vendor files

This commit is contained in:
Serguei Bezverkhi
2018-01-09 13:57:14 -05:00
parent 558bc6c02a
commit 7b24313bd6
16547 changed files with 4527373 additions and 0 deletions

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// Code generated by protoc-gen-go. DO NOT EDIT.
// source: google/cloud/ml/v1/operation_metadata.proto
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_protobuf2 "github.com/golang/protobuf/ptypes/timestamp"
// Reference imports to suppress errors if they are not otherwise used.
var _ = proto.Marshal
var _ = fmt.Errorf
var _ = math.Inf
// The operation type.
type OperationMetadata_OperationType int32
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
)
var OperationMetadata_OperationType_name = map[int32]string{
0: "OPERATION_TYPE_UNSPECIFIED",
1: "CREATE_VERSION",
2: "DELETE_VERSION",
3: "DELETE_MODEL",
}
var OperationMetadata_OperationType_value = map[string]int32{
"OPERATION_TYPE_UNSPECIFIED": 0,
"CREATE_VERSION": 1,
"DELETE_VERSION": 2,
"DELETE_MODEL": 3,
}
func (x OperationMetadata_OperationType) String() string {
return proto.EnumName(OperationMetadata_OperationType_name, int32(x))
}
func (OperationMetadata_OperationType) EnumDescriptor() ([]byte, []int) {
return fileDescriptor2, []int{0, 0}
}
// Represents the metadata of the long-running operation.
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 `protobuf:"varint,4,opt,name=is_cancellation_requested,json=isCancellationRequested" json:"is_cancellation_requested,omitempty"`
// The operation type.
OperationType OperationMetadata_OperationType `protobuf:"varint,5,opt,name=operation_type,json=operationType,enum=google.cloud.ml.v1.OperationMetadata_OperationType" json:"operation_type,omitempty"`
// 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"`
}
func (m *OperationMetadata) Reset() { *m = OperationMetadata{} }
func (m *OperationMetadata) String() string { return proto.CompactTextString(m) }
func (*OperationMetadata) ProtoMessage() {}
func (*OperationMetadata) Descriptor() ([]byte, []int) { return fileDescriptor2, []int{0} }
func (m *OperationMetadata) GetCreateTime() *google_protobuf2.Timestamp {
if m != nil {
return m.CreateTime
}
return nil
}
func (m *OperationMetadata) GetStartTime() *google_protobuf2.Timestamp {
if m != nil {
return m.StartTime
}
return nil
}
func (m *OperationMetadata) GetEndTime() *google_protobuf2.Timestamp {
if m != nil {
return m.EndTime
}
return nil
}
func (m *OperationMetadata) GetIsCancellationRequested() bool {
if m != nil {
return m.IsCancellationRequested
}
return false
}
func (m *OperationMetadata) GetOperationType() OperationMetadata_OperationType {
if m != nil {
return m.OperationType
}
return OperationMetadata_OPERATION_TYPE_UNSPECIFIED
}
func (m *OperationMetadata) GetModelName() string {
if m != nil {
return m.ModelName
}
return ""
}
func (m *OperationMetadata) GetVersion() *Version {
if m != nil {
return m.Version
}
return nil
}
func init() {
proto.RegisterType((*OperationMetadata)(nil), "google.cloud.ml.v1.OperationMetadata")
proto.RegisterEnum("google.cloud.ml.v1.OperationMetadata_OperationType", OperationMetadata_OperationType_name, OperationMetadata_OperationType_value)
}
func init() { proto.RegisterFile("google/cloud/ml/v1/operation_metadata.proto", fileDescriptor2) }
var fileDescriptor2 = []byte{
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}

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// Code generated by protoc-gen-go. DO NOT EDIT.
// source: google/cloud/ml/v1/prediction_service.proto
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_api3 "google.golang.org/genproto/googleapis/api/httpbody"
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
// 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.
