPECIFIED OperationMetadata_OperationType = 0
	// An operation to create a new version.
	OperationMetadata_CREATE_VERSION OperationMetadata_OperationType = 1
	// An operation to delete an existing version.
	OperationMetadata_DELETE_VERSION OperationMetadata_OperationType = 2
	// An operation to delete an existing model.
	OperationMetadata_DELETE_MODEL OperationMetadata_OperationType = 3
)

func (OperationMetadata_OperationType) EnumDescriptor 

func (OperationMetadata_OperationType) EnumDescriptor() ([]byte, []int)

func (OperationMetadata_OperationType) String 

func (x OperationMetadata_OperationType) String() string

type ParameterSpec 

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.v1.ParameterSpec_ParameterType" json:"type,omitempty"`
	// Required if type is `DOUBLE` or `INTEGER`. This field
	// should be unset if type is `CATEGORICAL`. This value should be integers if
	// type is INTEGER.
	MinValue float64 `protobuf:"fixed64,2,opt,name=min_value,json=minValue" 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 `` /* 130-byte string literal not displayed */
}

Represents a single hyperparameter to optimize.

func (*ParameterSpec) Descriptor 

func (*ParameterSpec) Descriptor() ([]byte, []int)

func (*ParameterSpec) GetCategoricalValues 

func (m *ParameterSpec) GetCategoricalValues() []string

func (*ParameterSpec) GetDiscreteValues 

func (m *ParameterSpec) GetDiscreteValues() []float64

func (*ParameterSpec) GetMaxValue 

func (m *ParameterSpec) GetMaxValue() float64

func (*ParameterSpec) GetMinValue 

func (m *ParameterSpec) GetMinValue() float64

func (*ParameterSpec) GetParameterName 

func (m *ParameterSpec) GetParameterName() string

func (*ParameterSpec) GetScaleType 

func (m *ParameterSpec) GetScaleType() ParameterSpec_ScaleType

func (*ParameterSpec) GetType 

func (m *ParameterSpec) GetType() ParameterSpec_ParameterType

func (*ParameterSpec) ProtoMessage 

func (*ParameterSpec) ProtoMessage()

func (*ParameterSpec) Reset 

func (m *ParameterSpec) Reset()

func (*ParameterSpec) String 

func (m *ParameterSpec) String() string

type ParameterSpec_ParameterType 

type ParameterSpec_ParameterType int32

The type of the parameter. 

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
)

func (ParameterSpec_ParameterType) EnumDescriptor 

func (ParameterSpec_ParameterType) EnumDescriptor() ([]byte, []int)

func (ParameterSpec_ParameterType) String 

func (x ParameterSpec_ParameterType) String() string

type ParameterSpec_ScaleType 

type ParameterSpec_ScaleType int32

The type of scaling that should be applied to this parameter. 

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
)

func (ParameterSpec_ScaleType) EnumDescriptor 

func (ParameterSpec_ScaleType) EnumDescriptor() ([]byte, []int)

func (ParameterSpec_ScaleType) String 

func (x ParameterSpec_ScaleType) String() string

type PredictRequest 

type PredictRequest struct {
	// Required. The resource name of a model or a version.
	//
	// Authorization: requires `Viewer` role on the parent project.
	Name string `protobuf:"bytes,1,opt,name=name" json:"name,omitempty"`
	//
	// Required. The prediction request body.
	HttpBody *google_api3.HttpBody `protobuf:"bytes,2,opt,name=http_body,json=httpBody" json:"http_body,omitempty"`
}

Request for predictions to be issued against a trained model.

The body of the request is a single JSON object with a single top-level field:

<dl> 

<dt>instances</dt>
<dd>A JSON array containing values representing the instances to use for
    prediction.</dd>

</dl>

The structure of each element of the instances list is determined by your model's input definition. Instances can include named inputs or can contain only unlabeled values.

