| // Code generated by protoc-gen-go. DO NOT EDIT. |
| // source: google/cloud/ml/v1/prediction_service.proto |
| |
| package ml // import "google.golang.org/genproto/googleapis/cloud/ml/v1" |
| |
| import proto "github.com/golang/protobuf/proto" |
| import fmt "fmt" |
| import math "math" |
| import _ "google.golang.org/genproto/googleapis/api/annotations" |
| import httpbody "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 |
| |
| // This is a compile-time assertion to ensure that this generated file |
| // is compatible with the proto package it is being compiled against. |
| // A compilation error at this line likely means your copy of the |
| // proto package needs to be updated. |
| const _ = proto.ProtoPackageIsVersion2 // please upgrade the proto package |
| |
| // 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,proto3" json:"name,omitempty"` |
| // |
| // Required. The prediction request body. |
| HttpBody *httpbody.HttpBody `protobuf:"bytes,2,opt,name=http_body,json=httpBody,proto3" json:"http_body,omitempty"` |
| XXX_NoUnkeyedLiteral struct{} `json:"-"` |
| XXX_unrecognized []byte `json:"-"` |
| XXX_sizecache int32 `json:"-"` |
| } |
| |
| func (m *PredictRequest) Reset() { *m = PredictRequest{} } |
| func (m *PredictRequest) String() string { return proto.CompactTextString(m) } |
| func (*PredictRequest) ProtoMessage() {} |
| func (*PredictRequest) Descriptor() ([]byte, []int) { |
| return fileDescriptor_prediction_service_92107018d3d8c7da, []int{0} |
| } |
| func (m *PredictRequest) XXX_Unmarshal(b []byte) error { |
| return xxx_messageInfo_PredictRequest.Unmarshal(m, b) |
| } |
| func (m *PredictRequest) XXX_Marshal(b []byte, deterministic bool) ([]byte, error) { |
| return xxx_messageInfo_PredictRequest.Marshal(b, m, deterministic) |
| } |
| func (dst *PredictRequest) XXX_Merge(src proto.Message) { |
| xxx_messageInfo_PredictRequest.Merge(dst, src) |
| } |
| func (m *PredictRequest) XXX_Size() int { |
| return xxx_messageInfo_PredictRequest.Size(m) |
| } |
| func (m *PredictRequest) XXX_DiscardUnknown() { |
| xxx_messageInfo_PredictRequest.DiscardUnknown(m) |
| } |
| |
| var xxx_messageInfo_PredictRequest proto.InternalMessageInfo |
| |
| func (m *PredictRequest) GetName() string { |
| if m != nil { |
| return m.Name |
| } |
| return "" |
| } |
| |
| func (m *PredictRequest) GetHttpBody() *httpbody.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 |
| |
| // OnlinePredictionServiceClient is the client API for OnlinePredictionService service. |
| // |
| // For semantics around ctx use and closing/ending streaming RPCs, please refer to https://godoc.org/google.golang.org/grpc#ClientConn.NewStream. |
| type OnlinePredictionServiceClient interface { |
| // Performs prediction on the data in the request. |
| // |
| // **** REMOVE FROM GENERATED DOCUMENTATION |
| Predict(ctx context.Context, in *PredictRequest, opts ...grpc.CallOption) (*httpbody.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) (*httpbody.HttpBody, error) { |
| out := new(httpbody.HttpBody) |
| err := c.cc.Invoke(ctx, "/google.cloud.ml.v1.OnlinePredictionService/Predict", in, out, opts...) |
| if err != nil { |
| return nil, err |
| } |
| return out, nil |
| } |
| |
| // OnlinePredictionServiceServer is the server API for OnlinePredictionService service. |
| type OnlinePredictionServiceServer interface { |
| // Performs prediction on the data in the request. |
| // |
| // **** REMOVE FROM GENERATED DOCUMENTATION |
| Predict(context.Context, *PredictRequest) (*httpbody.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", fileDescriptor_prediction_service_92107018d3d8c7da) |
| } |
| |
| var fileDescriptor_prediction_service_92107018d3d8c7da = []byte{ |
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