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"`
}
func (m *PredictRequest) Reset() { *m = PredictRequest{} }
func (m *PredictRequest) String() string { return proto.CompactTextString(m) }
func (*PredictRequest) ProtoMessage() {}
func (*PredictRequest) Descriptor() ([]byte, []int) { return fileDescriptor3, []int{0} }
func (m *PredictRequest) GetName() string {
if m != nil {
return m.Name
}
return ""
}
func (m *PredictRequest) GetHttpBody() *google_api3.HttpBody {
if m != nil {
return m.HttpBody
}
return nil
}
func init() {
proto.RegisterType((*PredictRequest)(nil), "google.cloud.ml.v1.PredictRequest")
}
// 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 OnlinePredictionService service
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)
}
type onlinePredictionServiceClient struct {
cc *grpc.ClientConn
}
func NewOnlinePredictionServiceClient(cc *grpc.ClientConn) OnlinePredictionServiceClient {
return &onlinePredictionServiceClient{cc}
}
func (c *onlinePredictionServiceClient) Predict(ctx context.Context, in *PredictRequest, opts ...grpc.CallOption) (*google_api3.HttpBody, error) {
out := new(google_api3.HttpBody)
err := grpc.Invoke(ctx, "/google.cloud.ml.v1.OnlinePredictionService/Predict", in, out, c.cc, opts...)
if err != nil {
return nil, err
}
return out, nil
}
// Server API for OnlinePredictionService service
type OnlinePredictionServiceServer interface {
// Performs prediction on the data in the request.
//
// **** REMOVE FROM GENERATED DOCUMENTATION
Predict(context.Context, *PredictRequest) (*google_api3.HttpBody, error)
}
func RegisterOnlinePredictionServiceServer(s *grpc.Server, srv OnlinePredictionServiceServer) {
s.RegisterService(&_OnlinePredictionService_serviceDesc, srv)
}
func _OnlinePredictionService_Predict_Handler(srv interface{}, ctx context.Context, dec func(interface{}) error, interceptor grpc.UnaryServerInterceptor) (interface{}, error) {
in := new(PredictRequest)
if err := dec(in); err != nil {
return nil, err
}
if interceptor == nil {
return srv.(OnlinePredictionServiceServer).Predict(ctx, in)
}
info := &grpc.UnaryServerInfo{
Server: srv,
FullMethod: "/google.cloud.ml.v1.OnlinePredictionService/Predict",
}
handler := func(ctx context.Context, req interface{}) (interface{}, error) {
return srv.(OnlinePredictionServiceServer).Predict(ctx, req.(*PredictRequest))
}
return interceptor(ctx, in, info, handler)
}
var _OnlinePredictionService_serviceDesc = grpc.ServiceDesc{
ServiceName: "google.cloud.ml.v1.OnlinePredictionService",
HandlerType: (*OnlinePredictionServiceServer)(nil),
Methods: []grpc.MethodDesc{
{
MethodName: "Predict",
Handler: _OnlinePredictionService_Predict_Handler,
},
},
Streams: []grpc.StreamDesc{},
Metadata: "google/cloud/ml/v1/prediction_service.proto",
}
func init() { proto.RegisterFile("google/cloud/ml/v1/prediction_service.proto", fileDescriptor3) }
var fileDescriptor3 = []byte{
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0xd5, 0x01, 0x00, 0x00,
}

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// Code generated by protoc-gen-go. DO NOT EDIT.
// source: google/cloud/ml/v1/project_service.proto
package ml
import proto "github.com/golang/protobuf/proto"
import fmt "fmt"
import math "math"
import _ "google.golang.org/genproto/googleapis/api/annotations"
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
// Requests service account information associated with a project.
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"`
}
func (m *GetConfigRequest) Reset() { *m = GetConfigRequest{} }
func (m *GetConfigRequest) String() string { return proto.CompactTextString(m) }
func (*GetConfigRequest) ProtoMessage() {}
func (*GetConfigRequest) Descriptor() ([]byte, []int) { return fileDescriptor4, []int{0} }
func (m *GetConfigRequest) GetName() string {
if m != nil {
return m.Name
}
return ""
}
// Returns service account information associated with a project.
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"`
}
func (m *GetConfigResponse) Reset() { *m = GetConfigResponse{} }
func (m *GetConfigResponse) String() string { return proto.CompactTextString(m) }
func (*GetConfigResponse) ProtoMessage() {}
func (*GetConfigResponse) Descriptor() ([]byte, []int) { return fileDescriptor4, []int{1} }
func (m *GetConfigResponse) GetServiceAccount() string {
if m != nil {
return m.ServiceAccount
}
return ""
}
func (m *GetConfigResponse) GetServiceAccountProject() int64 {
if m != nil {
return m.ServiceAccountProject
}
return 0
}
func init() {
proto.RegisterType((*GetConfigRequest)(nil), "google.cloud.ml.v1.GetConfigRequest")
proto.RegisterType((*GetConfigResponse)(nil), "google.cloud.ml.v1.GetConfigResponse")
}
// 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 ProjectManagementService service
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)
}
type projectManagementServiceClient struct {
cc *grpc.ClientConn
}
func NewProjectManagementServiceClient(cc *grpc.ClientConn) ProjectManagementServiceClient {
return &projectManagementServiceClient{cc}
}
func (c *projectManagementServiceClient) GetConfig(ctx context.Context, in *GetConfigRequest, opts ...grpc.CallOption) (*GetConfigResponse, error) {
out := new(GetConfigResponse)
err := grpc.Invoke(ctx, "/google.cloud.ml.v1.ProjectManagementService/GetConfig", in, out, c.cc, opts...)