Not all data includes named inputs. Some instances will be simple JSON values (boolean, number, or string). However, instances are often lists of simple values, or complex nested lists. Here are some examples of request bodies:

CSV data with each row encoded as a string value: <pre> {"instances": ["1.0,true,\\"x\\"", "-2.0,false,\\"y\\""]} </pre> Plain text: <pre> {"instances": ["the quick brown fox", "la bruja le dio"]} </pre> Sentences encoded as lists of words (vectors of strings): <pre> 

{
  "instances": [
    ["the","quick","brown"],
    ["la","bruja","le"],
    ...
  ]
}

</pre> Floating point scalar values: <pre> {"instances": [0.0, 1.1, 2.2]} </pre> Vectors of integers: <pre> 

{
  "instances": [
    [0, 1, 2],
    [3, 4, 5],
    ...
  ]
}

</pre> Tensors (in this case, two-dimensional tensors): <pre> 

{
  "instances": [
    [
      [0, 1, 2],
      [3, 4, 5]
    ],
    ...
  ]
}

</pre> Images can be represented different ways. In this encoding scheme the first two dimensions represent the rows and columns of the image, and the third contains lists (vectors) of the R, G, and B values for each pixel. <pre> 

{
  "instances": [
    [
      [
        [138, 30, 66],
        [130, 20, 56],
        ...
      ],
      [
        [126, 38, 61],
        [122, 24, 57],
        ...
      ],
      ...
    ],
    ...
  ]
}

</pre> JSON strings must be encoded as UTF-8. To send binary data, you must base64-encode the data and mark it as binary. To mark a JSON string as binary, replace it with a JSON object with a single attribute named `b64`: <pre>{"b64": "..."} </pre> For example:

Two Serialized tf.Examples (fake data, for illustrative purposes only): <pre> {"instances": [{"b64": "X5ad6u"}, {"b64": "IA9j4nx"}]} </pre> Two JPEG image byte strings (fake data, for illustrative purposes only): <pre> {"instances": [{"b64": "ASa8asdf"}, {"b64": "JLK7ljk3"}]} </pre> If your data includes named references, format each instance as a JSON object with the named references as the keys:

JSON input data to be preprocessed: <pre> 

{
  "instances": [
    {
      "a": 1.0,
      "b": true,
      "c": "x"
    },
    {
      "a": -2.0,
      "b": false,
      "c": "y"
    }
  ]
}

</pre> Some models have an underlying TensorFlow graph that accepts multiple input tensors. In this case, you should use the names of JSON name/value pairs to identify the input tensors, as shown in the following exmaples:

For a graph with input tensor aliases "tag" (string) and "image" (base64-encoded string): <pre> 

{
  "instances": [
    {
      "tag": "beach",
      "image": {"b64": "ASa8asdf"}
    },
    {
      "tag": "car",
      "image": {"b64": "JLK7ljk3"}
    }
  ]
}

</pre> For a graph with input tensor aliases "tag" (string) and "image" (3-dimensional array of 8-bit ints): <pre> 

{
  "instances": [
    {
      "tag": "beach",
      "image": [
        [
          [138, 30, 66],
          [130, 20, 56],
          ...
        ],
        [
          [126, 38, 61],
          [122, 24, 57],
          ...
        ],
        ...
      ]
    },
    {
      "tag": "car",
      "image": [
        [
          [255, 0, 102],
          [255, 0, 97],
          ...
        ],
        [
          [254, 1, 101],
          [254, 2, 93],
          ...
        ],
        ...
      ]
    },
    ...
  ]
}

</pre> If the call is successful, the response body will contain one prediction entry per instance in the request body. If prediction fails for any instance, the response body will contain no predictions and will contian a single error entry instead.

func (*PredictRequest) Descriptor 

func (*PredictRequest) Descriptor() ([]byte, []int)

func (*PredictRequest) GetHttpBody 

func (m *PredictRequest) GetHttpBody() *google_api3.HttpBody

func (*PredictRequest) GetName 

func (m *PredictRequest) GetName() string

func (*PredictRequest) ProtoMessage 

func (*PredictRequest) ProtoMessage()

func (*PredictRequest) Reset 

func (m *PredictRequest) Reset()

func (*PredictRequest) String 

func (m *PredictRequest) String() string

type PredictionInput 

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 `` /* 136-byte string literal not displayed */
	// 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"`
}

Represents input parameters for a prediction job.

