--- /dev/null
+// 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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+}
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