if err != nil {
return nil, err
}
return out, nil
}
// Server API for ProjectManagementService service
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)
}
func RegisterProjectManagementServiceServer(s *grpc.Server, srv ProjectManagementServiceServer) {
s.RegisterService(&_ProjectManagementService_serviceDesc, srv)
}
func _ProjectManagementService_GetConfig_Handler(srv interface{}, ctx context.Context, dec func(interface{}) error, interceptor grpc.UnaryServerInterceptor) (interface{}, error) {
in := new(GetConfigRequest)
if err := dec(in); err != nil {
return nil, err
}
if interceptor == nil {
return srv.(ProjectManagementServiceServer).GetConfig(ctx, in)
}
info := &grpc.UnaryServerInfo{
Server: srv,
FullMethod: "/google.cloud.ml.v1.ProjectManagementService/GetConfig",
}
handler := func(ctx context.Context, req interface{}) (interface{}, error) {
return srv.(ProjectManagementServiceServer).GetConfig(ctx, req.(*GetConfigRequest))
}
return interceptor(ctx, in, info, handler)
}
var _ProjectManagementService_serviceDesc = grpc.ServiceDesc{
ServiceName: "google.cloud.ml.v1.ProjectManagementService",
HandlerType: (*ProjectManagementServiceServer)(nil),
Methods: []grpc.MethodDesc{
{
MethodName: "GetConfig",
Handler: _ProjectManagementService_GetConfig_Handler,
},
},
Streams: []grpc.StreamDesc{},
Metadata: "google/cloud/ml/v1/project_service.proto",
}
func init() { proto.RegisterFile("google/cloud/ml/v1/project_service.proto", fileDescriptor4) }
var fileDescriptor4 = []byte{
// 319 bytes of a gzipped FileDescriptorProto
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}

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// Code generated by protoc-gen-go. DO NOT EDIT.
// source: google/cloud/ml/v1beta1/operation_metadata.proto
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_protobuf2 "github.com/golang/protobuf/ptypes/timestamp"
// Reference imports to suppress errors if they are not otherwise used.
var _ = proto.Marshal
var _ = fmt.Errorf
var _ = math.Inf
// The operation type.
type OperationMetadata_OperationType int32
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
)
var OperationMetadata_OperationType_name = map[int32]string{
0: "OPERATION_TYPE_UNSPECIFIED",
1: "CREATE_VERSION",
2: "DELETE_VERSION",
3: "DELETE_MODEL",
}
var OperationMetadata_OperationType_value = map[string]int32{
"OPERATION_TYPE_UNSPECIFIED": 0,
"CREATE_VERSION": 1,
"DELETE_VERSION": 2,
"DELETE_MODEL": 3,
}
func (x OperationMetadata_OperationType) String() string {
return proto.EnumName(OperationMetadata_OperationType_name, int32(x))
}
func (OperationMetadata_OperationType) EnumDescriptor() ([]byte, []int) {
return fileDescriptor2, []int{0, 0}
}
// Represents the metadata of the long-running operation.
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 `protobuf:"varint,4,opt,name=is_cancellation_requested,json=isCancellationRequested" json:"is_cancellation_requested,omitempty"`
// The operation type.