func (*PredictionInput) Descriptor 

func (*PredictionInput) Descriptor() ([]byte, []int)

func (*PredictionInput) GetDataFormat 

func (m *PredictionInput) GetDataFormat() PredictionInput_DataFormat

func (*PredictionInput) GetInputPaths 

func (m *PredictionInput) GetInputPaths() []string

func (*PredictionInput) GetMaxWorkerCount 

func (m *PredictionInput) GetMaxWorkerCount() int64

func (*PredictionInput) GetModelName 

func (m *PredictionInput) GetModelName() string

func (*PredictionInput) GetModelVersion 

func (m *PredictionInput) GetModelVersion() isPredictionInput_ModelVersion

func (*PredictionInput) GetOutputPath 

func (m *PredictionInput) GetOutputPath() string

func (*PredictionInput) GetRegion 

func (m *PredictionInput) GetRegion() string

func (*PredictionInput) GetRuntimeVersion 

func (m *PredictionInput) GetRuntimeVersion() string

func (*PredictionInput) GetUri 

func (m *PredictionInput) GetUri() string

func (*PredictionInput) GetVersionName 

func (m *PredictionInput) GetVersionName() string

func (*PredictionInput) ProtoMessage 

func (*PredictionInput) ProtoMessage()

func (*PredictionInput) Reset 

func (m *PredictionInput) Reset()

func (*PredictionInput) String 

func (m *PredictionInput) String() string

func (*PredictionInput) XXX_OneofFuncs 

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{})

XXX_OneofFuncs is for the internal use of the proto package.

type PredictionInput_DataFormat 

type PredictionInput_DataFormat int32

The format used to separate data instances in the source files. 

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
)

func (PredictionInput_DataFormat) EnumDescriptor 

func (PredictionInput_DataFormat) EnumDescriptor() ([]byte, []int)

func (PredictionInput_DataFormat) String 

func (x PredictionInput_DataFormat) String() string

type PredictionInput_ModelName 

type PredictionInput_ModelName struct {
	ModelName string `protobuf:"bytes,1,opt,name=model_name,json=modelName,oneof"`
}

type PredictionInput_Uri 

type PredictionInput_Uri struct {
	Uri string `protobuf:"bytes,9,opt,name=uri,oneof"`
}

type PredictionInput_VersionName 

type PredictionInput_VersionName struct {
	VersionName string `protobuf:"bytes,2,opt,name=version_name,json=versionName,oneof"`
}

type PredictionOutput 

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"`
}

Represents results of a prediction job.

func (*PredictionOutput) Descriptor 

func (*PredictionOutput) Descriptor() ([]byte, []int)

func (*PredictionOutput) GetErrorCount 

func (m *PredictionOutput) GetErrorCount() int64

func (*PredictionOutput) GetNodeHours 

func (m *PredictionOutput) GetNodeHours() float64

func (*PredictionOutput) GetOutputPath 

func (m *PredictionOutput) GetOutputPath() string

func (*PredictionOutput) GetPredictionCount 

func (m *PredictionOutput) GetPredictionCount() int64

func (*PredictionOutput) ProtoMessage 

func (*PredictionOutput) ProtoMessage()

func (*PredictionOutput) Reset 

func (m *PredictionOutput) Reset()

func (*PredictionOutput) String 

func (m *PredictionOutput) String() string

type ProjectManagementServiceClient 

type ProjectManagementServiceClient interface {
	// Get the service account information associated with your project. You need
	// this information in order to grant the service account persmissions for
	// the Google Cloud Storage location where you put your model training code
	// for training the model with Google Cloud Machine Learning.
	GetConfig(ctx context.Context, in *GetConfigRequest, opts ...grpc.CallOption) (*GetConfigResponse, error)
}

func NewProjectManagementServiceClient 

func NewProjectManagementServiceClient(cc *grpc.ClientConn) ProjectManagementServiceClient

type ProjectManagementServiceServer 

type ProjectManagementServiceServer interface {
	// Get the service account information associated with your project. You need
	// this information in order to grant the service account persmissions for
	// the Google Cloud Storage location where you put your model training code
	// for training the model with Google Cloud Machine Learning.
	GetConfig(context.Context, *GetConfigRequest) (*GetConfigResponse, error)
}

type SetDefaultVersionRequest 

type SetDefaultVersionRequest struct {
	// Required. The name of the version to make the default for the model. You
	// can get the names of all the versions of a model by calling
	// [projects.models.versions.list](/ml/reference/rest/v1/projects.models.versions/list).
	//
	// Authorization: requires `Editor` role on the parent project.
	Name string `protobuf:"bytes,1,opt,name=name" json:"name,omitempty"`
}

