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// Copyright 2017 Google Inc.
//
// Licensed under the Apache License, Version 2.0 (the "License");
// you may not use this file except in compliance with the License.
// You may obtain a copy of the License at
//
// http://www.apache.org/licenses/LICENSE-2.0
//
// Unless required by applicable law or agreed to in writing, software
// distributed under the License is distributed on an "AS IS" BASIS,
// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
// See the License for the specific language governing permissions and
// limitations under the License.
syntax = "proto3";
package google.cloud.ml.v1;
import "google/api/annotations.proto";
import "google/api/httpbody.proto";
option go_package = "google.golang.org/genproto/googleapis/cloud/ml/v1;ml";
option java_multiple_files = true;
option java_outer_classname = "PredictionServiceProto";
option java_package = "com.google.cloud.ml.api.v1";
// Copyright 2017 Google Inc. All Rights Reserved.
//
// Proto file for the Google Cloud Machine Learning Engine.
// Describes the online prediction service.
// The Prediction API, which serves predictions for models managed by
// ModelService.
service OnlinePredictionService {
// Performs prediction on the data in the request.
//
// **** REMOVE FROM GENERATED DOCUMENTATION
rpc Predict(PredictRequest) returns (google.api.HttpBody) {
option (google.api.http) = { post: "/v1/{name=projects/**}:predict" body: "*" };
}
}
// 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.
message PredictRequest {
// Required. The resource name of a model or a version.
//
// Authorization: requires `Viewer` role on the parent project.
string name = 1;
//
// Required. The prediction request body.
google.api.HttpBody http_body = 2;
}
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