OperationType OperationMetadata_OperationType `protobuf:"varint,5,opt,name=operation_type,json=operationType,enum=google.cloud.ml.v1beta1.OperationMetadata_OperationType" json:"operation_type,omitempty"`
// 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"`
}
func (m *OperationMetadata) Reset() { *m = OperationMetadata{} }
func (m *OperationMetadata) String() string { return proto.CompactTextString(m) }
func (*OperationMetadata) ProtoMessage() {}
func (*OperationMetadata) Descriptor() ([]byte, []int) { return fileDescriptor2, []int{0} }
func (m *OperationMetadata) GetCreateTime() *google_protobuf2.Timestamp {
if m != nil {
return m.CreateTime
}
return nil
}
func (m *OperationMetadata) GetStartTime() *google_protobuf2.Timestamp {
if m != nil {
return m.StartTime
}
return nil
}
func (m *OperationMetadata) GetEndTime() *google_protobuf2.Timestamp {
if m != nil {
return m.EndTime
}
return nil
}
func (m *OperationMetadata) GetIsCancellationRequested() bool {
if m != nil {
return m.IsCancellationRequested
}
return false
}
func (m *OperationMetadata) GetOperationType() OperationMetadata_OperationType {
if m != nil {
return m.OperationType
}
return OperationMetadata_OPERATION_TYPE_UNSPECIFIED
}
func (m *OperationMetadata) GetModelName() string {
if m != nil {
return m.ModelName
}
return ""
}
func (m *OperationMetadata) GetVersion() *Version {
if m != nil {
return m.Version
}
return nil
}
func init() {
proto.RegisterType((*OperationMetadata)(nil), "google.cloud.ml.v1beta1.OperationMetadata")
proto.RegisterEnum("google.cloud.ml.v1beta1.OperationMetadata_OperationType", OperationMetadata_OperationType_name, OperationMetadata_OperationType_value)
}
func init() { proto.RegisterFile("google/cloud/ml/v1beta1/operation_metadata.proto", fileDescriptor2) }
var fileDescriptor2 = []byte{
// 457 bytes of a gzipped FileDescriptorProto
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}

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@ -0,0 +1,343 @@
// Code generated by protoc-gen-go. DO NOT EDIT.
// source: google/cloud/ml/v1beta1/prediction_service.proto
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_api3 "google.golang.org/genproto/googleapis/api/httpbody"
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
// 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.
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"`
}
func (m *PredictRequest) Reset() { *m = PredictRequest{} }
func (m *PredictRequest) String() string { return proto.CompactTextString(m) }
func (*PredictRequest) ProtoMessage() {}
func (*PredictRequest) Descriptor() ([]byte, []int) { return fileDescriptor3, []int{0} }
func (m *PredictRequest) GetName() string {
if m != nil {
return m.Name
}
return ""
}
func (m *PredictRequest) GetHttpBody() *google_api3.HttpBody {
if m != nil {
return m.HttpBody
}
return nil
}
func init() {
proto.RegisterType((*PredictRequest)(nil), "google.cloud.ml.v1beta1.PredictRequest")
}
// 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 OnlinePredictionService service
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)
}
type onlinePredictionServiceClient struct {
cc *grpc.ClientConn
}
func NewOnlinePredictionServiceClient(cc *grpc.ClientConn) OnlinePredictionServiceClient {
return &onlinePredictionServiceClient{cc}
}
func (c *onlinePredictionServiceClient) Predict(ctx context.Context, in *PredictRequest, opts ...grpc.CallOption) (*google_api3.HttpBody, error) {
out := new(google_api3.HttpBody)
err := grpc.Invoke(ctx, "/google.cloud.ml.v1beta1.OnlinePredictionService/Predict", in, out, c.cc, opts...)
if err != nil {
return nil, err
}
return out, nil
}
// Server API for OnlinePredictionService service
type OnlinePredictionServiceServer interface {
// Performs prediction on the data in the request.
//
// **** REMOVE FROM GENERATED DOCUMENTATION
Predict(context.Context, *PredictRequest) (*google_api3.HttpBody, error)
}
func RegisterOnlinePredictionServiceServer(s *grpc.Server, srv OnlinePredictionServiceServer) {
s.RegisterService(&_OnlinePredictionService_serviceDesc, srv)
}
func _OnlinePredictionService_Predict_Handler(srv interface{}, ctx context.Context, dec func(interface{}) error, interceptor grpc.UnaryServerInterceptor) (interface{}, error) {
in := new(PredictRequest)
if err := dec(in); err != nil {
return nil, err
}
if interceptor == nil {
return srv.(OnlinePredictionServiceServer).Predict(ctx, in)
}
info := &grpc.UnaryServerInfo{
Server: srv,
FullMethod: "/google.cloud.ml.v1beta1.OnlinePredictionService/Predict",
}
handler := func(ctx context.Context, req interface{}) (interface{}, error) {
return srv.(OnlinePredictionServiceServer).Predict(ctx, req.(*PredictRequest))
}
return interceptor(ctx, in, info, handler)
}
var _OnlinePredictionService_serviceDesc = grpc.ServiceDesc{
ServiceName: "google.cloud.ml.v1beta1.OnlinePredictionService",
HandlerType: (*OnlinePredictionServiceServer)(nil),
Methods: []grpc.MethodDesc{
{
MethodName: "Predict",
Handler: _OnlinePredictionService_Predict_Handler,
},
},
Streams: []grpc.StreamDesc{},
Metadata: "google/cloud/ml/v1beta1/prediction_service.proto",
}
func init() { proto.RegisterFile("google/cloud/ml/v1beta1/prediction_service.proto", fileDescriptor3) }
var fileDescriptor3 = []byte{
// 312 bytes of a gzipped FileDescriptorProto
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}

View File

@ -0,0 +1,178 @@
// Code generated by protoc-gen-go. DO NOT EDIT.