Request message for the SetDefaultVersion request.

func (*SetDefaultVersionRequest) Descriptor 

func (*SetDefaultVersionRequest) Descriptor() ([]byte, []int)

func (*SetDefaultVersionRequest) GetName 

func (m *SetDefaultVersionRequest) GetName() string

func (*SetDefaultVersionRequest) ProtoMessage 

func (*SetDefaultVersionRequest) ProtoMessage()

func (*SetDefaultVersionRequest) Reset 

func (m *SetDefaultVersionRequest) Reset()

func (*SetDefaultVersionRequest) String 

func (m *SetDefaultVersionRequest) String() string

type TrainingInput 

type TrainingInput struct {
	// Required. Specifies the machine types, the number of replicas for workers
	// and parameter servers.
	ScaleTier TrainingInput_ScaleTier `` /* 130-byte string literal not displayed */
	// 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"`
}

Represents input parameters for a training job.

func (*TrainingInput) Descriptor 

func (*TrainingInput) Descriptor() ([]byte, []int)

func (*TrainingInput) GetArgs 

func (m *TrainingInput) GetArgs() []string

func (*TrainingInput) GetHyperparameters 

func (m *TrainingInput) GetHyperparameters() *HyperparameterSpec

func (*TrainingInput) GetJobDir 

func (m *TrainingInput) GetJobDir() string

func (*TrainingInput) GetMasterType 

func (m *TrainingInput) GetMasterType() string

func (*TrainingInput) GetPackageUris 

func (m *TrainingInput) GetPackageUris() []string

func (*TrainingInput) GetParameterServerCount 

func (m *TrainingInput) GetParameterServerCount() int64

func (*TrainingInput) GetParameterServerType 

func (m *TrainingInput) GetParameterServerType() string

func (*TrainingInput) GetPythonModule 

func (m *TrainingInput) GetPythonModule() string

func (*TrainingInput) GetRegion 

func (m *TrainingInput) GetRegion() string

func (*TrainingInput) GetRuntimeVersion 

func (m *TrainingInput) GetRuntimeVersion() string

func (*TrainingInput) GetScaleTier 

func (m *TrainingInput) GetScaleTier() TrainingInput_ScaleTier

func (*TrainingInput) GetWorkerCount 

func (m *TrainingInput) GetWorkerCount() int64

func (*TrainingInput) GetWorkerType 

func (m *TrainingInput) GetWorkerType() string

func (*TrainingInput) ProtoMessage 

func (*TrainingInput) ProtoMessage()

func (*TrainingInput) Reset 

func (m *TrainingInput) Reset()

func (*TrainingInput) String 

func (m *TrainingInput) String() string

type TrainingInput_ScaleTier 

type TrainingInput_ScaleTier int32

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). 

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
)

func (TrainingInput_ScaleTier) EnumDescriptor 

func (TrainingInput_ScaleTier) EnumDescriptor() ([]byte, []int)

func (TrainingInput_ScaleTier) String 

func (x TrainingInput_ScaleTier) String() string

type TrainingOutput 

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 `` /* 134-byte string literal not displayed */
}

Represents results of a training job. Output only.

