/
prediction_service.pb.go
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/
prediction_service.pb.go
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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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}