// source: google/cloud/ml/v1beta1/project_service.proto
package ml
import proto "github.com/golang/protobuf/proto"
import fmt "fmt"
import math "math"
import _ "google.golang.org/genproto/googleapis/api/annotations"
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
// Requests service account information associated with a project.
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"`
}
func (m *GetConfigRequest) Reset() { *m = GetConfigRequest{} }
func (m *GetConfigRequest) String() string { return proto.CompactTextString(m) }
func (*GetConfigRequest) ProtoMessage() {}
func (*GetConfigRequest) Descriptor() ([]byte, []int) { return fileDescriptor4, []int{0} }
func (m *GetConfigRequest) GetName() string {
if m != nil {
return m.Name
}
return ""
}
// Returns service account information associated with a project.
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"`
}
func (m *GetConfigResponse) Reset() { *m = GetConfigResponse{} }
func (m *GetConfigResponse) String() string { return proto.CompactTextString(m) }
func (*GetConfigResponse) ProtoMessage() {}
func (*GetConfigResponse) Descriptor() ([]byte, []int) { return fileDescriptor4, []int{1} }
func (m *GetConfigResponse) GetServiceAccount() string {
if m != nil {
return m.ServiceAccount
}
return ""
}
func (m *GetConfigResponse) GetServiceAccountProject() int64 {
if m != nil {
return m.ServiceAccountProject
}
return 0
}
func init() {
proto.RegisterType((*GetConfigRequest)(nil), "google.cloud.ml.v1beta1.GetConfigRequest")
proto.RegisterType((*GetConfigResponse)(nil), "google.cloud.ml.v1beta1.GetConfigResponse")
}
// 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 ProjectManagementService service
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)
}
type projectManagementServiceClient struct {
cc *grpc.ClientConn
}
func NewProjectManagementServiceClient(cc *grpc.ClientConn) ProjectManagementServiceClient {
return &projectManagementServiceClient{cc}
}
func (c *projectManagementServiceClient) GetConfig(ctx context.Context, in *GetConfigRequest, opts ...grpc.CallOption) (*GetConfigResponse, error) {
out := new(GetConfigResponse)
err := grpc.Invoke(ctx, "/google.cloud.ml.v1beta1.ProjectManagementService/GetConfig", in, out, c.cc, opts...)
if err != nil {
return nil, err
}
return out, nil
}
// Server API for ProjectManagementService service
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)
}
func RegisterProjectManagementServiceServer(s *grpc.Server, srv ProjectManagementServiceServer) {
s.RegisterService(&_ProjectManagementService_serviceDesc, srv)
}
func _ProjectManagementService_GetConfig_Handler(srv interface{}, ctx context.Context, dec func(interface{}) error, interceptor grpc.UnaryServerInterceptor) (interface{}, error) {
in := new(GetConfigRequest)
if err := dec(in); err != nil {
return nil, err
}
if interceptor == nil {
return srv.(ProjectManagementServiceServer).GetConfig(ctx, in)
}
info := &grpc.UnaryServerInfo{
Server: srv,
FullMethod: "/google.cloud.ml.v1beta1.ProjectManagementService/GetConfig",
}
handler := func(ctx context.Context, req interface{}) (interface{}, error) {
return srv.(ProjectManagementServiceServer).GetConfig(ctx, req.(*GetConfigRequest))
}
return interceptor(ctx, in, info, handler)
}
var _ProjectManagementService_serviceDesc = grpc.ServiceDesc{
ServiceName: "google.cloud.ml.v1beta1.ProjectManagementService",
HandlerType: (*ProjectManagementServiceServer)(nil),
Methods: []grpc.MethodDesc{
{
MethodName: "GetConfig",
Handler: _ProjectManagementService_GetConfig_Handler,
},
},
Streams: []grpc.StreamDesc{},
Metadata: "google/cloud/ml/v1beta1/project_service.proto",
}
func init() { proto.RegisterFile("google/cloud/ml/v1beta1/project_service.proto", fileDescriptor4) }
var fileDescriptor4 = []byte{
// 327 bytes of a gzipped FileDescriptorProto
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}