func (*TrainingOutput) Descriptor 

func (*TrainingOutput) Descriptor() ([]byte, []int)

func (*TrainingOutput) GetCompletedTrialCount 

func (m *TrainingOutput) GetCompletedTrialCount() int64

func (*TrainingOutput) GetConsumedMlUnits 

func (m *TrainingOutput) GetConsumedMlUnits() float64

func (*TrainingOutput) GetIsHyperparameterTuningJob 

func (m *TrainingOutput) GetIsHyperparameterTuningJob() bool

func (*TrainingOutput) GetTrials 

func (m *TrainingOutput) GetTrials() []*HyperparameterOutput

func (*TrainingOutput) ProtoMessage 

func (*TrainingOutput) ProtoMessage()

func (*TrainingOutput) Reset 

func (m *TrainingOutput) Reset()

func (*TrainingOutput) String 

func (m *TrainingOutput) String() string

type Version 

type Version struct {
	// Required.The name specified for the version when it was created.
	//
	// The version name must be unique within the model it is created in.
	Name string `protobuf:"bytes,1,opt,name=name" json:"name,omitempty"`
	// Optional. The description specified for the version when it was created.
	Description string `protobuf:"bytes,2,opt,name=description" json:"description,omitempty"`
	// Output only. If true, this version will be used to handle prediction
	// requests that do not specify a version.
	//
	// You can change the default version by calling
	// [projects.methods.versions.setDefault](/ml/reference/rest/v1/projects.models.versions/setDefault).
	IsDefault bool `protobuf:"varint,3,opt,name=is_default,json=isDefault" json:"is_default,omitempty"`
	// Required. The Google Cloud Storage location of the trained model used to
	// create the version. See the
	// [overview of model deployment](/ml/docs/concepts/deployment-overview) for
	// more informaiton.
	//
	// When passing Version to
	// [projects.models.versions.create](/ml/reference/rest/v1/projects.models.versions/create)
	// the model service uses the specified location as the source of the model.
	// Once deployed, the model version is hosted by the prediction service, so
	// this location is useful only as a historical record.
	DeploymentUri string `protobuf:"bytes,4,opt,name=deployment_uri,json=deploymentUri" json:"deployment_uri,omitempty"`
	// Output only. The time the version was created.
	CreateTime *google_protobuf2.Timestamp `protobuf:"bytes,5,opt,name=create_time,json=createTime" json:"create_time,omitempty"`
	// Output only. The time the version was last used for prediction.
	LastUseTime *google_protobuf2.Timestamp `protobuf:"bytes,6,opt,name=last_use_time,json=lastUseTime" json:"last_use_time,omitempty"`
	// Optional. The Google Cloud ML runtime version to use for this deployment.
	// If not set, Google Cloud ML will choose a version.
	RuntimeVersion string `protobuf:"bytes,8,opt,name=runtime_version,json=runtimeVersion" json:"runtime_version,omitempty"`
	// Optional. Manually select the number of nodes to use for serving the
	// model. If unset (i.e., by default), the number of nodes used to serve
	// the model automatically scales with traffic. However, care should be
	// taken to ramp up traffic according to the model's ability to scale. If
	// your model needs to handle bursts of traffic beyond it's ability to
	// scale, it is recommended you set this field appropriately.
	ManualScaling *ManualScaling `protobuf:"bytes,9,opt,name=manual_scaling,json=manualScaling" json:"manual_scaling,omitempty"`
}

Represents a version of the model.

Each version is a trained model deployed in the cloud, ready to handle prediction requests. A model can have multiple versions. You can get information about all of the versions of a given model by calling [projects.models.versions.list](/ml/reference/rest/v1/projects.models.versions/list).

func (*Version) Descriptor 

func (*Version) Descriptor() ([]byte, []int)

func (*Version) GetCreateTime 

func (m *Version) GetCreateTime() *google_protobuf2.Timestamp

func (*Version) GetDeploymentUri 

func (m *Version) GetDeploymentUri() string

func (*Version) GetDescription 

func (m *Version) GetDescription() string

func (*Version) GetIsDefault 

func (m *Version) GetIsDefault() bool

func (*Version) GetLastUseTime 

func (m *Version) GetLastUseTime() *google_protobuf2.Timestamp

func (*Version) GetManualScaling 

func (m *Version) GetManualScaling() *ManualScaling

func (*Version) GetName 

func (m *Version) GetName() string

func (*Version) GetRuntimeVersion 

func (m *Version) GetRuntimeVersion() string

func (*Version) ProtoMessage 

func (*Version) ProtoMessage()

func (*Version) Reset 

func (m *Version) Reset()

func (*Version) String 

func (m *Version) String() string

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